{"meta":{"query_hash":"4d71120b810e","filters":{"topic":"Optimal Experimental Design Methods"},"cohort_total":442,"direct_labels_cover":0,"predictions_cover":442,"exported":442,"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/4d71120b810e","api":"https://metacan.xera.ac/api/v1/cohort?topic=Optimal+Experimental+Design+Methods"},"results":[{"id":"W1031741470","doi":"10.1016/b978-075067618-2/50014-7","title":"Introduction to Analysis of Variance","year":2003,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Variance (accounting); Analysis of variance; Factorial; One-way analysis of variance; Replication (statistics); Main effect; Statistics; Variable (mathematics); Factorial analysis; Variance-based sensitivity analysis; Computer science; Quantitative analysis (chemistry); Variables; Mathematics; Econometrics","score_opus":0.07176784849284745,"score_gpt":0.3845153044998531,"score_spread":0.31274745600700565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1031741470","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000023658842,0.00041047338,0.0044656545,0.00027605545,0.0009324615,0.00042624178,0.00004718218,0.000023530163,0.99339473],"genre_scores_gemma":[0.00010465532,0.000010095479,0.06708037,0.0005339252,0.00036023936,0.00001832338,0.000006885685,0.000042780288,0.93184274],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99527264,0.0002694194,0.0013264364,0.0011522296,0.0017228913,0.00025638094],"domain_scores_gemma":[0.9962655,0.00047003265,0.00073282834,0.0018107472,0.00051780563,0.00020311399],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0037724725,0.0003850307,0.0013734989,0.0016428215,0.000071904,0.00010440649,0.00087036705,0.00028275105,0.0048137126],"category_scores_gemma":[0.0009131061,0.0003252874,0.0006818697,0.0004317962,0.00016463916,0.00008051222,0.00018298239,0.0002758696,0.0007256064],"study_design_candidate":"not_applicable","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":[0.000036770514,0.0000111338495,0.000005665625,0.0000045240204,0.0005701815,0.0000050621643,0.00022177985,0.00016669698,0.001919177,0.021774229,0.003660031,0.97162473],"study_design_scores_gemma":[0.00007322481,0.00010119399,0.00003964336,0.000021571777,0.0006283216,0.0000032789294,0.00001742257,0.00005273118,0.0017330084,0.020269576,0.9767456,0.00031441447],"about_ca_topic_score_codex":6.3674145e-7,"about_ca_topic_score_gemma":0.0000061897476,"teacher_disagreement_score":0.9730856,"about_ca_system_score_codex":0.00012606026,"about_ca_system_score_gemma":0.00007123251,"threshold_uncertainty_score":0.9999199},"labels":[],"label_agreement":null},{"id":"W109547034","doi":"10.1007/s00184-013-0461-9","title":"Some results on constructing general minimum lower order confounding $$2^{n-m}$$ 2 n - m designs for $$n\\le 2^{n-m-2}$$ n ≤ 2 n - m - 2","year":2013,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Combinatorics; Order (exchange); Zhàng; Statistics","score_opus":0.21584645927730445,"score_gpt":0.4501946779864947,"score_spread":0.23434821870919026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W109547034","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72100925,0.0010328909,0.20023675,0.00404277,0.011266639,0.0050116642,0.00034124297,0.00048355735,0.05657524],"genre_scores_gemma":[0.34926704,0.000008838144,0.6348654,0.0013894114,0.0009890705,0.00024788195,0.000022722159,0.000084297164,0.013125358],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9942446,0.00069039816,0.0014246955,0.001241164,0.0015270191,0.0008721604],"domain_scores_gemma":[0.9884434,0.008846829,0.0005724344,0.0010596839,0.0007208745,0.0003568185],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006412335,0.00041868672,0.00071667205,0.0009185899,0.0004685348,0.00097067264,0.0011149396,0.00023007004,0.00084201887],"category_scores_gemma":[0.022011872,0.00033293082,0.00029923732,0.0018739898,0.0002970428,0.0010920377,0.00020426213,0.00027570926,0.0010868325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018061275,0.0007756439,0.0007324273,0.00002681161,0.0002420461,0.000046724297,0.0011725018,0.0010315342,0.48286828,0.11444703,0.20965958,0.1871913],"study_design_scores_gemma":[0.013917529,0.00392186,0.0011163066,0.00013416618,0.0000858581,0.00009065209,0.006399761,0.036901724,0.5315659,0.21476705,0.18833137,0.0027677934],"about_ca_topic_score_codex":0.00015783249,"about_ca_topic_score_gemma":0.000002562574,"teacher_disagreement_score":0.43462864,"about_ca_system_score_codex":0.00015544446,"about_ca_system_score_gemma":0.00020782619,"threshold_uncertainty_score":0.99991226},"labels":[],"label_agreement":null},{"id":"W110861836","doi":"","title":"Recursive Constructions of Balanced Incomplete Block Designs with Block Size of 7.","year":2001,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Block (permutation group theory); Mathematics; Block size; Arithmetic; Combinatorics; Computer science; Operating system","score_opus":0.0795173013893631,"score_gpt":0.3721314229376046,"score_spread":0.2926141215482415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W110861836","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9848796,0.00010845839,0.00065728254,0.00012044842,0.00084223045,0.00037705683,0.00001985261,0.000035474874,0.012959602],"genre_scores_gemma":[0.9298379,0.000012847503,0.06972485,0.000036454367,0.000006464525,0.000015199142,8.46308e-7,0.000018843319,0.0003466083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966932,0.00051959505,0.0008486434,0.00047141287,0.0011627901,0.00030438983],"domain_scores_gemma":[0.99457175,0.003063296,0.00064610504,0.00080898916,0.0007539891,0.0001559028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013072271,0.00021746015,0.00064666616,0.00020640074,0.00011537897,0.00003956484,0.0007548068,0.00010290856,0.00027285932],"category_scores_gemma":[0.001812926,0.000171305,0.00013407395,0.0016256685,0.0006326657,0.00022429513,0.00013558804,0.0001672283,0.00003561454],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00136892,0.001042261,0.103345335,0.00002348574,0.0002824195,0.00007000009,0.00229799,0.0007466676,0.3945101,0.488936,0.0038835355,0.0034933041],"study_design_scores_gemma":[0.004298183,0.0022554782,0.025073662,0.0001303794,0.00007935425,0.00027259503,0.0032873838,0.00042423228,0.28962326,0.6724909,0.001504871,0.0005596855],"about_ca_topic_score_codex":0.00005679742,"about_ca_topic_score_gemma":0.0000044356966,"teacher_disagreement_score":0.18355493,"about_ca_system_score_codex":0.000057413185,"about_ca_system_score_gemma":0.00015208038,"threshold_uncertainty_score":0.6985615},"labels":[],"label_agreement":null},{"id":"W122717491","doi":"","title":"Nested Partially Balanced Incomplete Block Design.","year":2007,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Block (permutation group theory); Combinatorics","score_opus":0.12638211415481782,"score_gpt":0.4114496631792185,"score_spread":0.2850675490244007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W122717491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89613265,0.0003132786,0.07420997,0.00039063272,0.006644701,0.0009184189,0.0000051458733,0.00035848978,0.021026725],"genre_scores_gemma":[0.922419,0.000002740587,0.07629014,0.00040390872,0.000025808497,0.000016037573,0.0000017163636,0.00003303249,0.00080759585],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99495786,0.0008318998,0.0010398005,0.00074247655,0.0017296025,0.00069837115],"domain_scores_gemma":[0.9941455,0.003770007,0.0003229511,0.0010199924,0.00037325485,0.00036825924],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.010235237,0.00029093088,0.0005025743,0.00028833415,0.00024732726,0.0002745909,0.0012952423,0.00016455783,0.0003208074],"category_scores_gemma":[0.0028721953,0.00024536336,0.0001587254,0.00153742,0.0001820463,0.00039686682,0.0002770944,0.00024826004,0.0011858719],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011127668,0.0008503338,0.026922328,0.000008202701,0.00010496947,0.00040988898,0.0017417122,0.00085921487,0.44467804,0.45680982,0.02761433,0.038888384],"study_design_scores_gemma":[0.0024657594,0.0007004546,0.04618728,0.000023689947,0.000021410891,0.000049594448,0.00045086985,0.0039832313,0.20011784,0.7326602,0.012613688,0.000726008],"about_ca_topic_score_codex":0.000027389971,"about_ca_topic_score_gemma":0.0000054395255,"teacher_disagreement_score":0.27585036,"about_ca_system_score_codex":0.00012142292,"about_ca_system_score_gemma":0.00010606596,"threshold_uncertainty_score":0.9999999},"labels":[],"label_agreement":null},{"id":"W135223351","doi":"10.1007/978-1-4613-0049-6_7","title":"Additional Selected Topics","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Maximization; Computer science; TRACE (psycholinguistics); Variety (cybernetics); Block (permutation group theory); Mathematical optimization; Optimal design; Mathematics; Artificial intelligence; Combinatorics; Machine learning","score_opus":0.11270832870251576,"score_gpt":0.3942728854141296,"score_spread":0.28156455671161384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135223351","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":[4.0139076e-7,0.0003819849,0.63044447,0.00014548826,0.0005612327,0.00028629194,0.045785014,0.000053194373,0.32234192],"genre_scores_gemma":[0.000060930703,0.000043483135,0.73878556,0.00056762603,0.00050470827,0.000021643573,0.0027042674,0.00007434717,0.25723746],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99591976,0.00017260098,0.00096340606,0.00081059895,0.0017699674,0.0003636711],"domain_scores_gemma":[0.9859672,0.012264739,0.0004264504,0.0006517057,0.00055745686,0.00013244704],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004645802,0.00046930963,0.00069352967,0.00044614897,0.00009288735,0.00019004216,0.0006934394,0.0006199037,0.35389036],"category_scores_gemma":[0.01428279,0.00040951063,0.00010798938,0.00030290138,0.00024198828,0.00007377109,0.0001464536,0.0009385272,0.0018200491],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030198475,0.000038188853,0.000014536093,0.000011174744,0.00003543269,0.00023980433,0.00016975505,0.00038003622,0.000030681647,0.081546865,0.58352315,0.33398017],"study_design_scores_gemma":[0.00012446541,0.000085109416,0.000066831584,0.00005031097,0.000012100941,0.000021178079,9.690871e-7,0.002168739,0.00006170221,0.45161453,0.545468,0.00032609407],"about_ca_topic_score_codex":0.0000055170894,"about_ca_topic_score_gemma":0.000049395963,"teacher_disagreement_score":0.37006766,"about_ca_system_score_codex":0.00023109313,"about_ca_system_score_gemma":0.00015871678,"threshold_uncertainty_score":0.99983567},"labels":[],"label_agreement":null},{"id":"W1481493156","doi":"","title":"Optimization in 2 m 3 n Factorial Experiments","year":2012,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Integer programming; Factorial experiment; Post hoc; Factorial; Design of experiments; Mathematical optimization; Orthogonal array; Theoretical computer science; Industrial engineering; Algorithm; Mathematics; Machine learning; Taguchi methods; Engineering; Statistics","score_opus":0.5038899504498755,"score_gpt":0.6029256541613165,"score_spread":0.09903570371144099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1481493156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23840894,0.0025131654,0.71609247,0.00094525336,0.006299328,0.002927239,0.000044079206,0.00012954127,0.03264],"genre_scores_gemma":[0.70286393,0.000032597247,0.29317614,0.00003685612,0.00093018217,0.00022477067,0.0000148288955,0.000022829217,0.0026978727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932349,0.002293016,0.0006763457,0.0004735161,0.0025520646,0.0007701914],"domain_scores_gemma":[0.997428,0.0010665925,0.000026987458,0.00065283244,0.0005592691,0.00026628986],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01176098,0.00014610356,0.00023310022,0.0010159255,0.000401866,0.0005375537,0.000770458,0.00013376471,0.0023566736],"category_scores_gemma":[0.004133842,0.00012071648,0.00005813989,0.0024692228,0.00017544747,0.0017350237,0.0003108729,0.00041273137,0.0013013971],"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":[0.00032095125,0.004315772,0.027707951,0.0000086750315,0.00007605277,0.000034077493,0.038646206,0.5490734,0.21689399,0.038168106,0.018050104,0.10670472],"study_design_scores_gemma":[0.0019553772,0.00032182207,0.0050887866,0.000020661331,0.0000037099892,0.000018499453,0.008321367,0.89448833,0.07641145,0.0015082584,0.01128106,0.00058069295],"about_ca_topic_score_codex":0.00039334942,"about_ca_topic_score_gemma":0.00001837832,"teacher_disagreement_score":0.46445498,"about_ca_system_score_codex":0.0003397849,"about_ca_system_score_gemma":0.00017190917,"threshold_uncertainty_score":0.9994762},"labels":[],"label_agreement":null},{"id":"W1491175567","doi":"10.1002/cjs.11194","title":"Techniques for the construction of robust regression designs","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Minimax; Robust regression; Regression; Computer science; Regression analysis; Mathematics; Outlier; Mathematical optimization; Statistics; Algorithm","score_opus":0.233197658014244,"score_gpt":0.4119830676601403,"score_spread":0.17878540964589631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491175567","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":[0.0015745286,0.00030276013,0.99636793,0.00043305918,0.00049787475,0.00025415968,0.00012384183,0.0000021444246,0.00044368912],"genre_scores_gemma":[0.092013,0.000018734143,0.90759724,0.00007609013,0.000074407864,0.000006007137,7.6086775e-7,0.0000088492225,0.00020490812],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99849623,0.00017251055,0.0006692486,0.000092006136,0.00040456743,0.00016541495],"domain_scores_gemma":[0.99499387,0.002558167,0.000651241,0.00019804745,0.0013304623,0.00026821523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019431713,0.00008186401,0.00021967587,0.00025796652,0.0001562163,0.00013851408,0.0004758253,0.00005473792,0.0005915861],"category_scores_gemma":[0.004258457,0.00004624863,0.00006752367,0.00023197525,0.0003528033,0.00020918048,0.000010443073,0.00012010923,0.000006951768],"study_design_candidate":"theoretical_or_conceptual","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":[0.000044171167,0.000014046845,0.002632461,0.000011364346,0.00004184347,0.000017280281,0.0007256306,0.00032254463,0.012261497,0.032036472,0.26759106,0.6843016],"study_design_scores_gemma":[0.0011989691,0.002528712,0.015879622,0.00037791685,0.00018599466,0.0008587724,0.01552784,0.021701964,0.17334417,0.6785207,0.08924981,0.00062553474],"about_ca_topic_score_codex":0.0013560944,"about_ca_topic_score_gemma":0.00053525815,"teacher_disagreement_score":0.6836761,"about_ca_system_score_codex":0.00007863155,"about_ca_system_score_gemma":0.0006554173,"threshold_uncertainty_score":0.6477453},"labels":[],"label_agreement":null},{"id":"W1516925883","doi":"10.1080/00224065.2002.11980155","title":"Analyzing Unreplicated Blocked or Split-Plot Fractional Factorial Designs","year":2002,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":27,"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","keywords":"Fractional factorial design; Split plot; Factorial experiment; Factorial; Replication (statistics); Plackett–Burman design; Mathematics; Plot (graphics); Randomization; Statistics; Computer science; Main effect; Design of experiments; Algorithm; Mathematical optimization; Arithmetic; Response surface methodology","score_opus":0.49810525736246597,"score_gpt":0.5329440955844972,"score_spread":0.03483883822203121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516925883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61317325,0.0007109909,0.37120515,0.0106561985,0.0022462406,0.0002784842,0.00001662248,0.00020278463,0.001510278],"genre_scores_gemma":[0.85893345,0.000042961543,0.13939196,0.00024042348,0.00039495414,0.000005322749,4.7211273e-7,0.000022434522,0.000968056],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.994149,0.0010615971,0.0023302096,0.00046962543,0.0015853713,0.00040416257],"domain_scores_gemma":[0.99298143,0.0031455725,0.0019304974,0.0007693708,0.0009767256,0.00019641107],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0077430364,0.00023745855,0.00087573176,0.0013977533,0.00019694922,0.00015181304,0.001487142,0.00044535496,0.003764129],"category_scores_gemma":[0.017919129,0.00016099222,0.0003129657,0.0025007236,0.00037925606,0.00051477423,0.00018269166,0.00084463606,0.00026127038],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021884644,0.0017564181,0.020269133,0.000012358115,0.00053138286,0.0003486445,0.00083954027,0.00025854234,0.68785626,0.041449163,0.029827392,0.2146627],"study_design_scores_gemma":[0.008343837,0.0057622395,0.016121496,0.000114542876,0.00022449053,0.0032775584,0.005786781,0.0052173454,0.4071437,0.39040878,0.15586673,0.001732483],"about_ca_topic_score_codex":0.000015713673,"about_ca_topic_score_gemma":0.000004493174,"teacher_disagreement_score":0.34895962,"about_ca_system_score_codex":0.00022812738,"about_ca_system_score_gemma":0.00011347035,"threshold_uncertainty_score":0.99714655},"labels":[],"label_agreement":null},{"id":"W1527212461","doi":"10.1002/bimj.201200200","title":"Modified Gaussian estimation for correlated binary data","year":2013,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Windsor","funders":"","keywords":"Mathematics; Estimator; Statistics; Generalized estimating equation; Gaussian; Consistency (knowledge bases); Correlation; Binary data; Regression analysis; Binary number; Regression; Applied mathematics","score_opus":0.4222998253043632,"score_gpt":0.5111461501127252,"score_spread":0.08884632480836202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527212461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029238224,0.00059014827,0.9650745,0.0014959573,0.0013422151,0.00048996997,0.000041369243,0.00004483691,0.0016827977],"genre_scores_gemma":[0.5217568,0.000012227589,0.47699302,0.00016909822,0.00016645058,0.000013285595,0.0000233475,0.000016479942,0.00084924116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962466,0.0004040613,0.0009704176,0.00049480214,0.0014976045,0.00038652794],"domain_scores_gemma":[0.99486136,0.0029924023,0.00042846412,0.00081042305,0.00044913418,0.0004582206],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004994838,0.00016478481,0.00032728416,0.0021392829,0.00030583693,0.0010986098,0.0019830465,0.00015614198,0.001237537],"category_scores_gemma":[0.0149659915,0.00010863628,0.00013166059,0.0052513327,0.00010694735,0.0015155664,0.00037035666,0.00026511427,0.0009610516],"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":[0.00012817407,0.00029776426,0.00042318608,0.0000028297095,0.000043142634,0.000016823633,0.000062895764,0.0008404147,0.026050849,0.0006142336,0.16707724,0.80444247],"study_design_scores_gemma":[0.0010830443,0.0005241086,0.011457089,0.000012407784,0.000020598918,0.00019165964,0.00015770154,0.9565219,0.0010879842,0.022085324,0.006618823,0.00023932637],"about_ca_topic_score_codex":0.000017269951,"about_ca_topic_score_gemma":7.1352666e-8,"teacher_disagreement_score":0.9556815,"about_ca_system_score_codex":0.00010877209,"about_ca_system_score_gemma":0.000093210045,"threshold_uncertainty_score":0.99993837},"labels":[],"label_agreement":null},{"id":"W1538737853","doi":"10.1002/mren.201400060","title":"D‐Optimality in Model‐Based Experimental Designs: Applications in NMRP of Styrene","year":2015,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Waterloo","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Polystyrene; Optimal design; Work (physics); Computer science; Applied mathematics; Experimental data; Statistical physics; Biological system; Mathematics; Materials science; Mathematical optimization; Thermodynamics; Physics; Statistics; Polymer","score_opus":0.182083592544223,"score_gpt":0.42628530482558014,"score_spread":0.24420171228135715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1538737853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34676883,0.00022369856,0.6518264,0.000015655687,0.00005669834,0.00029855268,0.0000036310666,0.0000364764,0.0007700412],"genre_scores_gemma":[0.85222113,0.0000010194635,0.14757468,0.000016145339,0.00000907548,0.00013018961,0.0000060441216,0.000022551278,0.000019139467],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976279,0.00018194113,0.0007125274,0.00044193232,0.00078016514,0.00025554775],"domain_scores_gemma":[0.9989143,0.00019728843,0.00013776642,0.00050678814,0.00010102994,0.0001428302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002229825,0.00018754421,0.00033837822,0.00061345886,0.000017957542,0.000045088942,0.00036130752,0.00009878079,0.000016640019],"category_scores_gemma":[0.00047037168,0.00018964008,0.000092582915,0.0011851488,0.0000393508,0.00028687352,0.00007218257,0.00017503553,0.000013739332],"study_design_candidate":"bench_or_experimental","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":[0.000026158592,0.00014213946,0.00031558485,0.0000031916459,0.0000023984053,0.0000065337967,0.00018161253,0.44349238,0.5548303,0.00067370385,0.000004923502,0.00032104677],"study_design_scores_gemma":[0.00048821315,0.000042419484,0.00053550425,0.000012935895,0.0000020217176,0.0000031486686,0.00032828437,0.5308052,0.46712345,0.0004301854,0.000096253534,0.00013240601],"about_ca_topic_score_codex":0.00011936627,"about_ca_topic_score_gemma":0.000004941558,"teacher_disagreement_score":0.50545233,"about_ca_system_score_codex":0.0003332004,"about_ca_system_score_gemma":0.00008839407,"threshold_uncertainty_score":0.7733298},"labels":[],"label_agreement":null},{"id":"W1557325407","doi":"10.1111/j.1467-9574.2009.00444.x","title":"Multi-sample simple step-stress experiment under time constraints","year":2009,"lang":"en","type":"article","venue":"Statistica Neerlandica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Inference; Accelerated life testing; Stress (linguistics); Context (archaeology); Sample (material); Statistical inference; Maximum likelihood; Mathematics; Simple (philosophy); Statistics; Stress testing (software); Sample size determination; Computer science; Econometrics; Artificial intelligence","score_opus":0.10592661547284808,"score_gpt":0.44287912075045915,"score_spread":0.33695250527761106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557325407","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":[0.003583794,0.00020127447,0.9861663,0.00047430804,0.00024033066,0.00048162733,0.0016322949,0.00014984555,0.0070702513],"genre_scores_gemma":[0.43940064,0.0000048688958,0.5585429,0.0006376509,0.00006114938,0.000019628398,0.00009797017,0.000022610922,0.0012126075],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995391,0.00054751814,0.000904832,0.0008598366,0.0016211332,0.0006756817],"domain_scores_gemma":[0.99463356,0.00364141,0.00025426308,0.00081331923,0.00016986496,0.00048761012],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014336422,0.00035169034,0.0005740158,0.00024709836,0.0002576567,0.0003966266,0.0008273825,0.0001293025,0.014345807],"category_scores_gemma":[0.002779217,0.00027979087,0.0001312077,0.00049989164,0.00047909902,0.00021909358,0.00012225592,0.00021468624,0.002033608],"study_design_candidate":"bench_or_experimental","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":[0.0006387486,0.0024855265,0.0017189708,0.000014926636,0.00020081527,0.0003892731,0.0034239066,0.0006275816,0.28099334,0.07168791,0.2544221,0.38339692],"study_design_scores_gemma":[0.016964374,0.006609948,0.08248689,0.00020027338,0.0002506344,0.00023578206,0.013281829,0.272506,0.15346715,0.35071597,0.09749868,0.0057824817],"about_ca_topic_score_codex":0.000051242707,"about_ca_topic_score_gemma":0.000004450825,"teacher_disagreement_score":0.43581685,"about_ca_system_score_codex":0.0001137784,"about_ca_system_score_gemma":0.00012599972,"threshold_uncertainty_score":0.9999654},"labels":[],"label_agreement":null},{"id":"W1561674584","doi":"10.1002/9781118445112.stat00522","title":"Box‐Cox Transformations: Selecting for Symmetry","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Citation; Encyclopedia; Library science; Computer science","score_opus":0.16126139943251264,"score_gpt":0.46637625494535256,"score_spread":0.3051148555128399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561674584","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":[0.000006015603,0.0011794544,0.86392725,0.000103785685,0.00089233776,0.0012073724,0.032567475,0.0002821678,0.09983414],"genre_scores_gemma":[0.00017543475,0.0004885921,0.78477556,0.00031497527,0.00044426395,0.0001338966,0.0030459429,0.00052879454,0.21009251],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9930536,0.00068853685,0.0019058342,0.0013272772,0.0021141546,0.00091056083],"domain_scores_gemma":[0.99218404,0.0040859827,0.0013615529,0.0012177835,0.00075287034,0.00039779258],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002794013,0.0008190909,0.0014071616,0.0011618751,0.00028206623,0.00047096593,0.0015836537,0.0006104552,0.0045330543],"category_scores_gemma":[0.0063044685,0.0006847426,0.00019709276,0.0010132008,0.00028318257,0.0002156025,0.00014084944,0.00070597796,0.00077737955],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000753249,0.0002135323,0.000023793342,0.00015314626,0.000100810255,0.000005502634,0.0001933215,0.000026863641,0.0002903688,0.04138153,0.77503324,0.18250257],"study_design_scores_gemma":[0.0010110369,0.0005726121,0.000019503683,0.00039545412,0.00011622554,0.0000121273815,0.000534556,0.009617072,0.0002245529,0.061753277,0.9247575,0.0009860805],"about_ca_topic_score_codex":0.0001320845,"about_ca_topic_score_gemma":0.00043781535,"teacher_disagreement_score":0.18151648,"about_ca_system_score_codex":0.00017990392,"about_ca_system_score_gemma":0.00042413527,"threshold_uncertainty_score":0.99956036},"labels":[],"label_agreement":null},{"id":"W1563682158","doi":"","title":"RANCANGAN FAKTORIAL 25 DENGAN SEPEREMPAT ULANGAN","year":2006,"lang":"id","type":"dissertation","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Factorial experiment; Mathematics; Fraction (chemistry); Fractional factorial design; Block (permutation group theory); Statistics; Factorial; Main effect; Confounding; Quarter (Canadian coin); Randomized block design; Combinatorics; Arithmetic","score_opus":0.07190565509468562,"score_gpt":0.4254744187676641,"score_spread":0.3535687636729785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563682158","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5022483,0.0029021692,0.009103261,0.00015337206,0.046235252,0.0028728512,0.00019909827,0.0005348325,0.43575087],"genre_scores_gemma":[0.3162307,0.000075368414,0.09053773,0.0004322931,0.005172345,0.00014236133,0.0019027743,0.00035648447,0.58514994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98588467,0.0024233249,0.002751894,0.002715446,0.004863782,0.0013608695],"domain_scores_gemma":[0.9924389,0.0025009569,0.0012407593,0.0021081853,0.001037046,0.0006741201],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.005254017,0.0014906942,0.001998675,0.001054178,0.0008773871,0.001700885,0.0029288991,0.0016266898,0.005926829],"category_scores_gemma":[0.0026378988,0.0012528299,0.0011940912,0.0020146358,0.0002938914,0.00082630565,0.0002422166,0.0012875032,0.005804832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017482288,0.0013428135,0.0034672318,0.00018229969,0.000364292,0.00035350496,0.0100568505,0.00015963463,0.71873635,0.009161374,0.17306837,0.08135906],"study_design_scores_gemma":[0.005150715,0.0012922011,0.012833731,0.0003751334,0.0004702558,0.00006755802,0.031793907,0.0020878855,0.75445294,0.0043077664,0.1827305,0.00443741],"about_ca_topic_score_codex":0.00278962,"about_ca_topic_score_gemma":0.0015073034,"teacher_disagreement_score":0.18601759,"about_ca_system_score_codex":0.00045821842,"about_ca_system_score_gemma":0.0008101988,"threshold_uncertainty_score":0.99978423},"labels":[],"label_agreement":null},{"id":"W1564432298","doi":"10.1002/9780470061572.eqr012","title":"<scp>L</scp>atin Hypercube Designs","year":2007,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Quality and Reliability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"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":"Latin hypercube sampling; Orthogonality; Hypercube; Computer science; Stratification (seeds); Class (philosophy); Computer experiment; Strengths and weaknesses; Univariate; Space (punctuation); Theoretical computer science; Mathematics; Parallel computing; Simulation; Geometry; Statistics; Artificial intelligence; Multivariate statistics; Monte Carlo method","score_opus":0.1368736867543813,"score_gpt":0.455217255129216,"score_spread":0.3183435683748347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1564432298","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027185795,0.001327115,0.16592112,0.00003140689,0.0009612823,0.00075293606,0.0014348588,0.00006301836,0.8267897],"genre_scores_gemma":[0.00051028584,0.0019326169,0.6493705,0.00010777345,0.00016524162,0.000023921957,0.000039465474,0.00016687361,0.34768334],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9911224,0.0025558297,0.002488799,0.001258067,0.0020415657,0.0005333392],"domain_scores_gemma":[0.9714189,0.025556969,0.0011710129,0.0012980491,0.00027436228,0.00028069702],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.019250145,0.0004925161,0.0014203389,0.00077051297,0.000069025155,0.000069152156,0.00083859294,0.0007619409,0.0013469654],"category_scores_gemma":[0.051606346,0.00040728963,0.00013236418,0.00092157966,0.0012529072,0.0001011619,0.00031843208,0.0007104406,0.00011997292],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009039782,0.0011863719,0.041806623,0.00084116345,0.00005744695,0.000060705574,0.004141852,0.000059045506,0.00018018007,0.053991534,0.8390514,0.05853325],"study_design_scores_gemma":[0.00086765166,0.00030704084,0.046327308,0.0002029776,0.000048364265,0.0000051277784,0.0025454904,0.00024924852,0.00021013002,0.21277726,0.7359985,0.00046087714],"about_ca_topic_score_codex":0.0011685054,"about_ca_topic_score_gemma":0.0001741556,"teacher_disagreement_score":0.48344937,"about_ca_system_score_codex":0.00010451325,"about_ca_system_score_gemma":0.00022362641,"threshold_uncertainty_score":0.9998379},"labels":[],"label_agreement":null},{"id":"W1587669494","doi":"10.1002/cjs.5550360305","title":"Marginally restricted sequential D‐optimal designs","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"A priori and a posteriori; Mathematical optimization; Mathematics; Set (abstract data type); Variable (mathematics); Optimal design; Value (mathematics); Sequential analysis; Computer science; Algorithm; Statistics","score_opus":0.2719362671563821,"score_gpt":0.4007528114557431,"score_spread":0.128816544299361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587669494","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":[0.10016779,0.0003961904,0.89396185,0.00023703864,0.0012800648,0.00011963449,0.0003417927,0.000007405224,0.0034882447],"genre_scores_gemma":[0.40126872,0.000021324357,0.5969679,0.00017231626,0.00017293381,7.701217e-7,0.00000336513,0.000019060153,0.0013736285],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9965944,0.00049358123,0.0010608223,0.00021850923,0.001175895,0.00045678823],"domain_scores_gemma":[0.9955645,0.0010695097,0.00058135693,0.00030000703,0.0011990332,0.0012855944],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015736802,0.00017244082,0.00039373612,0.00078204495,0.00033268842,0.0002068537,0.0008962671,0.00009449886,0.001894018],"category_scores_gemma":[0.00525126,0.0001469501,0.00011216293,0.0007602699,0.0004279653,0.00033584313,0.000024065415,0.00034757162,0.00016682413],"study_design_candidate":"not_applicable","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":[0.0004505289,0.000114575465,0.024062697,0.000010392619,0.0002029831,0.034743894,0.004743278,0.0065599717,0.019742547,0.028604904,0.83185506,0.0489092],"study_design_scores_gemma":[0.007282978,0.0063433284,0.44545025,0.00019288505,0.0003408935,0.031701274,0.004916675,0.017203318,0.01617656,0.08235399,0.3851445,0.0028933508],"about_ca_topic_score_codex":0.0011945732,"about_ca_topic_score_gemma":0.00086076,"teacher_disagreement_score":0.44671053,"about_ca_system_score_codex":0.0002770247,"about_ca_system_score_gemma":0.0040842583,"threshold_uncertainty_score":0.9990184},"labels":[],"label_agreement":null},{"id":"W1589049529","doi":"10.1002/mats.201200085","title":"Design of Experiments for Reactivity Ratio Estimation in Multicomponent Polymerizations Using the Error‐<scp>I</scp>n‐<scp>V</scp>ariables Approach","year":2013,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":20,"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","keywords":"Context (archaeology); Design of experiments; Reactivity (psychology); Computer science; Nonlinear system; Nonlinear regression; Mathematics; Regression analysis; Statistics; Physics","score_opus":0.12193078032005286,"score_gpt":0.399763141765593,"score_spread":0.27783236144554013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1589049529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4108765,0.00020438834,0.58730364,0.00001289134,0.00006179226,0.0012116934,0.000021351543,0.000018040573,0.00028970183],"genre_scores_gemma":[0.77420294,0.0000021258531,0.22529654,0.000060683313,0.00001373901,0.00016953392,0.0000313179,0.000026626009,0.00019651033],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951092,0.002567072,0.0008161481,0.0005585072,0.00059593335,0.00035316643],"domain_scores_gemma":[0.9828784,0.015737167,0.0004119208,0.00062631007,0.00023394346,0.00011228376],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003524877,0.00027371035,0.00040471155,0.0003321775,0.0005566845,0.00030679125,0.0004271882,0.00014237012,0.000024985771],"category_scores_gemma":[0.009159176,0.00021010988,0.000115798764,0.00076706783,0.00031046636,0.000669399,0.00014979758,0.00014557946,0.0000060541274],"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":[0.000012907489,0.00023374928,0.00018855979,0.000006459403,0.000029695038,5.856712e-7,0.004066499,0.44418618,0.541009,0.009666757,0.000017077826,0.00058251957],"study_design_scores_gemma":[0.0005681711,0.0000536697,0.0005103755,0.000016068703,0.00003399,0.0000048814727,0.0048277113,0.79044145,0.17106766,0.032376777,0.00003925198,0.000060001294],"about_ca_topic_score_codex":0.000060315975,"about_ca_topic_score_gemma":9.141064e-7,"teacher_disagreement_score":0.36994135,"about_ca_system_score_codex":0.00005858917,"about_ca_system_score_gemma":0.000080610065,"threshold_uncertainty_score":0.9991871},"labels":[],"label_agreement":null},{"id":"W1597786057","doi":"10.1002/9780470057339.van010","title":"Nested Experimental Designs","year":2006,"lang":"en","type":"other","venue":"Encyclopedia of Environmetrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Nested set model; Differential (mechanical device); Subclass; Environmental science; Computer science; Mathematics; Engineering; Data mining; Biology","score_opus":0.1035037793585716,"score_gpt":0.3919361837683146,"score_spread":0.28843240440974305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597786057","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027320255,0.010540917,0.021032931,0.000010867442,0.0012301696,0.0005699054,0.00011939117,0.00012236046,0.9661003],"genre_scores_gemma":[0.0010543072,0.000555541,0.15511003,0.000043664742,0.00044855324,0.000037109687,0.00003786684,0.00056600914,0.84214693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99364704,0.0006434827,0.0012860055,0.0011043587,0.0028221817,0.00049691263],"domain_scores_gemma":[0.99521255,0.0017216055,0.0012418678,0.0015622869,0.000034749606,0.00022694332],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001671264,0.00060970074,0.0011144595,0.0026266412,0.000057292196,0.00007299876,0.0016944477,0.00068427925,0.009309728],"category_scores_gemma":[0.0019396106,0.0005264347,0.00038894147,0.0025450978,0.00045370634,0.00014933271,0.0004177067,0.00033768325,0.0013776864],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015420606,0.00044375475,0.0017869932,0.00000932307,0.000029311888,0.00003134739,0.00007285571,0.000053843934,0.0020477986,0.00026962586,0.98351395,0.011725773],"study_design_scores_gemma":[0.00041067562,0.00021960701,0.0013272945,0.000025813288,0.000033833705,0.000005968748,0.0001446064,0.000049898335,0.0051178257,0.00034754685,0.99173737,0.00057957735],"about_ca_topic_score_codex":0.0002756745,"about_ca_topic_score_gemma":0.000009128874,"teacher_disagreement_score":0.1340771,"about_ca_system_score_codex":0.00013335655,"about_ca_system_score_gemma":0.00007737661,"threshold_uncertainty_score":0.9997187},"labels":[],"label_agreement":null},{"id":"W1690361420","doi":"10.1002/mats.201400049","title":"Bayesian Design of Experiments Applied to a Complex Polymerization System: Nitrile Butadiene Rubber Production in a Train of CSTRs","year":2014,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"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","keywords":"Bayesian probability; Flexibility (engineering); Computer science; Bayesian optimization; Natural rubber; Bayesian inference; Process engineering; Materials science; Artificial intelligence; Engineering; Mathematics; Statistics","score_opus":0.07603592521570766,"score_gpt":0.3803337126649136,"score_spread":0.30429778744920594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1690361420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3376639,0.000028879318,0.66094,0.000016787786,0.000048029826,0.0005270433,0.000014119192,0.000013150182,0.00074810575],"genre_scores_gemma":[0.9484235,2.9755668e-7,0.05141405,0.000036287576,0.000013470389,0.000033999244,0.0000098136525,0.000015408086,0.000053163803],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965773,0.0017095017,0.0006916359,0.00040601974,0.0004444131,0.00017108678],"domain_scores_gemma":[0.99837726,0.000799966,0.00022300109,0.00040663322,0.000104542516,0.00008857968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034477634,0.00014473719,0.00036674176,0.0004324485,0.00009574898,0.000036578585,0.00020768239,0.00006412963,0.000072910705],"category_scores_gemma":[0.0010542575,0.00013200233,0.000049563012,0.00091835187,0.00012843886,0.00010572356,0.00006212922,0.00005003233,0.000003858827],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027070026,0.00009470992,0.00024412028,0.000012603089,0.000011850059,7.715175e-7,0.0021579019,0.10769523,0.84519374,0.039598834,0.0000027436633,0.0047167707],"study_design_scores_gemma":[0.0008281091,0.00023626692,0.0013768973,0.000057098263,0.000022849557,0.000006900228,0.0024099778,0.16899115,0.80349886,0.022315914,0.000029319675,0.00022664967],"about_ca_topic_score_codex":0.000010547143,"about_ca_topic_score_gemma":9.541088e-7,"teacher_disagreement_score":0.6107596,"about_ca_system_score_codex":0.000030109122,"about_ca_system_score_gemma":0.000022360995,"threshold_uncertainty_score":0.53828984},"labels":[],"label_agreement":null},{"id":"W1709872326","doi":"10.1007/978-3-319-30379-6_53","title":"Optimal Robust Designs of Step-Stress Accelerated Life Testing Experiments for Proportional Hazards Models","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Extrapolation; Reliability (semiconductor); Hazard; Proportional hazards model; Accelerated life testing; Delta method; Mathematics; Statistics; Reliability engineering; Function (biology); Weibull distribution; Applied mathematics; Computer science; Engineering","score_opus":0.650024958779505,"score_gpt":0.4754724647418152,"score_spread":0.17455249403768974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1709872326","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":[0.00012852081,0.00038980253,0.6108289,0.00005537786,0.00037446315,0.0014697777,0.00049089384,0.00009712359,0.38616514],"genre_scores_gemma":[0.003533686,0.000009147334,0.63706785,0.00010925195,0.00023082542,0.0001713784,0.00003623419,0.00013155931,0.35871008],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99198836,0.00018140713,0.0025685523,0.0015760045,0.0030537846,0.0006319148],"domain_scores_gemma":[0.9911737,0.002920076,0.0017574821,0.001080012,0.0026075586,0.00046119664],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030715268,0.00081542454,0.0014198183,0.0006220103,0.00023697005,0.000298982,0.0016329028,0.00065786164,0.0045826505],"category_scores_gemma":[0.0022457521,0.0005570734,0.00052871625,0.00019536402,0.0004604653,0.0008432869,0.00050988724,0.00027337542,0.00011189637],"study_design_candidate":"theoretical_or_conceptual","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":[0.0029775088,0.00096027594,0.00012641594,0.00021096935,0.0012106807,0.00006588561,0.0006395934,0.07707889,0.113726124,0.64416736,0.04467927,0.114157006],"study_design_scores_gemma":[0.006501995,0.0037009045,0.0000363473,0.0021006481,0.00032475957,0.000058640922,0.00090454466,0.51029116,0.30627346,0.15399086,0.011115632,0.0047010756],"about_ca_topic_score_codex":0.000013185301,"about_ca_topic_score_gemma":0.0000012750796,"teacher_disagreement_score":0.49017653,"about_ca_system_score_codex":0.00019976564,"about_ca_system_score_gemma":0.0011135648,"threshold_uncertainty_score":0.9996881},"labels":[],"label_agreement":null},{"id":"W1779186027","doi":"10.1002/cjs.11270","title":"Robust model‐based stratification sampling designs","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Alberta; MacEwan University","funders":"","keywords":"Statistics; Mean squared error; Robustness (evolution); Stratified sampling; Mathematics; Minimax; Sampling design; Sampling (signal processing); Econometrics; Regression analysis; Variance (accounting); Computer science; Population; Mathematical optimization","score_opus":0.6658353299386891,"score_gpt":0.46093345414289444,"score_spread":0.20490187579579466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1779186027","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":[0.0024073361,0.00015497125,0.99487746,0.00022837998,0.0004470225,0.000077120734,0.000172819,0.0000035695837,0.0016313295],"genre_scores_gemma":[0.3507094,7.3283366e-7,0.6489313,0.0001394295,0.000051082137,7.136914e-7,0.0000036290307,0.000010310571,0.00015336138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975995,0.00026959644,0.00081228436,0.0001677532,0.0008901101,0.0002607626],"domain_scores_gemma":[0.9955981,0.00080386474,0.00049219467,0.00027225874,0.0015177896,0.0013158162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004019205,0.00011599392,0.00024624047,0.00045236704,0.00012004741,0.00038199918,0.0005841911,0.000064806685,0.00018514402],"category_scores_gemma":[0.006311455,0.00009891335,0.000051287694,0.0003924644,0.00013781719,0.00029420326,0.000007467913,0.0001938367,0.000047945512],"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":[0.000046685673,0.00002364482,0.0011114479,0.0000030959068,0.000014525456,0.00011903482,0.0010757835,0.8926625,0.0019349636,0.02790461,0.048746362,0.026357338],"study_design_scores_gemma":[0.00061646156,0.00035714114,0.00069001055,0.000027061029,0.00003106569,0.000067437235,0.0017698171,0.8294401,0.0017854217,0.16206409,0.0028984307,0.00025292725],"about_ca_topic_score_codex":0.0004689924,"about_ca_topic_score_gemma":0.0019607279,"teacher_disagreement_score":0.34830207,"about_ca_system_score_codex":0.0003222383,"about_ca_system_score_gemma":0.005056224,"threshold_uncertainty_score":0.89695245},"labels":[],"label_agreement":null},{"id":"W1805695232","doi":"10.5267/j.msl.2015.8.001","title":"Optimal laptop VDU parameter setting using Taguchi method","year":2015,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Taguchi methods; Laptop; Computer science; Statistics; Engineering drawing; Industrial engineering; Mathematics; Machine learning; Engineering; Operating system","score_opus":0.2086883616781507,"score_gpt":0.4746946301537337,"score_spread":0.266006268475583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1805695232","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":[0.3652032,0.000024329216,0.6218372,0.002859717,0.0009748914,0.00038904432,0.0000018881594,0.000091245536,0.008618462],"genre_scores_gemma":[0.118043885,5.832308e-7,0.8731644,0.007966548,0.00006975648,0.000018778754,6.281029e-7,0.000020802938,0.0007145929],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99194056,0.0006998656,0.00075629674,0.0014500674,0.004175235,0.0009779988],"domain_scores_gemma":[0.9972413,0.00065877574,0.00033113934,0.0012071421,0.00011124336,0.00045039988],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02256607,0.0003091144,0.00036490816,0.0010466271,0.00048411882,0.0014892861,0.0027996346,0.000048226277,0.00014020692],"category_scores_gemma":[0.0016075763,0.00024915198,0.00014793176,0.003426273,0.0008082815,0.0019009969,0.0013748012,0.0002094678,0.00043624415],"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":[0.0001644407,0.00024858676,0.020801503,0.000022460557,0.00009932384,0.0004758446,0.0059925844,0.33548883,0.42272747,0.011825111,0.042250864,0.159903],"study_design_scores_gemma":[0.0018095033,0.00023947933,0.0075096944,0.000066605135,0.00009551448,0.00009763976,0.012793424,0.8690144,0.07169423,0.009507825,0.025567863,0.0016038447],"about_ca_topic_score_codex":0.00005950817,"about_ca_topic_score_gemma":5.6055086e-7,"teacher_disagreement_score":0.5335256,"about_ca_system_score_codex":0.0003632433,"about_ca_system_score_gemma":0.000040406703,"threshold_uncertainty_score":0.99999607},"labels":[],"label_agreement":null},{"id":"W183476718","doi":"","title":"Correction to \"All c-Bhaskar Rao designs with block size 3 and c >= -1 exist\".","year":2002,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Block (permutation group theory); Block size; Arithmetic; Combinatorics; Computer science","score_opus":0.12105832927030821,"score_gpt":0.37764119276679703,"score_spread":0.25658286349648884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W183476718","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94198626,0.0004181537,0.0015517696,0.00078964065,0.0058881566,0.00074660627,0.0000037466498,0.00018171217,0.048433956],"genre_scores_gemma":[0.9531594,0.000010936674,0.034313876,0.0006875463,0.000015664931,0.000038436454,3.470968e-7,0.000030790452,0.011743024],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969787,0.00045136578,0.00042716946,0.0007196219,0.0010401049,0.00038305638],"domain_scores_gemma":[0.99608004,0.002577881,0.00014437865,0.00063273264,0.0002113539,0.00035359143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001603072,0.00024667126,0.0003643673,0.00017054335,0.00020045757,0.00033974208,0.00048024344,0.00008528445,0.0004452592],"category_scores_gemma":[0.0024728838,0.00019100119,0.0000602124,0.0009601218,0.00012006414,0.00035115218,0.00015395538,0.00019722803,0.0006653816],"study_design_candidate":"not_applicable","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":[0.0011229516,0.001716371,0.018691251,0.000017447728,0.00021103905,0.00027699897,0.0125789605,0.0005168703,0.17514746,0.09448724,0.61319923,0.08203417],"study_design_scores_gemma":[0.0107945325,0.012213567,0.03767676,0.00026212036,0.00018881782,0.0007900412,0.0051097474,0.012860641,0.18012881,0.5696546,0.16668388,0.0036364696],"about_ca_topic_score_codex":0.00005002812,"about_ca_topic_score_gemma":0.00000699787,"teacher_disagreement_score":0.47516736,"about_ca_system_score_codex":0.000095227966,"about_ca_system_score_gemma":0.000022123877,"threshold_uncertainty_score":0.85523564},"labels":[],"label_agreement":null},{"id":"W1858569781","doi":"10.3968/j.sms.1923845220120501.1995","title":"Evaluation of Optimal Split-Plot Designs","year":2012,"lang":"en","type":"article","venue":"Studies in mathematical sciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorical variable; Plot (graphics); Set (abstract data type); Split plot; Mathematics; Algorithm; Optimal design; Mathematical optimization; Sample size determination; Computer science; Statistics","score_opus":0.8332496111643519,"score_gpt":0.6522853637196269,"score_spread":0.18096424744472506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1858569781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87021106,0.0057732835,0.021985801,0.0002416247,0.00069168414,0.00069205964,0.0000025692198,0.000029855863,0.10037204],"genre_scores_gemma":[0.75158256,0.000013644725,0.2482128,0.000023764313,0.00004225996,0.000046139103,4.8862596e-8,0.000004227916,0.00007457338],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9916283,0.0015228707,0.00094950374,0.0003845445,0.005019028,0.00049578527],"domain_scores_gemma":[0.9936063,0.0051904134,0.00024997018,0.00033567447,0.0005117551,0.00010584205],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.077058114,0.0001550305,0.0004968174,0.00026875272,0.00016915723,0.00005655055,0.0008145476,0.000050337032,0.00079025264],"category_scores_gemma":[0.03276842,0.00009385297,0.00009239977,0.0016229404,0.0020475965,0.0006733756,0.0003480011,0.000088011366,0.0002248573],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000677867,0.0020834862,0.025823537,0.00011002572,0.00016575423,0.0000037037398,0.06653111,0.0058132433,0.015329236,0.70394963,0.0025411844,0.17758131],"study_design_scores_gemma":[0.0005128723,0.0003695619,0.005916618,0.00014088058,0.000084652966,0.000011820172,0.057471912,0.049734596,0.016624803,0.86866236,0.00012683075,0.00034312494],"about_ca_topic_score_codex":0.0000037121365,"about_ca_topic_score_gemma":0.0000016273902,"teacher_disagreement_score":0.226227,"about_ca_system_score_codex":0.00012806927,"about_ca_system_score_gemma":0.0000904888,"threshold_uncertainty_score":0.975379},"labels":[],"label_agreement":null},{"id":"W1877248584","doi":"10.1080/00224065.2015.11918139","title":"Response Surface Methodology Using Split-Plot Definitive Screening Designs","year":2015,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"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 Manitoba","funders":"National Science Council","keywords":"Plot (graphics); Split plot; Computer science; Restricted randomization; Column (typography); Projection (relational algebra); Surface (topology); Design of experiments; Statistics; Mathematics; Algorithm; Geometry","score_opus":0.8766149783657292,"score_gpt":0.6257243463458664,"score_spread":0.25089063201986284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1877248584","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52145267,0.0005858399,0.4736762,0.0035335717,0.0003781445,0.00009101697,0.00000519066,0.000044294542,0.00023306985],"genre_scores_gemma":[0.31590745,0.0000050303743,0.6834188,0.00050328317,0.000044337376,9.636371e-7,1.4558302e-7,0.000020848784,0.00009915994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.978144,0.016422566,0.002542284,0.00052234426,0.001800416,0.0005683753],"domain_scores_gemma":[0.97986585,0.014582905,0.0023018632,0.0007978507,0.0021158746,0.00033567127],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.09523331,0.00027620376,0.0012612445,0.0014089108,0.00015064795,0.0001162444,0.001642203,0.000528835,0.000091992326],"category_scores_gemma":[0.09993301,0.00021824722,0.0002928179,0.0022456462,0.00080657506,0.00057293475,0.00046692838,0.0008682674,0.00006366226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010632718,0.00028684127,0.00796242,0.0000043795203,0.00019311804,0.00043453235,0.0023904368,0.0030563695,0.9004233,0.040255874,0.0017842432,0.032575753],"study_design_scores_gemma":[0.00349327,0.0037959213,0.003614446,0.00007096022,0.00009463952,0.0036517913,0.03892493,0.0015713852,0.4683966,0.46617,0.009503321,0.00071273546],"about_ca_topic_score_codex":0.00004917053,"about_ca_topic_score_gemma":0.000003743869,"teacher_disagreement_score":0.4320267,"about_ca_system_score_codex":0.00032392604,"about_ca_system_score_gemma":0.00064202864,"threshold_uncertainty_score":0.9316477},"labels":[],"label_agreement":null},{"id":"W1902241019","doi":"10.1016/j.ejor.2015.08.033","title":"A new Bayesian approach to multi-response surface optimization integrating loss function with posterior probability","year":2015,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Robustness (evolution); Computer science; Bayesian optimization; Bayesian probability; Reliability (semiconductor); Mathematical optimization; Multivariate statistics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.3873727016503927,"score_gpt":0.486740999509749,"score_spread":0.09936829785935625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902241019","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":[0.16470094,0.00006307018,0.82943016,0.0014021313,0.00014054078,0.00043485503,0.0000035500361,0.000010648789,0.0038141233],"genre_scores_gemma":[0.4091906,4.909356e-7,0.5888735,0.000084748484,0.0001268911,0.0000018800536,0.0000018113167,0.000022537206,0.0016975735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9810716,0.012762787,0.0010713711,0.00049470353,0.0042494955,0.00035007755],"domain_scores_gemma":[0.9919962,0.0013098419,0.00026564635,0.00041500462,0.005166068,0.00084722973],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.062289976,0.00018390943,0.0003012998,0.0005028056,0.00027635568,0.001142208,0.0009708447,0.000035456287,0.00020573547],"category_scores_gemma":[0.019546252,0.0001177795,0.000081242084,0.0015873977,0.00015447175,0.001145633,0.00025543387,0.0005855267,0.00016266554],"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":[0.015324126,0.00039142373,0.0018496558,0.0000040244504,0.000036778023,0.00009373774,0.0054187286,0.9307947,0.028751703,0.00043759242,0.005294857,0.011602687],"study_design_scores_gemma":[0.016930087,0.03668132,0.05747248,0.0005389283,0.000057134894,0.003227124,0.040100716,0.80920345,0.018079802,0.0015132349,0.014432647,0.0017630841],"about_ca_topic_score_codex":0.000017083295,"about_ca_topic_score_gemma":0.000002756976,"teacher_disagreement_score":0.24448964,"about_ca_system_score_codex":0.00034342465,"about_ca_system_score_gemma":0.0016007083,"threshold_uncertainty_score":0.9998947},"labels":[],"label_agreement":null},{"id":"W191350240","doi":"10.22237/jmasm/1241136180","title":"Aligned Rank Tests for Interactions in Split-Plot Designs: Distributional Assumptions and Stochastic Heterogeneity","year":2009,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Univariate; Rank (graph theory); Statistics; Mathematics; Multivariate statistics; Ranking (information retrieval); Plot (graphics); Context (archaeology); Code (set theory); R package; Split plot; Restricted randomization; Syntax; Econometrics; Data mining; Computer science; Artificial intelligence; Programming language","score_opus":0.2996917279586515,"score_gpt":0.5578593513974754,"score_spread":0.25816762343882393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W191350240","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":[0.007930221,0.00028904638,0.9902214,0.00040439927,0.00023746018,0.00047220904,0.00020373926,0.000014452459,0.0002270982],"genre_scores_gemma":[0.44363415,0.0000035651587,0.5561363,0.00011417934,0.0000623647,0.000022167262,0.0000048300894,0.0000098358805,0.000012589009],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9952986,0.0012346507,0.0016479421,0.00048743084,0.0009282462,0.0004031022],"domain_scores_gemma":[0.9804412,0.017926224,0.0005838671,0.00028625803,0.00038379533,0.00037863982],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00963979,0.0002634239,0.0007972405,0.00041626065,0.00018183963,0.00023521857,0.00043925835,0.00009974143,0.00010951932],"category_scores_gemma":[0.01009665,0.00020977599,0.00015887416,0.00040433867,0.00020900955,0.0002998759,0.00006705794,0.00026885737,0.0000071906943],"study_design_candidate":"theoretical_or_conceptual","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":[0.0016582682,0.00065980013,0.00009528934,0.000009007834,0.00006186534,0.000017448445,0.00025617873,0.0035008905,0.30919442,0.13048097,0.0007352259,0.55333066],"study_design_scores_gemma":[0.0016918381,0.0005112989,0.021708941,0.000031267715,0.00007472335,0.00015187396,0.00011404029,0.10322079,0.005337424,0.8666543,0.00023975167,0.00026377788],"about_ca_topic_score_codex":0.000002216764,"about_ca_topic_score_gemma":0.0000035319854,"teacher_disagreement_score":0.7361733,"about_ca_system_score_codex":0.00021130318,"about_ca_system_score_gemma":0.00013781134,"threshold_uncertainty_score":0.9982417},"labels":[],"label_agreement":null},{"id":"W1931398059","doi":"10.1002/cjs.11256","title":"Efficient semiparametric mixture inferences on cure rate models for competing risks","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"Health Canada; National Institutes of Health","keywords":"Nonparametric statistics; Statistics; Semiparametric regression; Multinomial distribution; Econometrics; Soft tissue sarcoma; Cancer; Multinomial logistic regression; Mixture model; Medicine; Proportional hazards model; Mathematics; Internal medicine; Sarcoma; Pathology","score_opus":0.40898035703447816,"score_gpt":0.4564198491778415,"score_spread":0.04743949214336335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1931398059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037229653,0.00070693257,0.95558935,0.00034444,0.0014872599,0.00022849906,0.0007131226,0.0000049047926,0.003695828],"genre_scores_gemma":[0.62312555,0.000004371597,0.37627095,0.00023756493,0.00011897108,0.000002299284,0.0000033546198,0.000014370005,0.0002225817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971537,0.00048560597,0.000857868,0.0002293809,0.00088589336,0.0003875505],"domain_scores_gemma":[0.99171126,0.0043466724,0.0006956619,0.000232331,0.0017320375,0.0012820328],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00622783,0.00017468903,0.0004230431,0.00085521373,0.0001791772,0.00038771244,0.00069282745,0.00009568182,0.00007746567],"category_scores_gemma":[0.015089781,0.00013086888,0.00009133874,0.0007998094,0.00015583317,0.000110602305,0.000019328416,0.00031685983,0.00003121479],"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":[0.00015642111,0.000050441187,0.0014458311,0.000009818798,0.00004399032,0.00019026923,0.002697918,0.8233405,0.00010789294,0.037774365,0.086206466,0.04797609],"study_design_scores_gemma":[0.0014930732,0.0016848026,0.0009698601,0.00012074392,0.000059594837,0.000096168355,0.0056636618,0.7987638,0.0006835338,0.16960406,0.020415341,0.00044532886],"about_ca_topic_score_codex":0.000545329,"about_ca_topic_score_gemma":0.00073767296,"teacher_disagreement_score":0.5858959,"about_ca_system_score_codex":0.0002665094,"about_ca_system_score_gemma":0.0019446647,"threshold_uncertainty_score":0.99320656},"labels":[],"label_agreement":null},{"id":"W1940783033","doi":"10.1002/sim.6110","title":"Shift-invariant target in allocation problems","year":2014,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Scale invariance; Computer science; Mathematical optimization; Optimal allocation; Mathematics; Statistics","score_opus":0.10073763129737792,"score_gpt":0.4457221730689747,"score_spread":0.34498454177159676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1940783033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029360862,0.0001539038,0.980892,0.0014756591,0.0006680117,0.00036648108,0.000015141968,0.000017046948,0.013475696],"genre_scores_gemma":[0.5223239,0.000014420307,0.47680235,0.00039586448,0.00010655999,0.00004212634,0.000019337282,0.000012998452,0.00028241982],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996263,0.0008511324,0.0009688251,0.0004505407,0.0011798951,0.00028660204],"domain_scores_gemma":[0.99477303,0.004379948,0.00018827323,0.0004439641,0.0001048358,0.00010994662],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01153594,0.00014499544,0.00041677136,0.00045856385,0.00003471802,0.0000353867,0.00048655106,0.00007107073,0.0009022531],"category_scores_gemma":[0.020138092,0.00010568843,0.000013677191,0.0009168888,0.00023069282,0.00011868111,0.000072308525,0.00023138423,0.00013696354],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011295514,0.00027868757,0.03978525,0.000045704677,0.0000097191605,0.00007736907,0.010331881,0.005049238,0.014162682,0.8081876,0.017032547,0.10492636],"study_design_scores_gemma":[0.001482693,0.0004457093,0.07327709,0.00011993083,0.0000045487363,0.000003897174,0.0005493037,0.12924705,0.00049696333,0.7825622,0.011607573,0.00020304201],"about_ca_topic_score_codex":0.000473505,"about_ca_topic_score_gemma":0.00036705338,"teacher_disagreement_score":0.51938784,"about_ca_system_score_codex":0.0000946958,"about_ca_system_score_gemma":0.000049731734,"threshold_uncertainty_score":0.9881157},"labels":[],"label_agreement":null},{"id":"W1941690598","doi":"10.3968/j.sms.1923845220120401.1522","title":"Sensitivity of Design Optimality Criteria to Second-order Response SurfaceModel Restrictions and Center Point Replications","year":2012,"lang":"en","type":"article","venue":"Studies in mathematical sciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Center (category theory); Mathematics; Point (geometry); Variance (accounting); Sensitivity (control systems); Order (exchange); Statistics; Linear model; Mathematical optimization; Engineering; Geometry","score_opus":0.4462560441076815,"score_gpt":0.5532982038796652,"score_spread":0.10704215977198372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1941690598","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71865165,0.0003099416,0.2746455,0.0017560478,0.0001869239,0.00053813786,0.000019134133,0.000024842362,0.0038678255],"genre_scores_gemma":[0.58523464,0.0000073436427,0.41448486,0.0000900979,0.000010556054,0.00003007086,5.9315074e-8,0.0000037115997,0.00013864251],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9946144,0.00272161,0.0008254866,0.00053535926,0.0008680268,0.00043510317],"domain_scores_gemma":[0.984464,0.014385225,0.00016690213,0.0005105102,0.0002809887,0.0001924104],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03956196,0.00016491977,0.0004903081,0.00028700472,0.00030582256,0.000087505316,0.0003343639,0.000051664756,0.0001953172],"category_scores_gemma":[0.032859612,0.000112211506,0.00005493514,0.0017761836,0.0014554481,0.0005908824,0.0005473451,0.0001011043,0.000051508952],"study_design_candidate":"theoretical_or_conceptual","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":[0.002726196,0.0061716717,0.15510345,0.0003085693,0.0002651653,0.000025038864,0.14857489,0.006444834,0.45204988,0.19153088,0.025263628,0.011535782],"study_design_scores_gemma":[0.0013426223,0.0016098496,0.27055615,0.00046166303,0.000065191576,0.00022316421,0.07972487,0.07220943,0.053442035,0.517669,0.0011596794,0.0015363011],"about_ca_topic_score_codex":0.000018971712,"about_ca_topic_score_gemma":0.000011892785,"teacher_disagreement_score":0.39860785,"about_ca_system_score_codex":0.00007296472,"about_ca_system_score_gemma":0.00005374275,"threshold_uncertainty_score":0.9889731},"labels":[],"label_agreement":null},{"id":"W194305686","doi":"10.1023/a:1019288220413","title":"Maximization of Manufacturing Yield of Systems with Arbitrary Distributions of Component Values","year":2000,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Theory of computation; Bounded function; Hypercube; Monte Carlo method; Mathematical optimization; Probability density function; Nonlinear system; Mathematics; Applied mathematics; Function (biology); Random variable; Yield (engineering); Computer science; Algorithm; Discrete mathematics; Statistics; Mathematical analysis","score_opus":0.4890581218960466,"score_gpt":0.5390253233917611,"score_spread":0.04996720149571454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W194305686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9783577,0.0004966091,0.013918187,0.00030987026,0.000027242615,0.00048096644,0.00021282624,0.0000061378223,0.006190505],"genre_scores_gemma":[0.9845866,0.00013555822,0.014345731,0.0000051610846,0.000012525236,0.00002269939,0.000021489417,0.0000078088415,0.00086243666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99589133,0.0008344238,0.0009583235,0.00024699958,0.001863795,0.00020510351],"domain_scores_gemma":[0.9963526,0.0012152918,0.00012656809,0.00059613906,0.0016314313,0.00007797206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043354747,0.00009099196,0.0003809317,0.00044887426,0.00014366585,0.000056359087,0.0005394144,0.000060196002,0.00081309985],"category_scores_gemma":[0.00081984495,0.00006642669,0.00008376158,0.0009299661,0.00053563406,0.00037097864,0.000079270336,0.00014262382,0.000012004197],"study_design_candidate":"bench_or_experimental","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":[0.0009829028,0.0021348146,0.0031148938,0.00023873898,0.00023185997,0.0000069942894,0.0030329232,0.5470456,0.40130678,0.02345353,0.0032290728,0.015221922],"study_design_scores_gemma":[0.0001676814,0.00055305666,0.0069087828,0.00015905336,0.0000050990457,0.0000037512905,0.0011871379,0.016955722,0.9730361,0.0008245536,0.00012846795,0.000070605296],"about_ca_topic_score_codex":0.00091066724,"about_ca_topic_score_gemma":0.000012649123,"teacher_disagreement_score":0.5717293,"about_ca_system_score_codex":0.000013372856,"about_ca_system_score_gemma":0.00015622926,"threshold_uncertainty_score":0.89028734},"labels":[],"label_agreement":null},{"id":"W1943160398","doi":"10.3968/j.pam.1925252820120402.1517","title":"Comparison of Optimality Criteria of Reduced Models for Response Surface Designs with Restricted Randomization","year":2012,"lang":"en","type":"article","venue":"Progress in applied mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Restricted randomization; Mathematics; Statistics; Variance (accounting); Central composite design; Set (abstract data type); Split plot; Response surface methodology; Design of experiments; Computer science; Randomization","score_opus":0.3305761640566564,"score_gpt":0.5173610822794575,"score_spread":0.18678491822280113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943160398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.527951,0.00024449857,0.4698488,0.000015908441,0.0000388715,0.0013685285,0.000013219151,0.00002010903,0.00049904274],"genre_scores_gemma":[0.50742805,0.0000012400175,0.49243167,0.0000013130722,0.000005388338,0.000103490565,0.0000023451719,0.000015816508,0.000010694862],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99639446,0.00048059603,0.0015414024,0.0002988132,0.0009517082,0.00033303504],"domain_scores_gemma":[0.99287206,0.005022916,0.0010748777,0.000620658,0.00031394066,0.00009553973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009062295,0.00020759004,0.0009760997,0.00021973997,0.00004818106,0.000048745886,0.00050969305,0.00012690008,0.000026063153],"category_scores_gemma":[0.0013051764,0.00015211951,0.00007610706,0.00089779287,0.0002891098,0.00023455567,0.000099838915,0.00009516291,0.0000020017164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.06440887,0.006551221,0.011049681,0.00074379856,0.00012153702,7.487648e-7,0.03467753,0.03388595,0.77823824,0.061303567,0.0002850795,0.008733749],"study_design_scores_gemma":[0.004143565,0.00029283736,0.000678413,0.00009756347,0.000044228913,0.0000020262803,0.002635605,0.29687524,0.67527646,0.019720765,0.000008296057,0.00022501918],"about_ca_topic_score_codex":0.0000013262108,"about_ca_topic_score_gemma":2.6163633e-7,"teacher_disagreement_score":0.26298928,"about_ca_system_score_codex":0.000052445095,"about_ca_system_score_gemma":0.00007555167,"threshold_uncertainty_score":0.6203253},"labels":[],"label_agreement":null},{"id":"W1955080364","doi":"10.1002/cjs.11260","title":"Covariate‐adjusted response adaptive designs incorporating covariates with and without treatment interactions","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Covariate; Statistical inference; Inference; Computer science; Conditional independence; Independence (probability theory); R package; Econometrics; Machine learning; Statistics; Artificial intelligence; Mathematics","score_opus":0.3704392104197118,"score_gpt":0.4250319695548762,"score_spread":0.054592759135164426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1955080364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080762096,0.00020564861,0.91694677,0.00040067098,0.0004412378,0.00021095268,0.0003170253,0.000006639362,0.00070892985],"genre_scores_gemma":[0.52191496,0.000001408216,0.4776653,0.000050929582,0.000033714543,0.0000021789078,0.0000015621538,0.000012046819,0.00031786415],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99728453,0.0010443851,0.00067513174,0.00021167532,0.00053829595,0.00024600347],"domain_scores_gemma":[0.9944557,0.002096804,0.0006916328,0.00020598316,0.001303562,0.001246352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031637938,0.00018526718,0.00040107724,0.0005310769,0.00019345731,0.00036187243,0.00024114021,0.000048516977,0.00008811449],"category_scores_gemma":[0.004673653,0.00012706054,0.00003107614,0.00044423432,0.00029093216,0.0004034546,0.000016334952,0.00018309106,0.000019230645],"study_design_candidate":"theoretical_or_conceptual","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":[0.049557064,0.0007417905,0.294321,0.000023340133,0.0024102181,0.011912932,0.10642894,0.037320312,0.02221658,0.26275212,0.054871853,0.15744388],"study_design_scores_gemma":[0.030893948,0.07359219,0.21493143,0.0009412357,0.0015931004,0.018062772,0.17128359,0.1496139,0.0144560905,0.28019714,0.040653054,0.0037815517],"about_ca_topic_score_codex":0.0043720296,"about_ca_topic_score_gemma":0.01348434,"teacher_disagreement_score":0.44115287,"about_ca_system_score_codex":0.0006025529,"about_ca_system_score_gemma":0.003979989,"threshold_uncertainty_score":0.75245786},"labels":[],"label_agreement":null},{"id":"W1963676859","doi":"10.1002/cjs.5550340404","title":"Some robust design strategies for percentile estimation in binary response models","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Percentile; Minimax; Binary number; Mathematical optimization; Estimation; Computer science; Estimation theory; Mathematics; Algorithm; Statistics; Engineering","score_opus":0.2014466815106305,"score_gpt":0.3944487031194451,"score_spread":0.19300202160881458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963676859","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":[0.031091245,0.00039066287,0.96735364,0.00018045896,0.00036139358,0.00021753536,0.00027400345,0.0000027772865,0.0001282823],"genre_scores_gemma":[0.40158916,0.0000024540996,0.5980772,0.000045960496,0.000045858007,0.0000035241153,0.0000038617736,0.000012101257,0.00021992139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970726,0.00075181766,0.0010903452,0.00018924772,0.00056911283,0.00032688683],"domain_scores_gemma":[0.9954792,0.003042085,0.0004405994,0.00020165803,0.0005346025,0.00030185297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066978224,0.00013606084,0.00032747712,0.0008661293,0.00013354736,0.00047892745,0.00045681986,0.000076356024,0.00012620293],"category_scores_gemma":[0.0024205097,0.00011854368,0.000068854555,0.0003544786,0.00013911827,0.0010430611,0.000009700692,0.00015060631,0.000012706561],"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":[0.0003668505,0.000021580527,0.000076438926,0.0000036942838,0.000004647569,0.0002071812,0.0005050138,0.9282731,0.002048105,0.036064003,0.030074814,0.0023545623],"study_design_scores_gemma":[0.00035024743,0.00032267664,0.0030441287,0.00002172842,0.0000080573745,0.000040265768,0.0013302284,0.53155416,0.00021765985,0.46273014,0.00026880475,0.00011191204],"about_ca_topic_score_codex":0.001448218,"about_ca_topic_score_gemma":0.0019450194,"teacher_disagreement_score":0.42666614,"about_ca_system_score_codex":0.00034081133,"about_ca_system_score_gemma":0.0027159217,"threshold_uncertainty_score":0.48340708},"labels":[],"label_agreement":null},{"id":"W1965247940","doi":"10.2307/3315869","title":"Bounds on the maximum number of clear two‐factor interactions for 2<sup><i>m‐p</i></sup> designs of resolution III and IV","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Factor (programming language); Fractional factorial design; Resolution (logic); Mathematics; Upper and lower bounds; Combinatorics; Factorial experiment; Statistics; Computer science; Mathematical analysis; Artificial intelligence","score_opus":0.21832012318510416,"score_gpt":0.40652571316754493,"score_spread":0.18820558998244077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965247940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.301332,0.00044638477,0.6863183,0.001717146,0.0009505664,0.00056861056,0.002370642,0.000005064264,0.006291338],"genre_scores_gemma":[0.8302414,0.00001860951,0.16896506,0.00016635527,0.00006436411,0.0000022323513,0.0000011933851,0.0000145389295,0.00052623247],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980324,0.0002990836,0.00081584457,0.0001416737,0.000484651,0.00022636351],"domain_scores_gemma":[0.9946932,0.0034816316,0.0006104297,0.00023493431,0.00064263266,0.0003371287],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013855732,0.00011713252,0.0003100065,0.0002615145,0.00019353084,0.000108629974,0.0003520234,0.000046160614,0.0016282413],"category_scores_gemma":[0.0033562242,0.00008324439,0.00008887415,0.000278574,0.00035339204,0.00019139757,0.000015315121,0.00020529461,0.000015198525],"study_design_candidate":"not_applicable","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":[0.0015352229,0.00040444487,0.008450942,0.00008092225,0.0005183105,0.00020330782,0.029836677,0.007922927,0.010182769,0.26458475,0.48889288,0.18738684],"study_design_scores_gemma":[0.008388393,0.0079572555,0.013059557,0.00078570785,0.00047717587,0.0016372881,0.031620882,0.3617197,0.015975144,0.3796494,0.1773097,0.0014197916],"about_ca_topic_score_codex":0.00078895694,"about_ca_topic_score_gemma":0.0008883373,"teacher_disagreement_score":0.52890944,"about_ca_system_score_codex":0.00012533239,"about_ca_system_score_gemma":0.00023975736,"threshold_uncertainty_score":0.9992844},"labels":[],"label_agreement":null},{"id":"W1967636732","doi":"10.1016/j.cherd.2009.01.001","title":"A new definition of mixing and segregation: Three dimensions of a key process variable","year":2009,"lang":"en","type":"article","venue":"Process Safety and Environmental Protection","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":127,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mixing (physics); Variance (accounting); Mathematics; Dimension (graph theory); Context (archaeology); Robustness (evolution); Scale (ratio); Field (mathematics); Statistical physics; Computer science; Physics; Combinatorics; Pure mathematics","score_opus":0.06359765893898006,"score_gpt":0.32389812291503584,"score_spread":0.2603004639760558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967636732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27787307,0.00085578265,0.7177608,0.00023815178,0.000056689463,0.00080199615,0.000019008196,0.000028691256,0.0023658029],"genre_scores_gemma":[0.9581725,0.000066877794,0.041646622,0.000028036573,0.000015591022,0.000014887729,0.0000043934056,0.0000072965913,0.00004382157],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99831694,0.000088876404,0.0005196116,0.00037374755,0.0005643354,0.00013645909],"domain_scores_gemma":[0.99928117,0.000103211394,0.0003369011,0.00015685117,0.000030230598,0.000091644986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008563123,0.00014209456,0.0002596575,0.00012077122,0.00018386496,0.00003133289,0.000109640925,0.000096319854,0.0001006676],"category_scores_gemma":[0.00019804556,0.000109876906,0.00003329894,0.0003606208,0.00012840702,0.0006088636,0.00004413584,0.00011707275,0.0000030819601],"study_design_candidate":"theoretical_or_conceptual","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":[0.0028824874,0.0006311695,0.005328865,0.00028768854,0.00004934296,0.0000022837412,0.0059364694,0.008321824,0.59637713,0.00892018,0.0000081350745,0.37125444],"study_design_scores_gemma":[0.0016058682,0.0014097844,0.021750433,0.00018170298,0.00005621991,0.000090031965,0.0026236959,0.026513688,0.13791385,0.80740345,0.00008581479,0.00036545726],"about_ca_topic_score_codex":0.000038300794,"about_ca_topic_score_gemma":0.000002207332,"teacher_disagreement_score":0.79848325,"about_ca_system_score_codex":0.000030833482,"about_ca_system_score_gemma":0.000028794128,"threshold_uncertainty_score":0.448065},"labels":[],"label_agreement":null},{"id":"W1967860508","doi":"10.1016/s0378-3758(02)00248-3","title":"Max–min multiple comparison procedure for isotonic dose–response curves","year":2002,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Studentized range; Pairwise comparison; Isotonic; Statistics; Confidence interval; Range (aeronautics); Multiple comparisons problem; Simple (philosophy); Combinatorics; Standard deviation","score_opus":0.2953533969958073,"score_gpt":0.5057111480488095,"score_spread":0.21035775105300214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967860508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1698503,0.005955613,0.8222235,0.00077964924,0.00028147863,0.0002653338,0.00010448657,0.000015674956,0.0005239796],"genre_scores_gemma":[0.8031389,0.000047846886,0.19627284,0.00022738203,0.000041407744,0.000005279241,0.0000010874256,0.0000076084025,0.00025762897],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99743074,0.0003736658,0.00094247423,0.00023976115,0.0007628787,0.00025046224],"domain_scores_gemma":[0.9832043,0.015588518,0.0004832484,0.00013959128,0.00034106674,0.00024326066],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0032640828,0.00015064495,0.00050273445,0.00016883625,0.00013535604,0.00020878973,0.0003466747,0.00007004523,0.00031397588],"category_scores_gemma":[0.0306784,0.00010378809,0.000064442844,0.00020082417,0.00018090475,0.00033684628,0.000054071537,0.00028989709,0.000022205892],"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":[0.0138812605,0.0017166189,0.24166687,0.000513209,0.00021555756,0.0003962523,0.0108656995,0.0029342035,0.05725012,0.009767713,0.42705843,0.23373407],"study_design_scores_gemma":[0.0047578663,0.008314757,0.27012956,0.0017081732,0.000119412565,0.0005415017,0.0038297183,0.6506261,0.00478683,0.027722191,0.026555646,0.00090822513],"about_ca_topic_score_codex":0.0000023701289,"about_ca_topic_score_gemma":4.0296533e-7,"teacher_disagreement_score":0.6476919,"about_ca_system_score_codex":0.00002880347,"about_ca_system_score_gemma":0.000060252136,"threshold_uncertainty_score":0.9774866},"labels":[],"label_agreement":null},{"id":"W1969203999","doi":"10.5539/jmr.v3n2p200","title":"An Algorithm for Constructing a D-Optimal 2^K Factorial Design for Linear Model Containing Main Effects and One-Two Factor Interaction","year":2011,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Factorial experiment; Matrix (chemical analysis); Factorial; Algorithm; Design matrix; Construct (python library); Java Programming Language; Order (exchange); Fractional factorial design; Java; Mathematical optimization; Linear programming; Factor (programming language); Optimal design; Combinatorics; Linear model; Computer science; Statistics; Programming language","score_opus":0.6601359548389913,"score_gpt":0.5883080652928527,"score_spread":0.07182788954613861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969203999","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":[0.17507447,0.000033886252,0.8235162,0.0000143338375,0.00036558637,0.000891964,0.000019740823,0.000008521769,0.000075291246],"genre_scores_gemma":[0.2370138,0.0000046653136,0.762579,0.0000064300193,0.00029707237,0.000033501372,4.040273e-7,0.000029385239,0.000035731333],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99550116,0.0009337551,0.0011431484,0.0003165708,0.001632848,0.0004725006],"domain_scores_gemma":[0.98031336,0.016797695,0.0007128574,0.00028237284,0.0015819588,0.00031176754],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.020874878,0.00018507993,0.0006280985,0.00064267777,0.00026262953,0.00036853316,0.000643405,0.000128949,0.000037654598],"category_scores_gemma":[0.0110003315,0.00013850477,0.0001780744,0.00023182872,0.00018532926,0.00093442394,0.00011167673,0.00049783103,0.0000036410313],"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":[0.005179497,0.0016893167,0.0001115179,0.00031386505,0.00038156958,0.000049927537,0.040406592,0.0024370207,0.57783103,0.009981411,0.00033327535,0.36128497],"study_design_scores_gemma":[0.0018878235,0.0037665705,0.0000065096137,0.000114147115,0.000021904187,0.00010804223,0.007106888,0.77181965,0.13981736,0.07520417,0.000014298678,0.00013263554],"about_ca_topic_score_codex":0.000004890559,"about_ca_topic_score_gemma":9.295388e-7,"teacher_disagreement_score":0.76938266,"about_ca_system_score_codex":0.00015407318,"about_ca_system_score_gemma":0.0003475579,"threshold_uncertainty_score":0.9973304},"labels":[],"label_agreement":null},{"id":"W1970823692","doi":"10.1002/masy.201000046","title":"Diagnostic Checks and Measures of Information in the Bayesian Design of Experiments with Complex Polymerizations","year":2011,"lang":"en","type":"article","venue":"Macromolecular Symposia","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"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","keywords":"Bayesian probability; Factorial experiment; Fractional factorial design; Computer science; Reliability engineering; Quality (philosophy); Polymerization; Nitroxide mediated radical polymerization; Design of experiments; Mathematics; Radical polymerization; Statistics; Chemistry; Artificial intelligence; Machine learning; Engineering; Organic chemistry","score_opus":0.15654033627861919,"score_gpt":0.36001906494376495,"score_spread":0.20347872866514577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970823692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13902928,0.00035196668,0.8559216,0.00003883013,0.000027690052,0.0006103583,0.000009807646,0.000008903715,0.0040015467],"genre_scores_gemma":[0.90840805,0.000008764295,0.09142896,0.00009623155,0.0000023150862,0.000040430656,0.0000032272483,0.0000074475747,0.000004588924],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975963,0.000727306,0.00056488166,0.00017238026,0.0007888114,0.00015029128],"domain_scores_gemma":[0.9986042,0.0005471636,0.0002729558,0.00041047242,0.000116542484,0.000048697333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013259278,0.0001303825,0.00024185696,0.00023209635,0.0000559488,0.00005678793,0.0004918617,0.00004620917,0.00006610198],"category_scores_gemma":[0.0005884659,0.00008294436,0.000037432506,0.000621346,0.00024831377,0.00031492996,0.000066786255,0.000057969075,0.0000038391618],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026126354,0.00031916186,0.018874219,0.000016413045,0.00005149464,0.000024495777,0.03549188,0.00054198445,0.9271598,0.011120103,0.00004698528,0.006092195],"study_design_scores_gemma":[0.0009844983,0.00081205834,0.0671674,0.00006052382,0.00003333662,0.000054291846,0.0047591976,0.004727911,0.9193882,0.0017014848,0.00007759099,0.0002335605],"about_ca_topic_score_codex":0.00014180198,"about_ca_topic_score_gemma":0.0000024560838,"teacher_disagreement_score":0.7693788,"about_ca_system_score_codex":0.00001118034,"about_ca_system_score_gemma":0.000041497875,"threshold_uncertainty_score":0.3382373},"labels":[],"label_agreement":null},{"id":"W1973353515","doi":"10.2307/3316054","title":"An adaptive randomized design with application to estimation","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":83,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptive design; Estimation; Simple (philosophy); Computer science; Mathematics; Mathematical optimization; Population; Completely randomized design; Statistics; Engineering","score_opus":0.10743609521977451,"score_gpt":0.4002492733428469,"score_spread":0.2928131781230724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973353515","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":[0.0024430894,0.00005066706,0.9961929,0.0001909107,0.000119664895,0.00040754257,0.0000389021,0.0000039847523,0.00055237644],"genre_scores_gemma":[0.38180727,0.0000018498799,0.6179112,0.00016024701,0.00003388387,0.000007251043,0.0000016008179,0.000009222777,0.00006743713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753076,0.0007427041,0.0006641314,0.00017835968,0.0006695469,0.00021447381],"domain_scores_gemma":[0.99559426,0.0017722399,0.000453053,0.00024912317,0.00096462254,0.0009667091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004714634,0.00011756927,0.00040432703,0.0005004883,0.0001288031,0.00023825004,0.00045122812,0.00004121084,0.00020495182],"category_scores_gemma":[0.00300912,0.00008293283,0.000037185742,0.00059089,0.00015899778,0.00035998784,0.000004768621,0.00011646168,0.00006954601],"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":[0.034831807,0.000067077315,0.00060873234,0.0000024086494,0.00008830046,0.00056005537,0.004149945,0.39557794,0.0017878317,0.052986912,0.011716413,0.49762258],"study_design_scores_gemma":[0.028173544,0.0035102041,0.002176408,0.00005892594,0.0001287106,0.0007048476,0.0022686024,0.7939001,0.0017851752,0.16363317,0.0031454966,0.0005148315],"about_ca_topic_score_codex":0.0011303901,"about_ca_topic_score_gemma":0.0019072596,"teacher_disagreement_score":0.49710774,"about_ca_system_score_codex":0.00018398791,"about_ca_system_score_gemma":0.00093499897,"threshold_uncertainty_score":0.3602415},"labels":[],"label_agreement":null},{"id":"W1973360772","doi":"10.1016/j.jspi.2009.02.010","title":"De-aliasing effects using semifoldover techniques","year":2009,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Factor (programming language); Aliasing; Factorial experiment; Fractional factorial design; Factorial; Plackett–Burman design; Design of experiments; Statistics; Arithmetic; Computer science; Programming language; Artificial intelligence; Response surface methodology","score_opus":0.14964504147306526,"score_gpt":0.5156544275279231,"score_spread":0.3660093860548579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973360772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16852373,0.0004279309,0.82949764,0.000056595643,0.00008332863,0.000035570934,0.0000032537664,0.000010429101,0.0013615155],"genre_scores_gemma":[0.57126355,0.0000056737695,0.42845052,0.00021877918,0.000044932713,1.1402341e-7,9.599745e-8,0.0000025755623,0.000013787967],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998195,0.00029417814,0.00054842344,0.00014888034,0.000613885,0.00019962297],"domain_scores_gemma":[0.9953499,0.0038686157,0.00031080865,0.00009703294,0.00018394263,0.00018969679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024295666,0.00011107803,0.0003246697,0.0001783847,0.00009545842,0.00033167726,0.00020906741,0.00006613702,0.000036094378],"category_scores_gemma":[0.007480277,0.00007718079,0.000038774404,0.00018174431,0.00010003653,0.00039772244,0.000034763,0.00025532724,0.0000021318997],"study_design_candidate":"theoretical_or_conceptual","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":[0.00032334676,0.00013918756,0.02834294,0.000022219589,0.000028717119,0.00071216765,0.0016960349,0.00058372656,0.3345441,0.025383823,0.0031101396,0.6051136],"study_design_scores_gemma":[0.0009301426,0.00428151,0.21586142,0.0014653365,0.00010663626,0.0017374434,0.0010055902,0.06356848,0.12161424,0.5869138,0.0018153015,0.00070008746],"about_ca_topic_score_codex":0.000005339027,"about_ca_topic_score_gemma":2.432618e-8,"teacher_disagreement_score":0.6044135,"about_ca_system_score_codex":0.000045745903,"about_ca_system_score_gemma":0.00007614173,"threshold_uncertainty_score":0.8955131},"labels":[],"label_agreement":null},{"id":"W1975354213","doi":"10.1007/s13595-011-0179-7","title":"Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials","year":2012,"lang":"en","type":"article","venue":"Annals of Forest Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of British Columbia; University of British Columbia; Canadian Forest Service","funders":"","keywords":"Statistics; Bayesian probability; Gibbs sampling; Bayesian inference; Sampling (signal processing); Spatial analysis; Mathematics; Tree (set theory); Computer science","score_opus":0.30021762239114985,"score_gpt":0.4666888968853303,"score_spread":0.16647127449418042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975354213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3894099,0.00097648246,0.607251,0.000040948944,0.0001560021,0.0018516455,0.00025245038,0.000012178104,0.000049406],"genre_scores_gemma":[0.64819115,0.000089035864,0.35101503,0.00017287623,0.000076060445,0.00040401067,0.000005965961,0.000020325062,0.000025547839],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9943736,0.0004532927,0.0010969029,0.0009922823,0.0019331985,0.0011507224],"domain_scores_gemma":[0.9919261,0.0055035907,0.00083989004,0.0007183744,0.00044030068,0.0005717456],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0116829295,0.00039099756,0.0008781733,0.0004670381,0.00045965926,0.00028653367,0.001267379,0.00012715261,0.000023367975],"category_scores_gemma":[0.0062629697,0.00027319774,0.00020756604,0.0006096888,0.001146754,0.0013004318,0.00040130326,0.00010481491,0.000009822829],"study_design_candidate":"observational","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":[0.0043709977,0.0035069084,0.24307513,0.00030902828,0.00036990485,0.0000162729,0.011184062,0.038346272,0.09488294,0.038779456,0.0024487865,0.5627102],"study_design_scores_gemma":[0.003144212,0.003737063,0.6813145,0.000104037,0.0000963843,0.000010233505,0.0005466168,0.24713512,0.03272984,0.0301777,0.00037304155,0.00063122524],"about_ca_topic_score_codex":0.00005407378,"about_ca_topic_score_gemma":0.00010180524,"teacher_disagreement_score":0.562079,"about_ca_system_score_codex":0.00003125592,"about_ca_system_score_gemma":0.00022976584,"threshold_uncertainty_score":0.99997205},"labels":[],"label_agreement":null},{"id":"W1977348009","doi":"10.1016/j.jspi.2005.09.008","title":"Existence and symmetry of minimax regression designs","year":2005,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Mathematics; Symmetry (geometry); Optimal design; Minimax approximation algorithm; Regression; Regression analysis; Mathematical optimization; Applied mathematics; Combinatorics; Statistics","score_opus":0.26716237170947266,"score_gpt":0.5117761184801407,"score_spread":0.24461374677066805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977348009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46716762,0.002289543,0.52776814,0.00017477055,0.00010492998,0.00004533795,0.000018564779,0.000004419993,0.0024266941],"genre_scores_gemma":[0.59401023,0.000040314157,0.4058221,0.000044351746,0.000029348643,2.2292186e-7,1.6957694e-7,0.0000026573339,0.000050616756],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99788487,0.0002506741,0.0008066083,0.0001780958,0.0007326391,0.00014709076],"domain_scores_gemma":[0.9934628,0.005444467,0.0005089666,0.00011722168,0.00025729256,0.00020923914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023314704,0.00010972304,0.00039224196,0.00018727862,0.00006777056,0.00010246525,0.00022459068,0.000060622075,0.00008930363],"category_scores_gemma":[0.007340286,0.000068567526,0.000029951578,0.00017869157,0.00031295736,0.00037794185,0.00008079855,0.00021908696,0.0000038887074],"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":[0.0010123403,0.00025125034,0.08802936,0.00006045801,0.000049753125,0.00020349937,0.0037382895,0.0001348622,0.06305361,0.076091744,0.0054872786,0.76188755],"study_design_scores_gemma":[0.0034998434,0.007486497,0.54602283,0.002565983,0.00015440925,0.0016736251,0.009207328,0.054501876,0.0577173,0.31073478,0.005395246,0.0010402657],"about_ca_topic_score_codex":0.0000036573865,"about_ca_topic_score_gemma":1.4648325e-7,"teacher_disagreement_score":0.7608473,"about_ca_system_score_codex":0.000014813105,"about_ca_system_score_gemma":0.00005914842,"threshold_uncertainty_score":0.8787538},"labels":[],"label_agreement":null},{"id":"W1977717747","doi":"10.1063/1.2748617","title":"Weighted complex projective 2-designs from bases: Optimal state determination by orthogonal measurements","year":2007,"lang":"en","type":"article","venue":"Journal of Mathematical Physics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":76,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Queensland Government","keywords":"Orthonormal basis; Mutually unbiased bases; Abelian group; Dimension (graph theory); Prime (order theory); Projective test; State (computer science); Linear subspace; Constant (computer programming)","score_opus":0.31893650638736837,"score_gpt":0.46898697359942826,"score_spread":0.1500504672120599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977717747","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":[0.25132716,0.00003165511,0.7464122,0.000058249527,0.0001315508,0.00021521669,0.00003115276,0.000012512369,0.0017803117],"genre_scores_gemma":[0.45804575,0.0000015564785,0.5415081,0.00013229965,0.00017132684,0.0000027037865,0.0000039345005,0.000024890796,0.000109485656],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99351764,0.00060817634,0.0017046729,0.0003221869,0.0034824412,0.00036490284],"domain_scores_gemma":[0.9932235,0.0037723256,0.0012970677,0.00031941207,0.0010941084,0.00029361254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007714035,0.00026275043,0.0006841116,0.00016735271,0.000120701814,0.00022213497,0.0007226569,0.000081743114,0.0005501329],"category_scores_gemma":[0.0018764162,0.00018380341,0.0003005201,0.0005681241,0.00018446338,0.00083207316,0.00010247511,0.00034051685,0.00014036002],"study_design_candidate":"bench_or_experimental","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":[0.0013860476,0.0032393965,0.0010128957,0.000027058588,0.0003011128,0.00011683527,0.0051480923,0.0002102559,0.8080656,0.0023458246,0.008014967,0.17013194],"study_design_scores_gemma":[0.0023526545,0.0012334769,0.0026223334,0.00016508582,0.00013117652,0.00007222972,0.0016626386,0.03640939,0.34163326,0.61237586,0.0007655748,0.0005763464],"about_ca_topic_score_codex":0.0000029893201,"about_ca_topic_score_gemma":5.3925595e-7,"teacher_disagreement_score":0.61003,"about_ca_system_score_codex":0.00021125918,"about_ca_system_score_gemma":0.00012874846,"threshold_uncertainty_score":0.7495286},"labels":[],"label_agreement":null},{"id":"W1979736402","doi":"10.1016/j.csda.2015.04.001","title":"Robust and efficient estimation of effective dose","year":2015,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Alberta","funders":"Humanities and Social Science Fund of Ministry of Education of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Estimator; Nonparametric statistics; Parametric statistics; Robustness (evolution); Asymptotic distribution; Mathematics; Monte Carlo method; Semiparametric model; Parametric model; Context (archaeology); Semiparametric regression; Robust statistics; Sample size determination; Statistics; Econometrics; Computer science","score_opus":0.266889376336155,"score_gpt":0.4777968272809065,"score_spread":0.2109074509447515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979736402","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":[0.02437102,0.0001494869,0.97289205,0.00003661604,0.000065889755,0.00015738174,0.0021485898,0.000011320959,0.00016767455],"genre_scores_gemma":[0.43756956,0.0000010820398,0.56146604,0.000016602697,0.00000822504,0.0000031396169,0.0009125301,0.0000039190263,0.000018910323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967082,0.0005246803,0.0006163049,0.00055422494,0.0014760947,0.00012050951],"domain_scores_gemma":[0.99408394,0.004105168,0.0003578694,0.0006088023,0.0006775903,0.00016662595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037583392,0.0001233866,0.0003965088,0.00048438407,0.000078865196,0.0001610167,0.0005102126,0.00003492702,0.00008299944],"category_scores_gemma":[0.005941482,0.0001034719,0.0000451722,0.0017599264,0.00020539833,0.00021308575,0.00044522452,0.000063323416,0.000042931097],"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":[0.000035384437,0.00007114694,0.002196849,0.0000031776926,0.00019883044,0.000003407404,0.00023334814,0.9424308,0.000012806942,0.007773932,0.0018433403,0.045197006],"study_design_scores_gemma":[0.00028859323,0.00005855289,0.06366749,0.000002875841,0.00033767772,0.000001993059,0.00009540806,0.9000221,0.000023950219,0.03536003,0.000047681802,0.00009364975],"about_ca_topic_score_codex":0.00018201178,"about_ca_topic_score_gemma":0.0000150557635,"teacher_disagreement_score":0.41319853,"about_ca_system_score_codex":0.000051460553,"about_ca_system_score_gemma":0.00009201911,"threshold_uncertainty_score":0.71129376},"labels":[],"label_agreement":null},{"id":"W1979760414","doi":"10.1108/17542731111139509","title":"Statistical, technical and sociological dimensions of design of experiments","year":2011,"lang":"en","type":"article","venue":"The TQM Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"JDA Software (Canada)","funders":"","keywords":"Originality; Computer science; Relation (database); Value (mathematics); Management science; Interpretation (philosophy); Design of experiments; Operations research; Risk analysis (engineering); Sociology; Business; Economics; Engineering; Social science; Data mining; Statistics; Mathematics; Machine learning; Qualitative research","score_opus":0.5071897920712852,"score_gpt":0.4986354264586207,"score_spread":0.008554365612664538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979760414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2658953,0.0010109518,0.72994083,0.00010591284,0.00018917116,0.00022762522,0.000009012667,0.000011879645,0.002609298],"genre_scores_gemma":[0.64219284,0.000052042866,0.35766152,0.000034818757,0.0000143573825,0.000002217968,5.4889504e-8,0.000004313349,0.00003780566],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99684125,0.0013666006,0.0007244626,0.00016776695,0.00071953336,0.00018039715],"domain_scores_gemma":[0.9963007,0.0027657857,0.00036280905,0.00028614732,0.00015480525,0.00012974553],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0059158183,0.00010138444,0.00032783355,0.000085022235,0.00014977588,0.000026354168,0.000589441,0.00007271856,0.0011500657],"category_scores_gemma":[0.002146756,0.000043944525,0.00007111573,0.0001670091,0.0010408035,0.000105657564,0.00021563322,0.00024756658,0.000017646957],"study_design_candidate":"bench_or_experimental","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":[0.0006696782,0.00046909976,0.002694176,0.0000019603099,0.000054578937,0.000029055593,0.0037156318,0.00005123285,0.9595975,0.01876687,0.004298612,0.009651574],"study_design_scores_gemma":[0.0013386941,0.0035473534,0.059181273,0.000052684954,0.000082741615,0.0009307916,0.006079593,0.0015958276,0.36965197,0.55696726,0.00024361069,0.00032822374],"about_ca_topic_score_codex":0.0000056671042,"about_ca_topic_score_gemma":5.9643746e-8,"teacher_disagreement_score":0.58994555,"about_ca_system_score_codex":0.000015484206,"about_ca_system_score_gemma":0.000052263305,"threshold_uncertainty_score":0.999763},"labels":[],"label_agreement":null},{"id":"W1979949183","doi":"10.1002/(sici)1097-0258(20000215)19:3<323::aid-sim372>3.0.co;2-d","title":"Saddlepoint approximations for small sample logistic regression problems","year":2000,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Cancer Institute","keywords":"Inference; Mathematics; Logistic regression; Sequence (biology); Applied mathematics; Binomial distribution; Statistics; Binomial regression; Econometrics; Computer science; Artificial intelligence","score_opus":0.33454370973748493,"score_gpt":0.5060935000628242,"score_spread":0.17154979032533924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979949183","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":[0.001397748,0.00028912516,0.9906279,0.00067347963,0.00043494327,0.0010290026,0.00039113662,0.00003538699,0.005121268],"genre_scores_gemma":[0.01912435,0.000062879444,0.97677296,0.0002842873,0.00015322356,0.00025911097,0.00011598076,0.000024158338,0.00320306],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969002,0.00026220077,0.0010741615,0.00053748215,0.00087134924,0.00035463177],"domain_scores_gemma":[0.9862924,0.012565968,0.00021150324,0.0005477025,0.0002322574,0.00015016488],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004664233,0.00019652749,0.0004912204,0.00028773892,0.00014043362,0.000055040342,0.00051701633,0.000082817,0.0047482],"category_scores_gemma":[0.027215404,0.0001292332,0.000038528557,0.00067464146,0.00039360102,0.00008282831,0.000046594825,0.00018488076,0.00010001028],"study_design_candidate":"theoretical_or_conceptual","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":[0.00029358105,0.00030393808,0.0013484625,0.00010510345,0.000018440272,0.000028675739,0.0038826256,0.003411537,0.0038929384,0.1262474,0.041984297,0.818483],"study_design_scores_gemma":[0.0016928657,0.0006980078,0.0021364647,0.00020209183,0.000019542498,0.00001126417,0.00068874453,0.09709856,0.00035018966,0.85270154,0.044154648,0.00024608],"about_ca_topic_score_codex":0.00027401204,"about_ca_topic_score_gemma":0.00012251636,"teacher_disagreement_score":0.8182369,"about_ca_system_score_codex":0.00009269758,"about_ca_system_score_gemma":0.000062012696,"threshold_uncertainty_score":0.9961616},"labels":[],"label_agreement":null},{"id":"W1982700054","doi":"10.1016/s0378-3758(00)00213-5","title":"On the orthogonal designs of order 40","year":2001,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"Natural Sciences and Engineering Research Council of Canada; Centre International de Recherche sur le Cancer","keywords":"Mathematics; Orthogonal array; Order (exchange); Construct (python library); Combinatorics; Statistics; Taguchi methods; Computer science","score_opus":0.29387407187914283,"score_gpt":0.4945985188595861,"score_spread":0.20072444698044328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982700054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22682017,0.00015198506,0.767815,0.00034129288,0.00011313276,0.000037857044,0.000012037125,0.0000024396322,0.0047060936],"genre_scores_gemma":[0.9118361,0.000016354965,0.08774128,0.0002708072,0.0000316159,5.559829e-7,2.6697185e-7,0.0000037994284,0.00009923709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977676,0.00036941012,0.0006655281,0.00012879042,0.0009240294,0.0001446137],"domain_scores_gemma":[0.98458725,0.0143959345,0.00038153827,0.0001323757,0.00037912806,0.00012376571],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0030369353,0.000094652394,0.00028012658,0.00011955005,0.000088709945,0.00010081303,0.00031806473,0.0000415943,0.0007508075],"category_scores_gemma":[0.014787714,0.000047726782,0.00003717191,0.00029686175,0.00025799818,0.00015116867,0.00004520916,0.00027632815,0.000014611923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025910044,0.00039747896,0.05734242,0.0000123638765,0.00009836279,0.00048771565,0.002233885,0.0041963807,0.015897248,0.7855217,0.020542732,0.1106787],"study_design_scores_gemma":[0.0010599586,0.004212873,0.22300139,0.00035958953,0.000048640573,0.00053505885,0.0025127053,0.023869444,0.0026877285,0.73859125,0.0027732004,0.00034815146],"about_ca_topic_score_codex":0.0000034490836,"about_ca_topic_score_gemma":1.9548467e-7,"teacher_disagreement_score":0.6850159,"about_ca_system_score_codex":0.000010626064,"about_ca_system_score_gemma":0.00008905016,"threshold_uncertainty_score":0.99351114},"labels":[],"label_agreement":null},{"id":"W1982712605","doi":"10.1016/j.laa.2008.04.039","title":"Robustness of optimal designs for correlated random variables","year":2008,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Acadia University","funders":"","keywords":"Mathematics; Robustness (evolution); Random variable; Statistics; Combinatorics; Applied mathematics","score_opus":0.15584439858390192,"score_gpt":0.40223299507339727,"score_spread":0.24638859648949535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982712605","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":[0.070297435,0.00065841404,0.9270003,0.000100836514,0.00007039239,0.001018493,0.00005371201,0.000037661845,0.0007627611],"genre_scores_gemma":[0.4532842,0.000112218964,0.5436442,0.00006276344,0.00012481157,0.00052541605,0.000023647568,0.000024316048,0.0021984307],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838173,0.00011811981,0.0005727411,0.0003937893,0.00034894331,0.00018466877],"domain_scores_gemma":[0.9967684,0.0021820522,0.00020661647,0.00034860658,0.0003763163,0.00011800861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010381045,0.00013355962,0.00033655023,0.00013118576,0.00029583092,0.000031393498,0.00037036953,0.00010519464,0.00015704021],"category_scores_gemma":[0.0006928473,0.00010565507,0.00009418026,0.00062204024,0.00015867564,0.0001860663,0.00007080536,0.00007911832,0.000025082098],"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":[0.0023103778,0.0019554785,0.0012233177,0.00011540338,0.00034582333,0.0000053717895,0.0035647573,0.17822322,0.3877157,0.3639119,0.017076034,0.043552596],"study_design_scores_gemma":[0.0033318058,0.00027551738,0.00042947734,0.000015371006,0.00006836355,0.000059549442,0.00042745547,0.88589865,0.08598213,0.0064333663,0.016701031,0.00037728235],"about_ca_topic_score_codex":0.0000038761536,"about_ca_topic_score_gemma":2.5892575e-7,"teacher_disagreement_score":0.70767546,"about_ca_system_score_codex":0.000009442108,"about_ca_system_score_gemma":0.00006912059,"threshold_uncertainty_score":0.43084887},"labels":[],"label_agreement":null},{"id":"W1983981102","doi":"10.1093/biomet/asn057","title":"Orthogonal and nearly orthogonal designs for computer experiments","year":2009,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":142,"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; Washington State University","keywords":"Latin hypercube sampling; Generalization; Orthogonal array; Mathematics; Fractional factorial design; Computer experiment; Orthographic projection; Orthogonal basis; Orthogonal transformation; Base (topology); Design of experiments; Projection (relational algebra); Factorial experiment; Algorithm; Statistics; Geometry; Mathematical analysis","score_opus":0.35594747965270723,"score_gpt":0.5003541112410744,"score_spread":0.1444066315883672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983981102","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":[0.38980144,0.001698119,0.6037072,0.00040299422,0.0008523652,0.0008465113,0.000067110384,0.00010504806,0.0025192513],"genre_scores_gemma":[0.48119828,0.000007313895,0.5170013,0.00070349785,0.00022382203,0.000022480384,0.000007955905,0.00001521073,0.00082015543],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966256,0.00025045688,0.0006567619,0.00081233,0.0011935955,0.00046127723],"domain_scores_gemma":[0.99730766,0.0014935598,0.00019508161,0.0004432132,0.00024327086,0.0003172063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028683764,0.000258713,0.00042159943,0.0011279676,0.00023158682,0.0005251875,0.0005848093,0.00013913409,0.00025467828],"category_scores_gemma":[0.00077694433,0.0002030645,0.00018218433,0.0022234635,0.00015877208,0.00043424618,0.00012610332,0.00009257206,0.000119022014],"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":[0.0004889032,0.00044276236,0.005844913,0.000004302895,0.00004858212,0.000019197982,0.0004888416,0.000015109038,0.22999054,0.011329139,0.012055947,0.73927176],"study_design_scores_gemma":[0.010454595,0.0133870635,0.43087775,0.00007586806,0.000087300126,0.00023435423,0.0008418007,0.026425127,0.26622212,0.063275754,0.18518856,0.0029297087],"about_ca_topic_score_codex":0.0000029408404,"about_ca_topic_score_gemma":2.766905e-7,"teacher_disagreement_score":0.7363421,"about_ca_system_score_codex":0.000050790786,"about_ca_system_score_gemma":0.00006824843,"threshold_uncertainty_score":0.82807297},"labels":[],"label_agreement":null},{"id":"W1986907660","doi":"10.1016/j.jspi.2011.08.003","title":"Creating catalogues of two-level nonregular fractional factorial designs based on the criteria of generalized aberration","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Queen's University","funders":"","keywords":"Fractional factorial design; Mathematics; Rank (graph theory); Factorial experiment; Plackett–Burman design; Mathematical optimization; Factorial; Optimal design; Algorithm; Applied mathematics; Statistics; Combinatorics; Response surface methodology; Mathematical analysis","score_opus":0.4635216322827111,"score_gpt":0.4912496423066186,"score_spread":0.027728010023907512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986907660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12411178,0.000034419252,0.8745089,0.000028485632,0.00026999065,0.00005239993,0.0000978052,0.0000021621863,0.0008940161],"genre_scores_gemma":[0.6936058,0.0000013110607,0.30628502,0.00003807635,0.00005245447,9.0771675e-7,0.000003947574,0.0000033706347,0.000009099223],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9974438,0.0005868867,0.0008600143,0.0001375483,0.000859182,0.00011254636],"domain_scores_gemma":[0.9912977,0.007245547,0.00075508165,0.00013576695,0.00047829267,0.00008760405],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0028793628,0.00010797303,0.00034305773,0.00014004429,0.00009208033,0.00006293589,0.00024090362,0.000051606807,0.0004640689],"category_scores_gemma":[0.0084346775,0.00006276358,0.000053202642,0.00013647214,0.00023533209,0.0002484104,0.0000280795,0.00018512909,0.0000023189662],"study_design_candidate":"bench_or_experimental","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":[0.006257899,0.0009278436,0.05834381,0.000053798045,0.00019377282,0.000088065426,0.014874457,0.006178326,0.68087196,0.2140022,0.0052652797,0.012942573],"study_design_scores_gemma":[0.0033637406,0.00554576,0.32452658,0.0006774765,0.00013387867,0.0000691465,0.0027932841,0.23238598,0.18464169,0.24521372,0.00013985194,0.00050890585],"about_ca_topic_score_codex":0.000071885035,"about_ca_topic_score_gemma":7.356479e-7,"teacher_disagreement_score":0.569494,"about_ca_system_score_codex":0.000016596345,"about_ca_system_score_gemma":0.00012220138,"threshold_uncertainty_score":0.9999177},"labels":[],"label_agreement":null},{"id":"W1986927305","doi":"10.1002/sim.2782","title":"Methods for the statistical analysis of binary data in split‐mouth designs with baseline measurements","year":2006,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Baseline (sea); Computer science; Context (archaeology); Binary number; Binary data; Outcome (game theory); Statistics; Statistical model; Statistical analysis; Artificial intelligence; Mathematics","score_opus":0.46426087040142916,"score_gpt":0.5858141738594048,"score_spread":0.12155330345797566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986927305","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":[0.0008335083,0.00071054103,0.9955638,0.00027652862,0.00015204425,0.00062455446,0.0014762399,0.000007510513,0.0003552921],"genre_scores_gemma":[0.081468344,0.000015043542,0.9177305,0.00011568208,0.00004959842,0.000040628027,0.0004274618,0.000018469247,0.00013425804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932846,0.0022525233,0.0016022255,0.0007495187,0.0017445575,0.0003666014],"domain_scores_gemma":[0.9621985,0.035516914,0.00039338038,0.0014075283,0.00040029685,0.00008339951],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.037652254,0.00022372465,0.0009615553,0.0009250014,0.000068491834,0.000031990792,0.0013184365,0.00006179615,0.00054034934],"category_scores_gemma":[0.023903245,0.00012225445,0.00003308874,0.0033811582,0.000692188,0.00011347503,0.00018705828,0.00020234894,0.0000025010877],"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":[0.004788393,0.0018206479,0.19527625,0.00016271695,0.0014862525,0.00019535588,0.0029782203,0.046817727,0.04121157,0.10346026,0.04311538,0.5586872],"study_design_scores_gemma":[0.0024088512,0.00060508726,0.2012737,0.00006981817,0.0010028332,0.00000227643,0.0017454216,0.75952256,0.000462509,0.03175549,0.00091407506,0.00023737726],"about_ca_topic_score_codex":0.0016501333,"about_ca_topic_score_gemma":0.0012757982,"teacher_disagreement_score":0.71270484,"about_ca_system_score_codex":0.00008959585,"about_ca_system_score_gemma":0.00013583327,"threshold_uncertainty_score":0.9909395},"labels":[],"label_agreement":null},{"id":"W1987072912","doi":"10.1016/j.jkss.2010.04.004","title":"Multi-treatment optimal response-adaptive designs for phase III clinical trials","year":2010,"lang":"en","type":"article","venue":"Journal of the Korean Statistical Society","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Optimal design; Clinical trial; Mathematical optimization; Adaptive design; Viewpoints; Constraint (computer-aided design); Mathematics; Computer science; Machine learning; Medicine","score_opus":0.5882728797378395,"score_gpt":0.6229738739250534,"score_spread":0.03470099418721395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987072912","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":[0.1199676,0.00006002995,0.8756505,0.00094331626,0.0020591896,0.00083969074,0.00041847464,0.0000119353745,0.000049221635],"genre_scores_gemma":[0.20207542,0.0000134486445,0.79628575,0.00030602663,0.0005506451,0.000018603476,0.0000013946561,0.000025355712,0.00072337274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9871457,0.0068502226,0.0035565987,0.0004760865,0.0015158984,0.0004554909],"domain_scores_gemma":[0.9239107,0.071907334,0.0021529032,0.00062633195,0.00085248455,0.0005502394],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07483641,0.00030852444,0.0014847182,0.00006209683,0.00041191367,0.00027923283,0.0012573402,0.00028033345,0.00046242418],"category_scores_gemma":[0.07825569,0.00015089732,0.002337387,0.0003221722,0.000919349,0.00025411588,0.00016882675,0.0008011293,0.000024565368],"study_design_candidate":"bench_or_experimental","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":[0.06584304,0.0073257824,0.00079131057,0.0000069695757,0.0017081957,0.00008279895,0.004904933,0.00039282005,0.37619746,0.010659829,0.15312783,0.37895903],"study_design_scores_gemma":[0.14441083,0.047409635,0.025869656,0.00014305543,0.0030308727,0.00079354,0.017252559,0.4297044,0.13903104,0.07957151,0.11055192,0.0022309772],"about_ca_topic_score_codex":0.000010051669,"about_ca_topic_score_gemma":0.000003883415,"teacher_disagreement_score":0.4293116,"about_ca_system_score_codex":0.0001986961,"about_ca_system_score_gemma":0.0006668828,"threshold_uncertainty_score":0.9526506},"labels":[],"label_agreement":null},{"id":"W1991868481","doi":"10.2307/3316012","title":"Robustness properties of minimally‐supported Bayesian D‐optimal designs for heteroscedastic models","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Optimal design; Heteroscedasticity; Mathematics; Bayesian probability; Variance (accounting); Mathematical optimization; Computer science; Statistics","score_opus":0.27805095404610747,"score_gpt":0.3822553040869715,"score_spread":0.10420435004086404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991868481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08072422,0.0003208235,0.91749,0.00011625809,0.00046242957,0.00027416812,0.0003618657,0.0000035555724,0.00024667],"genre_scores_gemma":[0.55020225,0.0000057367865,0.44934386,0.000052642583,0.000054082015,0.0000041824273,0.0000024381488,0.000022466511,0.0003123632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960844,0.00034263596,0.001832634,0.00028194953,0.000933711,0.0005246874],"domain_scores_gemma":[0.9946785,0.000986534,0.0010896779,0.00036268297,0.0019727186,0.0009099054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003046413,0.00023502682,0.00071841304,0.0006819194,0.00016811215,0.000232334,0.00094326324,0.00011065141,0.0004069252],"category_scores_gemma":[0.004528823,0.00018341921,0.00018699825,0.0004674687,0.00038939936,0.0004820196,0.000023297505,0.00018631866,0.000004476436],"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":[0.0017052531,0.00026694292,0.0044346065,0.00013009795,0.0003550486,0.0012522056,0.0049971226,0.80775565,0.083173804,0.011455161,0.036271498,0.048202623],"study_design_scores_gemma":[0.0018964086,0.0023563588,0.0009945676,0.00021779485,0.00017809389,0.00076338043,0.0034702022,0.9519378,0.020337384,0.015501352,0.0017162832,0.00063038885],"about_ca_topic_score_codex":0.0003577481,"about_ca_topic_score_gemma":0.0011985063,"teacher_disagreement_score":0.469478,"about_ca_system_score_codex":0.00017032106,"about_ca_system_score_gemma":0.002116844,"threshold_uncertainty_score":0.7479618},"labels":[],"label_agreement":null},{"id":"W1992113455","doi":"10.1063/1.3663521","title":"Robust Designs for Three Commonly Used Nonlinear Models","year":2011,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Brock University","funders":"","keywords":"Homoscedasticity; Heteroscedasticity; Extrapolation; Nonlinear system; Minimax; Exponential function; Computer science; Mathematics; Non-linear least squares; Least-squares function approximation; Mathematical optimization; Applied mathematics; Estimation theory; Econometrics; Statistics","score_opus":0.6086791475359449,"score_gpt":0.4204054572866362,"score_spread":0.18827369024930868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992113455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044965327,0.000064173044,0.91570574,0.00022380287,0.00023613662,0.0009962384,0.000025864536,0.00016842897,0.03761428],"genre_scores_gemma":[0.5798589,0.0000035584083,0.4192052,0.00016790119,0.000059991788,0.00013566882,0.0000020611963,0.000030694715,0.00053601875],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640423,0.000045275523,0.0008217992,0.0009482428,0.0010799681,0.00070046814],"domain_scores_gemma":[0.99677795,0.00063288666,0.00038302658,0.00041158017,0.0014944674,0.00030007833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037118143,0.00035469653,0.0005670646,0.00027324018,0.00029871252,0.0005257882,0.0018580336,0.00020797896,0.00062839297],"category_scores_gemma":[0.001282652,0.00028389445,0.00020525043,0.00063223456,0.0002903456,0.0016181157,0.00031682194,0.0002379156,0.00020488784],"study_design_candidate":"theoretical_or_conceptual","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":[0.0018178269,0.0013013594,0.058180075,0.00009090983,0.00018149341,0.000014887897,0.041540805,0.00012121933,0.2453892,0.54102254,0.014760542,0.095579125],"study_design_scores_gemma":[0.00081085216,0.00071287935,0.0011787805,0.00004583157,0.000031250238,0.000012224749,0.0027476256,0.6065565,0.025047854,0.36143655,0.0008995801,0.0005201413],"about_ca_topic_score_codex":0.00008060415,"about_ca_topic_score_gemma":0.000029397892,"teacher_disagreement_score":0.60643524,"about_ca_system_score_codex":0.00006148486,"about_ca_system_score_gemma":0.0001887874,"threshold_uncertainty_score":0.9999613},"labels":[],"label_agreement":null},{"id":"W1993387761","doi":"10.1002/asmb.861","title":"Robust designs for Haar wavelet approximation models","year":2010,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Optimal design; Mathematics; Mathematical optimization; Heteroscedasticity; Haar; Wavelet; Computer science; Applied mathematics; Algorithm; Statistics; Artificial intelligence","score_opus":0.2836707091881809,"score_gpt":0.3801837382328027,"score_spread":0.09651302904462183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993387761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07491507,0.00003734196,0.9190806,0.00020888635,0.0003173689,0.000978743,0.000023895123,0.000041549843,0.0043965275],"genre_scores_gemma":[0.723973,0.000001914258,0.27519938,0.0001265527,0.00011880116,0.00039263317,0.000010024144,0.00003194688,0.00014576218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99709696,0.000057601883,0.000776674,0.00086415117,0.0007345857,0.0004700266],"domain_scores_gemma":[0.99772614,0.0009437575,0.00024164854,0.00059005414,0.00032285438,0.00017554552],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026077155,0.00031846695,0.0005054244,0.00036637695,0.00021526136,0.00029869165,0.0005614706,0.00072630023,0.00007611843],"category_scores_gemma":[0.0005452024,0.0002672639,0.000051034152,0.0008787326,0.00023937572,0.00078014244,0.00018266495,0.0007339571,0.000007718522],"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":[0.00016266317,0.000122759,0.0000059455356,0.000015221428,0.0000071420495,0.0000012110684,0.0004332767,0.6409721,0.014761112,0.30921075,0.00028278457,0.034025036],"study_design_scores_gemma":[0.00082730764,0.000019448356,0.000087441826,0.000017720628,0.000008443955,0.000007515667,0.00037217268,0.6735083,0.00062247337,0.32426676,0.000023399316,0.0002390442],"about_ca_topic_score_codex":0.000043482418,"about_ca_topic_score_gemma":0.00001058718,"teacher_disagreement_score":0.6490579,"about_ca_system_score_codex":0.00003811019,"about_ca_system_score_gemma":0.00013175292,"threshold_uncertainty_score":0.99997795},"labels":[],"label_agreement":null},{"id":"W1993481816","doi":"10.1002/cjs.11190","title":"The factor aliased effect number pattern and its application in experimental planning","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia; McMaster University","funders":"National Natural Science Foundation of China","keywords":"Fractional factorial design; Rank (graph theory); Ranking (information retrieval); Factorial experiment; Factor (programming language); Design of experiments; Computer science; Statistics; Paired comparison; Mathematics; Machine learning; Combinatorics","score_opus":0.08315733229787388,"score_gpt":0.40718472789242044,"score_spread":0.3240273955945466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993481816","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9623229,0.0008106849,0.035772648,0.00014851028,0.00030839612,0.0002662224,0.00005467457,0.0000018201642,0.00031416715],"genre_scores_gemma":[0.99299574,0.0000036364588,0.006760514,0.00010637835,0.000041187217,0.000010227957,0.0000010110418,0.00000960296,0.000071723895],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998474,0.00030594916,0.00049014756,0.00012747293,0.00037907835,0.00022334201],"domain_scores_gemma":[0.99729437,0.0017639648,0.0002533641,0.00012470759,0.00016112062,0.00040245242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010737056,0.00009978627,0.00018354178,0.00012437931,0.00014493593,0.00031714005,0.00031763816,0.00004132875,0.0003230914],"category_scores_gemma":[0.0010554648,0.00006436049,0.000024890218,0.00015754437,0.000084634026,0.00022130822,0.000016022632,0.00016060499,0.0000935523],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006242903,0.000033299348,0.5134249,0.000011069052,0.000044256445,0.00030772394,0.006942016,0.00018609688,0.044764966,0.0018439405,0.02002789,0.41235143],"study_design_scores_gemma":[0.0029588595,0.0013596531,0.8060932,0.00017036448,0.00002894682,0.00055330124,0.01218833,0.04576333,0.10319885,0.01416523,0.012615581,0.0009043133],"about_ca_topic_score_codex":0.0020796005,"about_ca_topic_score_gemma":0.0010274281,"teacher_disagreement_score":0.4114471,"about_ca_system_score_codex":0.00013018408,"about_ca_system_score_gemma":0.00016904245,"threshold_uncertainty_score":0.35376242},"labels":[],"label_agreement":null},{"id":"W1995381013","doi":"10.1002/cjs.11203","title":"Selection of partial replication on two‐level orthogonal arrays","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Science Council","keywords":"Replication (statistics); Selection (genetic algorithm); Variance (accounting); Orthogonal array; Variance components; Computer science; Statistics; Mathematics; Algorithm; Artificial intelligence; Accounting","score_opus":0.18985514452340868,"score_gpt":0.41972196113959115,"score_spread":0.22986681661618247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995381013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057030562,0.000015458807,0.93955827,0.00017813857,0.00056868995,0.00006807217,0.00017458729,0.0000024044473,0.002403818],"genre_scores_gemma":[0.70000064,0.000001378288,0.29954162,0.00013423068,0.00015183253,7.921548e-7,0.0000027785356,0.00000851425,0.00015818066],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99766713,0.00038690135,0.00082527136,0.00019405058,0.0007307646,0.00019586404],"domain_scores_gemma":[0.99661356,0.001042129,0.000736756,0.00029005125,0.0008533169,0.0004641599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037066021,0.00009597112,0.00025905168,0.00038777012,0.00010572116,0.00008083029,0.00035425866,0.00004835996,0.0005276787],"category_scores_gemma":[0.00788281,0.00008069501,0.00006268513,0.00037504022,0.00012917107,0.00013387563,0.0000070609153,0.00018026771,0.000038304217],"study_design_candidate":"theoretical_or_conceptual","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":[0.0002918134,0.00009877458,0.029253455,0.000011913468,0.000070170194,0.000045915694,0.001150975,0.008647116,0.032329693,0.43607685,0.048452474,0.44357085],"study_design_scores_gemma":[0.003512851,0.006221513,0.18759,0.0001962617,0.0001505755,0.0006470525,0.00088895706,0.11220213,0.18897328,0.4214585,0.07718647,0.0009724099],"about_ca_topic_score_codex":0.00043682175,"about_ca_topic_score_gemma":0.0014961143,"teacher_disagreement_score":0.6429701,"about_ca_system_score_codex":0.00012601349,"about_ca_system_score_gemma":0.00075265503,"threshold_uncertainty_score":0.94370294},"labels":[],"label_agreement":null},{"id":"W1998682508","doi":"10.1080/01621459.2000.10474272","title":"Integer-Valued, Minimax Robust Designs for Estimation and Extrapolation in Heteroscedastic, Approximately Linear Models","year":2000,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":61,"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 Alberta","funders":"","keywords":"Heteroscedasticity; Mathematics; Minimax; Extrapolation; Polynomial regression; Integer (computer science); Mathematical optimization; Variance (accounting); Regression; Linear regression; Simulated annealing; Minification; Regression analysis; Applied mathematics; Statistics; Computer science","score_opus":0.16231053548561797,"score_gpt":0.43708298046806854,"score_spread":0.27477244498245057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998682508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18272631,0.00003310243,0.81626683,0.00042061982,0.000113490474,0.00024191535,0.000046695513,0.000005839636,0.0001451665],"genre_scores_gemma":[0.5209032,0.0000118488115,0.47876775,0.00012740852,0.000046004476,0.000007105044,0.0000017084252,0.000010335735,0.00012461682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964612,0.0009065037,0.0010808898,0.00022862692,0.0010821453,0.00024060339],"domain_scores_gemma":[0.9934017,0.004643064,0.0013716543,0.00015373851,0.00032897896,0.00010084476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047331722,0.00014046658,0.00047471022,0.00017586708,0.000110332316,0.00016861604,0.0003093254,0.000058187492,0.00005741967],"category_scores_gemma":[0.0075470945,0.00009481218,0.00011116354,0.0005344274,0.00013840172,0.0005503477,0.000031403164,0.0002242605,0.0000073284577],"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":[0.0022113076,0.0004691662,0.00941339,0.000018448713,0.00009179724,0.0000062850495,0.0018642645,0.33218512,0.008465904,0.0071948282,0.0027431368,0.63533634],"study_design_scores_gemma":[0.00060029014,0.00043989395,0.022080366,0.00002871306,0.000035591038,0.000012907828,0.00025960887,0.91867095,0.00041206967,0.05729602,0.00005293875,0.00011064161],"about_ca_topic_score_codex":0.00003764842,"about_ca_topic_score_gemma":0.000005739574,"teacher_disagreement_score":0.6352257,"about_ca_system_score_codex":0.0004000427,"about_ca_system_score_gemma":0.00007420917,"threshold_uncertainty_score":0.90351224},"labels":[],"label_agreement":null},{"id":"W1998973039","doi":"10.2307/3316099","title":"Robust sequential designs for nonlinear regression","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Winnipeg","funders":"","keywords":"Heteroscedasticity; Nonlinear regression; Parametric statistics; Computer science; Inference; Nonlinear system; Regression; Algorithm; Regression analysis; Statistics; Mathematics; Artificial intelligence","score_opus":0.43052303994889,"score_gpt":0.4281379789056735,"score_spread":0.0023850610432165076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998973039","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":[0.0019555807,0.00058012124,0.993769,0.00037317147,0.0013583497,0.00014859256,0.00071468577,0.0000034388293,0.001097043],"genre_scores_gemma":[0.040470786,0.000016181071,0.95687795,0.00022723382,0.00033039687,0.0000017705366,0.0000044817707,0.000022046135,0.0020491693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99776834,0.00024019819,0.00081420556,0.00018357055,0.00064967346,0.00034400955],"domain_scores_gemma":[0.996176,0.0013329285,0.0005036822,0.00023115923,0.000896422,0.0008598069],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002070515,0.00013500704,0.00031521538,0.00042180574,0.00022927158,0.00027943964,0.0006046036,0.000082758736,0.002456952],"category_scores_gemma":[0.005791555,0.00010197648,0.00011262238,0.00031075126,0.00016674591,0.0002426453,0.000013556296,0.0001853481,0.00007867548],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008401054,0.000054797358,0.0009145874,0.000011848579,0.00005231565,0.00076604506,0.001444119,0.002917852,0.003690988,0.0077775614,0.8340095,0.14827633],"study_design_scores_gemma":[0.004040811,0.0032064777,0.0011867085,0.0002614687,0.00019750618,0.0013442471,0.002808258,0.31685945,0.014129426,0.079064064,0.5757455,0.0011560633],"about_ca_topic_score_codex":0.00019372106,"about_ca_topic_score_gemma":0.00091887335,"teacher_disagreement_score":0.3139416,"about_ca_system_score_codex":0.00017581158,"about_ca_system_score_gemma":0.000513254,"threshold_uncertainty_score":0.9984549},"labels":[],"label_agreement":null},{"id":"W2000581731","doi":"10.1016/j.jspi.2007.06.020","title":"Robust designs for one-point extrapolation","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University; University of Alberta","funders":"","keywords":"Mathematics; Extrapolation; Statistics; Applied mathematics; Econometrics","score_opus":0.48900906829941165,"score_gpt":0.519636374492855,"score_spread":0.030627306193443393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000581731","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0171786,0.00024247917,0.9807762,0.00007937167,0.00018640069,0.000079926496,0.000014979525,0.0000055699747,0.0014364638],"genre_scores_gemma":[0.5124655,0.000003846626,0.48737103,0.00005577549,0.0000615026,4.9202265e-7,6.8875937e-7,0.0000035765142,0.000037653685],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979873,0.00012360477,0.0008582671,0.00016765595,0.0006516069,0.00021157459],"domain_scores_gemma":[0.9879794,0.010895418,0.00040388838,0.0000939434,0.00040764082,0.00021971023],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0070514614,0.000096380165,0.00028603335,0.00020883164,0.00011203311,0.00019013855,0.00019843626,0.00006376401,0.00010557275],"category_scores_gemma":[0.010246655,0.00007001333,0.000045361165,0.0001654455,0.000121629135,0.00038846838,0.00002758279,0.0001903672,0.0000050629305],"study_design_candidate":"theoretical_or_conceptual","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":[0.0033292428,0.00043312297,0.03984521,0.00005036379,0.000104231796,0.00018689992,0.00416833,0.005089732,0.09897284,0.23477378,0.010876584,0.60216963],"study_design_scores_gemma":[0.0027202317,0.005721266,0.29529923,0.00047246442,0.00010476195,0.00031639886,0.0044014584,0.08253373,0.017297147,0.5870652,0.0033789107,0.0006891968],"about_ca_topic_score_codex":0.0000031575717,"about_ca_topic_score_gemma":4.7621114e-7,"teacher_disagreement_score":0.6014805,"about_ca_system_score_codex":0.000030583702,"about_ca_system_score_gemma":0.00006149792,"threshold_uncertainty_score":0.99809045},"labels":[],"label_agreement":null},{"id":"W2000683594","doi":"10.1007/s10463-014-0470-0","title":"Minimax design criterion for fractional factorial designs","year":2014,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Minimax; Mathematics; Optimal design; Fractional factorial design; Estimator; Factorial experiment; Optimality criterion; Minimax estimator; Mathematical optimization; Applied mathematics; Mean squared error; TRACE (psycholinguistics); Matrix (chemical analysis); Statistics; Minimum-variance unbiased estimator","score_opus":0.5060403338934417,"score_gpt":0.5212604005461885,"score_spread":0.015220066652746778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000683594","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":[0.0033747915,0.000016266113,0.9930506,0.00038939647,0.0012022451,0.00047904867,0.00018743747,0.000011259721,0.0012889566],"genre_scores_gemma":[0.24485427,0.0000025566271,0.75474864,0.00011574185,0.00010323965,0.000018723824,0.00000257353,0.000012829319,0.00014141183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99679846,0.00034113755,0.0011167775,0.00026855484,0.0012501519,0.00022493917],"domain_scores_gemma":[0.98792803,0.010133014,0.0006245501,0.00056297134,0.0006473363,0.00010411024],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004352587,0.00017377455,0.0005396308,0.0001030282,0.00012649196,0.00006642228,0.00085910095,0.00009932423,0.00014666918],"category_scores_gemma":[0.028347215,0.00010861109,0.00020364586,0.00025647003,0.0005524422,0.00027503606,0.00013175304,0.00009479989,0.000021881597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035994442,0.0005220115,0.000025132345,0.00012749758,0.000062974075,7.066706e-7,0.00053320755,0.0015393251,0.05358408,0.90991914,0.020701682,0.012624304],"study_design_scores_gemma":[0.00032849726,0.00042563365,0.00019674053,0.00006731471,0.000029033807,0.0000044145368,0.00007570734,0.028249461,0.10552683,0.8597339,0.00522525,0.00013725218],"about_ca_topic_score_codex":0.000011076339,"about_ca_topic_score_gemma":9.1813234e-7,"teacher_disagreement_score":0.24147947,"about_ca_system_score_codex":0.000013885069,"about_ca_system_score_gemma":0.000106643885,"threshold_uncertainty_score":0.9798374},"labels":[],"label_agreement":null},{"id":"W2001211064","doi":"10.3758/bf03207805","title":"Statistical power for the two-factor repeated measures ANOVA","year":2000,"lang":"en","type":"article","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":111,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Sample size determination; Mathematics; Univariate; Monte Carlo method; Type I and type II errors; Correlation; Analysis of variance; Unavailability; Variance (accounting); Power (physics); Statistical power; Mean squared error; Multivariate statistics; Physics; Thermodynamics","score_opus":0.5420606524719409,"score_gpt":0.6332398172327909,"score_spread":0.09117916476084997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001211064","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":[0.23459825,0.0002915699,0.7558526,0.0007477507,0.0020656448,0.003830631,0.00026983302,0.00017922369,0.0021645261],"genre_scores_gemma":[0.17490007,0.000034486315,0.821976,0.00026316335,0.00013739265,0.00058043347,0.000015346817,0.000072829345,0.0020202762],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98070556,0.009718955,0.0014872942,0.0016239651,0.004953129,0.0015111],"domain_scores_gemma":[0.97833693,0.017801069,0.00021030733,0.0020365613,0.0010207597,0.00059439015],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0294339,0.00046182878,0.0007302167,0.00061680796,0.0011699887,0.0014317855,0.0032173921,0.00018007604,0.005929198],"category_scores_gemma":[0.005755567,0.00030306372,0.0003605272,0.0019127617,0.0012325023,0.0005460372,0.0006030669,0.00093419536,0.00040259174],"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":[0.00043471812,0.0003004586,0.0017725434,0.000003070705,0.00006260627,0.000026223557,0.0004679795,0.000045865872,0.017241433,0.001310341,0.007556433,0.97077835],"study_design_scores_gemma":[0.009137262,0.004111735,0.13982205,0.00013592844,0.00017836511,0.000280156,0.0036895997,0.030770166,0.123315856,0.0202432,0.6659735,0.0023421766],"about_ca_topic_score_codex":0.000330883,"about_ca_topic_score_gemma":0.000008566113,"teacher_disagreement_score":0.9684361,"about_ca_system_score_codex":0.00035746276,"about_ca_system_score_gemma":0.00030724113,"threshold_uncertainty_score":0.9999421},"labels":[],"label_agreement":null},{"id":"W2001620004","doi":"10.1002/qre.1298","title":"Ensemble of Surrogates for Dual Response Surface Modeling in Robust Parameter Design","year":2012,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":61,"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 Calgary","funders":"National Natural Science Foundation of China; U.S. Department of Energy","keywords":"Kriging; Parametric statistics; Nonparametric statistics; Computer science; Basis (linear algebra); Basis function; Variance (accounting); Radial basis function; Ensemble forecasting; Function (biology); Regression; Machine learning; Mathematical optimization; Econometrics; Mathematics; Statistics; Artificial neural network","score_opus":0.3270333269392578,"score_gpt":0.45037196772559607,"score_spread":0.12333864078633827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001620004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5478636,0.00007824463,0.45137557,0.0001775614,0.0003025724,0.00015482666,0.0000145381755,0.000012799033,0.000020300122],"genre_scores_gemma":[0.6684568,0.000003863709,0.3314224,0.000012632289,0.000025675032,0.000013312169,0.0000019477975,0.0000071466693,0.000056249108],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742746,0.0006144884,0.0008334579,0.0003109031,0.00057257415,0.00024110057],"domain_scores_gemma":[0.98828614,0.011015594,0.00011565587,0.00025859213,0.00023198366,0.00009204127],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02066754,0.00013163654,0.00029752328,0.00014894713,0.000029623328,0.00005258147,0.00023783589,0.00010334461,0.00003513718],"category_scores_gemma":[0.017713653,0.00011239126,0.00009599431,0.00018845509,0.00005301399,0.00041181745,0.000089465364,0.000122763,0.0000033833999],"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":[0.00091297383,0.00015496313,0.010113903,0.000020506766,0.000012688491,2.2576522e-7,0.0008513802,0.94404364,0.041610077,0.0017886858,0.000020499137,0.00047044197],"study_design_scores_gemma":[0.000415269,0.00007199716,0.015458333,0.000022106478,0.0000035101182,0.0000032374553,0.0002253723,0.9573051,0.023215812,0.00299834,0.00013076686,0.00015021182],"about_ca_topic_score_codex":0.000066097535,"about_ca_topic_score_gemma":0.0000013007783,"teacher_disagreement_score":0.12059321,"about_ca_system_score_codex":0.00008628188,"about_ca_system_score_gemma":0.000030342355,"threshold_uncertainty_score":0.99056053},"labels":[],"label_agreement":null},{"id":"W2004668118","doi":"10.1007/pl00012581","title":"On PBIBD Designs Based on Triangular Schemes","year":2002,"lang":"en","type":"article","venue":"Annals of Combinatorics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Greo","funders":"","keywords":"Mathematics; Combinatorics; Block (permutation group theory); Discrete mathematics; Arithmetic","score_opus":0.5328576690974302,"score_gpt":0.4930455767340003,"score_spread":0.03981209236342986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004668118","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52276,0.003821494,0.097097635,0.0098667685,0.0040691346,0.0018280386,0.000066177956,0.00037711882,0.36011365],"genre_scores_gemma":[0.9906202,0.000034772966,0.005213769,0.001989102,0.000025849593,0.000013220682,0.0000011280268,0.000025567631,0.0020763783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9955289,0.00072637916,0.0006904277,0.0004945603,0.0022045635,0.0003552205],"domain_scores_gemma":[0.9940051,0.0039463947,0.0003401295,0.0010419638,0.00046267666,0.00020372844],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0035376358,0.0002369685,0.00048572652,0.00055168435,0.0001177139,0.00011223368,0.00094403484,0.00014197316,0.001468021],"category_scores_gemma":[0.006495645,0.00018948926,0.00027874947,0.0015373572,0.00012366829,0.00019930299,0.000062603445,0.00020237698,0.00066997565],"study_design_candidate":"theoretical_or_conceptual","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":[0.0005956019,0.003968942,0.0009860999,0.00001760987,0.000054584958,0.00007413603,0.0003825362,0.0018951624,0.008210969,0.70391226,0.23563905,0.04426306],"study_design_scores_gemma":[0.0019280667,0.004017746,0.0006680474,0.000068255205,0.000011682839,0.0000017153399,0.000113507114,0.038831208,0.5033148,0.41681007,0.033736825,0.00049806596],"about_ca_topic_score_codex":0.0000033124918,"about_ca_topic_score_gemma":5.921248e-8,"teacher_disagreement_score":0.49510384,"about_ca_system_score_codex":0.000027885546,"about_ca_system_score_gemma":0.00003169611,"threshold_uncertainty_score":0.9994448},"labels":[],"label_agreement":null},{"id":"W2006192744","doi":"10.1007/s13253-010-0043-5","title":"Statistical Modelling of Neighbor Treatment Effects in Aquaculture Clinical Trials","year":2010,"lang":"en","type":"article","venue":"Journal of Agricultural Biological and Environmental Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Economic and Social Research Council","keywords":"Block (permutation group theory); Mixed model; Covariance; Variance (accounting); Computer science; Design of experiments; Statistics; Data mining; Mathematics","score_opus":0.2926904964694667,"score_gpt":0.46131361704564366,"score_spread":0.16862312057617695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006192744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9275171,0.00033447993,0.071110755,0.00006345185,0.00036410845,0.00022368194,0.00032856833,0.0000020367042,0.00005579481],"genre_scores_gemma":[0.77129036,0.0004973578,0.22801007,0.000023989369,0.00011372515,0.0000022121862,0.00001862626,0.0000031902136,0.00004043972],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9949951,0.0015894558,0.0023242387,0.0002952271,0.00058010174,0.00021586553],"domain_scores_gemma":[0.98816335,0.010365894,0.0010434522,0.00011053303,0.000041011575,0.00027576755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005005747,0.00022658444,0.0011798138,0.000062131505,0.000053200914,0.00005658713,0.0002533555,0.00023851695,0.00034830466],"category_scores_gemma":[0.0038722358,0.00009138015,0.00021311559,0.00012019783,0.00043620996,0.00013453698,0.00008201432,0.00043718438,0.000013030179],"study_design_candidate":"observational","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":[0.0011846337,0.002400929,0.16825616,0.000012027726,0.0001742716,0.00018479924,0.00054491335,0.0013258888,0.56436956,0.0067069577,0.0009885462,0.2538513],"study_design_scores_gemma":[0.0025538346,0.0054354505,0.9624294,0.000023697161,0.00008453454,0.00017433755,0.0011570709,0.0025714315,0.007469068,0.016980963,0.0008266495,0.00029354892],"about_ca_topic_score_codex":0.000010612354,"about_ca_topic_score_gemma":0.000004231282,"teacher_disagreement_score":0.79417324,"about_ca_system_score_codex":0.000049402446,"about_ca_system_score_gemma":0.000014619422,"threshold_uncertainty_score":0.46357074},"labels":[],"label_agreement":null},{"id":"W2007573942","doi":"10.1007/s00184-005-0404-1","title":"A recursive method for orthogonal designs","year":2005,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge; University of Manitoba","funders":"","keywords":"Mathematics; Class (philosophy); Orthogonal array; Algorithm; Algebra over a field; Applied mathematics; Computer science; Pure mathematics; Artificial intelligence; Statistics","score_opus":0.35246597602917273,"score_gpt":0.5510554251208561,"score_spread":0.1985894490916834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007573942","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":[0.0029923155,0.00074568746,0.9832095,0.0013356627,0.00041078287,0.0005630949,0.000035677887,0.000058613532,0.010648696],"genre_scores_gemma":[0.025938883,0.0000051301945,0.9653576,0.00097311323,0.0003222901,0.00010659588,0.0000031197133,0.000026661302,0.0072666323],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960463,0.0008728302,0.00069000607,0.00071636966,0.0012309544,0.00044353062],"domain_scores_gemma":[0.9918412,0.0066884016,0.00024005343,0.00060173124,0.00040664332,0.00022199626],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.010869766,0.00020482772,0.00043661363,0.00063280325,0.00017736702,0.00021083088,0.00088355417,0.00012258765,0.0015210918],"category_scores_gemma":[0.012444907,0.00015644789,0.00031975,0.0018055225,0.00007324012,0.0003986522,0.0001199511,0.00014131499,0.0008161183],"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":[0.00024199154,0.00013723678,0.00015578774,0.0000021139715,0.00003505978,0.0000037413977,0.00048641628,0.00035542014,0.042113245,0.022867847,0.024841085,0.9087601],"study_design_scores_gemma":[0.0012885927,0.00058170425,0.00076082617,0.000008795505,0.000037872225,0.000031510866,0.0006303738,0.02289604,0.26159716,0.07154842,0.64016783,0.00045086627],"about_ca_topic_score_codex":0.000008708784,"about_ca_topic_score_gemma":0.000006114182,"teacher_disagreement_score":0.90830916,"about_ca_system_score_codex":0.00011989407,"about_ca_system_score_gemma":0.000099545614,"threshold_uncertainty_score":0.99996185},"labels":[],"label_agreement":null},{"id":"W2009242172","doi":"10.1111/j.0006-341x.2003.00124.x","title":"A Bayesian<i>A</i>‐Optimal and Model Robust Design Criterion","year":2003,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"McGill University; Montreal General Hospital","funders":"","keywords":"Bayesian probability; TRACE (psycholinguistics); Optimal design; Set (abstract data type); Mathematical optimization; Mathematics; Limit (mathematics); Function (biology); Optimality criterion; Basis (linear algebra); Bayesian experimental design; Bayesian inference; Computer science; Applied mathematics; Statistics; Bayesian statistics","score_opus":0.3170732228480225,"score_gpt":0.4331611706651947,"score_spread":0.11608794781717224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009242172","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":[0.011433378,0.0017097362,0.98196733,0.000061905455,0.00027222376,0.0002515335,0.0000103221955,0.000055884455,0.0042376975],"genre_scores_gemma":[0.28578272,0.000053259595,0.71279776,0.00018941089,0.000017592198,0.000011356332,6.838471e-7,0.000019906243,0.0011273284],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634445,0.00067196536,0.0005743596,0.00070534996,0.0012966159,0.00040724583],"domain_scores_gemma":[0.997286,0.0014655137,0.00016554372,0.00054937176,0.00023981988,0.0002937525],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0063292114,0.00022820011,0.00034143572,0.0021966486,0.0001852432,0.00052258064,0.00046860144,0.00016031499,0.00019564947],"category_scores_gemma":[0.0087798685,0.00018408873,0.000091315196,0.0073231626,0.00016366584,0.0004774207,0.00012894999,0.00012778403,0.00011438172],"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":[0.0005080558,0.0013397958,0.0055160983,0.000042738615,0.00010127834,0.00016469725,0.0027357566,0.14942262,0.43569583,0.029740635,0.05515502,0.3195775],"study_design_scores_gemma":[0.00081193063,0.0004401941,0.00044617598,0.000008917468,0.000020645308,0.00007978749,0.00040797054,0.9409352,0.036213942,0.012239127,0.007838743,0.00055734615],"about_ca_topic_score_codex":0.000004488971,"about_ca_topic_score_gemma":1.3670548e-7,"teacher_disagreement_score":0.7915126,"about_ca_system_score_codex":0.000075667354,"about_ca_system_score_gemma":0.00009344231,"threshold_uncertainty_score":0.9995696},"labels":[],"label_agreement":null},{"id":"W2010002412","doi":"10.1081/qen-120020772","title":"Minimizing Cost of Multiple Response Systems by Probabilistic Robust Design","year":2003,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"SNC-Lavalin (Canada); University of Waterloo","funders":"University of Waterloo","keywords":"Rework; Reliability engineering; Mathematical optimization; Probabilistic logic; Reliability (semiconductor); Product (mathematics); Quality (philosophy); Total cost; Probabilistic design; Manufacturing cost; Function (biology); Computer science; Engineering; Mathematics; Engineering design process; Power (physics)","score_opus":0.3095892073698175,"score_gpt":0.4293579533605675,"score_spread":0.11976874599074999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010002412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10099937,0.00061721244,0.89686424,0.000018268871,0.0004737159,0.00062301254,0.000025964375,0.00008127864,0.0002969278],"genre_scores_gemma":[0.7500977,0.0000018138999,0.24937706,0.000010232078,0.00001315126,0.00006297918,0.0000012183119,0.000026323554,0.00040951566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99399793,0.0028882292,0.0012047052,0.000495499,0.0010420092,0.00037160396],"domain_scores_gemma":[0.9848362,0.013814048,0.00027547445,0.00069772615,0.00020121939,0.0001753068],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.020663701,0.0002224864,0.00054213085,0.00020281042,0.00006532822,0.00014261442,0.00047926418,0.000112729,0.000055115863],"category_scores_gemma":[0.04533456,0.00019855499,0.00011143477,0.0007272181,0.0000708012,0.00023834112,0.00005233957,0.00015268769,0.000035694968],"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":[0.00018443227,0.00006459598,0.0002019744,0.000035089535,0.0000135894115,0.0000027943795,0.00041553273,0.5792144,0.41763425,0.0013786994,0.00054730126,0.0003073053],"study_design_scores_gemma":[0.0013076981,0.00027129336,0.0010620672,0.00014147746,0.000018838062,0.000025080608,0.002856033,0.6619619,0.32145274,0.0002579018,0.009874523,0.00077041716],"about_ca_topic_score_codex":0.000047995265,"about_ca_topic_score_gemma":4.5094566e-7,"teacher_disagreement_score":0.64909834,"about_ca_system_score_codex":0.00015030135,"about_ca_system_score_gemma":0.00007382322,"threshold_uncertainty_score":0.962707},"labels":[],"label_agreement":null},{"id":"W2010246637","doi":"10.1007/s00184-012-0389-5","title":"D-optimal two-level orthogonal arrays for estimating main effects and some specified two-factor interactions","year":2012,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Victoria","keywords":"Mathematics; Orthogonal array; Orthogonal matrix; Factor (programming language); Matrix (chemical analysis); Combinatorics; Construct (python library); Applied mathematics; Mathematical optimization; Algorithm; Orthogonal basis; Statistics; Taguchi methods; Computer science","score_opus":0.24529758869099522,"score_gpt":0.4749035942362205,"score_spread":0.2296060055452253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010246637","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":[0.38492474,0.0003917816,0.6104289,0.00008840591,0.0023354793,0.0005303588,0.00007386669,0.000055549008,0.0011708923],"genre_scores_gemma":[0.40218022,0.0000012951805,0.5960952,0.00013003389,0.0006972505,0.0000649158,0.000004694588,0.000026310294,0.00080008927],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99657416,0.0005208848,0.0006987725,0.0005964412,0.0009256558,0.00068409514],"domain_scores_gemma":[0.98813534,0.01054821,0.00030502,0.0004696904,0.00013420482,0.00040755305],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0041704797,0.00030816975,0.0005282599,0.00063093094,0.0003361921,0.00036108657,0.0004434913,0.0000740653,0.0004738838],"category_scores_gemma":[0.008681199,0.00024640816,0.00021112633,0.00085283216,0.00013492655,0.0013833229,0.00022689214,0.00026277817,0.0002273536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007677729,0.0007734816,0.010579915,0.00007500141,0.00021500596,0.00002007475,0.0035699415,0.0017094751,0.52248305,0.035138153,0.0046145488,0.42005357],"study_design_scores_gemma":[0.011717256,0.0014739662,0.0757645,0.0002057254,0.0002405157,0.0003771662,0.0029029555,0.2864211,0.5637947,0.03710878,0.017262248,0.002731124],"about_ca_topic_score_codex":0.000013261014,"about_ca_topic_score_gemma":0.000003748408,"teacher_disagreement_score":0.41732246,"about_ca_system_score_codex":0.00013417381,"about_ca_system_score_gemma":0.00004941707,"threshold_uncertainty_score":0.9999988},"labels":[],"label_agreement":null},{"id":"W2011491153","doi":"10.1016/s0378-3758(02)00387-7","title":"Robust regression designs for approximate polynomial models","year":2003,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Bounding overwatch; Polynomial regression; Robustness (evolution); Applied mathematics; Polynomial; Inference; Regression analysis; Regression; Statistics; Computer science; Artificial intelligence","score_opus":0.45190605868259315,"score_gpt":0.48887331860783384,"score_spread":0.03696725992524069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011491153","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":[0.009214541,0.00043986927,0.98765266,0.000047642876,0.0002496273,0.000093637966,0.000027398546,0.000005978882,0.0022686692],"genre_scores_gemma":[0.47300023,0.000009021605,0.52681047,0.00004704019,0.00003336357,0.0000019208635,5.9043026e-7,0.0000057970396,0.00009158467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99777526,0.00034731612,0.0007756494,0.00022652678,0.0006366481,0.00023857795],"domain_scores_gemma":[0.9933113,0.005559524,0.00043112924,0.00013069663,0.00032034624,0.000247041],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0036897506,0.00013963212,0.00040221948,0.00016398003,0.00015386778,0.00024920653,0.00024465533,0.000078733035,0.00007494312],"category_scores_gemma":[0.009227196,0.00008774739,0.000058492526,0.00014508926,0.0001452722,0.0004832645,0.000033116485,0.00021828704,0.0000029375271],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004743991,0.0007248982,0.012480971,0.00011896963,0.00016632686,0.00037681728,0.0057279216,0.09891989,0.058702834,0.523399,0.111707985,0.18293041],"study_design_scores_gemma":[0.0024634046,0.002508289,0.00198807,0.00036615535,0.00006481519,0.0003917632,0.0022018394,0.39371532,0.009007284,0.5833608,0.0034080204,0.00052426313],"about_ca_topic_score_codex":0.0000023541231,"about_ca_topic_score_gemma":8.074715e-8,"teacher_disagreement_score":0.46378568,"about_ca_system_score_codex":0.000026545442,"about_ca_system_score_gemma":0.000120410885,"threshold_uncertainty_score":0.9991185},"labels":[],"label_agreement":null},{"id":"W2012031618","doi":"10.1080/15598608.2010.10412013","title":"Some Constructions of Balanced Ternary Residual Treatment Effects Designs","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Residual; Mathematics; Ternary operation; Statistics; Sequence (biology); Algorithm; Chemistry; Computer science","score_opus":0.10312828801991125,"score_gpt":0.4876842862283958,"score_spread":0.3845559982084846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012031618","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":[0.2754803,0.0012960208,0.7097925,0.0010083129,0.0023755094,0.00033798002,0.00011020375,0.0000130923545,0.009586067],"genre_scores_gemma":[0.49859342,0.000108616056,0.5007734,0.0001335864,0.00021142136,0.0000019494955,3.5374197e-7,0.000007071624,0.00017023206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99482113,0.0034813506,0.00072608003,0.00017546458,0.00064910663,0.00014687437],"domain_scores_gemma":[0.9121135,0.08652065,0.0006894198,0.00019195769,0.0002871574,0.00019732346],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009806535,0.00012453439,0.00040542186,0.00014272687,0.00010878062,0.00009944147,0.00021490909,0.000075964024,0.0004027649],"category_scores_gemma":[0.04906282,0.00007882047,0.00006789104,0.00014899435,0.00055725925,0.0008706853,0.000042063002,0.0003396894,0.00002159701],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0052861357,0.00032454918,0.0002304593,0.000007891711,0.00011903357,0.00024713323,0.00043793352,0.000009696428,0.2054946,0.71787703,0.00041002815,0.06955553],"study_design_scores_gemma":[0.0016677158,0.00437438,0.0047990987,0.000030388563,0.00025476693,0.0037162022,0.0021927897,0.00010349896,0.04855849,0.9298316,0.004306761,0.0001643104],"about_ca_topic_score_codex":0.000004475761,"about_ca_topic_score_gemma":2.5662123e-7,"teacher_disagreement_score":0.22311312,"about_ca_system_score_codex":0.000019830617,"about_ca_system_score_gemma":0.00012543118,"threshold_uncertainty_score":0.9589473},"labels":[],"label_agreement":null},{"id":"W2012573364","doi":"10.1111/j.1467-9868.2009.00711.x","title":"Robust Discrimination Designs","year":2009,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":49,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Institute of Materials Science and Engineering, Washington University in St. Louis","keywords":"Divergence (linguistics); Neighbourhood (mathematics); Computer science; Kullback–Leibler divergence; Regression; Linear regression; Mathematics; Artificial intelligence; Machine learning; Algorithm; Statistics","score_opus":0.37932534623046377,"score_gpt":0.4644865830517543,"score_spread":0.08516123682129051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012573364","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":[0.0016155854,0.00019418196,0.98940605,0.0053028194,0.0014241256,0.00025377327,0.0001401246,0.00002588619,0.0016374621],"genre_scores_gemma":[0.058588326,0.000017781816,0.93821484,0.0016245625,0.00032581858,0.000004083111,0.000003583602,0.000024201549,0.0011967858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9876405,0.00656549,0.0021204392,0.0005581748,0.002431024,0.00068435445],"domain_scores_gemma":[0.9736848,0.023321306,0.0010855144,0.0005898725,0.0008217062,0.0004967883],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.015562847,0.00039314386,0.0011300519,0.00009032072,0.0005110531,0.0003478596,0.0016840844,0.00030214997,0.0016485491],"category_scores_gemma":[0.06375954,0.00022583085,0.00060681667,0.00072383997,0.0013075476,0.00042406458,0.00025483986,0.0010675221,0.000054911445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014647588,0.00056051277,0.0005560867,0.00002527735,0.0002005695,0.00012959856,0.0018013131,0.0053224913,0.007457546,0.6490361,0.14099516,0.19245057],"study_design_scores_gemma":[0.00096472836,0.0023668665,0.06852888,0.000043160933,0.0002789225,0.000274312,0.0022342207,0.015330629,0.003270216,0.9003861,0.005849018,0.00047295564],"about_ca_topic_score_codex":0.0000169808,"about_ca_topic_score_gemma":0.0000030155072,"teacher_disagreement_score":0.25134996,"about_ca_system_score_codex":0.00026130714,"about_ca_system_score_gemma":0.00022488285,"threshold_uncertainty_score":0.99926406},"labels":[],"label_agreement":null},{"id":"W2012612209","doi":"10.1016/s0378-3758(01)00133-1","title":"Orthogonal arrays of strength three from regular 3-wise balanced designs","year":2002,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Army Research Office","keywords":"Mathematics; Orthogonal array; Combinatorics; Statistics; Taguchi methods","score_opus":0.2229557193678256,"score_gpt":0.4284878804339385,"score_spread":0.20553216106611288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012612209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23602453,0.000828997,0.76130474,0.000055903987,0.00018569524,0.00004179003,0.0001005014,0.0000055111045,0.001452332],"genre_scores_gemma":[0.6386017,0.000022033513,0.36125553,0.000032996028,0.000050138973,4.803831e-7,0.0000012948046,0.0000048914085,0.000030903644],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99706966,0.000284791,0.0010428926,0.00024233766,0.0011501741,0.00021014162],"domain_scores_gemma":[0.9929108,0.005644076,0.0006516741,0.00020448018,0.00032766184,0.00026129608],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00143605,0.00015448146,0.00054938404,0.00016224722,0.0000760433,0.00012444853,0.00039498773,0.00008092523,0.0010559384],"category_scores_gemma":[0.006133082,0.00010778297,0.00006655134,0.00023066103,0.00028623885,0.00033909373,0.000065425345,0.00031217845,0.000015600213],"study_design_candidate":"observational","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":[0.0016607204,0.0010144298,0.3154108,0.00004690406,0.0003368077,0.0010536204,0.0055268304,0.003769497,0.13259326,0.09128425,0.026421826,0.42088106],"study_design_scores_gemma":[0.0028437884,0.0033159344,0.33724427,0.00070202496,0.0001289913,0.00017367645,0.001657252,0.15952799,0.010828403,0.4816902,0.0011825009,0.0007049796],"about_ca_topic_score_codex":0.000011101664,"about_ca_topic_score_gemma":8.833153e-7,"teacher_disagreement_score":0.42017606,"about_ca_system_score_codex":0.000018588722,"about_ca_system_score_gemma":0.00004797142,"threshold_uncertainty_score":0.99985725},"labels":[],"label_agreement":null},{"id":"W2012785947","doi":"10.1139/f01-004","title":"Permutation tests for univariate or multivariate analysis of variance and regression","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1416,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Permutation (music); Univariate; Resampling; Mathematics; Multivariate statistics; Statistics; Independent and identically distributed random variables; Null distribution; Variance (accounting); Omnibus test; Regression analysis; Random permutation; Linear regression; Statistical hypothesis testing; Econometrics; Test statistic; Random variable; Combinatorics","score_opus":0.17714782906897386,"score_gpt":0.4305256572153112,"score_spread":0.25337782814633736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012785947","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9427358,0.00080801046,0.053401094,0.001819561,0.00029194838,0.00015045257,0.000014740984,0.0000022749011,0.0007761102],"genre_scores_gemma":[0.910168,0.000049979364,0.08927039,0.00009784889,0.000025235895,0.0000016044271,4.5346582e-7,0.0000036511542,0.00038283065],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982663,0.00019981645,0.0006354429,0.00023221537,0.0004584844,0.00020773678],"domain_scores_gemma":[0.9966973,0.0020407026,0.00060846726,0.00012370096,0.00020061061,0.00032918958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003918683,0.00010497226,0.00041455572,0.0007662869,0.0003412035,0.00035357062,0.00035114514,0.00004633331,0.00016332015],"category_scores_gemma":[0.0054307757,0.00006162531,0.000079771125,0.0019618897,0.0006575108,0.00069183164,0.000017622337,0.000053544474,3.3421443e-7],"study_design_candidate":"observational","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":[0.00068427314,0.0000735861,0.45918876,0.000029613679,0.00051005534,0.00014676573,0.020859633,0.0015767716,0.015166771,0.008336632,0.0015896198,0.49183753],"study_design_scores_gemma":[0.0019861073,0.0031541407,0.5202607,0.00030715193,0.0007760612,0.00026028993,0.024830364,0.37766853,0.0015013983,0.057615537,0.0110364435,0.0006032783],"about_ca_topic_score_codex":0.0025052573,"about_ca_topic_score_gemma":0.0051195784,"teacher_disagreement_score":0.49123424,"about_ca_system_score_codex":0.000027906244,"about_ca_system_score_gemma":0.00058503984,"threshold_uncertainty_score":0.6501538},"labels":[],"label_agreement":null},{"id":"W2013774051","doi":"10.1080/10543406.2014.920854","title":"A Step-Up Test Procedure to Find the Minimum Effective Dose","year":2014,"lang":"en","type":"article","venue":"Journal of Biopharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Estimator; Mathematics; Statistics; Null hypothesis; Monotone polygon; Test (biology); Sequence (biology); Null (SQL); False discovery rate; Computer science; Data mining","score_opus":0.10704938510907043,"score_gpt":0.4854068352479506,"score_spread":0.37835745013888017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013774051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054987516,0.00031602013,0.93666506,0.0036264402,0.0019968261,0.0008270868,0.00016700625,0.00001628963,0.0013977559],"genre_scores_gemma":[0.74797755,0.000015800408,0.24748294,0.002924966,0.00052521616,0.00001343411,6.061589e-7,0.000027121923,0.0010323892],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951572,0.0010224138,0.0011257447,0.00031884562,0.0019698606,0.00040597512],"domain_scores_gemma":[0.9790886,0.018587377,0.0005343511,0.00033032784,0.0008453604,0.0006139561],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007126453,0.00023601769,0.0005041237,0.00019931667,0.00019595586,0.00035323692,0.0010912066,0.00009457838,0.00041146894],"category_scores_gemma":[0.035212327,0.00012732386,0.00014929431,0.00080191944,0.0002846335,0.00016944739,0.00020927527,0.00058867235,0.00041600064],"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":[0.0014934853,0.0005394081,0.0020377787,0.00003087552,0.00009866057,0.00011444361,0.0012055784,0.00016693858,0.17813878,0.006992394,0.10582514,0.7033565],"study_design_scores_gemma":[0.0097091235,0.012717123,0.14214522,0.0002605165,0.0006252173,0.0013888911,0.002480413,0.18860063,0.12493041,0.05722317,0.45833853,0.0015807551],"about_ca_topic_score_codex":0.0000021099193,"about_ca_topic_score_gemma":0.0000010815855,"teacher_disagreement_score":0.7017757,"about_ca_system_score_codex":0.00010480403,"about_ca_system_score_gemma":0.0001266466,"threshold_uncertainty_score":0.9729145},"labels":[],"label_agreement":null},{"id":"W2014468891","doi":"10.1007/s10463-009-0222-8","title":"Forms of four-word indicator functions with implications to two-level factorial designs","year":2009,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; DePaul University","keywords":"Fractional factorial design; Mathematics; Factorial; Factorial experiment; Statistics; Plackett–Burman design; Factorial analysis; Econometrics; Arithmetic; Mathematical analysis","score_opus":0.4405702350079293,"score_gpt":0.49258100385881126,"score_spread":0.05201076885088196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014468891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0756098,0.0000099872095,0.9186185,0.0010518521,0.00020133189,0.00052317034,0.0006249723,0.00001231046,0.0033480763],"genre_scores_gemma":[0.5454178,0.0000013802137,0.45436105,0.00010709301,0.000020962416,0.000009284884,0.0000021852481,0.0000069164034,0.00007332847],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99703395,0.00010030155,0.0011597965,0.00026487623,0.0012031341,0.0002379676],"domain_scores_gemma":[0.99599504,0.0016506987,0.0006970773,0.00090787606,0.0005636464,0.00018564945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014079531,0.00018242547,0.00057318417,0.00019971989,0.000117256706,0.000038311282,0.0010246994,0.00006550963,0.00009776635],"category_scores_gemma":[0.0047468566,0.000102687336,0.00013638588,0.0008558462,0.0004981922,0.00024645423,0.00013058993,0.000119572214,0.000027603084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024022395,0.00094088895,0.00029087462,0.000046937697,0.00009667524,0.0000016018423,0.0010689904,0.0014787763,0.043768656,0.92259,0.0046954555,0.02478088],"study_design_scores_gemma":[0.00055166695,0.001100582,0.016716212,0.00015114319,0.00007875265,0.000015856438,0.00038089725,0.00048757618,0.081214525,0.898277,0.0007744278,0.00025138562],"about_ca_topic_score_codex":0.000019975747,"about_ca_topic_score_gemma":0.000011256852,"teacher_disagreement_score":0.46980798,"about_ca_system_score_codex":0.000018618475,"about_ca_system_score_gemma":0.00019481945,"threshold_uncertainty_score":0.56827736},"labels":[],"label_agreement":null},{"id":"W2014721111","doi":"10.3758/brm.42.2.366","title":"Applying the permutation test to factorial designs","year":2010,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Permutation (music); Factorial; Fractional factorial design; Parametric statistics; Factorial experiment; Resampling; Mathematics; Computation; Limit (mathematics); Computer science; Test (biology); Algorithm; Statistics","score_opus":0.773946312954811,"score_gpt":0.7301019632369032,"score_spread":0.04384434971790774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014721111","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":[0.27137265,0.000059036167,0.7065371,0.00086721306,0.004397093,0.0077433586,0.00003478499,0.00015419169,0.00883458],"genre_scores_gemma":[0.22307844,0.0000013566142,0.76867574,0.0000882955,0.0005779443,0.005193413,0.0000019616377,0.00004228043,0.0023405731],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98392695,0.008940059,0.0007818003,0.0009818561,0.004453657,0.0009156653],"domain_scores_gemma":[0.96042836,0.035858408,0.00013177702,0.0017355456,0.001277749,0.0005681377],"candidate_categories":["metaresearch","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.083144024,0.00023555632,0.00034890446,0.00072152563,0.0010510096,0.0012881161,0.0026493943,0.00020687553,0.0019380576],"category_scores_gemma":[0.08366858,0.00014447318,0.0001807228,0.003008227,0.00051262253,0.00042561203,0.0007507112,0.0015902177,0.001065831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002846074,0.00008799047,0.002206996,5.861946e-7,0.0000017174015,0.0000073642263,0.0006991784,0.000004281858,0.62276196,0.00072649436,0.0011738605,0.3723011],"study_design_scores_gemma":[0.00046170017,0.00064292573,0.039532736,0.0000081743065,0.000017854998,0.000042099633,0.0041803312,0.0008135224,0.75725925,0.015219862,0.18138157,0.00043997492],"about_ca_topic_score_codex":0.00018863173,"about_ca_topic_score_gemma":0.000031660038,"teacher_disagreement_score":0.37186113,"about_ca_system_score_codex":0.00012025523,"about_ca_system_score_gemma":0.00027476472,"threshold_uncertainty_score":0.99974865},"labels":[],"label_agreement":null},{"id":"W2016255587","doi":"10.1139/x06-112","title":"Post hoc blocking to improve heritability and precision of best linear unbiased genetic predictions","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Blocking (statistics); Statistics; Heritability; Block design; Post hoc; Block (permutation group theory); Mathematics; Post-hoc analysis; Randomized block design; Selection (genetic algorithm); Block size; Computer science; Biology; Combinatorics; Ecology; Genetics; Medicine; Artificial intelligence; Key (lock)","score_opus":0.1413515098880563,"score_gpt":0.45646299505219756,"score_spread":0.3151114851641412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016255587","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9928007,0.0009827759,0.004054736,0.0005907204,0.00024722848,0.000364894,0.00008251218,0.0000024470914,0.0008739585],"genre_scores_gemma":[0.96116334,0.000008668762,0.038173933,0.000018363382,0.00019870908,0.0000054633283,8.024313e-7,0.000015346388,0.00041539487],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99569404,0.0007851812,0.0009876776,0.00034084977,0.0016647726,0.00052748586],"domain_scores_gemma":[0.9936522,0.0018533663,0.00021062493,0.0004887269,0.0026906906,0.0011043642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008519911,0.00012355807,0.00033265323,0.00135924,0.0003173437,0.00024887017,0.00080208323,0.00010650063,0.00018022982],"category_scores_gemma":[0.008158269,0.00009872613,0.00011112415,0.0013161745,0.00052517187,0.00026071505,0.00010116882,0.00043368316,0.000022604934],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007731862,0.0002532283,0.75982857,0.000039394712,0.000047830366,0.00035447296,0.0020949044,0.010638953,0.12603252,0.0010347624,0.0035175113,0.09538464],"study_design_scores_gemma":[0.0011037193,0.006252113,0.9429012,0.00020343487,0.000019876366,0.0002318712,0.003185463,0.0035593337,0.02120653,0.014404621,0.006663031,0.00026883543],"about_ca_topic_score_codex":0.01999786,"about_ca_topic_score_gemma":0.06386472,"teacher_disagreement_score":0.18307258,"about_ca_system_score_codex":0.00029718236,"about_ca_system_score_gemma":0.0018124067,"threshold_uncertainty_score":0.98652804},"labels":[],"label_agreement":null},{"id":"W2016263267","doi":"10.1016/j.jco.2013.04.002","title":"On blocked resolution IV designs containing clear two-factor interactions","year":2013,"lang":"en","type":"article","venue":"Journal of Complexity","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Mathematics; Resolution (logic); Block (permutation group theory); Homogeneous; Blocking (statistics); Factor (programming language); Algorithm; Combinatorics; Statistics; Computer science; Artificial intelligence","score_opus":0.45002715596824155,"score_gpt":0.49923935731208213,"score_spread":0.04921220134384058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016263267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90867347,0.000049374477,0.08038651,0.0010227232,0.0010141603,0.00020880338,0.0000050571566,0.000023050763,0.0086168535],"genre_scores_gemma":[0.82602507,0.0000017159497,0.17304718,0.00034882323,0.00019416721,0.0000028795666,3.4295365e-7,0.000012067726,0.00036774413],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958549,0.0011620782,0.001137314,0.00023516406,0.0013369697,0.00027357694],"domain_scores_gemma":[0.99466866,0.0028718417,0.0009842763,0.0003737553,0.00083282526,0.00026865507],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002791121,0.00016785479,0.0004543505,0.00041746852,0.0002426944,0.00037853295,0.0006923597,0.00005089399,0.005215305],"category_scores_gemma":[0.0035436628,0.000120661796,0.00027401897,0.00041785632,0.0001876186,0.00096500595,0.0001003827,0.00051710894,0.0007399796],"study_design_candidate":"theoretical_or_conceptual","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":[0.0076175104,0.0023370595,0.006014566,0.000012247241,0.00045639236,0.00015831571,0.0072538983,0.003935545,0.5436669,0.17305997,0.10486978,0.15061781],"study_design_scores_gemma":[0.004111462,0.0061289365,0.10994546,0.00019916416,0.00004980656,0.0005558069,0.0042690635,0.0687497,0.031127937,0.75902164,0.015136848,0.0007041672],"about_ca_topic_score_codex":0.000078158875,"about_ca_topic_score_gemma":0.000010517155,"teacher_disagreement_score":0.5859617,"about_ca_system_score_codex":0.00022907797,"about_ca_system_score_gemma":0.00009082887,"threshold_uncertainty_score":0.99569404},"labels":[],"label_agreement":null},{"id":"W2018021189","doi":"10.1007/s00184-013-0463-7","title":"Optimal and robust designs for trigonometric regression models","year":2013,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Trigonometry; Mathematics; Minimax; Optimal design; Trigonometric functions; Regression analysis; Applied mathematics; Regression; Differentiation of trigonometric functions; Function (biology); Inverse trigonometric functions; Mathematical optimization; Algorithm; Statistics; Mathematical analysis","score_opus":0.3965849092122878,"score_gpt":0.44954812061816074,"score_spread":0.052963211405872956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018021189","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":[0.11715357,0.0029170644,0.87424165,0.00020824857,0.0003018419,0.0010257247,0.000012361443,0.0000576498,0.0040818704],"genre_scores_gemma":[0.29401916,0.00004118477,0.7016501,0.00011069596,0.00006531829,0.0001658094,0.0000023043383,0.000024554669,0.0039208913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969438,0.000356936,0.0006517481,0.0007392657,0.0008829942,0.0004252555],"domain_scores_gemma":[0.9941409,0.004413602,0.00023547752,0.000568401,0.00034838024,0.00029320957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004260291,0.00023334596,0.0004775964,0.0015473153,0.00022440481,0.00056180015,0.0006516173,0.00014314077,0.00074581924],"category_scores_gemma":[0.0051587527,0.00015554465,0.0001590311,0.0030260584,0.00011887606,0.0011381591,0.00020883851,0.0001251921,0.00021025634],"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":[0.00034569314,0.00033859632,0.0011894973,0.000020322077,0.000061033374,0.0000055449714,0.00071207475,0.026751354,0.042869918,0.0056241853,0.05152092,0.8705609],"study_design_scores_gemma":[0.0027940914,0.0012229538,0.003451882,0.000037612852,0.000044088723,0.000024766845,0.001610721,0.85401666,0.069504626,0.058704212,0.007763904,0.0008244746],"about_ca_topic_score_codex":0.000040053947,"about_ca_topic_score_gemma":3.590646e-7,"teacher_disagreement_score":0.8697364,"about_ca_system_score_codex":0.000069147005,"about_ca_system_score_gemma":0.000042012365,"threshold_uncertainty_score":0.81661975},"labels":[],"label_agreement":null},{"id":"W2020172162","doi":"10.1007/s00362-006-0328-5","title":"2 m 41 designs with minimum aberration or weak minimum aberration","year":2007,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Nankai University; National Natural Science Foundation of China","keywords":"Mathematics; Zhàng; Combinatorics; Discrete mathematics","score_opus":0.13212961033756035,"score_gpt":0.4336010623331525,"score_spread":0.3014714519955921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020172162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0146192135,0.000038594826,0.8481829,0.00035236226,0.00052399666,0.00054493395,0.000050662537,0.000095333955,0.13559197],"genre_scores_gemma":[0.5642779,0.000003311927,0.426563,0.00046835837,0.0001236186,0.00002321572,0.000028331682,0.000033840533,0.008478472],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9950534,0.00052993314,0.0009614679,0.00084665365,0.0019791326,0.00062938244],"domain_scores_gemma":[0.9923096,0.0062052687,0.0002457087,0.0005399696,0.00026198858,0.0004374916],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0040661464,0.00031643617,0.0004237961,0.00023125566,0.00028105057,0.00031405705,0.00045617486,0.00016838867,0.0048498255],"category_scores_gemma":[0.004758219,0.00020314861,0.00006874556,0.0009030093,0.00047471732,0.00042284795,0.00006244923,0.00023785056,0.0006924746],"study_design_candidate":"bench_or_experimental","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":[0.0077339755,0.0007172558,0.0027384555,0.00003253649,0.00014033394,0.00047602705,0.0040407414,0.00036671918,0.54109174,0.11767547,0.06023729,0.26474944],"study_design_scores_gemma":[0.018650955,0.02721701,0.28242117,0.00038053162,0.0007375834,0.0007411949,0.08827372,0.065852225,0.15393509,0.13978471,0.21350802,0.008497788],"about_ca_topic_score_codex":0.000039801784,"about_ca_topic_score_gemma":0.00025725976,"teacher_disagreement_score":0.54965866,"about_ca_system_score_codex":0.00014807409,"about_ca_system_score_gemma":0.00021035201,"threshold_uncertainty_score":0.9960599},"labels":[],"label_agreement":null},{"id":"W2020209089","doi":"10.1002/cjs.5550360204","title":"Likelihood and bayesian approaches to inference for the stationary point of a quadratic response surface","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Bayesian probability; Stationary point; Bayesian inference; Parametrization (atmospheric modeling); Applied mathematics; Point (geometry); Inference; Quadratic equation; Polynomial; Quadratic function; Point estimation; Algorithm; Statistics; Computer science; Mathematical analysis; Artificial intelligence; Physics; Geometry","score_opus":0.32222074703924236,"score_gpt":0.38998170668840615,"score_spread":0.06776095964916379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020209089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1491691,0.0004459973,0.8482269,0.0012746144,0.00014417774,0.00024593322,0.00044333754,0.0000011440566,0.000048801558],"genre_scores_gemma":[0.5673027,0.0000081533735,0.4324596,0.00011523581,0.000013386642,0.0000018881203,6.1756117e-7,0.0000065394015,0.000091879934],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99802583,0.00042337936,0.0007021328,0.00014158193,0.00049293693,0.00021414597],"domain_scores_gemma":[0.9885907,0.009777216,0.00035967343,0.00021234118,0.0005073318,0.00055270747],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0038663824,0.000102987346,0.0002698316,0.00023881524,0.0002180886,0.00008162894,0.00040865727,0.00003580549,0.00006768001],"category_scores_gemma":[0.011599057,0.000069906244,0.000044553828,0.00033847246,0.00029039814,0.00015838188,0.000020039146,0.00011253168,0.0000034493717],"study_design_candidate":"observational","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":[0.009860446,0.00040119488,0.095478855,0.00018329569,0.00065469975,0.0015615001,0.19861472,0.058489893,0.026386429,0.1357484,0.20962827,0.2629923],"study_design_scores_gemma":[0.003986933,0.009348725,0.4578767,0.00028497307,0.00022774842,0.0018758291,0.05658402,0.12180979,0.011255412,0.31937027,0.016096333,0.0012832701],"about_ca_topic_score_codex":0.000540375,"about_ca_topic_score_gemma":0.0015531711,"teacher_disagreement_score":0.41813362,"about_ca_system_score_codex":0.00007615745,"about_ca_system_score_gemma":0.0020082693,"threshold_uncertainty_score":0.9967267},"labels":[],"label_agreement":null},{"id":"W2020343818","doi":"10.1016/j.cmpb.2007.05.001","title":"Experimental design for regression analysis when the responses are subject to censoring","year":2007,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western Forest Products","funders":"","keywords":"Censoring (clinical trials); Computer science; Regression analysis; Bayesian probability; Regression; Statistics; Econometrics; Artificial intelligence; Machine learning; Mathematics","score_opus":0.38358074031227707,"score_gpt":0.5497928935120152,"score_spread":0.16621215319973814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020343818","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":[0.08042359,0.0022758213,0.9146472,0.0008550904,0.0005885925,0.0011256763,0.0000023204075,0.00004289952,0.000038773414],"genre_scores_gemma":[0.10029309,0.0000074476375,0.8986278,0.0005231672,0.00025260585,0.00009383693,0.0000027645465,0.000017227338,0.00018203772],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9945718,0.0024072824,0.00091559,0.0008460074,0.00073806656,0.00052124],"domain_scores_gemma":[0.9889758,0.009676588,0.0002613556,0.0006622369,0.00015985878,0.00026412826],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.038264535,0.0002740445,0.00070048694,0.0011339526,0.00023222003,0.00024087024,0.0007127307,0.00011247562,0.000025297057],"category_scores_gemma":[0.0013117413,0.00014667283,0.00017486661,0.0029549256,0.00028464274,0.00012758566,0.00033759524,0.00015680531,0.0000026683304],"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":[0.001048741,0.00014923436,0.0057526864,0.0000056573226,0.00007405714,0.00002035245,0.00463762,0.00003530147,0.041765027,0.00012046968,0.00043085805,0.94596],"study_design_scores_gemma":[0.007626134,0.01023567,0.124282286,0.00070249155,0.00050757796,0.00014053607,0.028317831,0.11212793,0.59203696,0.019985605,0.102078594,0.001958384],"about_ca_topic_score_codex":0.000037300855,"about_ca_topic_score_gemma":0.000004989454,"teacher_disagreement_score":0.9440016,"about_ca_system_score_codex":0.00007194539,"about_ca_system_score_gemma":0.000020245916,"threshold_uncertainty_score":0.99030906},"labels":[],"label_agreement":null},{"id":"W2021002368","doi":"10.1016/j.jspi.2003.12.003","title":"Constructing optimal designs with constraints","year":2004,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Manitoba","funders":"","keywords":"Mathematics; Multiplicative function; Mathematical optimization; Convergence (economics); Class (philosophy); Construct (python library); Optimal design; Function (biology); Computer science","score_opus":0.19406125824831688,"score_gpt":0.46787717471823737,"score_spread":0.2738159164699205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021002368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16542105,0.00009521047,0.831502,0.00007902525,0.000087764754,0.00003762558,0.000015487596,0.0000063332095,0.0027554983],"genre_scores_gemma":[0.54500425,0.0000017148757,0.45491913,0.000041050655,0.00002212887,2.9062633e-7,2.9188672e-7,0.000003346994,0.000007794654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979483,0.00016037028,0.00067267724,0.0001954544,0.0008079138,0.000215301],"domain_scores_gemma":[0.995344,0.003533311,0.0004216758,0.00010134189,0.00033621583,0.0002634509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018651745,0.00013045069,0.00036818933,0.00015487625,0.000108445645,0.00027628697,0.00023711009,0.0000512113,0.00017604324],"category_scores_gemma":[0.0048952964,0.000081410144,0.000028318304,0.00020095463,0.0008232896,0.0003845115,0.000043663815,0.00033105005,0.0000097583215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015622918,0.00026287427,0.10494585,0.000029434259,0.00017622983,0.0032500925,0.005821826,0.01893974,0.013659443,0.4957795,0.00068509526,0.3548876],"study_design_scores_gemma":[0.018717851,0.023551285,0.13703603,0.0035210955,0.00036966696,0.03399995,0.11183247,0.01824064,0.04563169,0.6034089,0.0007964616,0.0028939953],"about_ca_topic_score_codex":0.000004462101,"about_ca_topic_score_gemma":2.810728e-7,"teacher_disagreement_score":0.3795832,"about_ca_system_score_codex":0.000044679724,"about_ca_system_score_gemma":0.0002386616,"threshold_uncertainty_score":0.58604807},"labels":[],"label_agreement":null},{"id":"W2021706749","doi":"10.1081/sta-100001556","title":"INTEGER-VALUED, MINIMAX ROBUST DESIGNS FOR APPROXIMATELY LINEAR MODELS WITH CORRELATED ERRORS","year":2001,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":24,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Mathematics; Integer (computer science); Linear regression; Polynomial regression; Polynomial; Minification; Mathematical optimization; Linear model; Regression; Applied mathematics; Algorithm; Statistics; Computer science","score_opus":0.3435779794204035,"score_gpt":0.520969719714822,"score_spread":0.17739174029441845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021706749","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":[0.002048138,0.0015871794,0.99137074,0.000073058356,0.00011617263,0.0008295738,0.00006775498,0.00005489545,0.0038525036],"genre_scores_gemma":[0.038981013,0.00029426382,0.95901304,0.00016852934,0.000012330243,0.0002312751,0.000036288897,0.00004179522,0.001221435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98849773,0.009212491,0.00096749724,0.00056672795,0.0004021875,0.00035337894],"domain_scores_gemma":[0.97717106,0.020477826,0.00042176186,0.0013056576,0.00045999317,0.0001637165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027031051,0.00028243204,0.0005628389,0.0003408762,0.00033680967,0.00016345434,0.0010737939,0.00016789099,0.0001419918],"category_scores_gemma":[0.00732435,0.00021817164,0.00005793081,0.0008031044,0.00070082746,0.00045323043,0.000266639,0.00037094226,0.000010558866],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026110907,0.0001984322,0.00020401765,0.000018563336,0.000037286052,0.0000050784674,0.0043899147,0.015116843,0.0012993757,0.8245332,0.00028386863,0.15130232],"study_design_scores_gemma":[0.00092194806,0.00024341806,0.00012612191,0.00006681703,0.00003271481,0.000028832572,0.0036308537,0.42815134,0.0011364256,0.56444657,0.0009685428,0.00024644044],"about_ca_topic_score_codex":0.000028253586,"about_ca_topic_score_gemma":0.0000136263425,"teacher_disagreement_score":0.4130345,"about_ca_system_score_codex":0.00007252011,"about_ca_system_score_gemma":0.00009270745,"threshold_uncertainty_score":0.9368477},"labels":[],"label_agreement":null},{"id":"W2023060931","doi":"10.1007/s00184-008-0173-8","title":"Minimax robust designs for field experiments","year":2008,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Minimax; Mathematics; Estimator; Covariance matrix; Covariance; Mathematical optimization; Field (mathematics); Optimal design; Least-squares function approximation; Robust statistics; Spatial correlation; Algorithm; Statistics","score_opus":0.6510149411035311,"score_gpt":0.5186700726877603,"score_spread":0.13234486841577076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023060931","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":[0.09346684,0.0017324559,0.87465906,0.00034019677,0.0013093065,0.0008356279,0.00001683484,0.00010848507,0.027531223],"genre_scores_gemma":[0.31608784,0.000019881973,0.66846305,0.0008426637,0.00015771348,0.00013161564,0.0000025509842,0.000028287692,0.014266396],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968915,0.00029263424,0.0006287255,0.0006344471,0.0011369014,0.00041580104],"domain_scores_gemma":[0.99419814,0.0045267646,0.00017224369,0.00068640773,0.00021964853,0.00019678482],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024025494,0.00020359024,0.0003997012,0.00047442733,0.0003041475,0.00011597097,0.0009061345,0.0001264453,0.0014597742],"category_scores_gemma":[0.0074743386,0.00016053383,0.00025073215,0.0012299173,0.000101898804,0.00033175395,0.0001377324,0.00010297024,0.0004610923],"study_design_candidate":"bench_or_experimental","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":[0.0010588418,0.0009058571,0.009250909,0.0000111634545,0.00011819814,0.00010806535,0.0037560845,0.0004393229,0.33443058,0.0033092478,0.5007689,0.1458428],"study_design_scores_gemma":[0.0011390671,0.0009187782,0.0010297152,0.000008009474,0.0000106066145,0.000033493954,0.00075926154,0.0021332817,0.92283016,0.0026462607,0.06813199,0.00035937468],"about_ca_topic_score_codex":0.000027609567,"about_ca_topic_score_gemma":9.794308e-7,"teacher_disagreement_score":0.5883996,"about_ca_system_score_codex":0.000067280125,"about_ca_system_score_gemma":0.00007081567,"threshold_uncertainty_score":0.999453},"labels":[],"label_agreement":null},{"id":"W2023389257","doi":"10.1093/biomet/asr022","title":"Robust designs through partially clear two-factor interactions","year":2011,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"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","keywords":"Mathematics; Factor (programming language); Upper and lower bounds; Class (philosophy); Computer science; Artificial intelligence","score_opus":0.7229823990637422,"score_gpt":0.5014948630397166,"score_spread":0.2214875360240256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023389257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124440886,0.0006595472,0.745294,0.0002364566,0.004166001,0.0006772033,0.00007167629,0.00035758305,0.12409665],"genre_scores_gemma":[0.6042582,0.000013180663,0.39305836,0.00023147826,0.00013640511,0.000019520461,0.0000018130156,0.00002738262,0.0022536905],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961933,0.00062429014,0.0007985221,0.0007330605,0.0011805883,0.00047025023],"domain_scores_gemma":[0.9968693,0.0013984438,0.00029377633,0.00091373955,0.00028231664,0.0002424229],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001916384,0.00024955484,0.00037791638,0.0010838326,0.00021918454,0.00029513641,0.001119532,0.00009752649,0.013596907],"category_scores_gemma":[0.0031125238,0.00019276503,0.00024591983,0.004974507,0.00022203462,0.0011125305,0.00026410073,0.00020173249,0.005051226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014763349,0.0024246087,0.017534968,0.000011288084,0.00032549826,0.0001586239,0.0133153545,0.00012594859,0.5149366,0.022532148,0.04304321,0.38411543],"study_design_scores_gemma":[0.0017787858,0.001645206,0.03239973,0.000039560087,0.00005745062,0.00007210522,0.0032673837,0.0021519172,0.7818092,0.01970633,0.15587577,0.0011965503],"about_ca_topic_score_codex":0.00022070589,"about_ca_topic_score_gemma":0.00001786072,"teacher_disagreement_score":0.4798173,"about_ca_system_score_codex":0.00011246278,"about_ca_system_score_gemma":0.00008123869,"threshold_uncertainty_score":0.9957235},"labels":[],"label_agreement":null},{"id":"W2025162236","doi":"10.1016/j.csda.2006.03.010","title":"New criteria for robust integer-valued designs in linear models","year":2006,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Integer (computer science); Generalized linear model; Mathematical optimization; Applied mathematics; Algorithm; Computer science; Statistics","score_opus":0.4267899798828539,"score_gpt":0.5165879401463285,"score_spread":0.08979796026347459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025162236","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":[0.0002738535,0.00011938381,0.99278635,0.00011469304,0.00015502553,0.00026543895,0.0060460223,0.000027023949,0.00021219441],"genre_scores_gemma":[0.03408457,0.0000025631032,0.9569024,0.00011123475,0.00012508433,0.000014685253,0.008030378,0.00002117609,0.0007079501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956281,0.0004908618,0.0012590208,0.0010125788,0.0012889626,0.00032043256],"domain_scores_gemma":[0.9936987,0.004331622,0.00033586784,0.0009272094,0.0005577738,0.00014880237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034138868,0.00023239337,0.00061431754,0.000896977,0.00014612993,0.0004431627,0.0013733042,0.00007702971,0.0007232089],"category_scores_gemma":[0.0018802978,0.00021467208,0.00014056514,0.0025566595,0.000089647576,0.00069124316,0.00036324584,0.00011529628,0.0000656111],"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":[0.0000603095,0.00008516601,0.00063705683,0.0000030082913,0.000120983466,0.000007846986,0.00006108424,0.8968484,0.000048808608,0.037593905,0.057745434,0.0067880163],"study_design_scores_gemma":[0.0003278907,0.000028842998,0.0029071297,0.0000032976716,0.0001862177,9.416228e-7,0.00003084646,0.6714126,0.000011511819,0.32455963,0.00037083207,0.00016023629],"about_ca_topic_score_codex":0.0019685833,"about_ca_topic_score_gemma":0.0005875235,"teacher_disagreement_score":0.28696573,"about_ca_system_score_codex":0.000107332184,"about_ca_system_score_gemma":0.0002533052,"threshold_uncertainty_score":0.87540734},"labels":[],"label_agreement":null},{"id":"W2025530900","doi":"10.1016/j.jspi.2007.12.003","title":"Folded over non-orthogonal designs","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Isomorphism (crystallography); Mathematics; Orthogonal array; Orthogonal matrix; Orthogonal basis; Statistics","score_opus":0.21802542119450613,"score_gpt":0.5196836354778618,"score_spread":0.30165821428335565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025530900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20458078,0.0001732505,0.7915654,0.000030948457,0.00022092939,0.000034400557,0.0000100623365,0.0000043813225,0.0033797906],"genre_scores_gemma":[0.73337376,0.00000484197,0.26630712,0.00014120979,0.00008280954,2.437441e-7,4.927495e-7,0.0000050320764,0.000084479216],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99728566,0.00015622829,0.0009533308,0.00020114135,0.001128403,0.0002752158],"domain_scores_gemma":[0.99075305,0.00805788,0.00041412644,0.00012741302,0.00029599716,0.0003515428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006052588,0.00013213999,0.00036682223,0.0002454411,0.0001038325,0.00021153192,0.00030309558,0.000075754215,0.0003744215],"category_scores_gemma":[0.0064755334,0.00008969501,0.000055682693,0.00025704104,0.00019790101,0.00037515358,0.00006910748,0.00033885616,0.000020455587],"study_design_candidate":"observational","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":[0.002434249,0.00045066664,0.37165812,0.000029679599,0.00014320489,0.0033239094,0.00400826,0.00076585216,0.10924861,0.10233728,0.02608665,0.3795135],"study_design_scores_gemma":[0.001153331,0.0016274878,0.8810376,0.00015250553,0.000035038618,0.00047107003,0.001094484,0.007703516,0.0047235554,0.09965278,0.0020060358,0.00034260348],"about_ca_topic_score_codex":0.0000050578665,"about_ca_topic_score_gemma":5.193661e-7,"teacher_disagreement_score":0.528793,"about_ca_system_score_codex":0.000027592716,"about_ca_system_score_gemma":0.000097156684,"threshold_uncertainty_score":0.7752286},"labels":[],"label_agreement":null},{"id":"W2026680199","doi":"10.1111/j.0006-341x.2004.00236.x","title":"Letter to the Editor of <i>Biometrics</i>","year":2004,"lang":"en","type":"letter","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Population; Mathematics; Biometrics; Statistics; Library science; Demography; Computer science; Artificial intelligence; Sociology","score_opus":0.16610295402904326,"score_gpt":0.4268696127936606,"score_spread":0.26076665876461735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026680199","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060342987,0.0027842803,0.083253235,0.84100884,0.06540259,0.0013869406,0.00081648206,0.00009638535,0.0051909024],"genre_scores_gemma":[0.00019656247,0.00003416408,0.08553365,0.83246183,0.07326039,0.000057218946,0.000045495337,0.000107924505,0.0083027575],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.987307,0.00084451836,0.001768532,0.0013254038,0.0079037,0.00085082406],"domain_scores_gemma":[0.985563,0.009351943,0.001050967,0.0027169413,0.0011004525,0.00021673128],"candidate_categories":["metaresearch","metaepi_narrow","bibliometrics","open_science","research_integrity","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.008369659,0.0006020074,0.0011143195,0.017236505,0.000171358,0.0006095623,0.0056265886,0.0013529754,0.0005091145],"category_scores_gemma":[0.022589521,0.00036437536,0.00065018,0.06899593,0.00035952128,0.00022405105,0.001044897,0.0015536874,0.00297151],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011429043,0.00005457927,0.000027124084,0.000019987063,0.0000351486,0.000048917085,0.000083450745,0.000014998793,0.0010314019,0.000020469302,0.98650664,0.012145865],"study_design_scores_gemma":[0.00022688974,0.00030867785,0.00009709276,0.000026730775,0.00003363907,0.000008424299,0.000024333673,0.000009717986,0.005759677,0.0006567909,0.99240375,0.00044430135],"about_ca_topic_score_codex":0.000090544425,"about_ca_topic_score_gemma":7.0236933e-7,"teacher_disagreement_score":0.05175943,"about_ca_system_score_codex":0.00042644344,"about_ca_system_score_gemma":0.00027604584,"threshold_uncertainty_score":0.9999435},"labels":[],"label_agreement":null},{"id":"W2027005019","doi":"10.5539/ijsp.v4n2p55","title":"Quantile Plots of the Prediction Variance for Partially Replicated Central Composite Design","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Quantile; Variance (accounting); Star (game theory); Cube (algebra); Replicate; Replication (statistics); Statistics; Mathematics; Algorithm; Computer science; Combinatorics","score_opus":0.20201414718977026,"score_gpt":0.4313331808864174,"score_spread":0.22931903369664716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027005019","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05604526,0.000060818675,0.94169265,0.0004905494,0.0010083115,0.0002522832,0.000383951,0.000003002167,0.000063184176],"genre_scores_gemma":[0.59125644,0.0000056283684,0.40861243,0.000037327813,0.00005766492,0.0000031128122,0.0000016410146,0.000003280719,0.000022464274],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975607,0.0003883278,0.0008348758,0.00015611674,0.0009652091,0.00009477694],"domain_scores_gemma":[0.9953908,0.0014027808,0.00072965416,0.00016806427,0.0022077537,0.000100954276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040001716,0.00006999971,0.00018799707,0.00004640338,0.000040114453,0.00009519224,0.00050360465,0.000034322693,0.00001592526],"category_scores_gemma":[0.005553134,0.00004339391,0.00006146291,0.000102934864,0.00016413628,0.00017347606,0.00007267244,0.00008500043,6.221826e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.01753729,0.0025376726,0.28635326,0.0000752409,0.0008423119,0.00003080573,0.0070438986,0.09200822,0.13764718,0.2545617,0.041713163,0.15964927],"study_design_scores_gemma":[0.0015252491,0.00082419015,0.13101542,0.000050135102,0.000039826322,0.00007630136,0.00010070358,0.09456152,0.025986942,0.7433997,0.0023194135,0.00010060024],"about_ca_topic_score_codex":0.000015062245,"about_ca_topic_score_gemma":0.0000027405476,"teacher_disagreement_score":0.5352112,"about_ca_system_score_codex":0.000072213676,"about_ca_system_score_gemma":0.00020613927,"threshold_uncertainty_score":0.66480213},"labels":[],"label_agreement":null},{"id":"W2027590663","doi":"10.1016/s0166-218x(99)00232-2","title":"On the orthogonal designs of order 24","year":2000,"lang":"en","type":"article","venue":"Discrete Applied Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Mathematics; Orthogonal array; Order (exchange); Construct (python library); Combinatorics; Mathematical optimization; Statistics; Computer science; Taguchi methods","score_opus":0.16773208217413743,"score_gpt":0.4070227853266725,"score_spread":0.2392907031525351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027590663","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24398054,0.00004071292,0.1817645,0.0005534725,0.00011148311,0.00088956446,0.000031174535,0.00006453409,0.572564],"genre_scores_gemma":[0.5575139,0.000007827857,0.43754134,0.00070037413,0.000030184834,0.00008221354,0.0000030195708,0.00004074916,0.004080398],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970185,0.00012726295,0.00077969924,0.00034216882,0.001470909,0.0002614488],"domain_scores_gemma":[0.9946956,0.0038923298,0.00024904154,0.0009877271,0.00009040179,0.00008488562],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0027154603,0.00020918077,0.00038401422,0.000085840235,0.00013323936,0.00010493991,0.0009651067,0.000077933255,0.0113398265],"category_scores_gemma":[0.00078124093,0.00011110279,0.00013504448,0.00074371666,0.00026210857,0.00006893662,0.00008306558,0.00016585946,0.0014909598],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008307853,0.00018619368,0.0000054662996,0.000010443532,0.000028008144,0.0000018971975,0.0018896164,0.0006171949,0.030144917,0.9486987,0.0045391265,0.013795371],"study_design_scores_gemma":[0.00036714802,0.0001450887,0.00006928545,0.00003109936,0.000026462254,0.0000066876396,0.0025755966,0.008236345,0.06860112,0.9173654,0.0022877608,0.00028805315],"about_ca_topic_score_codex":0.0000016921823,"about_ca_topic_score_gemma":6.9846294e-7,"teacher_disagreement_score":0.5684836,"about_ca_system_score_codex":0.000017180797,"about_ca_system_score_gemma":0.00004466994,"threshold_uncertainty_score":0.9992865},"labels":[],"label_agreement":null},{"id":"W2027757720","doi":"10.1080/03610920903511769","title":"Applications and Implementations of Continuous Robust Designs","year":2010,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Implementation; Computer science; Programming language","score_opus":0.18421064189139083,"score_gpt":0.5518130027677605,"score_spread":0.36760236087636966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027757720","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":[0.006197979,0.0008728477,0.9894105,0.00007181416,0.00005969397,0.00043615163,0.00010138626,0.0000144549085,0.0028351431],"genre_scores_gemma":[0.18153875,0.00017769008,0.81786186,0.00005717889,0.0000072637527,0.00014260926,0.000012468489,0.000009641386,0.00019255791],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9936791,0.0048786625,0.00078118173,0.00029149698,0.00022020757,0.00014931645],"domain_scores_gemma":[0.9783707,0.02001712,0.00033001738,0.0009417977,0.00024398896,0.00009635506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019007115,0.00012003902,0.00030659686,0.00023765907,0.00021720552,0.000096744785,0.0005612476,0.00007472401,0.00030085974],"category_scores_gemma":[0.005308839,0.00010750898,0.000024576308,0.00042566162,0.0007850871,0.00018112714,0.00028203582,0.00026745512,0.0000041633443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028907032,0.00004497978,0.0018111015,0.000005320062,0.00000598583,1.3385718e-7,0.0010709896,0.0000075718176,0.031211412,0.6533539,0.00005957852,0.3124001],"study_design_scores_gemma":[0.00044931084,0.000057652098,0.014111465,0.000012281067,0.000023530523,0.000009956367,0.0040293615,0.0013240604,0.008697498,0.96559405,0.0055400925,0.0001507587],"about_ca_topic_score_codex":0.000033638546,"about_ca_topic_score_gemma":0.000044482782,"teacher_disagreement_score":0.31224933,"about_ca_system_score_codex":0.00001066057,"about_ca_system_score_gemma":0.000044930035,"threshold_uncertainty_score":0.65875244},"labels":[],"label_agreement":null},{"id":"W2028387228","doi":"10.1016/j.jspi.2011.12.019","title":"Generalized minimum aberration two-level split-plot designs","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Plot (graphics); Split plot; Restricted randomization; Rank (graph theory); Algorithm; Combinatorics; Statistics","score_opus":0.41414051249315026,"score_gpt":0.5156379488757189,"score_spread":0.10149743638256864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028387228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20717841,0.0006049532,0.7901008,0.000083286395,0.00042774875,0.000050907183,0.000022805862,0.0000071965956,0.001523883],"genre_scores_gemma":[0.5930748,0.000007895238,0.406513,0.000121093704,0.00014022549,9.907667e-7,0.0000012930047,0.0000054866478,0.00013517971],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99715716,0.000510104,0.0009090185,0.00017091355,0.0009453924,0.0003074253],"domain_scores_gemma":[0.994597,0.0040950878,0.00045982853,0.00014483354,0.00030564037,0.00039761592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041131657,0.00014843227,0.00039508723,0.00018657355,0.00012630965,0.00024944072,0.00026401193,0.00006521169,0.00035950294],"category_scores_gemma":[0.0066943206,0.000101510734,0.00004914579,0.00020720113,0.00015998368,0.0007918997,0.00006201559,0.00026469244,0.00004386842],"study_design_candidate":"observational","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":[0.001673285,0.0007294438,0.18704832,0.000036296322,0.00016725382,0.00020109626,0.010759453,0.0018516842,0.21328549,0.35685804,0.034242343,0.19314729],"study_design_scores_gemma":[0.005178347,0.0031717191,0.6788901,0.00038076128,0.00020375042,0.0010900651,0.004282638,0.06548019,0.017912054,0.21577413,0.0063201473,0.0013160949],"about_ca_topic_score_codex":0.000011386133,"about_ca_topic_score_gemma":2.8666767e-7,"teacher_disagreement_score":0.4918418,"about_ca_system_score_codex":0.000034263518,"about_ca_system_score_gemma":0.000082000144,"threshold_uncertainty_score":0.80142105},"labels":[],"label_agreement":null},{"id":"W2034173718","doi":"10.1007/s10463-005-0020-x","title":"Connections Between the Resolutions of General Two-level Factorial Designs","year":2006,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractional factorial design; Mathematics; Factorial experiment; Factorial; Connection (principal bundle); Plackett–Burman design; Statistics; Mathematical analysis; Geometry","score_opus":0.4751025642495491,"score_gpt":0.5019135975057859,"score_spread":0.026811033256236794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034173718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070847385,0.00004837274,0.9184134,0.000733964,0.00066944095,0.00037669012,0.0012513013,0.000011352109,0.00764809],"genre_scores_gemma":[0.6035205,0.0000025648917,0.39599827,0.000024585841,0.00012537645,0.000006566486,0.0000040866425,0.00000884675,0.0003091704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9964613,0.00036205968,0.0014398596,0.000209216,0.0012996669,0.00022793998],"domain_scores_gemma":[0.99267036,0.0051414976,0.00074947416,0.00079147855,0.0005819233,0.00006526936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025293336,0.00016226286,0.00054568023,0.00010733644,0.00020885517,0.000040903218,0.0011033692,0.00007498014,0.00010339042],"category_scores_gemma":[0.006952905,0.000087329674,0.0002219069,0.0006239095,0.0014410182,0.00016778026,0.00023966891,0.00015042357,0.000013316509],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002299097,0.00025144473,0.0003110913,0.000024020013,0.000055047392,7.1248644e-7,0.00022388088,0.0022441042,0.017242117,0.97058207,0.008160217,0.0008823272],"study_design_scores_gemma":[0.0003035661,0.0001375385,0.0063984096,0.00005760806,0.000074590214,0.000004811852,0.00014882348,0.0028392468,0.09340784,0.8952117,0.001285917,0.00012993868],"about_ca_topic_score_codex":0.00057808263,"about_ca_topic_score_gemma":0.000038397204,"teacher_disagreement_score":0.5326731,"about_ca_system_score_codex":0.000016141561,"about_ca_system_score_gemma":0.00014446407,"threshold_uncertainty_score":0.8323779},"labels":[],"label_agreement":null},{"id":"W2035714589","doi":"10.1080/03610920903147812","title":"A Note on Bartlett's<i>M</i>Test for Homogeneity of Variances","year":2010,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Homogeneity (statistics); Mathematics; F-test of equality of variances; Levene's test; Statistics; Chi-square test; Econometrics; Statistical hypothesis testing","score_opus":0.12547914715936326,"score_gpt":0.539330779527931,"score_spread":0.4138516323685677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035714589","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":[0.0062382836,0.00039367587,0.98881143,0.00013399591,0.00027387353,0.00035472246,0.00022023376,0.000012967236,0.003560819],"genre_scores_gemma":[0.21111977,0.000056901063,0.7883601,0.00015768282,0.000016341435,0.00007071043,0.0000071182735,0.000010823364,0.00020050077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99373645,0.0047646873,0.00072258065,0.0003293504,0.0002774914,0.00016946055],"domain_scores_gemma":[0.9252693,0.07287774,0.00033259523,0.0011978688,0.0002446483,0.000077819226],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032062225,0.00013977288,0.00037043708,0.0001890234,0.00017504788,0.00007299176,0.00088984636,0.00010728954,0.00016695776],"category_scores_gemma":[0.044910517,0.00011518221,0.00005156257,0.0003375661,0.00060731167,0.00013788916,0.0001965199,0.0002941936,0.000006759458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020052999,0.000114045666,0.00044932065,0.000009366714,0.000004290501,2.3016803e-7,0.000829174,0.000012998834,0.092523776,0.6579759,0.0001548988,0.24772544],"study_design_scores_gemma":[0.0004896948,0.00018949986,0.005518204,0.000023390088,0.000012524779,0.0000029797118,0.00029911724,0.003475176,0.07790279,0.9051722,0.006772594,0.00014180089],"about_ca_topic_score_codex":0.000013382225,"about_ca_topic_score_gemma":0.000023317374,"teacher_disagreement_score":0.24758364,"about_ca_system_score_codex":0.0000130622975,"about_ca_system_score_gemma":0.000054769564,"threshold_uncertainty_score":0.99669564},"labels":[],"label_agreement":null},{"id":"W2037898831","doi":"10.1080/03610918.2011.594533","title":"Inferences on the Among-Group Variance Component in Unbalanced Heteroscedastic One-Fold Nested Design","year":2011,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"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":"Ministère de l’Éducation, Gouvernement de l’Ontario; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Heteroscedasticity; Variance components; Confidence interval; Variance (accounting); Component (thermodynamics); Statistics; Computer science; Group (periodic table); Econometrics; Mathematics; Economics","score_opus":0.6107567722578584,"score_gpt":0.5249015163457682,"score_spread":0.0858552559120902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037898831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07549069,0.00007161505,0.9225369,0.00013392423,0.000094618015,0.0006204027,0.000018780293,0.000029807034,0.0010032213],"genre_scores_gemma":[0.73028606,0.000021020489,0.2694617,0.00011445112,0.00000454274,0.00006130293,0.000027358596,0.000009256115,0.00001431218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957001,0.0023582245,0.00089249504,0.00035361218,0.0005128301,0.00018272853],"domain_scores_gemma":[0.98724496,0.011305606,0.00036496707,0.0008178177,0.00020606155,0.00006057937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025934877,0.00017064843,0.00026391566,0.0003442127,0.00024567565,0.0002029763,0.00078822253,0.00007730532,0.00005426124],"category_scores_gemma":[0.0018663403,0.00013976828,0.000023309545,0.0009044784,0.00035588874,0.00028425726,0.00020659057,0.00026752937,0.00003125286],"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":[0.00020866779,0.00078397413,0.04673083,0.0000073487618,0.000015103149,0.0000028571774,0.006924304,0.7174339,0.00038749873,0.17837964,0.00005730502,0.04906854],"study_design_scores_gemma":[0.0003301726,0.000096236174,0.29111403,0.000038316193,0.0000038725543,3.185729e-7,0.00036532743,0.62848556,0.000025187881,0.07942706,0.000013218712,0.00010065686],"about_ca_topic_score_codex":0.00014353047,"about_ca_topic_score_gemma":0.0001560479,"teacher_disagreement_score":0.65479535,"about_ca_system_score_codex":0.00008725685,"about_ca_system_score_gemma":0.000031919655,"threshold_uncertainty_score":0.5699585},"labels":[],"label_agreement":null},{"id":"W2038336950","doi":"10.1016/j.jspi.2008.05.022","title":"Robust designs for misspecified logistic models","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Mathematics; Statistics; Logistic regression; Econometrics; Applied mathematics","score_opus":0.7552309551786061,"score_gpt":0.5199634607146267,"score_spread":0.23526749446397932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038336950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00961722,0.0005439432,0.9874043,0.000075924334,0.00018248727,0.00008181318,0.0000360889,0.000007403923,0.002050804],"genre_scores_gemma":[0.5389971,0.000025953559,0.46070725,0.000070805865,0.000056139485,0.0000014764262,8.029173e-7,0.0000052272417,0.00013524378],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977995,0.0001972187,0.0008334528,0.00022420904,0.0007162626,0.00022932871],"domain_scores_gemma":[0.98890984,0.009849969,0.00036933768,0.00013591394,0.00047331295,0.00026162705],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00206897,0.0001326934,0.00042824412,0.00016836516,0.000189734,0.00013206931,0.00032895757,0.00006818544,0.00010085722],"category_scores_gemma":[0.009610993,0.000091090464,0.00006010709,0.0001602906,0.0003026346,0.00037822407,0.000043541302,0.00021889516,0.000007639567],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004683016,0.0007405368,0.01651424,0.00008348551,0.00020054482,0.0018787766,0.008037958,0.24573447,0.0164053,0.48697773,0.11685532,0.10188862],"study_design_scores_gemma":[0.0016270156,0.0025326102,0.015724609,0.00016406488,0.00004835043,0.0008943221,0.0011472263,0.3880327,0.0009888103,0.5868372,0.0015752406,0.0004278675],"about_ca_topic_score_codex":0.0000039976867,"about_ca_topic_score_gemma":1.0850636e-7,"teacher_disagreement_score":0.5293799,"about_ca_system_score_codex":0.000026647045,"about_ca_system_score_gemma":0.00012773555,"threshold_uncertainty_score":0.9987315},"labels":[],"label_agreement":null},{"id":"W2038338945","doi":"10.1111/1467-9469.00247","title":"Heteroscedastic Regression Models and Applications to Off‐line Quality Control","year":2001,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Concordia University","funders":"","keywords":"Heteroscedasticity; Estimator; Likelihood function; Mathematics; Computer science; Mathematical optimization; Minification; Variance function; Variance (accounting); Econometrics; Estimation theory; Statistics","score_opus":0.21101361298810462,"score_gpt":0.5027999072681889,"score_spread":0.2917862942800843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038338945","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0212026,0.00054471614,0.97680634,0.00029551284,0.0002238522,0.0002439645,0.00020212917,0.000007158513,0.00047370972],"genre_scores_gemma":[0.71584487,0.000092373746,0.28334954,0.00023174053,0.00012404549,0.0000068453833,0.0000014584051,0.0000122167985,0.0003368886],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99670357,0.00047804377,0.0011871546,0.00026822317,0.0011223136,0.00024071576],"domain_scores_gemma":[0.99584293,0.0019494137,0.00070708507,0.00032038943,0.00068977004,0.0004903785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003259244,0.00016323468,0.00049811596,0.00028318536,0.00016112921,0.00020321141,0.00044371214,0.00005685358,0.00012464708],"category_scores_gemma":[0.0019122196,0.0001142258,0.00006834125,0.00045550166,0.00014425225,0.00030997343,0.000066876935,0.00019917548,0.000028608158],"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":[0.0021303645,0.00043266543,0.021933436,0.00002667734,0.00008873651,0.00021722882,0.0014288446,0.013971152,0.029206226,0.04016603,0.008489705,0.88190895],"study_design_scores_gemma":[0.0073307785,0.0044787917,0.060554653,0.0005191415,0.00019936939,0.0014878521,0.003013204,0.06916111,0.0022714748,0.83759546,0.012271315,0.0011168635],"about_ca_topic_score_codex":0.000007734759,"about_ca_topic_score_gemma":0.000003529203,"teacher_disagreement_score":0.8807921,"about_ca_system_score_codex":0.000076889206,"about_ca_system_score_gemma":0.000057483085,"threshold_uncertainty_score":0.46579927},"labels":[],"label_agreement":null},{"id":"W2042833209","doi":"10.2307/3316025","title":"Partially replicated two‐level fractional factorial designs","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fractional factorial design; Covariance; Factorial experiment; Constant (computer programming); Series (stratigraphy); Term (time); Mathematics; Factorial; Plackett–Burman design; Construct (python library); Simple (philosophy); Algorithm; Computer science; Statistics","score_opus":0.3625785804372562,"score_gpt":0.44976741361792516,"score_spread":0.08718883318066895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042833209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0060410774,0.000083470724,0.9889222,0.0004182645,0.002569291,0.00009538884,0.00058021466,0.0000054330094,0.0012846906],"genre_scores_gemma":[0.5266748,0.0000029087944,0.47237617,0.00025140008,0.00045915984,0.0000011726968,0.000005095114,0.000015816637,0.00021344975],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969893,0.00025391387,0.0010353061,0.00023883856,0.0011130248,0.00036959513],"domain_scores_gemma":[0.9956713,0.0011048523,0.0006237673,0.00030390106,0.0011435243,0.0011526708],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023724085,0.00015627996,0.00033341782,0.00042245444,0.0002273422,0.00033673653,0.0006658282,0.00008374685,0.0015314282],"category_scores_gemma":[0.0076881144,0.0001315007,0.00009864748,0.00046800682,0.00021020485,0.00035488274,0.000016413167,0.0003370431,0.00020032996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008718016,0.00032290956,0.016273879,0.000011582568,0.000432576,0.006012576,0.0050850133,0.07685299,0.059750974,0.52232385,0.20749702,0.10456481],"study_design_scores_gemma":[0.004355292,0.0012809248,0.0324252,0.000061129664,0.00009124942,0.0011761877,0.0007935663,0.00095613836,0.018737957,0.8629161,0.07646262,0.0007436008],"about_ca_topic_score_codex":0.0034431466,"about_ca_topic_score_gemma":0.007220653,"teacher_disagreement_score":0.52063376,"about_ca_system_score_codex":0.00049517356,"about_ca_system_score_gemma":0.004532009,"threshold_uncertainty_score":0.9993813},"labels":[],"label_agreement":null},{"id":"W2043174953","doi":"10.1002/cjs.11128","title":"Sequential design for nonparametric inference","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Nonparametric statistics; Quantile; Statistics; Inference; Econometrics; Computer science; Artificial intelligence","score_opus":0.3601301529286638,"score_gpt":0.4679454129079356,"score_spread":0.10781525997927183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043174953","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":[0.0031635372,0.0005920866,0.993546,0.00006235119,0.0017163369,0.00015606883,0.00023544347,0.0000021384792,0.0005260185],"genre_scores_gemma":[0.41661423,0.000003666963,0.5828751,0.000102940074,0.00017692303,0.0000019625963,0.0000010742967,0.000009405007,0.000214729],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997728,0.00033322908,0.00074449554,0.0001163031,0.00060489937,0.0004731017],"domain_scores_gemma":[0.99220985,0.0050226618,0.00047575482,0.00019600001,0.0008891001,0.0012066081],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005281259,0.00011826885,0.00029383006,0.0007350514,0.00014722293,0.00024955184,0.00059049483,0.00006897961,0.00057127455],"category_scores_gemma":[0.020237302,0.00009597889,0.0000818325,0.00063162023,0.0001466233,0.00033948052,0.000013855818,0.00015927803,0.0000710438],"study_design_candidate":"theoretical_or_conceptual","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":[0.0002872605,0.00014969624,0.034777045,0.00002567396,0.00017582817,0.00024321461,0.004673825,0.006029734,0.0056936387,0.16280073,0.2772477,0.50789565],"study_design_scores_gemma":[0.004653025,0.0048858165,0.04646235,0.00013872524,0.0004231604,0.0012821143,0.0040552597,0.036794372,0.04549244,0.4949397,0.35878003,0.002092994],"about_ca_topic_score_codex":0.0003509263,"about_ca_topic_score_gemma":0.00028747242,"teacher_disagreement_score":0.50580263,"about_ca_system_score_codex":0.00020471548,"about_ca_system_score_gemma":0.0015269843,"threshold_uncertainty_score":0.98801565},"labels":[],"label_agreement":null},{"id":"W2044198898","doi":"10.1002/bimj.200410146","title":"Methods of Selecting Informative Variables","year":2006,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Mathematics; Dimensionality reduction; Principal component analysis; Dimension (graph theory); Eigenvalues and eigenvectors; Feature selection; Selection (genetic algorithm); Design matrix; Population; Set (abstract data type); Mathematical optimization; Statistics; Algorithm; Computer science; Data mining; Artificial intelligence; Regression analysis","score_opus":0.1827256570618646,"score_gpt":0.5352720553893084,"score_spread":0.35254639832744383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044198898","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":[0.022378407,0.00084790605,0.9435085,0.0000728745,0.00049629307,0.00007570759,0.000004120623,0.000018955192,0.032597248],"genre_scores_gemma":[0.24248818,0.0000061562673,0.7569492,0.000037787533,0.00015440625,0.0000010897279,3.490323e-7,0.0000071596046,0.00035568496],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99496037,0.0013803977,0.0014513585,0.00020248856,0.0016750228,0.00033035103],"domain_scores_gemma":[0.9910712,0.006977913,0.0008549796,0.00021924377,0.0007046531,0.0001719742],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015726859,0.00014004234,0.0004399343,0.0029810006,0.00018914999,0.00033796317,0.0007528075,0.00010811404,0.0006194416],"category_scores_gemma":[0.0141052045,0.0000923347,0.00021699746,0.011732627,0.00013547546,0.0006256408,0.0001590436,0.00030540102,0.000056013963],"study_design_candidate":"bench_or_experimental","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":[0.00011498421,0.00032224623,0.0078086043,0.0000054878806,0.00006178027,0.00001726129,0.0003744608,0.0006287299,0.28364885,0.007873123,0.011371231,0.6877732],"study_design_scores_gemma":[0.0015215535,0.0010046754,0.034066107,0.000037525115,0.00004244388,0.0008812308,0.0018203821,0.016570814,0.74345946,0.15326251,0.046777792,0.0005555084],"about_ca_topic_score_codex":0.00004308091,"about_ca_topic_score_gemma":1.16988225e-7,"teacher_disagreement_score":0.6872177,"about_ca_system_score_codex":0.000115927716,"about_ca_system_score_gemma":0.00010842471,"threshold_uncertainty_score":0.9941994},"labels":[],"label_agreement":null},{"id":"W2044499983","doi":"10.2307/3316089","title":"The analysis of unreplicated factorial experiments from a geometric perspective","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Factorial; Partition (number theory); Context (archaeology); Point (geometry); Mathematics; Factorial experiment; Unit vector; Unit sphere; Unit (ring theory); Space (punctuation); Computer science; Combinatorics; Statistics; Geometry; Mathematical analysis","score_opus":0.15826180918038166,"score_gpt":0.42955833407703603,"score_spread":0.27129652489665435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044499983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16840108,0.004702352,0.8153166,0.00012983657,0.0033962463,0.00024735447,0.0022780304,0.0000045346887,0.005523936],"genre_scores_gemma":[0.92269707,0.000019478583,0.077006765,0.00003369581,0.00005467421,0.0000011773305,0.0000030351737,0.000010254809,0.0001738666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968626,0.0006313119,0.0009896393,0.00021533137,0.001026937,0.00027417863],"domain_scores_gemma":[0.9921064,0.004341251,0.000890876,0.00042738553,0.0016585342,0.0005755853],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024041096,0.00013012264,0.0004951762,0.0013993066,0.0002093985,0.00022950752,0.0007490091,0.00006289333,0.0011061659],"category_scores_gemma":[0.020063566,0.00008599329,0.00019197799,0.003842496,0.00027708607,0.00012688755,0.0000142745375,0.000185842,0.000014298813],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092997187,0.0005061138,0.16688429,0.000005115504,0.017172199,0.0010406534,0.04360582,0.0073224944,0.034803722,0.5112587,0.13111582,0.08535509],"study_design_scores_gemma":[0.0046241907,0.002010184,0.30069396,0.000046932408,0.0038766435,0.00006989487,0.09478979,0.005447562,0.08652094,0.40030706,0.10015284,0.0014599786],"about_ca_topic_score_codex":0.010083482,"about_ca_topic_score_gemma":0.004111196,"teacher_disagreement_score":0.754296,"about_ca_system_score_codex":0.00043016084,"about_ca_system_score_gemma":0.001199043,"threshold_uncertainty_score":0.99980694},"labels":[],"label_agreement":null},{"id":"W2044892297","doi":"10.2307/3316055","title":"Restricted minimax robust designs for misspecified regression models","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Minimax; Robustness (evolution); Parametric statistics; Mathematics; Polynomial regression; Class (philosophy); Mathematical optimization; Regression; Polynomial; Regression analysis; Computer science; Applied mathematics; Statistics; Artificial intelligence","score_opus":0.4242684117404009,"score_gpt":0.4286993554738206,"score_spread":0.004430943733419679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044892297","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":[0.005067272,0.00056093035,0.98932713,0.0003947078,0.0008179343,0.00022680397,0.0003628739,0.000004508797,0.003237841],"genre_scores_gemma":[0.09089511,0.000051148523,0.9058377,0.00021233242,0.00021541449,0.0000034929815,0.000008103016,0.000030230909,0.002746439],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99661326,0.0004026534,0.0013369455,0.0002731738,0.0009204614,0.00045351344],"domain_scores_gemma":[0.9932262,0.0027730258,0.0008314897,0.0003879019,0.0016114295,0.0011699963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026517897,0.00018922355,0.00048070893,0.000708648,0.00026223378,0.0003537654,0.000889308,0.00011714862,0.0007622099],"category_scores_gemma":[0.007813448,0.00014183634,0.00013630404,0.0006802204,0.00015995844,0.00037546645,0.000016573116,0.00022259883,0.000026831209],"study_design_candidate":"not_applicable","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":[0.00060075126,0.00006184571,0.0012835633,0.000008234479,0.000057915626,0.0015816644,0.0013222024,0.018863225,0.0042029754,0.03054901,0.82392395,0.11754468],"study_design_scores_gemma":[0.0039497055,0.0024940178,0.006843665,0.00027237547,0.00013866517,0.001319747,0.004626969,0.17764921,0.003333395,0.55590695,0.24242233,0.0010429609],"about_ca_topic_score_codex":0.00040131478,"about_ca_topic_score_gemma":0.0011359877,"teacher_disagreement_score":0.5815016,"about_ca_system_score_codex":0.00025684875,"about_ca_system_score_gemma":0.0015284177,"threshold_uncertainty_score":0.9353992},"labels":[],"label_agreement":null},{"id":"W2045335212","doi":"10.1002/sim.776","title":"Power comparison of robust approximate and non‐parametric tests for the analysis of cross‐over trials","year":2001,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parametric statistics; Sample size determination; Mathematics; Ordinary least squares; Statistics; Robustness (evolution); Covariance; Applied mathematics; Statistical hypothesis testing","score_opus":0.3388194152568817,"score_gpt":0.5831102429260211,"score_spread":0.2442908276691394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045335212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14095023,0.0012432645,0.8559732,0.00007931126,0.00024001402,0.0005383845,0.00024515393,0.0000034554696,0.0007270189],"genre_scores_gemma":[0.7541279,0.00007101443,0.24557047,0.00004005836,0.000020116384,0.000015575768,0.000008811474,0.000007239758,0.00013881916],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99602103,0.00045880827,0.001887849,0.00032304338,0.001117831,0.00019145553],"domain_scores_gemma":[0.9556472,0.042484693,0.00091301196,0.0004679781,0.00042116744,0.00006596321],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01672762,0.00013864477,0.0014726241,0.0009799831,0.000054911776,0.000036074765,0.0004295249,0.000065127264,0.0006275351],"category_scores_gemma":[0.05933647,0.00007515881,0.00008708382,0.003695165,0.0007779125,0.00006681922,0.00007515302,0.00010993737,9.319462e-7],"study_design_candidate":"observational","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":[0.002279402,0.0006946144,0.81154805,0.00008253077,0.0011563926,0.000014068542,0.007040475,0.045477495,0.012404747,0.017062126,0.017407928,0.08483219],"study_design_scores_gemma":[0.0025876942,0.00066646485,0.44556972,0.000035181092,0.00061509985,0.0000012696075,0.002179885,0.53395236,0.001094863,0.012777186,0.00039213974,0.00012816722],"about_ca_topic_score_codex":0.0001499516,"about_ca_topic_score_gemma":0.000040254326,"teacher_disagreement_score":0.61317766,"about_ca_system_score_codex":0.000025373647,"about_ca_system_score_gemma":0.000030224153,"threshold_uncertainty_score":0.9485871},"labels":[],"label_agreement":null},{"id":"W2046447653","doi":"10.1037/a0014164","title":"Confidence intervals in repeated-measures designs: The number of observations principle.","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":191,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Factorial; Factorial experiment; Arithmetic; Psychology; Statistics; Confidence interval; Variety (cybernetics); Mathematics; Cognitive psychology; Computer science; Artificial intelligence","score_opus":0.3054079905872321,"score_gpt":0.4825623642055711,"score_spread":0.17715437361833897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046447653","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9802341,0.0031374318,0.0025263964,0.0028173034,0.0018952922,0.00056897587,0.00004116552,0.000013018532,0.00876632],"genre_scores_gemma":[0.97633773,0.000033602915,0.019766597,0.0033104538,0.00014335349,0.000028831148,0.000004259239,0.00003442006,0.00034074122],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99343854,0.0013691488,0.0025415898,0.00086866645,0.0004666611,0.0013154022],"domain_scores_gemma":[0.9947641,0.0007418569,0.0013321295,0.0014921343,0.00047209946,0.0011977182],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007991952,0.00046010365,0.0009241914,0.0009298141,0.00024599742,0.00017041108,0.003372748,0.00031261405,0.0012073587],"category_scores_gemma":[0.0025028647,0.0003725477,0.00043386046,0.001588755,0.00095766986,0.0006468757,0.00006284652,0.0007691163,0.000068594054],"study_design_candidate":"bench_or_experimental","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":[0.0007007308,0.0009171267,0.11606629,0.0000058642236,0.00015169864,0.0014612479,0.021707544,0.000341112,0.81735957,0.014313763,0.016041458,0.010933611],"study_design_scores_gemma":[0.0063788453,0.0042081564,0.57751584,0.00060427753,0.00008145311,0.0080374135,0.04791563,0.00023279687,0.2633139,0.07505245,0.01506741,0.0015918266],"about_ca_topic_score_codex":0.0031574457,"about_ca_topic_score_gemma":0.008157791,"teacher_disagreement_score":0.5540457,"about_ca_system_score_codex":0.0011967039,"about_ca_system_score_gemma":0.00075285684,"threshold_uncertainty_score":0.9998726},"labels":[],"label_agreement":null},{"id":"W2048147694","doi":"10.5539/mas.v3n4p19","title":"Markov Chain Monte Carlo-Based Bayesian Analysis of Binary Response Regression, with Illustration in Dose-Response Assessment","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Monte Carlo method; Markov chain; Bayesian inference; Metropolis–Hastings algorithm; Statistics; Mathematics; Artificial intelligence; Machine learning","score_opus":0.06289780851169717,"score_gpt":0.4142650047121909,"score_spread":0.3513671962004937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048147694","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7002777,0.000021687647,0.29779744,0.0005514589,0.000033464472,0.00043041233,0.000009184518,0.00003208381,0.00084656395],"genre_scores_gemma":[0.86809206,7.716662e-7,0.13138284,0.0002462085,0.0000056304375,0.00003976692,0.0000012560052,0.000010257599,0.00022122361],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9919987,0.0014862719,0.0009995193,0.0013245601,0.0036309299,0.0005600175],"domain_scores_gemma":[0.99572885,0.0017716886,0.00053935027,0.0013927164,0.00029778632,0.00026958613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027487257,0.00030971272,0.0006834986,0.0030564945,0.00029312778,0.00026129442,0.0014999235,0.000110534995,0.00007787259],"category_scores_gemma":[0.0009853136,0.00021590863,0.00013351477,0.011203361,0.0008486816,0.00052113115,0.00011727503,0.00023792108,0.000003371201],"study_design_candidate":"bench_or_experimental","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":[0.010023585,0.00022761596,0.0014259219,0.0000011070904,0.000010771886,0.00002585582,0.0009730419,0.12834333,0.8500207,0.00018427144,0.00001540207,0.008748396],"study_design_scores_gemma":[0.0006286888,0.00045755264,0.26230222,0.000019544741,0.00002802527,0.000001245604,0.00061061146,0.7095777,0.025711052,0.0004496365,0.000008086341,0.00020567478],"about_ca_topic_score_codex":0.000040966825,"about_ca_topic_score_gemma":0.000044757533,"teacher_disagreement_score":0.82430965,"about_ca_system_score_codex":0.00040471123,"about_ca_system_score_gemma":0.0011389253,"threshold_uncertainty_score":0.952659},"labels":[],"label_agreement":null},{"id":"W2048699385","doi":"10.1037/1082-989x.5.3.370","title":"How important is transient error in estimating reliability? Going beyond simulation studies.","year":2000,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":84,"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; University of Manitoba","funders":"","keywords":"Reliability (semiconductor); Transient (computer programming); Observational error; Statistics; Standard error; Range (aeronautics); Error analysis; Reliability engineering; Mathematics; Computer science; Algorithm; Applied mathematics; Engineering","score_opus":0.3949998641647077,"score_gpt":0.6121051813343663,"score_spread":0.21710531716965858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048699385","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":[0.47230467,0.0015148126,0.51456267,0.0029976415,0.0009272154,0.00070611306,0.0000075537855,0.00012263612,0.006856704],"genre_scores_gemma":[0.29604083,0.000024570772,0.70199835,0.0010388419,0.0000708488,0.000051591865,7.5041095e-7,0.000017202756,0.00075703685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9902726,0.004620101,0.0016888035,0.0016005272,0.001193172,0.0006247857],"domain_scores_gemma":[0.9881411,0.01004401,0.00034193808,0.0010491578,0.00019670077,0.00022709269],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.023291713,0.00037578234,0.0009214871,0.00026292045,0.00018118665,0.0002739564,0.0007994767,0.0002686549,0.00205451],"category_scores_gemma":[0.017181449,0.000254636,0.00029362578,0.0017651089,0.00035516635,0.0005637128,0.000091065114,0.00050563086,0.000081246995],"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":[0.00026605456,0.000391252,0.0025553815,0.000008725655,0.000016835626,0.000036148816,0.003024217,0.017020822,0.008320876,0.000102117374,0.000377097,0.9678805],"study_design_scores_gemma":[0.0012837817,0.0008190936,0.014884049,0.000056538884,0.000024990159,0.000025064164,0.0018673532,0.78964794,0.0030351207,0.17761946,0.010060064,0.0006765568],"about_ca_topic_score_codex":0.0000050794647,"about_ca_topic_score_gemma":9.944565e-7,"teacher_disagreement_score":0.9672039,"about_ca_system_score_codex":0.00014700013,"about_ca_system_score_gemma":0.000015383775,"threshold_uncertainty_score":0.9999906},"labels":[],"label_agreement":null},{"id":"W2048882670","doi":"10.1016/j.compchemeng.2012.08.015","title":"A powerful estimation scheme with the error-in-variables-model for nonlinear cases: Reactivity ratio estimation examples","year":2012,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Nonlinear system; Fraction (chemistry); Focus (optics); Computer science; Estimation; Scheme (mathematics); Algorithm; Estimation theory; Mathematical optimization; Mathematics; Iterative method; Applied mathematics; Engineering; Chemistry","score_opus":0.0938544274180493,"score_gpt":0.3726196765032083,"score_spread":0.27876524908515904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048882670","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":[0.198492,0.00005742639,0.80068016,0.00014277291,0.00014099726,0.00036703038,0.0000070424508,0.00006948345,0.000043062017],"genre_scores_gemma":[0.48119274,2.1129922e-7,0.5186336,0.00004275044,0.000056223016,0.000034771918,0.000011215712,0.000016533411,0.000011928314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981833,0.000058804053,0.00041821788,0.00038422554,0.00054016564,0.0004152588],"domain_scores_gemma":[0.996139,0.0030493524,0.00014096474,0.00043536568,0.000095908355,0.00013942391],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019127819,0.00023734962,0.0003148648,0.00013269839,0.00006610306,0.00016396123,0.00042596718,0.0000959836,0.000008015895],"category_scores_gemma":[0.0016559653,0.00016605516,0.000080750826,0.0004954347,0.00006013127,0.0008214734,0.0001140666,0.00021015079,0.000011571507],"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":[0.00007644813,0.00009119134,0.000075850505,0.000018895478,0.000013181717,0.0000017834589,0.00077688583,0.8389709,0.15204197,0.0011458395,0.00031667695,0.006470378],"study_design_scores_gemma":[0.00039211774,0.000033327793,0.000085188265,0.00004172849,0.000009723359,0.00002966587,0.000048593905,0.9532484,0.04555556,0.00015006142,0.00019904155,0.00020658664],"about_ca_topic_score_codex":0.000011135544,"about_ca_topic_score_gemma":5.426855e-7,"teacher_disagreement_score":0.28270072,"about_ca_system_score_codex":0.0001667603,"about_ca_system_score_gemma":0.00005088592,"threshold_uncertainty_score":0.67715335},"labels":[],"label_agreement":null},{"id":"W2049010725","doi":"10.1111/j.0006-341x.2004.00159.x","title":"A Note on One‐Sided Tests with Multiple Endpoints","year":2004,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Statistics; Computer science; Econometrics","score_opus":0.23374589013598263,"score_gpt":0.45472142168100044,"score_spread":0.2209755315450178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049010725","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7237896,0.00027398946,0.25267214,0.000724492,0.0008641135,0.00082664384,0.00005988386,0.0002493497,0.020539815],"genre_scores_gemma":[0.65703887,0.0000050897347,0.34222633,0.00027839522,0.000045460492,0.000008645699,0.0000021521269,0.000019719117,0.0003753453],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958728,0.0001652314,0.0004740883,0.0006810648,0.002415403,0.0003914387],"domain_scores_gemma":[0.99424696,0.0041845553,0.00022281094,0.0008174274,0.0002699327,0.00025832234],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020575942,0.0002187977,0.00032920716,0.0028462617,0.00013441972,0.00027242213,0.00077724335,0.00012090411,0.00015122023],"category_scores_gemma":[0.019313233,0.00015152081,0.00010209268,0.013328395,0.00014863258,0.00026379916,0.00014112768,0.00016249136,0.0018208815],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013654345,0.004019281,0.028038098,0.000014162983,0.00010804129,0.00036360204,0.0014820964,0.0018902925,0.52561384,0.0056866137,0.002074164,0.4293444],"study_design_scores_gemma":[0.0064464207,0.0043542725,0.18337436,0.00011787729,0.00003369909,0.0000549571,0.00039046849,0.0010998615,0.78026414,0.012764696,0.010058198,0.001041032],"about_ca_topic_score_codex":0.000085702006,"about_ca_topic_score_gemma":0.000018893592,"teacher_disagreement_score":0.42830333,"about_ca_system_score_codex":0.00027822173,"about_ca_system_score_gemma":0.00011969104,"threshold_uncertainty_score":0.9989563},"labels":[],"label_agreement":null},{"id":"W2049125983","doi":"10.5267/j.ijiec.2013.09.004","title":"Optimization of multi-response dynamic systems using principal component analysis (PCA)-based utility theory approach","year":2013,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Component (thermodynamics); Computer science; Mathematical optimization; Control theory (sociology); Mathematics; Artificial intelligence; Physics; Control (management)","score_opus":0.1604123791344658,"score_gpt":0.4085364242494176,"score_spread":0.2481240451149518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049125983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26609582,0.000057907404,0.73248214,0.00005106774,0.0010451449,0.00020844769,0.000029203393,0.0000148836825,0.000015407899],"genre_scores_gemma":[0.70104903,4.7010352e-7,0.29883355,0.0000073784254,0.000071345494,0.000004717084,0.00001004704,0.0000114016875,0.000012048491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99530464,0.0009946004,0.0017055488,0.00023866973,0.0015910912,0.00016545998],"domain_scores_gemma":[0.9934976,0.0028596302,0.0011886385,0.00023852444,0.0020657508,0.00014985137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005011392,0.00018579653,0.0005281783,0.0018092456,0.00005750479,0.0002892025,0.00089385937,0.00013098515,0.00012814483],"category_scores_gemma":[0.0039190715,0.00015845065,0.00037303288,0.0012310932,0.00008284751,0.00047484625,0.00007452257,0.00030298255,0.000003927765],"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":[0.0002717982,0.00023864918,0.00081296946,0.0000022762877,0.0005480626,0.000004371013,0.00015074236,0.9918551,0.0048274864,0.00019108116,0.000009948187,0.0010875448],"study_design_scores_gemma":[0.0011789463,0.00006938538,0.003950595,0.0000444194,0.00012337332,0.000025675534,0.00035994904,0.99360925,0.00045351082,0.000042087115,0.000015900805,0.00012689005],"about_ca_topic_score_codex":0.00006928895,"about_ca_topic_score_gemma":2.492965e-7,"teacher_disagreement_score":0.4349532,"about_ca_system_score_codex":0.00031011453,"about_ca_system_score_gemma":0.00026560272,"threshold_uncertainty_score":0.646143},"labels":[],"label_agreement":null},{"id":"W2049410843","doi":"10.1002/app.40443","title":"A statistical approach to engineer a biocomposite formulation from biofuel coproduct with balanced properties","year":2014,"lang":"en","type":"article","venue":"Journal of Applied Polymer Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Guelph","funders":"","keywords":"Biocomposite; Materials science; Factorial experiment; Design of experiments; Ultimate tensile strength; Response surface methodology; Composite material; Mathematics; Statistics; Composite number","score_opus":0.0539006769759734,"score_gpt":0.3382922479835923,"score_spread":0.2843915710076189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049410843","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5178515,0.00014195665,0.4764449,0.00020065671,0.0001979024,0.00022375342,0.0000055780897,0.000014941757,0.004918854],"genre_scores_gemma":[0.64160347,4.734565e-7,0.35800934,0.00022390166,0.00010465782,0.0000066091357,3.8381685e-7,0.00000867747,0.000042508065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957037,0.00009047148,0.00072320417,0.000540632,0.0025719139,0.000370072],"domain_scores_gemma":[0.99797875,0.0003551156,0.00039486086,0.00049010967,0.00039153043,0.00038962104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038768467,0.00019412875,0.00042315983,0.0005321315,0.0001896973,0.0004916408,0.0012484444,0.000042937194,0.00003287097],"category_scores_gemma":[0.00029652377,0.00010997841,0.000048876627,0.0017228728,0.00038986723,0.000639172,0.00014197642,0.00018706145,0.00005357251],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005286633,0.00010548583,0.0003155115,0.0000024747444,0.00001056291,0.0000011559808,0.0014272956,0.0007318715,0.96713126,0.007661669,0.00010864522,0.02197539],"study_design_scores_gemma":[0.00066632393,0.0004925413,0.0064943996,0.000023922481,0.000017537324,0.000035562512,0.00044380163,0.018000992,0.9709121,0.0022326978,0.00042032977,0.0002597713],"about_ca_topic_score_codex":0.000016821992,"about_ca_topic_score_gemma":4.365691e-7,"teacher_disagreement_score":0.12375199,"about_ca_system_score_codex":0.00007537939,"about_ca_system_score_gemma":0.00020450215,"threshold_uncertainty_score":0.4740904},"labels":[],"label_agreement":null},{"id":"W2050334918","doi":"10.1016/j.csda.2010.01.033","title":"A comparative study of robust designs for M-estimated regression models","year":2010,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Robust regression; Mean squared error; Regression; Regression analysis; Least-squares function approximation; Applied mathematics; Estimator","score_opus":0.6235838049097152,"score_gpt":0.568700766208028,"score_spread":0.0548830387016872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050334918","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":[0.053290088,0.000029211666,0.9398827,0.000017966855,0.000115399635,0.00052865944,0.0060251015,0.00002167967,0.000089199544],"genre_scores_gemma":[0.46873295,7.037074e-7,0.5294389,0.000010291686,0.000013358142,0.000017141549,0.0017369239,0.000007240747,0.000042495434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955532,0.0005411149,0.0011486128,0.0009039005,0.0016434363,0.00020976557],"domain_scores_gemma":[0.98870856,0.007626456,0.0007321127,0.0012564796,0.0015288604,0.00014754468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032377718,0.00022154255,0.0008452363,0.00066271337,0.00026779075,0.00022918182,0.0014847849,0.00006368041,0.00030604267],"category_scores_gemma":[0.0021927364,0.00017249481,0.00010405819,0.0022078305,0.00018830886,0.0005291486,0.00045333037,0.0001642014,0.000017773613],"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":[0.00014110572,0.0007603401,0.0025995648,0.0000038728513,0.0006959644,0.000003827965,0.0011156417,0.9748036,0.00073966425,0.0089564845,0.006682533,0.0034973451],"study_design_scores_gemma":[0.00057451485,0.00020615522,0.011173711,0.0000044268836,0.0007101682,0.0000010178111,0.0006422814,0.9055055,0.00008425339,0.08090133,0.000031577478,0.00016507319],"about_ca_topic_score_codex":0.0002329483,"about_ca_topic_score_gemma":0.00036703618,"teacher_disagreement_score":0.41544285,"about_ca_system_score_codex":0.00002427113,"about_ca_system_score_gemma":0.00013930148,"threshold_uncertainty_score":0.7034134},"labels":[],"label_agreement":null},{"id":"W2050485463","doi":"10.1080/10543401003618876","title":"Residuals and Outliers in Replicate Design Crossover Studies","year":2010,"lang":"en","type":"article","venue":"Journal of Biopharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Toronto","funders":"","keywords":"Outlier; Replicate; Crossover; Bioequivalence; Statistics; Computer science; Residual; Identification (biology); Econometrics; Data mining; Mathematics; Artificial intelligence; Algorithm; Bioinformatics","score_opus":0.409312864440003,"score_gpt":0.590393232651865,"score_spread":0.18108036821186207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050485463","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5633717,0.004477402,0.4248633,0.0025724592,0.0029183775,0.0005783717,0.000082261795,0.000022848812,0.0011132717],"genre_scores_gemma":[0.37102243,0.00038379658,0.62756133,0.0006960114,0.00011391427,0.0000025999382,1.4373043e-7,0.00001555323,0.00020419846],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962016,0.0006249652,0.0013798819,0.00030373473,0.0011734241,0.0003163905],"domain_scores_gemma":[0.9913364,0.0069429483,0.00054300495,0.00025746794,0.0005726334,0.00034752494],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010119736,0.00017484867,0.00055641157,0.00029683023,0.00009256073,0.00023414713,0.00046319526,0.00010273678,0.000142709],"category_scores_gemma":[0.012467373,0.0001191877,0.000060569495,0.00045141485,0.0006792126,0.00026994315,0.00016752678,0.0007360053,0.000029747962],"study_design_candidate":"bench_or_experimental","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":[0.0033175666,0.0005440364,0.014730109,0.00004174232,0.00023956368,0.0014925126,0.0029655092,0.00034311964,0.73564917,0.028814072,0.03972895,0.17213365],"study_design_scores_gemma":[0.009869346,0.002150933,0.060594127,0.00012005836,0.00026697386,0.0011874042,0.0049372795,0.0144227445,0.41658834,0.4423208,0.046298902,0.0012431153],"about_ca_topic_score_codex":0.0000022210738,"about_ca_topic_score_gemma":0.0000012855453,"teacher_disagreement_score":0.41350672,"about_ca_system_score_codex":0.00005393828,"about_ca_system_score_gemma":0.00009860679,"threshold_uncertainty_score":0.99585104},"labels":[],"label_agreement":null},{"id":"W2051453094","doi":"10.2307/3315856","title":"Extrapolation designs with constraints","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Extrapolation; Bounding overwatch; Mathematics; Norm (philosophy); Applied mathematics; Minimax; Variance (accounting); Mathematical optimization; Mean squared error; Space (punctuation); Statistics; Computer science; Artificial intelligence","score_opus":0.21705520395397465,"score_gpt":0.3988301582811531,"score_spread":0.18177495432717844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051453094","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006940353,0.00016459795,0.98065966,0.000053997628,0.00034384237,0.00007029173,0.00008907061,0.0000017947102,0.011676409],"genre_scores_gemma":[0.5220514,0.0000015628167,0.47763503,0.000080790654,0.000017259194,2.9491923e-7,5.107287e-7,0.0000069918715,0.00020615793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99810886,0.0003772167,0.0005460627,0.0001236428,0.00060484343,0.00023939661],"domain_scores_gemma":[0.9971125,0.0009462566,0.00037119017,0.00016202868,0.0006856175,0.0007224117],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023758912,0.00009901711,0.00021165481,0.00036164795,0.00012546119,0.00021083787,0.00026945534,0.000043530898,0.002274947],"category_scores_gemma":[0.0038863174,0.000072413466,0.000033653323,0.00037905978,0.00036262555,0.00021914003,0.0000022198988,0.00015719168,0.000043994678],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012068134,0.00006501022,0.06530328,0.0000088454,0.0001374219,0.0031536696,0.003768863,0.002339798,0.0068867644,0.65393376,0.064448416,0.19983348],"study_design_scores_gemma":[0.0062574674,0.0055105477,0.083690815,0.00027309023,0.0002645652,0.010828775,0.018760186,0.0034435461,0.027675644,0.64272106,0.19858326,0.0019910391],"about_ca_topic_score_codex":0.00015573273,"about_ca_topic_score_gemma":0.0014680463,"teacher_disagreement_score":0.515111,"about_ca_system_score_codex":0.00012221416,"about_ca_system_score_gemma":0.002171551,"threshold_uncertainty_score":0.9986371},"labels":[],"label_agreement":null},{"id":"W2051834255","doi":"10.1002/asmb.917","title":"A robust treatment of a dose–response study","year":2011,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Robustification; Flexibility (engineering); Link (geometry); Function (biology); Range (aeronautics); Computer science; Econometrics; Mathematical optimization; Mathematics; Statistics; Engineering; Combinatorics","score_opus":0.3887363654133567,"score_gpt":0.39580036784350786,"score_spread":0.007064002430151151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051834255","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8417125,0.00008677455,0.15429083,0.000019595263,0.00008898391,0.0007110862,0.0000072047,0.00001560303,0.0030674494],"genre_scores_gemma":[0.9844957,0.0000027487,0.01508441,0.000019030664,0.000022061693,0.00020244886,5.541068e-7,0.000018860117,0.0001541779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99755436,0.00021447513,0.0007285254,0.0006170637,0.0006097252,0.000275833],"domain_scores_gemma":[0.9982822,0.0006692703,0.00021182054,0.00057524303,0.00014226024,0.00011919473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016739357,0.00025607122,0.00057543913,0.0003947228,0.00006999531,0.000049134484,0.00034968785,0.00025208187,0.00019194216],"category_scores_gemma":[0.00022678183,0.00018695524,0.00003796393,0.0010650124,0.00020388913,0.00020645803,0.00014697351,0.00017013318,0.000008106912],"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":[0.03675553,0.024174878,0.0118195815,0.000037505633,0.0002877475,0.00026871313,0.108399324,0.48037174,0.021343665,0.060288426,0.00013926612,0.25611362],"study_design_scores_gemma":[0.028584808,0.0058819493,0.48228332,0.00018186124,0.00022567273,0.0000949726,0.0895863,0.24765536,0.0045559625,0.13864209,0.0000565643,0.0022511238],"about_ca_topic_score_codex":0.00036135755,"about_ca_topic_score_gemma":0.000011569867,"teacher_disagreement_score":0.47046375,"about_ca_system_score_codex":0.00006250622,"about_ca_system_score_gemma":0.000112999725,"threshold_uncertainty_score":0.7623813},"labels":[],"label_agreement":null},{"id":"W2051846647","doi":"10.1016/j.laa.2008.02.017","title":"Robustness of A-optimal designs","year":2008,"lang":"en","type":"article","venue":"Linear Algebra and its Applications","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Acadia University","funders":"","keywords":"Mathematics; Robustness (evolution); Covariance; Covariance matrix; Combinatorics; Set (abstract data type); Optimal design; Factorial experiment; Algorithm; Mathematical optimization; Statistics; Computer science","score_opus":0.19426680317455847,"score_gpt":0.4173525277194035,"score_spread":0.22308572454484504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051846647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31490147,0.00069749134,0.68103033,0.00016893451,0.000044930977,0.0003945266,0.000019706431,0.00003820963,0.0027044045],"genre_scores_gemma":[0.74929285,0.00009489046,0.24850227,0.000067647954,0.000109240034,0.00010901931,0.000004684177,0.000014241561,0.0018051708],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984385,0.00011005777,0.00046107985,0.00036055778,0.0004710726,0.00015874603],"domain_scores_gemma":[0.9984217,0.0006581776,0.00015221398,0.00040657705,0.00023379947,0.0001275256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063508446,0.000113109825,0.00023732123,0.00013253426,0.00021398933,0.00002393072,0.00039817856,0.000070203285,0.0002836928],"category_scores_gemma":[0.00029086013,0.00009010507,0.00006706974,0.00068452745,0.00018497767,0.00019502464,0.00010542758,0.00008670407,0.000105740844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024938732,0.0016984115,0.0046686525,0.00005965376,0.00014465355,0.000013677247,0.004163462,0.03350691,0.47759074,0.37666693,0.012096158,0.089141354],"study_design_scores_gemma":[0.0017887227,0.00061774673,0.012243969,0.000026536698,0.00007642291,0.0002963759,0.0017860421,0.44886371,0.4535378,0.012615073,0.067018434,0.0011291716],"about_ca_topic_score_codex":0.0000033820565,"about_ca_topic_score_gemma":3.377213e-7,"teacher_disagreement_score":0.43439135,"about_ca_system_score_codex":0.00000817455,"about_ca_system_score_gemma":0.00004944171,"threshold_uncertainty_score":0.3674378},"labels":[],"label_agreement":null},{"id":"W2052873271","doi":"10.1080/03610910701790376","title":"A Method for Analyzing Supersaturated Designs with a Block Orthogonal Structure","year":2008,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Supersaturation; Computer science; Block (permutation group theory); Algorithm; Set (abstract data type); Statistical analysis; Factorial experiment; Class (philosophy); Type I and type II errors; Statistics; Mathematics; Combinatorics; Artificial intelligence","score_opus":0.4028390374033875,"score_gpt":0.5540593349346938,"score_spread":0.15122029753130634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052873271","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":[0.04766917,0.00016190506,0.951231,0.00012951794,0.00003028815,0.00050881144,0.00014371969,0.000031646046,0.000093962975],"genre_scores_gemma":[0.4994266,0.0000083242285,0.50035346,0.00004584936,0.000006093409,0.00001591344,0.000111221445,0.000008944672,0.000023598455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975857,0.0008323909,0.00064252055,0.00035733814,0.00041865706,0.00016338145],"domain_scores_gemma":[0.9899179,0.008533716,0.00025804018,0.0005145365,0.0007001475,0.000075676886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121559,0.00015788153,0.0002716784,0.00037902617,0.00048865593,0.00014422268,0.00038240553,0.000077243036,0.000019386578],"category_scores_gemma":[0.0009936983,0.00013502946,0.00002926136,0.00097536104,0.00020473923,0.00026594536,0.000102107304,0.00016929019,0.0000025992972],"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":[0.00017796934,0.000083331746,0.007340929,0.000006523388,0.000024312596,0.0000023566977,0.0029519943,0.9193569,0.0011188615,0.013234318,0.00011351715,0.055589024],"study_design_scores_gemma":[0.00084212626,0.000119090655,0.019212037,0.000012341946,0.00001745266,0.00001874734,0.00040274338,0.9542348,0.00008325635,0.024605671,0.00028795266,0.00016378844],"about_ca_topic_score_codex":0.000022014907,"about_ca_topic_score_gemma":0.000076850665,"teacher_disagreement_score":0.45175743,"about_ca_system_score_codex":0.000066101165,"about_ca_system_score_gemma":0.00011012691,"threshold_uncertainty_score":0.55063415},"labels":[],"label_agreement":null},{"id":"W2053479559","doi":"10.1080/08982110108918693","title":"PROBABILISTIC ROBUST DESIGN WITH MULTIPLE QUALITY CHARACTERISTICS","year":2001,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"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":"Quality (philosophy); Reliability engineering; Probabilistic logic; Computer science; Statistics; Mathematics; Engineering","score_opus":0.3321672477924726,"score_gpt":0.4397579413038509,"score_spread":0.10759069351137829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053479559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27647355,0.00004194179,0.72206473,0.000088501445,0.00023567042,0.0003633369,0.000013811269,0.00020361795,0.000514855],"genre_scores_gemma":[0.66873294,0.0000038114551,0.33062348,0.0000642027,0.00009715747,0.000048332648,0.000004961294,0.000034536988,0.0003905574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951919,0.00091588276,0.001206318,0.0007274839,0.0014301521,0.00052830874],"domain_scores_gemma":[0.99271905,0.005391249,0.0003086058,0.0010109498,0.00029602472,0.0002741276],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009661885,0.00032938353,0.00064034184,0.00018234053,0.00012648036,0.00031398138,0.0007038659,0.00011906741,0.00023957415],"category_scores_gemma":[0.012764469,0.00025540995,0.00011514972,0.0009236429,0.000101997255,0.00042084826,0.00011915349,0.0002741861,0.00017171628],"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":[0.0014647522,0.000835495,0.07371281,0.00017408509,0.00015719335,0.00016922604,0.0031332877,0.7507667,0.10259677,0.013160247,0.0005052856,0.053324156],"study_design_scores_gemma":[0.0020419967,0.000556485,0.3671752,0.00012408398,0.000039385122,0.0001055221,0.0011710522,0.60765606,0.010083483,0.0017785225,0.007347651,0.0019205904],"about_ca_topic_score_codex":0.000091041664,"about_ca_topic_score_gemma":0.000008598364,"teacher_disagreement_score":0.3922594,"about_ca_system_score_codex":0.00016060787,"about_ca_system_score_gemma":0.00006891899,"threshold_uncertainty_score":0.9999898},"labels":[],"label_agreement":null},{"id":"W2054451345","doi":"10.1007/s10985-008-9108-y","title":"Review and implementation of cure models based on first hitting times for Wiener processes","year":2009,"lang":"en","type":"review","venue":"Lifetime Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inverse Gaussian distribution; Computer science; Wiener process; Variety (cybernetics); Gaussian process; Inverse; Mixture model; Gaussian; Expectation–maximization algorithm; Applied mathematics; Process (computing); Class (philosophy); Maximization; Mathematical optimization; Algorithm; Mathematics; Artificial intelligence; Statistics; Maximum likelihood; Physics","score_opus":0.3148332102313453,"score_gpt":0.5538587081506247,"score_spread":0.23902549791927935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054451345","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[6.077477e-8,0.9397576,0.053647064,0.00019612628,0.000019760484,0.0013590102,0.0047512255,0.000019814446,0.00024937303],"genre_scores_gemma":[0.0000031911864,0.9035496,0.09059369,0.00037232583,0.000048017388,0.00013685021,0.005125769,0.000030180863,0.00014037514],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.994118,0.00071192574,0.002192468,0.0015793161,0.0010976075,0.00030071178],"domain_scores_gemma":[0.99070334,0.004139764,0.0020332444,0.0025747945,0.00041060452,0.00013823382],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0064378795,0.00051123655,0.0036992957,0.0010540801,0.0001795462,0.0002188516,0.0021788464,0.0001711001,0.000672406],"category_scores_gemma":[0.0027175848,0.00034985362,0.0006653201,0.004703594,0.00007808853,0.0007154171,0.00035563696,0.00014737052,0.000019708132],"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":[0.000008400039,0.000077760145,0.0000069719986,0.01826873,0.00077055587,8.337509e-7,0.000024757584,0.00045963383,1.796276e-7,0.00005918737,0.019883115,0.96043986],"study_design_scores_gemma":[0.00017842039,0.00015030547,0.0000015622581,0.008676422,0.022808224,0.0000014952865,0.00004764889,0.03663307,0.0000066489515,0.00028151166,0.9307622,0.0004525071],"about_ca_topic_score_codex":0.000048382022,"about_ca_topic_score_gemma":0.00003716078,"teacher_disagreement_score":0.95998734,"about_ca_system_score_codex":0.000044990295,"about_ca_system_score_gemma":0.00028667165,"threshold_uncertainty_score":0.99989533},"labels":[],"label_agreement":null},{"id":"W2054487197","doi":"10.1111/j.1467-9876.2005.00523_1.x","title":"Corrigendum: Designing Fractional Factorial Split-Plot Experiments with Few Whole-Plot Factors","year":2005,"lang":"en","type":"erratum","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"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":"Mistake; Plot (graphics); Table (database); Fractional factorial design; Mathematics; Statistics; Split plot; Factorial; Factorial experiment; Combinatorics; Arithmetic; Computer science; Mathematical analysis; Data mining; Law","score_opus":0.09048864572351657,"score_gpt":0.37133249024216963,"score_spread":0.2808438445186531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054487197","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":[0.00015605618,0.00047594414,0.94420165,0.0002506296,0.0404431,0.00087543146,0.0062626335,0.00006866658,0.0072659],"genre_scores_gemma":[0.006551114,0.00009304617,0.8739381,0.0005615933,0.0071878913,0.000055096792,0.00038203935,0.0003788443,0.110852286],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9841901,0.0011148705,0.0034658404,0.0012860771,0.008621021,0.0013220843],"domain_scores_gemma":[0.98711574,0.0049115703,0.004430574,0.0011077716,0.0014629724,0.00097135943],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0031658735,0.0014438394,0.002556838,0.00019170115,0.001359892,0.0014940277,0.0030792044,0.0010376155,0.004304512],"category_scores_gemma":[0.00322802,0.0008762062,0.0008566146,0.0007673494,0.001655335,0.0005522126,0.0006844065,0.0043548113,0.0001760736],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013998213,0.0003960313,0.00014547438,0.000051178708,0.0008076047,0.000060101767,0.0017200236,0.0012603304,0.0006783707,0.013173766,0.97744286,0.0028644186],"study_design_scores_gemma":[0.0034617637,0.0025841906,0.0063860794,0.00036200634,0.0011145071,0.0001373745,0.008765286,0.0035471495,0.0018530816,0.034291074,0.9349293,0.0025681872],"about_ca_topic_score_codex":0.000089363006,"about_ca_topic_score_gemma":0.000023881532,"teacher_disagreement_score":0.10358639,"about_ca_system_score_codex":0.00148548,"about_ca_system_score_gemma":0.0019135133,"threshold_uncertainty_score":0.9999402},"labels":[],"label_agreement":null},{"id":"W2055156195","doi":"10.1080/03610918.2012.687065","title":"On Symmetrizing Transformation of the Sample Coefficient of Variation from a Normal Population","year":2013,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Transformation (genetics); Mathematics; Inference; Variation (astronomy); Population; Variance (accounting); Statistics; Sample (material); Applied mathematics; Homogeneity (statistics); Coefficient of variation; Scope (computer science); Econometrics; Computer science; Thermodynamics; Physics; Economics; Chemistry","score_opus":0.2378893196943069,"score_gpt":0.4950773787109019,"score_spread":0.257188059016595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055156195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29417756,0.000037054873,0.70484596,0.00009640469,0.000076030665,0.00042372622,0.00010566344,0.000007886888,0.00022968873],"genre_scores_gemma":[0.84493697,0.0000069431203,0.15483195,0.000038675487,0.0000042627644,0.000016416207,0.00015381792,0.0000064065503,0.00000457246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971777,0.0008162906,0.0010845992,0.0001817067,0.0006494053,0.00009027555],"domain_scores_gemma":[0.9885264,0.009725846,0.0006525897,0.0005917325,0.000472221,0.000031210766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011038341,0.0001020618,0.0002125316,0.0003448041,0.00015993646,0.00008216902,0.00041131297,0.00006460218,0.00005365771],"category_scores_gemma":[0.002622573,0.000083248,0.000039633953,0.0009497519,0.00011768004,0.0003484415,0.000107455104,0.00011832931,0.000006490799],"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":[0.00002879887,0.00015778876,0.0099491915,0.000007973296,0.0000075109356,1.6462478e-8,0.0031115126,0.8249706,0.00074077747,0.05909683,0.000013456426,0.10191556],"study_design_scores_gemma":[0.00026993328,0.000035410772,0.33015797,0.000022852764,0.000006430106,8.58409e-8,0.00018913033,0.5870849,0.0001766376,0.08200541,0.0000054024063,0.00004581623],"about_ca_topic_score_codex":0.0012136408,"about_ca_topic_score_gemma":0.000069905596,"teacher_disagreement_score":0.5507594,"about_ca_system_score_codex":0.000070147515,"about_ca_system_score_gemma":0.000027540123,"threshold_uncertainty_score":0.33947548},"labels":[],"label_agreement":null},{"id":"W2056122055","doi":"10.1080/03610918.2010.533226","title":"Nearly Optimal Orthogonally Blocked Designs for Four Mixture Components Based on F-Squares","year":2010,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"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":"McMaster University","keywords":"Quadratic equation; Latin square; Least-squares function approximation; Component (thermodynamics); Mathematics; Applied mathematics; Combinatorics; Computer science; Algorithm; Statistics; Geometry; Physics; Chemistry","score_opus":0.4051451671404312,"score_gpt":0.5393336135269894,"score_spread":0.1341884463865582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056122055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08114338,0.00002681393,0.9168895,0.00042386146,0.00019687648,0.00072190736,0.00018371134,0.00004410982,0.0003698357],"genre_scores_gemma":[0.52749324,0.0000016654634,0.47205442,0.00015719132,0.000014279286,0.000043524844,0.00018690331,0.000013070702,0.000035712197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724513,0.0006919613,0.000803538,0.0004202368,0.0006368045,0.0002023171],"domain_scores_gemma":[0.98439914,0.013630363,0.00033634432,0.00087054446,0.00065195543,0.00011163138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020969398,0.00019725956,0.00027324512,0.00043854606,0.00044201475,0.00037340098,0.00069115445,0.00013428692,0.000047299225],"category_scores_gemma":[0.0028856935,0.00019168704,0.000055997218,0.0005293081,0.00023726435,0.00022296135,0.00012938546,0.0003335549,0.000019722804],"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":[0.00023231824,0.00028983384,0.0024072146,0.000007766789,0.000008870761,0.0000012727673,0.0005747294,0.8728639,0.0028049012,0.023427112,0.00028369194,0.097098395],"study_design_scores_gemma":[0.0011109653,0.00018796271,0.039408945,0.000015611533,0.00001167718,0.0000012798182,0.000105047875,0.9329222,0.00008251465,0.024620842,0.0013434067,0.00018958404],"about_ca_topic_score_codex":0.00001630278,"about_ca_topic_score_gemma":0.00005706396,"teacher_disagreement_score":0.44634986,"about_ca_system_score_codex":0.000045398217,"about_ca_system_score_gemma":0.000090407426,"threshold_uncertainty_score":0.78167707},"labels":[],"label_agreement":null},{"id":"W2058359526","doi":"10.1021/ie900196u","title":"Systematic Statistical-Based Approach for Product Design: Application to Disinfectant Formulations","year":2009,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Computer science; Set (abstract data type); New product development; Process (computing); Product (mathematics); Product design; Process engineering; Biochemical engineering; Systems engineering; Engineering; Mathematics","score_opus":0.37387846103661887,"score_gpt":0.508732729996101,"score_spread":0.1348542689594821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058359526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026982587,0.00004755804,0.99247116,0.00029247368,0.000066616136,0.0038192552,0.000043239324,0.00010570625,0.00045570981],"genre_scores_gemma":[0.80880374,2.5332704e-7,0.18923873,0.000014719307,0.00038244325,0.0011809708,0.00003837018,0.000030010035,0.0003107931],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99549323,0.0003089378,0.00086445594,0.0008015102,0.0018751075,0.00065675954],"domain_scores_gemma":[0.9925662,0.0053776195,0.000107646345,0.00095599116,0.00061814004,0.00037439575],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.011464267,0.00023001358,0.00047890862,0.0002704272,0.00021758108,0.00042057983,0.0008804823,0.00018263186,0.000036290687],"category_scores_gemma":[0.030699652,0.00019300195,0.00009886012,0.0018384998,0.000051653402,0.00015035136,0.00006855696,0.0004482133,0.000034956833],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012978652,0.00013950236,0.000007997245,0.0003011445,0.000012938963,0.000001245093,0.00004769166,0.185751,0.8090316,0.0006381499,0.0011899868,0.0027489178],"study_design_scores_gemma":[0.00049978314,0.00017949828,0.00002561394,0.00016122093,0.000011664209,0.0000037122884,0.0000452807,0.46721107,0.5306511,0.00071364845,0.00028390833,0.00021350512],"about_ca_topic_score_codex":0.0000059473437,"about_ca_topic_score_gemma":3.2648934e-8,"teacher_disagreement_score":0.80610543,"about_ca_system_score_codex":0.00034736437,"about_ca_system_score_gemma":0.0002525901,"threshold_uncertainty_score":0.97746515},"labels":[],"label_agreement":null},{"id":"W2059324920","doi":"10.1002/cjce.5450790314","title":"On the costs of parameter uncertainties. Part 2: Impact of EVOP procedures on the optimization and design of experiments","year":2001,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Process (computing); Mathematical optimization; Computer science; Mathematics","score_opus":0.09256206850110352,"score_gpt":0.35432135954755895,"score_spread":0.26175929104645546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059324920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98569304,0.00040152183,0.013022326,0.00039608005,0.000089244975,0.00022999113,0.000006266246,0.0000021233777,0.00015941722],"genre_scores_gemma":[0.9955456,0.000009040095,0.0043295287,0.000062510066,0.000025679221,0.000003971018,1.6499145e-7,0.000011608117,0.000011859138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844,0.00017236489,0.00054901623,0.000100016696,0.00055414956,0.00018441968],"domain_scores_gemma":[0.9956782,0.003321951,0.000361686,0.00026792535,0.00020798446,0.00016226826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019760912,0.00012994805,0.0002762763,0.00014623845,0.00004603239,0.000047679318,0.0005748042,0.000054147426,0.0001655945],"category_scores_gemma":[0.0056975754,0.00005757529,0.000121818746,0.0003569038,0.00021861271,0.00008499979,0.000020168145,0.00019573445,5.643797e-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":[0.00016388032,0.000021870514,0.00013413477,0.0000037933278,0.00007489822,0.0000031894003,0.0008559368,0.84543425,0.15106757,0.0009852558,0.0008004788,0.00045471362],"study_design_scores_gemma":[0.00037186188,0.0005606222,0.00019667188,0.00033118806,0.000026558077,0.000057085756,0.0003473886,0.18204483,0.8144633,0.0014409762,0.000021035456,0.00013846967],"about_ca_topic_score_codex":0.00029634437,"about_ca_topic_score_gemma":0.000004374995,"teacher_disagreement_score":0.66339576,"about_ca_system_score_codex":0.00015003755,"about_ca_system_score_gemma":0.00026417492,"threshold_uncertainty_score":0.68209416},"labels":[],"label_agreement":null},{"id":"W2060230359","doi":"10.5539/ijsp.v3n4p67","title":"On the Behaviour of D-Optimal Exact Designs Under Changing Regression Polynomials","year":2014,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Optimal design; Point (geometry); Orthogonality; Equivalence (formal languages); Applied mathematics; Polynomial; Orthogonal array; Mathematical optimization; Combinatorics; Discrete mathematics; Statistics; Mathematical analysis; Taguchi methods; Geometry","score_opus":0.14392973343862575,"score_gpt":0.43516434692053674,"score_spread":0.291234613481911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060230359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45787606,0.00003136508,0.54040194,0.0008223174,0.00042497198,0.00008347542,0.000055401815,0.0000018726081,0.0003026212],"genre_scores_gemma":[0.85299253,0.000011023741,0.14674266,0.00011948196,0.000069260794,0.0000013865433,8.6191665e-7,0.0000052846995,0.00005749464],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99695295,0.0006865047,0.00079462706,0.00017110942,0.0012764079,0.000118381795],"domain_scores_gemma":[0.99235636,0.0057019666,0.0008130386,0.00019283447,0.0008570055,0.00007878949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008327871,0.00010561241,0.0002499905,0.00018950422,0.000075694596,0.00013358862,0.00056729116,0.000043093332,0.0003588436],"category_scores_gemma":[0.005784415,0.000056669902,0.0000804413,0.00011725182,0.00019876905,0.00013639228,0.00011191904,0.00016491632,0.0000044369717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017927727,0.0006970876,0.016354658,0.000016814352,0.00018071869,0.000024746818,0.0027735673,0.0050652055,0.03309228,0.7752028,0.004962503,0.15983684],"study_design_scores_gemma":[0.0007384389,0.0010140648,0.036229268,0.00014157653,0.000029692885,0.00006969731,0.0007096235,0.008699308,0.023127595,0.9287804,0.0002976929,0.00016264198],"about_ca_topic_score_codex":0.000011979422,"about_ca_topic_score_gemma":0.0000012637518,"teacher_disagreement_score":0.3951165,"about_ca_system_score_codex":0.00005748158,"about_ca_system_score_gemma":0.000060621955,"threshold_uncertainty_score":0.69249034},"labels":[],"label_agreement":null},{"id":"W2060696278","doi":"10.1080/00949650215868","title":"Random balanced resampling: A new method for estimating variance components in unbalanced designs","year":2003,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Resampling; Statistics; Estimator; Equating; Factorial; Generalized least squares; Variance (accounting); Normality; Set (abstract data type); Restricted maximum likelihood; Ordinary least squares; Least-squares function approximation; Maximum likelihood; Algorithm; Computer science","score_opus":0.28054596940125864,"score_gpt":0.5233086275550208,"score_spread":0.2427626581537622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060696278","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":[0.0075049615,0.000058050595,0.9916137,0.00010249782,0.00030500334,0.00030806186,0.0000072242015,0.00000692083,0.000093606715],"genre_scores_gemma":[0.4226744,6.9103544e-7,0.5772067,0.00006163868,0.000031811203,0.0000014256458,0.000002496784,0.0000058843916,0.000014984777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99673843,0.0009789953,0.0012233702,0.00024119171,0.00064643257,0.00017159668],"domain_scores_gemma":[0.9830466,0.015647879,0.00064541027,0.000076684926,0.00041726988,0.00016616576],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005657707,0.00012485794,0.00046845255,0.0002524417,0.00009578696,0.0001864791,0.000114063354,0.00006244874,0.000035905716],"category_scores_gemma":[0.015276621,0.00010189903,0.00005802766,0.00036687925,0.000035438654,0.0003194057,0.000013358194,0.00014528201,0.0000022498527],"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":[0.0006961285,0.000045401408,0.0004932935,0.0000072430325,0.000008941545,0.0000044640565,0.0004273485,0.9390429,0.0030775552,0.0052513485,0.00007465395,0.05087068],"study_design_scores_gemma":[0.0041956585,0.00019422856,0.004506003,0.000034577995,0.000011129296,0.000013078462,0.000092751965,0.8173179,0.00012708624,0.17331292,0.000106864456,0.000087828674],"about_ca_topic_score_codex":0.000006040834,"about_ca_topic_score_gemma":8.067866e-7,"teacher_disagreement_score":0.41516942,"about_ca_system_score_codex":0.00006614126,"about_ca_system_score_gemma":0.00010022488,"threshold_uncertainty_score":0.99301815},"labels":[],"label_agreement":null},{"id":"W2061052065","doi":"10.2307/3315853","title":"for misspecified regression models","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Minimax; Polynomial regression; Regression; Mathematics; Regression analysis; Applied mathematics; Polynomial; Function (biology); Mean squared error; Linear regression; Statistics; Mathematical optimization; Computer science","score_opus":0.29515400096401667,"score_gpt":0.4376875731719163,"score_spread":0.14253357220789964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061052065","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":[0.0017840877,0.000506711,0.9851847,0.00015673703,0.0010334387,0.00012179274,0.00023300396,0.0000014899052,0.010978035],"genre_scores_gemma":[0.16052175,0.000007413433,0.8368256,0.00020339606,0.000068676774,0.0000013960706,0.00000147204,0.00001540895,0.0023548917],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99772096,0.00033575302,0.0008807331,0.00016179189,0.00062450516,0.00027624884],"domain_scores_gemma":[0.99568987,0.0016828247,0.0005171646,0.00026091927,0.0010391608,0.0008100858],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004070666,0.000111527785,0.00031910138,0.00035098652,0.00016036503,0.00021739758,0.0004716943,0.00006199443,0.0008524712],"category_scores_gemma":[0.009627031,0.00007957284,0.000098976765,0.0002551328,0.00010297396,0.00022804351,0.0000050674316,0.00013398753,0.000024819377],"study_design_candidate":"theoretical_or_conceptual","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":[0.000063176514,0.000019298692,0.0005432168,0.0000047827093,0.000023291152,0.00021221876,0.00089065544,0.0021014665,0.0012607779,0.47128865,0.48294893,0.040643524],"study_design_scores_gemma":[0.0006499799,0.00029727418,0.00027567716,0.000028578259,0.00001648359,0.0001594785,0.0011652268,0.005349616,0.003336867,0.75870943,0.2298378,0.00017361347],"about_ca_topic_score_codex":0.000096126605,"about_ca_topic_score_gemma":0.00045041132,"teacher_disagreement_score":0.28742075,"about_ca_system_score_codex":0.00013286718,"about_ca_system_score_gemma":0.0012522595,"threshold_uncertainty_score":0.9987153},"labels":[],"label_agreement":null},{"id":"W2061206455","doi":"10.1016/j.jspi.2005.11.007","title":"Linear programming bounds for balanced arrays","year":2006,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Generality; Fractional factorial design; Linear programming; Mathematical optimization; Ingredient; Factorial; Factorial experiment; Key (lock); Applied mathematics; Algorithm; Statistics; Computer science; Mathematical analysis","score_opus":0.13858068401954668,"score_gpt":0.48154932624395025,"score_spread":0.3429686422244036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061206455","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":[0.029733388,0.00028706033,0.9685689,0.000104426406,0.00020356818,0.000079778416,0.000024439325,0.000008521514,0.0009899255],"genre_scores_gemma":[0.49396163,0.0000018974021,0.50577766,0.000038063048,0.00012032984,0.00000202471,0.000001627267,0.0000043405767,0.00009241186],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980979,0.00011077819,0.00076923944,0.00018289231,0.00061388273,0.0002253076],"domain_scores_gemma":[0.9944701,0.004548096,0.00038317245,0.000097923905,0.00036441014,0.000136284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022677938,0.00011106594,0.00034262214,0.00012750663,0.00013021997,0.00029332953,0.00022789891,0.000055788154,0.00004111451],"category_scores_gemma":[0.0053505073,0.00007584529,0.0000536942,0.0001670389,0.00016398153,0.00027557096,0.00003256765,0.00018673834,0.0000046644745],"study_design_candidate":"theoretical_or_conceptual","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":[0.0021583585,0.0006519383,0.11960185,0.00010423248,0.00010385933,0.00034470521,0.0018578904,0.007030805,0.056118876,0.22166044,0.038358934,0.5520081],"study_design_scores_gemma":[0.0039264103,0.0058286316,0.1063706,0.0004637381,0.00010237456,0.00042941392,0.0022085211,0.14293313,0.0076731467,0.65759486,0.071558945,0.00091020326],"about_ca_topic_score_codex":0.000008696244,"about_ca_topic_score_gemma":3.7992402e-7,"teacher_disagreement_score":0.5510979,"about_ca_system_score_codex":0.000020865758,"about_ca_system_score_gemma":0.0000724739,"threshold_uncertainty_score":0.64054435},"labels":[],"label_agreement":null},{"id":"W2064101451","doi":"10.1002/qre.721","title":"Optimal Mean and Tolerance Allocation Using Conformance‐based Design","year":2005,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":19,"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":"Robustness (evolution); Reliability engineering; Computer science; Mathematical optimization; Process capability index; Limit (mathematics); Process capability; Engineering; Work in process; Mathematics; Operations management","score_opus":0.17420414038011733,"score_gpt":0.4444025892382796,"score_spread":0.27019844885816224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064101451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4511818,0.000081633356,0.54769135,0.0005768166,0.00021496836,0.00010677973,0.0000062822205,0.000034438384,0.00010591818],"genre_scores_gemma":[0.59503746,0.0000065976465,0.40469068,0.0001280913,0.000073264375,0.0000060955026,0.0000021208273,0.000005473849,0.000050213872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801254,0.00017752708,0.0005961629,0.00039293565,0.0006639262,0.00015687667],"domain_scores_gemma":[0.9983187,0.0010031168,0.00012277807,0.00024152527,0.00021095402,0.00010297911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004546223,0.00014091929,0.00020490788,0.00013015224,0.00008092444,0.0001864431,0.0002501479,0.00008031144,0.00008912438],"category_scores_gemma":[0.0013742807,0.00012280536,0.000048023452,0.00014666417,0.00010088484,0.0005397538,0.000072352195,0.00013358366,0.00000886507],"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":[0.000076335964,0.0000472653,0.0010168519,0.000011460769,0.00000851902,4.0496644e-7,0.0004107941,0.9783127,0.012123863,0.002079681,0.000028434526,0.005883719],"study_design_scores_gemma":[0.00034395454,0.00003368528,0.0095286025,0.000017981474,0.0000035787714,0.000006493743,0.000072122246,0.97709185,0.010295707,0.00021202196,0.0022401393,0.00015385503],"about_ca_topic_score_codex":0.000027150347,"about_ca_topic_score_gemma":8.876581e-7,"teacher_disagreement_score":0.14385565,"about_ca_system_score_codex":0.000105778534,"about_ca_system_score_gemma":0.000038947335,"threshold_uncertainty_score":0.5007857},"labels":[],"label_agreement":null},{"id":"W2064642572","doi":"10.1016/j.jprocont.2011.12.004","title":"Designing priors for robust Bayesian optimal experimental design","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"A priori and a posteriori; Prior probability; Process (computing); Computer science; Design of experiments; Bayesian probability; Engineering design process; Process modeling; Machine learning; Data mining; Mathematical optimization; Artificial intelligence; Engineering; Work in process; Mathematics; Statistics","score_opus":0.1733677444323993,"score_gpt":0.451264019980269,"score_spread":0.27789627554786966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064642572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017156309,0.0033288863,0.9774506,0.00019035971,0.0008602712,0.00069904956,0.000004549595,0.000021570688,0.00028837172],"genre_scores_gemma":[0.5517708,0.0000017467443,0.447449,0.00016449727,0.00045050358,0.000036499627,1.4775422e-7,0.000026925969,0.000099888864],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9949181,0.00076468696,0.0015524962,0.00031293483,0.0017416965,0.0007100689],"domain_scores_gemma":[0.9938369,0.0029983385,0.0014362856,0.00029013312,0.0008984535,0.00053992035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011734467,0.0003075964,0.00082941254,0.00044797352,0.00024679973,0.0003379961,0.0010905485,0.0001461707,0.0003079908],"category_scores_gemma":[0.0041938,0.00022034628,0.0004049605,0.00045467936,0.000124666,0.0022127999,0.000043793487,0.00028385973,0.00002642329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008281742,0.0018655774,0.0060351146,0.00004150882,0.0004241278,0.000052061736,0.013341773,0.11803998,0.80764514,0.00079316134,0.008048866,0.03543095],"study_design_scores_gemma":[0.008909639,0.0030841467,0.0004237559,0.00009626774,0.000171422,0.00053571834,0.015926752,0.07897729,0.8881717,0.0015580958,0.0014254373,0.00071978616],"about_ca_topic_score_codex":8.3588793e-7,"about_ca_topic_score_gemma":5.4644584e-8,"teacher_disagreement_score":0.5346145,"about_ca_system_score_codex":0.00015641554,"about_ca_system_score_gemma":0.000272013,"threshold_uncertainty_score":0.89854604},"labels":[],"label_agreement":null},{"id":"W2065015301","doi":"10.1016/j.csda.2009.03.001","title":"Robustness of design for the testing of lack of fit and for estimation in binary response models","year":2009,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covariate; Mathematics; Statistics; Estimator; Binary data; Test statistic; Regression analysis; Econometrics; Statistical hypothesis testing; Binary number","score_opus":0.6072277121095938,"score_gpt":0.5422912207812377,"score_spread":0.0649364913283561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065015301","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":[0.014258888,0.00013473968,0.9821345,0.000088854234,0.000013687283,0.0003547389,0.0030094814,0.000002618655,0.000002498557],"genre_scores_gemma":[0.39232928,0.0000020189486,0.6074759,0.000008908078,0.0000026165028,0.0000060181274,0.00016687816,0.0000028116444,0.000005602082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777234,0.00039790137,0.0008794941,0.00030399254,0.0005511451,0.00009514747],"domain_scores_gemma":[0.95850104,0.039783597,0.00056942785,0.0003922055,0.00072634633,0.000027397777],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006578871,0.00008794312,0.0003969733,0.00040938804,0.00006441473,0.000035400022,0.0005015395,0.00003069212,0.000007771087],"category_scores_gemma":[0.011335416,0.00006698576,0.00004914416,0.0014114318,0.00013789629,0.0002458413,0.00009251924,0.0000317131,9.5176695e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006132792,0.000072394854,0.00025791934,0.000012448438,0.000080152466,2.611897e-7,0.000120103956,0.97269166,0.00041268198,0.0035187644,0.00034076767,0.021879585],"study_design_scores_gemma":[0.00030750738,0.0002032147,0.019146515,0.000011888433,0.00023248239,4.3064546e-7,0.00007126873,0.8773245,0.00013291223,0.102512695,0.0000017706271,0.000054816173],"about_ca_topic_score_codex":0.000041390515,"about_ca_topic_score_gemma":0.0000048637357,"teacher_disagreement_score":0.37807038,"about_ca_system_score_codex":0.000016881646,"about_ca_system_score_gemma":0.000112634094,"threshold_uncertainty_score":0.9969925},"labels":[],"label_agreement":null},{"id":"W2067267828","doi":"10.1080/02664760500449170","title":"A note comparing component-slope, Scheffé and Cox parameterizations of the linear mixture experiment model","year":2006,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"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":"Pacific Northwest National Laboratory; Simon Fraser University","keywords":"Component (thermodynamics); Mixture model; Linear model; Statistics; Mathematics; Thermodynamics; Physics","score_opus":0.08472300885455115,"score_gpt":0.39775683772838866,"score_spread":0.3130338288738375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067267828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13498057,0.00012639268,0.86260283,0.00006387981,0.00015436207,0.00017194147,0.000070458096,0.000004266217,0.0018253153],"genre_scores_gemma":[0.5176285,0.000004756095,0.48223576,0.000043619457,0.00002881052,0.0000016791222,0.0000013740392,0.000008116012,0.00004741001],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972794,0.00013940867,0.0011213471,0.00017723154,0.001119687,0.00016292819],"domain_scores_gemma":[0.99740154,0.00086674915,0.0010073712,0.00029167783,0.0003419906,0.000090691545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011771474,0.00015101767,0.00046280696,0.0001314694,0.00014393147,0.00010469049,0.00048385913,0.00006374852,0.000024304834],"category_scores_gemma":[0.0003028196,0.000096616124,0.00007270403,0.00032342668,0.00025423817,0.00009117627,0.00016409658,0.00024149336,0.000003329843],"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":[0.00038702154,0.00036375935,0.0008082284,0.000019074681,0.000041564177,0.000009426383,0.001580731,0.25983083,0.6480241,0.08096479,0.004179008,0.003791498],"study_design_scores_gemma":[0.0013371065,0.00015199145,0.0034441797,0.00003952465,0.00007091792,0.0000395326,0.0006268672,0.80750406,0.12851457,0.057082456,0.00096057064,0.00022824726],"about_ca_topic_score_codex":0.000008833971,"about_ca_topic_score_gemma":0.0000027026474,"teacher_disagreement_score":0.5476732,"about_ca_system_score_codex":0.000045233846,"about_ca_system_score_gemma":0.000099973506,"threshold_uncertainty_score":0.39398912},"labels":[],"label_agreement":null},{"id":"W2067883763","doi":"10.1111/j.1541-0420.2008.01013.x","title":"Bayesian Estimation of Inverse Dose Response","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Maximum a posteriori estimation; Posterior probability; Bayesian probability; Bayesian inference; Mathematics; Posterior predictive distribution; Statistics; Prior probability; A priori and a posteriori; Computer science; Bayes estimator; Inverse problem; Bayesian linear regression; Algorithm; Maximum likelihood","score_opus":0.26296731177884397,"score_gpt":0.46296430263117344,"score_spread":0.19999699085232947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067883763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7203886,0.00021026256,0.2762051,0.00010627097,0.00036202354,0.00016264047,0.0000151745535,0.000038709113,0.0025112354],"genre_scores_gemma":[0.6680736,0.00001099337,0.33092108,0.000056488,0.000012235143,0.000002776216,0.0000011573795,0.0000073336464,0.0009143303],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9967359,0.0006487875,0.0006089654,0.0003131198,0.0015160367,0.00017723971],"domain_scores_gemma":[0.9953295,0.0034314243,0.00027902296,0.00057453115,0.00024561665,0.0001398824],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005490692,0.00010973231,0.00025052734,0.0034889465,0.00009705705,0.00003928715,0.0005290622,0.0000964971,0.00030133553],"category_scores_gemma":[0.025135763,0.00008852012,0.000106297055,0.013515117,0.00023641867,0.00031087722,0.00012123982,0.00006547537,0.00033064553],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031303382,0.00092190754,0.019285845,0.000017065551,0.000042069165,0.00018355018,0.0035960558,0.0034869583,0.52172196,0.0012920131,0.04290957,0.4034127],"study_design_scores_gemma":[0.0027099035,0.0019163305,0.27190554,0.0000248324,0.000025725087,0.00017045025,0.0011438184,0.23784266,0.44868752,0.008078442,0.026675962,0.00081878685],"about_ca_topic_score_codex":0.000020945643,"about_ca_topic_score_gemma":2.7538528e-7,"teacher_disagreement_score":0.4025939,"about_ca_system_score_codex":0.00007565884,"about_ca_system_score_gemma":0.00011621827,"threshold_uncertainty_score":0.9830759},"labels":[],"label_agreement":null},{"id":"W2068276015","doi":"10.1002/cjs.11154","title":"Three‐level regular designs with general minimum lower‐order confounding","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Confounding; Mathematics; Component (thermodynamics); Order (exchange); Econometrics; Computer science; Economics","score_opus":0.2639097558166017,"score_gpt":0.4098164920617417,"score_spread":0.14590673624514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068276015","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":[0.03306716,0.00055014365,0.96283793,0.00015998262,0.0015077874,0.00011327029,0.00029079142,0.000003664843,0.0014692916],"genre_scores_gemma":[0.23818013,0.0000035842254,0.7604178,0.00026663407,0.00035578848,0.0000012784634,0.0000025547843,0.000027915188,0.00074427394],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970439,0.00024452407,0.00079675764,0.00018822776,0.0010582507,0.0006683225],"domain_scores_gemma":[0.9954732,0.0009671213,0.00055464276,0.00033706758,0.0010326456,0.0016352943],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029905364,0.00020590908,0.00041283385,0.00047103397,0.0002387102,0.00033929694,0.0006544849,0.00008826248,0.0015417169],"category_scores_gemma":[0.0026538104,0.00015152934,0.00006530358,0.000577081,0.00034068414,0.0005395492,0.000020844533,0.00027889686,0.00007275598],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006026269,0.00022862677,0.118483685,0.000029861696,0.0005106731,0.003570588,0.006248073,0.002154265,0.0165718,0.25403443,0.46559098,0.1319744],"study_design_scores_gemma":[0.009210599,0.008237676,0.24112687,0.00057739246,0.0008274898,0.010595098,0.01249919,0.019545443,0.013561293,0.22246498,0.45663765,0.004716323],"about_ca_topic_score_codex":0.0009289852,"about_ca_topic_score_gemma":0.004387547,"teacher_disagreement_score":0.20511298,"about_ca_system_score_codex":0.00028053415,"about_ca_system_score_gemma":0.0017493713,"threshold_uncertainty_score":0.999371},"labels":[],"label_agreement":null},{"id":"W2068480341","doi":"10.1007/s00362-013-0569-z","title":"Analyzing supersaturated designs for discrete responses via generalized linear models","year":2013,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Technical University of Athens","keywords":"Factorial experiment; Design of experiments; Supersaturation; Computer science; Identification (biology); Optimal design; Linear regression; Factorial; Mathematical optimization; Discrete time and continuous time; Mathematics; Statistics","score_opus":0.19680439901753044,"score_gpt":0.4569285278511817,"score_spread":0.26012412883365127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068480341","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":[0.016041046,0.000113071415,0.9785831,0.00049169327,0.00022194938,0.00080772187,0.00023665883,0.000082775434,0.0034220077],"genre_scores_gemma":[0.41456217,0.000005122047,0.5831292,0.00028016078,0.000050053626,0.00011419917,0.000028434128,0.000029045108,0.0018015798],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958349,0.0011134745,0.0008042531,0.0007860075,0.00086238567,0.0005990176],"domain_scores_gemma":[0.9899132,0.00866436,0.00012593115,0.00052107195,0.00036592822,0.0004094837],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002160614,0.0002851728,0.00051432813,0.0002008278,0.00027405858,0.00035182908,0.0005701294,0.00012984408,0.0029620554],"category_scores_gemma":[0.0068563027,0.00020180494,0.00017519413,0.0005463364,0.0003507511,0.00052141957,0.000098937264,0.00016575296,0.00037444071],"study_design_candidate":"bench_or_experimental","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":[0.0011443006,0.000083367355,0.0002452779,0.000011996257,0.00010707744,0.000020692112,0.0006700975,0.0037652133,0.8790966,0.059567682,0.009292902,0.04599479],"study_design_scores_gemma":[0.0012417185,0.00050143857,0.001254504,0.000012763255,0.000055066535,0.0000082785855,0.0009023365,0.78064066,0.012342226,0.20093976,0.0015491477,0.0005520947],"about_ca_topic_score_codex":0.00020948541,"about_ca_topic_score_gemma":0.0000072689836,"teacher_disagreement_score":0.86675435,"about_ca_system_score_codex":0.00008076627,"about_ca_system_score_gemma":0.00009412835,"threshold_uncertainty_score":0.99794936},"labels":[],"label_agreement":null},{"id":"W2068618230","doi":"10.1016/s0378-3758(99)00185-8","title":"Crossover designs for two-treatment clinical trials","year":2000,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Crossover; Crossover study; Mathematics; Repeated measures design; Clinical trial; Statistics; Sample size determination; Design of experiments; Optimal design; Sequential analysis; Treatment and control groups; Clinical study design; Computer science; Medicine; Machine learning","score_opus":0.7098386298084299,"score_gpt":0.672377921310636,"score_spread":0.037460708497793926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068618230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08902163,0.0005379848,0.90762675,0.00013876874,0.00035793608,0.00019701038,0.00010709118,0.0000074192008,0.0020053985],"genre_scores_gemma":[0.6173777,0.00005684201,0.38145947,0.0001864719,0.00025570704,0.000004551997,0.0000015986724,0.000007717722,0.0006499065],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953322,0.0014125465,0.002147434,0.00026505068,0.00061093207,0.00023183458],"domain_scores_gemma":[0.95206946,0.04653335,0.00063162984,0.00015653983,0.0002747634,0.00033426477],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.016260376,0.00015227564,0.0009337456,0.00009720597,0.00013345436,0.0003706447,0.00025320612,0.00008214239,0.0012638154],"category_scores_gemma":[0.031694014,0.000088742694,0.00018563884,0.000117216565,0.00024517742,0.00028892964,0.000020444675,0.00018886577,0.000036498917],"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":[0.0032183025,0.000278601,0.009905938,0.0000043715245,0.00010241139,0.00009292165,0.0005152105,0.0008180674,0.0013336028,0.008902488,0.00903574,0.96579236],"study_design_scores_gemma":[0.027996149,0.03354097,0.11295147,0.00051987235,0.0006630694,0.00067682593,0.0018256903,0.08983385,0.0059755296,0.600633,0.123944476,0.0014390921],"about_ca_topic_score_codex":0.00000728087,"about_ca_topic_score_gemma":2.9317727e-7,"teacher_disagreement_score":0.96435326,"about_ca_system_score_codex":0.000034305605,"about_ca_system_score_gemma":0.00015936357,"threshold_uncertainty_score":0.99964917},"labels":[],"label_agreement":null},{"id":"W2069118402","doi":"10.1007/s11336-014-9422-0","title":"On the Asymptotic Relative Efficiency of Planned Missingness Designs","year":2014,"lang":"en","type":"article","venue":"Psychometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Missing data; Mathematics; Latent variable; Regression analysis; Efficiency; Sample size determination; Latent variable model; Regression; Set (abstract data type); Linear regression; Econometrics; Variable (mathematics); Computer science","score_opus":0.21779276082377713,"score_gpt":0.44365606500622917,"score_spread":0.22586330418245204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069118402","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42358243,0.00034486374,0.41857356,0.0011440612,0.0012225335,0.0005518478,0.0000098190185,0.0000601873,0.1545107],"genre_scores_gemma":[0.9793056,0.0000026963514,0.018752601,0.0003512974,0.000044793367,0.000013098533,5.6989654e-7,0.000018331806,0.001510976],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99517214,0.0015083386,0.00070010364,0.00056068745,0.0017381837,0.0003205554],"domain_scores_gemma":[0.9701863,0.028032381,0.0004104849,0.0010278951,0.00021073104,0.00013221256],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0090433275,0.00020341526,0.00039890766,0.00082079816,0.00021015976,0.00013838954,0.0013356112,0.00009760136,0.0009165765],"category_scores_gemma":[0.03155631,0.00011329512,0.00017958735,0.004212299,0.00030747443,0.00017887056,0.000073384384,0.00021888001,0.0006582824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022465165,0.0027270431,0.02140242,0.000026867809,0.00018996415,0.000007882151,0.006477767,0.0017058253,0.11751991,0.5585734,0.04813561,0.24098678],"study_design_scores_gemma":[0.0036404945,0.008364165,0.08381246,0.00025949776,0.00007340871,0.000023763076,0.002594776,0.02369199,0.2723637,0.58998334,0.013874322,0.0013181039],"about_ca_topic_score_codex":0.000004353087,"about_ca_topic_score_gemma":3.2059876e-7,"teacher_disagreement_score":0.5557232,"about_ca_system_score_codex":0.000040401403,"about_ca_system_score_gemma":0.000032395157,"threshold_uncertainty_score":0.9999967},"labels":[],"label_agreement":null},{"id":"W2070691432","doi":"10.1016/j.jspi.2015.02.005","title":"Effective designs based on individual word length patterns","year":2015,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Aliasing; Fractional factorial design; Column (typography); Rank (graph theory); Measure (data warehouse); Design of experiments; Factorial experiment; Word length; Plackett–Burman design; Mathematical optimization; Statistics; Computer science; Data mining; Artificial intelligence; Combinatorics","score_opus":0.3239244489964962,"score_gpt":0.4980734602817696,"score_spread":0.17414901128527338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070691432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12537009,0.00009176569,0.8727668,0.000092890055,0.00026040853,0.00008189957,0.000052090865,0.000007751746,0.001276322],"genre_scores_gemma":[0.88392836,0.0000014616251,0.11570383,0.00026676268,0.00007214115,0.0000021909468,0.0000015884816,0.0000067085098,0.00001698302],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9967365,0.00077692873,0.00060134294,0.00022464282,0.0014490776,0.00021146866],"domain_scores_gemma":[0.98736227,0.011383294,0.00034971943,0.00014285112,0.00033738036,0.00042448245],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00482532,0.0001504493,0.0003823413,0.00026337308,0.000073272946,0.0003025809,0.0003436382,0.00006961795,0.00009701118],"category_scores_gemma":[0.01493489,0.000098445955,0.00004215052,0.00020204844,0.00014032959,0.0002823422,0.00006015896,0.0004098937,0.000024530062],"study_design_candidate":"observational","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":[0.0031465783,0.000629703,0.31326935,0.000024684,0.000113184964,0.0011424015,0.0057514613,0.014444491,0.0012055611,0.011851748,0.017650886,0.63076997],"study_design_scores_gemma":[0.0042967373,0.0132138375,0.79027027,0.00067719136,0.00010246599,0.00019343988,0.0042935186,0.087679684,0.002866744,0.094204225,0.0014940263,0.0007078895],"about_ca_topic_score_codex":0.0000073679316,"about_ca_topic_score_gemma":2.486609e-7,"teacher_disagreement_score":0.7585582,"about_ca_system_score_codex":0.000046297944,"about_ca_system_score_gemma":0.00014643617,"threshold_uncertainty_score":0.9933627},"labels":[],"label_agreement":null},{"id":"W2071064599","doi":"10.1016/j.chemolab.2006.08.003","title":"Mixture designs and models for the simultaneous selection of ingredients and their ratios","year":2006,"lang":"en","type":"article","venue":"Chemometrics and Intelligent Laboratory Systems","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Raw material; Process engineering; Selection (genetic algorithm); Product (mathematics); Process (computing); Design of experiments; Computer science; Property (philosophy); Degrees of freedom (physics and chemistry); Mixture model; Biochemical engineering; Materials science; Biological system; Mathematics; Artificial intelligence; Chemistry; Statistics; Engineering; Thermodynamics; Organic chemistry","score_opus":0.11102235451616162,"score_gpt":0.3588083474859413,"score_spread":0.2477859929697797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071064599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3048182,0.028319497,0.6650413,0.00003517036,0.00046007277,0.0010113094,0.00009740888,0.000021426906,0.00019559194],"genre_scores_gemma":[0.99590373,0.00033179112,0.0033175102,0.000034492892,0.00008862464,0.00003613206,0.0000022096388,0.000015520654,0.00026996568],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99825424,0.00017126832,0.0005918944,0.0003888282,0.0004151137,0.000178633],"domain_scores_gemma":[0.9942107,0.004397953,0.00030193324,0.00020891511,0.0007986848,0.00008176554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026513417,0.00017977809,0.00034410405,0.00042024013,0.00020273829,0.00031973212,0.00019521131,0.00012369722,0.0000042862325],"category_scores_gemma":[0.0017222159,0.00010518036,0.000038770195,0.0019273567,0.00015398655,0.00021853199,0.00006764275,0.00008993208,7.136411e-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":[0.0007345372,0.000756082,0.029161451,0.000778017,0.00043848352,0.000007720506,0.008544055,0.06820402,0.645214,0.03960127,0.012466542,0.1940938],"study_design_scores_gemma":[0.00031954361,0.0004317541,0.00016188768,0.00003295702,0.00002479573,0.000013525484,0.0033112287,0.77968466,0.20172597,0.0028904204,0.011169292,0.00023393],"about_ca_topic_score_codex":0.00004375121,"about_ca_topic_score_gemma":0.0000029492453,"teacher_disagreement_score":0.7114807,"about_ca_system_score_codex":0.000036449797,"about_ca_system_score_gemma":0.00003630511,"threshold_uncertainty_score":0.4289131},"labels":[],"label_agreement":null},{"id":"W2071488757","doi":"10.1002/cjs.5550340405","title":"Some robust designs for polynomial regression models","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Polynomial regression; Polynomial; Optimality criterion; Degree (music); Regression; Applied mathematics; Regression analysis; Polynomial and rational function modeling; Mathematical optimization; Statistics; Mathematical analysis","score_opus":0.24069616774055105,"score_gpt":0.39896563257433343,"score_spread":0.15826946483378238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071488757","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":[0.0036139542,0.0009100495,0.99221593,0.000278057,0.0012444683,0.00016361124,0.00067036325,0.0000031413258,0.0009004349],"genre_scores_gemma":[0.16233426,0.0000066967214,0.83501685,0.00017734077,0.00069974334,0.0000025054997,0.000007260337,0.000024563527,0.0017308075],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972567,0.00026293236,0.0011441056,0.00021145488,0.00073344505,0.00039135915],"domain_scores_gemma":[0.9959929,0.0015923006,0.0007047974,0.00026273166,0.0008095854,0.0006376681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030599239,0.00015766529,0.00040646165,0.00052236207,0.00024052383,0.00034821648,0.00066935504,0.000094338095,0.00022570757],"category_scores_gemma":[0.0021459742,0.00011726909,0.0001353991,0.00024291436,0.00016482007,0.0005186909,0.000014580196,0.00016898429,0.000020196907],"study_design_candidate":"theoretical_or_conceptual","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":[0.00012534508,0.000028726612,0.00044068584,0.0000051530374,0.000018172428,0.00023576361,0.00025526367,0.04548968,0.0033458937,0.07370388,0.85551393,0.020837538],"study_design_scores_gemma":[0.00163811,0.0007833846,0.0015860804,0.00008197228,0.00005763611,0.00022418074,0.00066537043,0.0942217,0.005479347,0.85957766,0.03521606,0.00046848806],"about_ca_topic_score_codex":0.0015938652,"about_ca_topic_score_gemma":0.002256945,"teacher_disagreement_score":0.82029784,"about_ca_system_score_codex":0.0002519653,"about_ca_system_score_gemma":0.0015205367,"threshold_uncertainty_score":0.47820947},"labels":[],"label_agreement":null},{"id":"W2073055376","doi":"10.1016/s0012-365x(03)00277-2","title":"Some new orthogonal designs in orders 32 and 40","year":2003,"lang":"en","type":"article","venue":"Discrete Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Mathematics; Combinatorics; Complement (music); Order (exchange); Orthogonal array; Resolution (logic); Discrete mathematics; Orthogonal complement; Statistics; Mathematical analysis; Computer science","score_opus":0.1952039077306143,"score_gpt":0.43652435207368806,"score_spread":0.24132044434307376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073055376","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":[0.46079424,0.0020108086,0.47983533,0.0006329878,0.00047838385,0.0009906751,0.000014686325,0.00010333272,0.05513956],"genre_scores_gemma":[0.2759763,0.000029691244,0.72100914,0.00023264735,0.00003773296,0.00001301264,0.000001237263,0.00003260044,0.0026676543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99741095,0.00032609707,0.00067625893,0.00041214342,0.0008472844,0.00032724702],"domain_scores_gemma":[0.99775845,0.0013462834,0.00017039315,0.0004717125,0.00004463678,0.00020849849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002948103,0.00020400246,0.00039163322,0.00022411026,0.0000737136,0.00023125547,0.00036628224,0.00008941645,0.00047852934],"category_scores_gemma":[0.0030709123,0.00015198824,0.00007829442,0.0005791763,0.00013537613,0.00038156906,0.00009161381,0.00014493507,0.00017339735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044624834,0.00019762876,0.003211394,0.000036602516,0.000027263923,0.000044306318,0.0071269386,0.00022964507,0.0129369255,0.9664295,0.0027525993,0.006962616],"study_design_scores_gemma":[0.0007912332,0.0001167526,0.00086952734,0.00004883388,0.000012967409,0.000040931267,0.004346552,0.006633276,0.007444256,0.9765922,0.0027245781,0.00037888237],"about_ca_topic_score_codex":0.000011291486,"about_ca_topic_score_gemma":0.000008591753,"teacher_disagreement_score":0.2411738,"about_ca_system_score_codex":0.000033438253,"about_ca_system_score_gemma":0.00010585648,"threshold_uncertainty_score":0.6197901},"labels":[],"label_agreement":null},{"id":"W207338156","doi":"","title":"Partial Diallel Cross Block Designs.","year":2002,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Diallel cross; Mathematics; Block (permutation group theory); Arithmetic; Combinatorics; Horticulture; Biology","score_opus":0.18328870662151253,"score_gpt":0.42360412442249823,"score_spread":0.2403154178009857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W207338156","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9386048,0.0007694254,0.00088281225,0.0004085664,0.00625259,0.00040946426,0.000007217344,0.00020102852,0.0524641],"genre_scores_gemma":[0.98458236,0.000013930871,0.009129683,0.00027323322,0.00003691182,0.000035030498,7.78048e-7,0.00003145808,0.00589662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956079,0.0007074351,0.00076696486,0.0007442453,0.0016225997,0.0005508452],"domain_scores_gemma":[0.99674207,0.0014774194,0.00021787873,0.0010107835,0.00024089456,0.00031094113],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0027762598,0.0002577727,0.0004076561,0.00016293101,0.0003107882,0.00060448184,0.0012598964,0.00016171554,0.0031612974],"category_scores_gemma":[0.002631046,0.00021600342,0.00020321553,0.00087250705,0.0002676984,0.00052508834,0.00027571822,0.00023042873,0.0051195915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002522961,0.0018387515,0.03952323,0.000008614731,0.00010768686,0.00027444708,0.0036724287,0.0006061411,0.04952231,0.680885,0.1831071,0.04020195],"study_design_scores_gemma":[0.003501877,0.0007366842,0.00898629,0.000016229817,0.000024423021,0.000050282553,0.00026240826,0.01569523,0.1010718,0.8100709,0.058581278,0.0010026429],"about_ca_topic_score_codex":0.000019231878,"about_ca_topic_score_gemma":7.107114e-7,"teacher_disagreement_score":0.12918581,"about_ca_system_score_codex":0.00008029929,"about_ca_system_score_gemma":0.00002776184,"threshold_uncertainty_score":0.9977499},"labels":[],"label_agreement":null},{"id":"W2073476612","doi":"10.1287/moor.2014.0682","title":"K -Optimal Design via Semidefinite Programming and Entropy Optimization","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Semidefinite programming; Mathematical optimization; Semidefinite embedding; Mathematics; Computer science; Quadratically constrained quadratic program; Quadratic programming","score_opus":0.08293965647548455,"score_gpt":0.3422714412133001,"score_spread":0.25933178473781554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073476612","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":[0.0069110743,0.002281687,0.977733,0.0022884011,0.0003639312,0.0010788055,0.000033554825,0.0002922461,0.009017305],"genre_scores_gemma":[0.07427868,0.00025112898,0.9208184,0.000086561224,0.000038040045,0.00015591663,0.00016346842,0.000081778875,0.0041260486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97391856,0.020414138,0.0013699426,0.0017956168,0.0018840708,0.000617675],"domain_scores_gemma":[0.9835845,0.006709511,0.0012474847,0.0028406803,0.005060281,0.0005575306],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.039103054,0.0006130489,0.0008194022,0.00055662193,0.0004569425,0.00220252,0.0022047362,0.0005361195,0.00029453656],"category_scores_gemma":[0.012342283,0.000583868,0.0002531709,0.00096865074,0.00056937255,0.0004858862,0.0031041915,0.0008534497,0.000104321254],"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":[0.00037444144,0.0021274765,0.003502761,0.00020166008,0.00037015617,0.000064125146,0.04608843,0.2928845,0.01720063,0.037216034,0.008652057,0.5913177],"study_design_scores_gemma":[0.0009779693,0.0000037389939,0.00024038817,0.00069038145,0.000085408,0.000049984068,0.00057547796,0.93454975,0.036704887,0.017572522,0.0075633787,0.0009860874],"about_ca_topic_score_codex":0.000340627,"about_ca_topic_score_gemma":0.000033726603,"teacher_disagreement_score":0.6416653,"about_ca_system_score_codex":0.00024494954,"about_ca_system_score_gemma":0.000495068,"threshold_uncertainty_score":0.99966127},"labels":[],"label_agreement":null},{"id":"W2073515716","doi":"10.1002/cjce.20233","title":"Comparison of experimental designs using neural networks","year":2009,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial neural network; Design of experiments; Factor (programming language); Nonlinear system; Mathematics; Computer science; Algorithm; Statistics; Artificial intelligence","score_opus":0.18773288497715854,"score_gpt":0.4236921190970609,"score_spread":0.23595923411990236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073515716","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.960019,0.0021120496,0.037124217,0.00015365193,0.00037780582,0.000067783476,0.0000010784217,0.000005399349,0.00013902302],"genre_scores_gemma":[0.97812486,1.73721e-7,0.021623688,0.00007345649,0.00016182296,2.697502e-7,1.6200077e-7,0.000010779427,0.00000477724],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813414,0.000089500725,0.000796031,0.00011616646,0.0005563129,0.00030783468],"domain_scores_gemma":[0.9984519,0.00042733803,0.00030661115,0.0002262152,0.00014863523,0.00043933344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001334179,0.00013787666,0.0004041497,0.00020324693,0.00006674322,0.00011065419,0.00081802736,0.000078191864,0.00008023325],"category_scores_gemma":[0.0007049674,0.00009562965,0.00017232064,0.00043770534,0.000102812664,0.00019250593,0.000019796367,0.00035573373,0.0000011028837],"study_design_candidate":"bench_or_experimental","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":[0.00001844986,0.000013105469,0.00021625496,5.093737e-7,0.000009137794,0.000011597645,0.00039779878,0.3658415,0.63175726,0.00024624402,0.00013010972,0.0013580305],"study_design_scores_gemma":[0.0001288049,0.00008574743,0.000102591766,0.000017054426,0.000008251758,0.00008546538,0.00009773001,0.52867675,0.47056302,0.00012405495,0.00003344459,0.00007704873],"about_ca_topic_score_codex":0.00015170565,"about_ca_topic_score_gemma":0.0000049304886,"teacher_disagreement_score":0.16283527,"about_ca_system_score_codex":0.00019521038,"about_ca_system_score_gemma":0.00015009938,"threshold_uncertainty_score":0.3899664},"labels":[],"label_agreement":null},{"id":"W2076340142","doi":"10.1007/s00170-007-1090-0","title":"Evaluating performance of a fuel nozzle test stand under varying configurations using Taguchi parameter design - An industrial application","year":2007,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Barrick Gold (Canada)","funders":"","keywords":"Taguchi methods; Orthogonal array; Nozzle; Design of experiments; Engineering; Noise (video); Transducer; Pressure sensor; Calibration; Process variable; Mechanical engineering; Process (computing); Computer science; Mathematics; Electrical engineering; Statistics","score_opus":0.26429948188824487,"score_gpt":0.4834107795474762,"score_spread":0.21911129765923132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076340142","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6059565,0.000074844706,0.39312315,0.00027024816,0.00031928238,0.00017646584,0.0000030752208,0.000017240423,0.000059214093],"genre_scores_gemma":[0.71553254,0.000014688196,0.2842818,0.000052479387,0.00008846763,0.0000038210687,6.1001356e-7,0.000012042836,0.000013556614],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99668914,0.00016674885,0.001286492,0.00026400352,0.001356124,0.00023747221],"domain_scores_gemma":[0.99397194,0.003097182,0.0016655484,0.00042311687,0.0007680129,0.00007421292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006040271,0.00016570462,0.00031230177,0.0008081091,0.00014600989,0.00009075097,0.0016887293,0.00014786556,0.00005630622],"category_scores_gemma":[0.003510456,0.00011656888,0.000088514,0.00036243783,0.00026470612,0.0007550111,0.00017583638,0.0004784643,0.0000038276094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004864509,0.00008420594,0.0002900362,0.0000017138198,0.00004249667,0.000004839756,0.0001560316,0.17826705,0.6914342,0.0003897827,0.0000044433223,0.12883872],"study_design_scores_gemma":[0.00095642614,0.0006647149,0.0006761194,0.00005535697,0.00002392156,0.00024633788,0.00091843196,0.036352124,0.92487985,0.0350011,0.000102210004,0.00012340315],"about_ca_topic_score_codex":0.000009013016,"about_ca_topic_score_gemma":0.000002205026,"teacher_disagreement_score":0.23344561,"about_ca_system_score_codex":0.00025628766,"about_ca_system_score_gemma":0.00020749916,"threshold_uncertainty_score":0.4753541},"labels":[],"label_agreement":null},{"id":"W2076423843","doi":"10.1093/biomet/asr071","title":"Optimal fractions of two-level factorials under a baseline parameterization","year":2011,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":32,"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","keywords":"Mathematics; Fractional factorial design; Baseline (sea); Context (archaeology); Isomorphism (crystallography); Factorial experiment; Orthogonal array; Optimal design; Applied mathematics; Mathematical optimization; Statistics; Combinatorics","score_opus":0.5168909803614168,"score_gpt":0.48915894728996917,"score_spread":0.027732033071447615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076423843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22592427,0.00012460697,0.7696459,0.00002805957,0.0012169098,0.0002154343,0.00011690453,0.000041669846,0.0026862456],"genre_scores_gemma":[0.57432175,0.0000065305267,0.42511377,0.000044072152,0.00007435425,0.000009204127,0.000006757511,0.000012065179,0.00041149202],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965553,0.0006449618,0.00092600705,0.00046946062,0.00115947,0.00024474494],"domain_scores_gemma":[0.9961221,0.0021703409,0.00047673308,0.00064129557,0.00043431413,0.00015520131],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00425404,0.00017383971,0.00040115378,0.001690217,0.000084486914,0.00008350651,0.0005790103,0.00011918935,0.0041020224],"category_scores_gemma":[0.006341956,0.00013554601,0.00016935857,0.0049040616,0.00016212856,0.00047536028,0.00013393551,0.0000876184,0.0003381811],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021620629,0.00066303863,0.004240252,0.0000048604807,0.00007266514,0.0000039710108,0.00068671396,0.00026457154,0.95144415,0.0040795235,0.000852153,0.037471864],"study_design_scores_gemma":[0.0010360454,0.0005215742,0.026548486,0.000013329874,0.000037777398,0.000008671318,0.0008414504,0.0031664036,0.95718235,0.0076367036,0.0026522623,0.00035492695],"about_ca_topic_score_codex":0.000192194,"about_ca_topic_score_gemma":0.0000019409144,"teacher_disagreement_score":0.3483975,"about_ca_system_score_codex":0.000055950695,"about_ca_system_score_gemma":0.00007951918,"threshold_uncertainty_score":0.99680835},"labels":[],"label_agreement":null},{"id":"W2076644634","doi":"10.3758/pbr.17.1.135","title":"Revisiting confidence intervals for repeated measures designs","year":2010,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Psychology; Factorial; Factorial experiment; Value (mathematics); Arithmetic; Statistics; Confidence interval; Relation (database); Cognitive psychology; Mathematics; Computer science; Data mining","score_opus":0.27839042303392475,"score_gpt":0.4936683544901512,"score_spread":0.21527793145622642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076644634","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":[0.0077708573,0.30257595,0.5274844,0.057057425,0.009691709,0.011447401,0.00009672495,0.0006501044,0.08322542],"genre_scores_gemma":[0.066738494,0.022229783,0.88149345,0.018655298,0.0012701208,0.001212239,0.000014953334,0.00017152449,0.008214138],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947356,0.0010134449,0.0019236738,0.0011702068,0.0006690166,0.00048804563],"domain_scores_gemma":[0.99324405,0.0035091331,0.00086995546,0.0015964604,0.00052004436,0.0002603446],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.021549122,0.00035228243,0.0010352698,0.00013709452,0.00020134942,0.00033487612,0.0017258012,0.00013083912,0.0077253203],"category_scores_gemma":[0.017106386,0.00027482232,0.00053991494,0.00040432657,0.00018689247,0.00013827386,0.00013931164,0.00039927202,0.0032519838],"study_design_candidate":"not_applicable","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":[0.00009835164,0.00005654481,0.00015707943,0.00034950615,0.000040272935,0.0000036320816,0.000086966604,0.0000026112737,0.074281886,0.003831955,0.25337353,0.66771764],"study_design_scores_gemma":[0.00038208478,0.000087450106,0.00016584055,0.0017627573,0.000050117513,0.000052554926,0.00004411228,0.00009784641,0.0048043136,0.004037089,0.9881331,0.00038273216],"about_ca_topic_score_codex":0.000020276395,"about_ca_topic_score_gemma":0.000001923572,"teacher_disagreement_score":0.73475957,"about_ca_system_score_codex":0.000044220866,"about_ca_system_score_gemma":0.000070351234,"threshold_uncertainty_score":0.9999704},"labels":[],"label_agreement":null},{"id":"W2078249808","doi":"10.1198/jasa.2009.0119","title":"Screening Experiments for Developing Dynamic Treatment Regimes","year":2009,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":41,"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":"National Institute on Drug Abuse; National Institute of Mental Health","keywords":"Fractional factorial design; Factorial experiment; Computer science; Treatment effect; Factorial; Design of experiments; Machine learning; Mathematical optimization; Medicine; Mathematics; Statistics","score_opus":0.12215089528701961,"score_gpt":0.4961811498570252,"score_spread":0.3740302545700056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078249808","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":[0.0854026,0.000079555546,0.9088184,0.0048430883,0.00033591833,0.00021035702,0.000030004247,0.0000088662355,0.00027117663],"genre_scores_gemma":[0.4697489,0.000010299538,0.5288751,0.0005568006,0.000070484915,0.000003582939,9.1906946e-7,0.0000069470084,0.0007269392],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9968966,0.0006134523,0.0008200403,0.00018487837,0.0012326782,0.00025231327],"domain_scores_gemma":[0.99333286,0.0037740455,0.0022424236,0.00018664713,0.0003792248,0.00008479656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024445856,0.00013349406,0.00047962726,0.00011884604,0.0001823996,0.00016692522,0.00044735213,0.000031431133,0.000016342687],"category_scores_gemma":[0.007091734,0.000077243625,0.00022083275,0.00045362022,0.00006502683,0.00020847787,0.00003100138,0.00010125562,0.000006849025],"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":[0.00076765683,0.00029061353,0.007806007,0.0000014464308,0.00023910661,0.000009385299,0.0010130709,0.00036500333,0.031109106,0.008203857,0.011304109,0.93889064],"study_design_scores_gemma":[0.0041844686,0.007686215,0.68916756,0.00011842602,0.0003035532,0.00007473346,0.004877194,0.025070392,0.04167277,0.20911805,0.016944487,0.0007821683],"about_ca_topic_score_codex":0.000011216345,"about_ca_topic_score_gemma":0.000001073442,"teacher_disagreement_score":0.93810844,"about_ca_system_score_codex":0.0011924217,"about_ca_system_score_gemma":0.00012437782,"threshold_uncertainty_score":0.84899807},"labels":[],"label_agreement":null},{"id":"W207882731","doi":"","title":"Balanced Bipartite Row-Column Designs.","year":2001,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Column (typography); Mathematics; Bipartite graph; Combinatorics; Algorithm; Geometry; Graph","score_opus":0.13705619323265078,"score_gpt":0.4176691317247561,"score_spread":0.28061293849210533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W207882731","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93718415,0.00047246178,0.0039443173,0.00054064364,0.0042303354,0.00043696206,0.0000040474138,0.00022046697,0.05296661],"genre_scores_gemma":[0.97806793,0.000025386955,0.015747195,0.00050308224,0.000022591084,0.000047349084,0.0000021660398,0.00003246224,0.0055518453],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957825,0.0006883123,0.00072385994,0.0007365851,0.0014999812,0.00056876044],"domain_scores_gemma":[0.9968634,0.0012572301,0.00023281886,0.0010703001,0.00027114621,0.00030508352],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003441122,0.0002528396,0.0004506222,0.00023330103,0.00023768265,0.00035124598,0.0012114693,0.00014045154,0.0016615345],"category_scores_gemma":[0.0020839819,0.000218179,0.00017504193,0.001662018,0.00021502821,0.000609107,0.00022969065,0.00020813936,0.0030391344],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054191553,0.0009936028,0.12487839,0.000006775395,0.0000915687,0.00036172185,0.0016137757,0.00032772255,0.17158264,0.5226712,0.11680268,0.060128022],"study_design_scores_gemma":[0.0016779307,0.00038490517,0.022559626,0.00001847766,0.000014489562,0.000042904267,0.00043898236,0.0026291783,0.038743198,0.8700092,0.06293614,0.00054496503],"about_ca_topic_score_codex":0.00004273652,"about_ca_topic_score_gemma":0.000003887498,"teacher_disagreement_score":0.34733802,"about_ca_system_score_codex":0.000110387526,"about_ca_system_score_gemma":0.0000788079,"threshold_uncertainty_score":0.99925107},"labels":[],"label_agreement":null},{"id":"W2080431129","doi":"10.1111/1755-0998.12303","title":"<i>Q</i><sub>ST</sub>–<i>F</i><sub>ST</sub> comparisons with unbalanced half‐sib designs","year":2014,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology","score_opus":0.050577434184904284,"score_gpt":0.34199443307018357,"score_spread":0.2914169988852793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080431129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8893914,0.00051123905,0.10061708,0.00094424805,0.0004403819,0.00084591674,0.000029817938,0.00028557493,0.0069343345],"genre_scores_gemma":[0.9655793,0.00001946817,0.030774921,0.0029667816,0.0001483879,0.00021704502,0.00002562511,0.00014412127,0.00012437918],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9889085,0.0038854503,0.0014364803,0.0021476373,0.0020457467,0.001576155],"domain_scores_gemma":[0.9928281,0.0030502263,0.000881326,0.0020542373,0.0004819345,0.00070419733],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004899491,0.00089492096,0.0016049695,0.0007014795,0.0007877018,0.0005781789,0.0023262862,0.0006105475,0.00016914685],"category_scores_gemma":[0.0022459028,0.0007321631,0.00043953763,0.0017992897,0.0013594542,0.00036355868,0.000653511,0.0008287452,0.0010058138],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045810355,0.0003995045,0.006629011,0.000014071482,0.00017898215,0.00025782327,0.00059285894,0.0044712285,0.97200143,0.0009797363,0.008234146,0.0057830852],"study_design_scores_gemma":[0.0023611595,0.0017751086,0.015384328,0.000047019475,0.00013736488,0.00017833141,0.00064379553,0.007432206,0.9449739,0.0024334597,0.023507897,0.0011254275],"about_ca_topic_score_codex":0.00002326549,"about_ca_topic_score_gemma":0.00015360376,"teacher_disagreement_score":0.076187864,"about_ca_system_score_codex":0.00017745387,"about_ca_system_score_gemma":0.0001681227,"threshold_uncertainty_score":0.999772},"labels":[],"label_agreement":null},{"id":"W2081290294","doi":"10.1016/j.csda.2008.01.014","title":"Approximation of powers of some tests in one-way MANOVA type multivariate generalized linear model","year":2008,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Pacific Institute for the Mathematical Sciences","keywords":"Mathematics; Multivariate analysis of variance; Multivariate statistics; Generalized linear model; Deviance (statistics); Statistics; Applied mathematics; Sample size determination; Statistic; Test statistic; Linear model; Type I and type II errors; Statistical hypothesis testing","score_opus":0.3767989769850297,"score_gpt":0.48334965061897744,"score_spread":0.10655067363394777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081290294","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":[0.064064935,0.000078722805,0.93293995,0.000025408106,0.000036739828,0.0001330075,0.0026139999,0.000008689758,0.00009856807],"genre_scores_gemma":[0.37272543,0.000013016308,0.62527645,0.000022266204,0.000008960885,0.0000017964661,0.0018650012,0.0000075053567,0.000079565994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960795,0.0003929508,0.0012935224,0.00055505766,0.0015164288,0.00016252257],"domain_scores_gemma":[0.9959353,0.0017293502,0.0006787029,0.00078174577,0.00079965923,0.00007523163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018862577,0.00015118402,0.0006426529,0.0008873873,0.00006506065,0.000027273903,0.00083052367,0.00006188448,0.0002611996],"category_scores_gemma":[0.0026879578,0.00014186263,0.00008445494,0.0026788805,0.00020350715,0.00044391133,0.00031508127,0.000094940675,0.000036760834],"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":[0.00009204673,0.0002610563,0.003949263,0.000008482892,0.00025395674,0.000005260176,0.00033204796,0.97566426,0.0023697137,0.014989588,0.000560299,0.0015140091],"study_design_scores_gemma":[0.00044836616,0.000036698406,0.037527315,0.000005517559,0.00014668301,9.308958e-7,0.000022464668,0.9104659,0.0003324959,0.050878897,0.000012944116,0.00012177184],"about_ca_topic_score_codex":0.00050561485,"about_ca_topic_score_gemma":0.000034148627,"teacher_disagreement_score":0.3086605,"about_ca_system_score_codex":0.00005071765,"about_ca_system_score_gemma":0.00017828928,"threshold_uncertainty_score":0.578499},"labels":[],"label_agreement":null},{"id":"W2083828390","doi":"10.1016/j.jspi.2005.02.018","title":"Constructing non-regular robust parameter designs","year":2005,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Fractional factorial design; Mathematics; Selection (genetic algorithm); Orthogonal array; Variance (accounting); Mathematical optimization; Design of experiments; Rank (graph theory); Optimal design; Noise (video); Word (group theory); Factorial experiment; Algorithm; Statistics; Computer science; Artificial intelligence; Combinatorics","score_opus":0.25226363154183207,"score_gpt":0.4727185011069364,"score_spread":0.22045486956510435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083828390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11243136,0.00023825551,0.8848039,0.0001626088,0.00015589055,0.000040774765,0.000011604039,0.0000059107215,0.0021497104],"genre_scores_gemma":[0.522921,0.0000033766737,0.47686425,0.000087379696,0.00007184531,3.8946197e-7,3.302041e-7,0.0000039126076,0.000047495603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975756,0.00025902892,0.0009050255,0.00021389018,0.0008088782,0.00023762014],"domain_scores_gemma":[0.99111795,0.007734176,0.00045206642,0.0001416508,0.0002901582,0.00026400728],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0028126296,0.00013793456,0.00041280122,0.00018474224,0.00010977053,0.00033628027,0.00030774428,0.00007081952,0.0003098938],"category_scores_gemma":[0.009788826,0.000094827774,0.000051310286,0.0001899365,0.0002947229,0.0005217918,0.00007013725,0.0003505716,0.00002691679],"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":[0.00043564668,0.00015712461,0.09323312,0.00001765075,0.0000877866,0.0003653161,0.0021423297,0.009385065,0.012714928,0.0331956,0.009601093,0.83866435],"study_design_scores_gemma":[0.0045226114,0.0045836563,0.13193357,0.0010308579,0.00021759342,0.0039285226,0.016198572,0.605402,0.026865914,0.19652483,0.0070660315,0.0017258489],"about_ca_topic_score_codex":0.0000037468062,"about_ca_topic_score_gemma":3.3152637e-7,"teacher_disagreement_score":0.8369385,"about_ca_system_score_codex":0.000041327352,"about_ca_system_score_gemma":0.00008345594,"threshold_uncertainty_score":0.99855214},"labels":[],"label_agreement":null},{"id":"W2083839279","doi":"10.1198/004017007000000038","title":"Incorporating Prior Information in Optimal Design for Model Selection","year":2007,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Interpretability; Bayesian information criterion; Set (abstract data type); Selection (genetic algorithm); Computer science; Prior probability; Bayesian probability; Model selection; Hellinger distance; Machine learning; Identification (biology); Design of experiments; Mathematics; Mathematical optimization; Artificial intelligence; Data mining; Statistics","score_opus":0.21412141502088103,"score_gpt":0.4495098550792087,"score_spread":0.23538844005832768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083839279","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":[0.06615272,0.000042075615,0.9316128,0.00003131581,0.000089980014,0.0007781978,0.0000046164564,0.00013139482,0.0011568831],"genre_scores_gemma":[0.43867457,0.0000010705663,0.5611628,0.000042859498,0.000011743683,0.000031850137,0.0000018671931,0.0000073527617,0.00006586518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974188,0.000054163895,0.0009785307,0.00028291237,0.000929598,0.00033596056],"domain_scores_gemma":[0.9969063,0.0019911418,0.00039857315,0.0002622111,0.00036337727,0.00007838851],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.015956486,0.00014200121,0.0002317745,0.004548046,0.00013565649,0.00020760953,0.00051318243,0.00018585518,0.000011248908],"category_scores_gemma":[0.0126791755,0.00012812453,0.00006626178,0.01074741,0.000048455095,0.0014434806,0.00010019158,0.00018465257,0.00003897474],"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":[0.00026832835,0.00013443267,0.0024994682,0.00000985681,0.000004510731,0.0000012210234,0.00044192528,0.34858626,0.016953694,0.012202318,0.0006528465,0.6182451],"study_design_scores_gemma":[0.00050735514,0.00022012851,0.00061069964,0.0000049314326,0.0000028202833,0.0000036166316,0.0004570906,0.9215321,0.055754155,0.020386005,0.0003505315,0.00017054631],"about_ca_topic_score_codex":0.000007816485,"about_ca_topic_score_gemma":0.0000020916118,"teacher_disagreement_score":0.6180746,"about_ca_system_score_codex":0.000331651,"about_ca_system_score_gemma":0.00009877978,"threshold_uncertainty_score":0.9956375},"labels":[],"label_agreement":null},{"id":"W2084811700","doi":"10.1002/cjs.10069","title":"Testing for equivalence of variances using Hartley's ratio","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Equivalence (formal languages); Mathematics; Statistics; Econometrics; Discrete mathematics","score_opus":0.32429380419992876,"score_gpt":0.4553709992244614,"score_spread":0.13107719502453263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084811700","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":[0.07111055,0.00011517436,0.92612123,0.000060463724,0.0015453026,0.00011181528,0.00039511448,0.0000015796863,0.00053876516],"genre_scores_gemma":[0.37756255,3.8069712e-7,0.62221855,0.000036015917,0.000109426044,4.816242e-7,4.1281245e-7,0.000007328107,0.00006483592],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817806,0.00010551448,0.00085398724,0.00013489624,0.0004836298,0.00024393063],"domain_scores_gemma":[0.9935518,0.0033798814,0.0008206833,0.00019393612,0.0016131152,0.0004405818],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0031882366,0.00009410244,0.00028843107,0.00029592164,0.00015622676,0.00017221825,0.0005672676,0.00005330258,0.0003033478],"category_scores_gemma":[0.024130218,0.00007894306,0.00005632483,0.00037645432,0.00026793827,0.0002936264,0.000014003608,0.00018724451,0.0000038016487],"study_design_candidate":"bench_or_experimental","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":[0.000056975903,0.0000317299,0.028240183,0.000037037447,0.00004457661,0.0001519734,0.001408439,0.0023787755,0.794771,0.055402335,0.00879663,0.10868031],"study_design_scores_gemma":[0.002643033,0.0030056043,0.049105447,0.00042754135,0.00025575806,0.0013797324,0.004025824,0.24180241,0.14363542,0.5309687,0.021564513,0.0011859804],"about_ca_topic_score_codex":0.0008610877,"about_ca_topic_score_gemma":0.0016619381,"teacher_disagreement_score":0.6511356,"about_ca_system_score_codex":0.000048045098,"about_ca_system_score_gemma":0.002051721,"threshold_uncertainty_score":0.98409},"labels":[],"label_agreement":null},{"id":"W2084976008","doi":"10.1002/cjs.11174","title":"D‐optimal minimax fractional factorial designs","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Fractional factorial design; Mathematics; Factorial experiment; Plackett–Burman design; Factorial; Invariant (physics); Optimal design; Mathematical optimization; Applied mathematics; Statistics; Response surface methodology","score_opus":0.19626501042954558,"score_gpt":0.40383266140906765,"score_spread":0.20756765097952207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084976008","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":[0.029048152,0.00015873558,0.96277666,0.00034105303,0.00406196,0.00014897286,0.00031310125,0.0000045229363,0.0031468477],"genre_scores_gemma":[0.40147477,0.0000032098908,0.5967094,0.00020972415,0.0005936492,0.000002367662,0.000003189338,0.00001714656,0.0009865995],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706453,0.00031076875,0.0009477604,0.00019143385,0.0010992654,0.00038623507],"domain_scores_gemma":[0.9944585,0.0021237356,0.00054460013,0.00022266674,0.0013883851,0.0012621256],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015428,0.00015695656,0.00033486733,0.00050596055,0.00019238617,0.0005756181,0.0006799877,0.000098337885,0.014003413],"category_scores_gemma":[0.0061784806,0.00012648935,0.00010427435,0.0003411091,0.00022417084,0.00063111965,0.00001781461,0.00032373276,0.0008245278],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065595064,0.000046145084,0.007552873,0.000003324989,0.000083144325,0.0004369981,0.001341905,0.001831465,0.0065082405,0.01352088,0.9241287,0.0444807],"study_design_scores_gemma":[0.003519478,0.0031451923,0.12555131,0.00008186988,0.0001464426,0.0017088127,0.0072184983,0.01462366,0.007907743,0.30939987,0.5250378,0.001659295],"about_ca_topic_score_codex":0.0025784406,"about_ca_topic_score_gemma":0.00074653194,"teacher_disagreement_score":0.3990909,"about_ca_system_score_codex":0.00025691834,"about_ca_system_score_gemma":0.002037391,"threshold_uncertainty_score":0.99995345},"labels":[],"label_agreement":null},{"id":"W2087870069","doi":"10.1016/j.jspi.2014.01.005","title":"New optimal design criteria for regression models with asymmetric errors","year":2014,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Regression; Regression analysis; Econometrics; Applied mathematics","score_opus":0.25324346801563835,"score_gpt":0.49172098730036595,"score_spread":0.2384775192847276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087870069","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":[0.004185024,0.0002388394,0.9943968,0.00011058523,0.00015657558,0.00009773414,0.000010279039,0.000007791012,0.0007963623],"genre_scores_gemma":[0.40918636,0.0000040173186,0.5906046,0.000053018626,0.000058921225,0.0000011794131,5.403183e-7,0.000006757833,0.00008457067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975662,0.00039385344,0.0007044003,0.0002567519,0.00083925,0.0002395162],"domain_scores_gemma":[0.9888809,0.009731909,0.0004675888,0.00015081598,0.00039485417,0.0003739471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003949828,0.00016342282,0.00046343997,0.0003147057,0.00012531894,0.00031946157,0.00033478902,0.00007266513,0.00006181084],"category_scores_gemma":[0.008155651,0.00009261876,0.000041627172,0.00031093668,0.00010945477,0.0005906408,0.00004931992,0.00021810544,0.0000036186545],"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":[0.007475519,0.00019920885,0.003148452,0.000049256254,0.0000990331,0.0001308352,0.002957353,0.09402903,0.0075819804,0.11434142,0.06614708,0.7038408],"study_design_scores_gemma":[0.0017383092,0.0060492274,0.0039889337,0.00034706193,0.000053742497,0.0002029067,0.0005951745,0.78209466,0.0023034674,0.20126548,0.0010406687,0.00032036364],"about_ca_topic_score_codex":0.000006301077,"about_ca_topic_score_gemma":8.1689585e-8,"teacher_disagreement_score":0.7035205,"about_ca_system_score_codex":0.000021560309,"about_ca_system_score_gemma":0.00013952688,"threshold_uncertainty_score":0.9763666},"labels":[],"label_agreement":null},{"id":"W2087938803","doi":"10.1198/00401700152672555","title":"A Robust Criterion for Experimental Designs for Serially Correlated Observations","year":2001,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractional factorial design; Mathematics; Simulated annealing; Algorithm; Variance (accounting); Factorial experiment; Design of experiments; Correlation; Statistics; Interval (graph theory); Mathematical optimization; Computer science","score_opus":0.6859603614227414,"score_gpt":0.4947408768853512,"score_spread":0.19121948453739024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087938803","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":[0.08513611,0.00058342767,0.91020125,0.00031775903,0.0008465046,0.0018818155,0.00009451379,0.0003001697,0.00063845],"genre_scores_gemma":[0.26880878,0.00001619942,0.72771573,0.00024892075,0.00010028566,0.0006841673,0.000038200073,0.000050461964,0.002337261],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970275,0.000086191794,0.00087451586,0.0007105304,0.00082203443,0.00047923482],"domain_scores_gemma":[0.9953382,0.0028965648,0.0003092808,0.0006951826,0.0006041562,0.00015660972],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0032163812,0.0002551297,0.00041849806,0.0016336055,0.00039413135,0.0003819684,0.0010372695,0.00024964483,0.00023435046],"category_scores_gemma":[0.010142225,0.00022919927,0.0002571454,0.00704126,0.00011246428,0.00057803903,0.00015793565,0.00011688161,0.000050982995],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094114034,0.001128658,0.0027689552,0.000016129854,0.00005339212,0.000009471121,0.00054690504,0.001414853,0.8887105,0.02476249,0.026242701,0.053404782],"study_design_scores_gemma":[0.008013995,0.005626437,0.004273654,0.000060325154,0.00011810479,0.000089268986,0.0055195307,0.31389052,0.346124,0.058240645,0.25597373,0.0020698162],"about_ca_topic_score_codex":0.000010081462,"about_ca_topic_score_gemma":0.0000016090675,"teacher_disagreement_score":0.5425865,"about_ca_system_score_codex":0.00024233427,"about_ca_system_score_gemma":0.00008288745,"threshold_uncertainty_score":0.99819577},"labels":[],"label_agreement":null},{"id":"W2088554048","doi":"10.2307/3315953","title":"Minimax A‐ and D‐optimal integer‐valued wavelet designs for estimation","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Estimator; Gompertz function; Nonparametric statistics; Wavelet; Mathematics; Integer (computer science); Simulated annealing; Heteroscedasticity; Mathematical optimization; Statistics; Algorithm; Computer science; Artificial intelligence","score_opus":0.27530869357263327,"score_gpt":0.40929619536195033,"score_spread":0.13398750178931707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088554048","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":[0.008095703,0.00049525214,0.9895887,0.0003185119,0.0004397865,0.00016687471,0.00033159685,0.0000031367538,0.00056045357],"genre_scores_gemma":[0.20772892,0.000009159001,0.7914179,0.00017177076,0.0000672234,0.0000025835986,0.0000024186547,0.000015096269,0.00058493105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802756,0.00019291851,0.0007969541,0.00018918942,0.0004832669,0.00031009942],"domain_scores_gemma":[0.9961995,0.001775935,0.00043564962,0.00017176343,0.00066233036,0.0007548606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021300323,0.00014118402,0.00032875882,0.00046362603,0.00019137897,0.00034444168,0.00035887916,0.00007255451,0.00077557575],"category_scores_gemma":[0.008162706,0.00011862186,0.00006471694,0.00025522913,0.00022831577,0.00030860913,0.0000128091515,0.00015609538,0.000031756543],"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":[0.00012617551,0.00005635174,0.00079306506,0.000027340402,0.000087963315,0.00038517476,0.0057760724,0.0023560065,0.0017733069,0.043447886,0.33225095,0.6129197],"study_design_scores_gemma":[0.001800053,0.001960773,0.0023182507,0.00007773294,0.00010555263,0.00080284703,0.0023447827,0.89272463,0.0021810122,0.07308112,0.022092799,0.0005104176],"about_ca_topic_score_codex":0.00017583498,"about_ca_topic_score_gemma":0.000359694,"teacher_disagreement_score":0.89036864,"about_ca_system_score_codex":0.0001424497,"about_ca_system_score_gemma":0.00032195327,"threshold_uncertainty_score":0.9772111},"labels":[],"label_agreement":null},{"id":"W2088655298","doi":"10.1198/004017004000000176","title":"The Design of Blocked Fractional Factorial Split-Plot Experiments","year":2004,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":29,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractional factorial design; Split plot; Blocking (statistics); Ranking (information retrieval); Plackett–Burman design; Mathematics; Factorial experiment; Restricted randomization; Design of experiments; Statistics; Plot (graphics); Computer science; Mathematical optimization; Algorithm; Randomization; Artificial intelligence; Response surface methodology","score_opus":0.30237494260827,"score_gpt":0.4710541812441454,"score_spread":0.16867923863587536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088655298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05311438,0.0011829564,0.94080865,0.00016861035,0.0017654314,0.0005832852,0.000015934938,0.00014667826,0.0022140497],"genre_scores_gemma":[0.7703283,0.000036058267,0.22914657,0.000029070065,0.00010565899,0.00003660619,9.4258303e-7,0.000019927016,0.00029685994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99569976,0.0001776318,0.000820713,0.0004383604,0.0025345087,0.00032900457],"domain_scores_gemma":[0.99450374,0.0037672361,0.0004207361,0.00085645384,0.00034031272,0.00011152218],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0041989386,0.00019082184,0.00031130566,0.001027231,0.00030030374,0.00018331065,0.0014694277,0.00015758594,0.00017553856],"category_scores_gemma":[0.0103959,0.000122040175,0.000144346,0.0061247894,0.00031608116,0.0002699916,0.00025320036,0.0002280858,0.00019992514],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001039988,0.0019091774,0.0032581212,0.000007983871,0.00018975562,0.000025374078,0.0011906455,0.011166231,0.5727378,0.20997752,0.007544029,0.19095337],"study_design_scores_gemma":[0.0016749452,0.00072247774,0.00224813,0.000011170425,0.00001635186,0.000011591573,0.0010802429,0.0008136627,0.7639751,0.206405,0.022647027,0.000394291],"about_ca_topic_score_codex":0.000030190573,"about_ca_topic_score_gemma":2.8114525e-7,"teacher_disagreement_score":0.7172139,"about_ca_system_score_codex":0.00024385858,"about_ca_system_score_gemma":0.00014793957,"threshold_uncertainty_score":0.99793994},"labels":[],"label_agreement":null},{"id":"W2088703188","doi":"10.2307/3316068","title":"Optimal design for the proportional odds model","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Cancer Institute; National Research Foundation","keywords":"Quantile; Optimal design; Odds; Ordinal data; Mathematics; Construct (python library); Statistics; Computer science; Mathematical optimization; Econometrics; Logistic regression","score_opus":0.2962670257650652,"score_gpt":0.4255418455873327,"score_spread":0.12927481982226752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088703188","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":[0.000488603,0.0004252931,0.99715257,0.00030173964,0.0005532592,0.0002310277,0.00023217792,0.0000016233853,0.00061370817],"genre_scores_gemma":[0.12495629,0.0000057604,0.87356037,0.00023708049,0.000062014034,0.0000065660915,9.0407246e-7,0.000014295435,0.0011567099],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783576,0.00026235185,0.00073257886,0.00014661266,0.0007128194,0.0003098845],"domain_scores_gemma":[0.9948976,0.0028843412,0.00041734165,0.00021441079,0.0010508978,0.00053541095],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0057540117,0.000114999275,0.00021902497,0.00021083129,0.00032456144,0.0002598703,0.000586232,0.000051859595,0.00048293913],"category_scores_gemma":[0.0099849645,0.000071847906,0.000094296796,0.0002468413,0.00023638688,0.00017798046,0.000006688986,0.00017119646,0.000017256458],"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":[0.000070516464,0.00002131801,0.00019525195,0.0000027735239,0.000050904728,0.00008156227,0.00065523596,0.5385623,0.00041575506,0.27858225,0.1691004,0.0122617055],"study_design_scores_gemma":[0.0008189896,0.0006101673,0.0004524344,0.0000147257,0.00007702289,0.0004283428,0.0013585644,0.68292123,0.0024676851,0.2673459,0.043220833,0.00028413272],"about_ca_topic_score_codex":0.00006489353,"about_ca_topic_score_gemma":0.00019595228,"teacher_disagreement_score":0.14435887,"about_ca_system_score_codex":0.00014616526,"about_ca_system_score_gemma":0.0038287987,"threshold_uncertainty_score":0.9983544},"labels":[],"label_agreement":null},{"id":"W2088944695","doi":"10.1002/cjs.10037","title":"Bayesian optimal design for changepoint problems","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Jewish General Hospital; McGill University Health Centre; McGill University","funders":"","keywords":"Optimal design; Bayesian probability; Measure (data warehouse); Bayesian experimental design; Computer science; Mathematical optimization; Statistics; Function (biology); Prior probability; Mathematics; Bayesian inference; Algorithm; Bayesian statistics; Data mining","score_opus":0.21331821375004886,"score_gpt":0.4045466788676051,"score_spread":0.19122846511755623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088944695","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":[0.00034078764,0.00042466592,0.9966205,0.00090119464,0.0005765063,0.00031975738,0.00020265575,0.000004178651,0.00060978945],"genre_scores_gemma":[0.13654786,0.0000067802052,0.8622815,0.00049062644,0.00018221761,0.0000034237266,0.0000022799738,0.000015327241,0.00046996828],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975208,0.0002531427,0.00092518475,0.00021101658,0.0006309277,0.00045894604],"domain_scores_gemma":[0.9960555,0.0012144775,0.0005393921,0.00024330846,0.0009097856,0.0010375674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00409407,0.00016241673,0.00038833218,0.00054754503,0.00019392568,0.00035582713,0.00068937795,0.000078596524,0.00038702547],"category_scores_gemma":[0.004365937,0.00013169418,0.000109041284,0.00037139663,0.00011534776,0.00026412975,0.0000075108114,0.00017754197,0.00002367973],"study_design_candidate":"theoretical_or_conceptual","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":[0.00030820578,0.00011012839,0.00035766416,0.000017756556,0.000089507645,0.0009127982,0.005532208,0.03787367,0.006303652,0.050084654,0.4176392,0.48077056],"study_design_scores_gemma":[0.0031510245,0.010959344,0.004216058,0.00019167532,0.00014571323,0.0013114545,0.0026574999,0.1296913,0.010718168,0.71182555,0.12395557,0.0011766179],"about_ca_topic_score_codex":0.00014866005,"about_ca_topic_score_gemma":0.00042884503,"teacher_disagreement_score":0.6617409,"about_ca_system_score_codex":0.00020213232,"about_ca_system_score_gemma":0.0013153821,"threshold_uncertainty_score":0.53703326},"labels":[],"label_agreement":null},{"id":"W2091280909","doi":"10.1080/02664760903301127","title":"Bayesian and likelihood inference for cure rates based on defective inverse Gaussian regression models","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":44,"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 Guelph","funders":"","keywords":"Gibbs sampling; Bayesian probability; Statistics; Bayesian inference; Econometrics; Bayesian linear regression; Mathematics; Inference; Regression; Biostatistics; Mixture model; Computer science; Artificial intelligence; Medicine","score_opus":0.06860073087418259,"score_gpt":0.4269199345563927,"score_spread":0.35831920368221015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091280909","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":[0.010460563,0.000015141839,0.9857051,0.00014254397,0.0003742512,0.00029369805,0.00012981569,0.000007841176,0.0028710924],"genre_scores_gemma":[0.47852457,0.000006428171,0.5211922,0.00017525052,0.00006205484,0.000006506622,0.0000018852031,0.000012785773,0.000018311157],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976749,0.00012976365,0.0007198938,0.0002997674,0.0009330348,0.00024267423],"domain_scores_gemma":[0.99351025,0.0045978157,0.0007968126,0.0002859896,0.000505593,0.00030355257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026535036,0.00020949122,0.00043318758,0.0003152258,0.00017072797,0.00022652205,0.00037713183,0.0001391181,0.00009024874],"category_scores_gemma":[0.0020510408,0.00013802593,0.000076445605,0.00025395476,0.00016641726,0.00022658156,0.00005153085,0.0005008054,0.000007247213],"study_design_candidate":"theoretical_or_conceptual","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":[0.0078109587,0.0010266464,0.0017674862,0.00008925852,0.00012093179,0.00011927248,0.0050735218,0.02633001,0.22524528,0.3401343,0.04442031,0.34786204],"study_design_scores_gemma":[0.001337075,0.00073170057,0.00047493205,0.000041706066,0.00003258339,0.000010202738,0.00066578924,0.43014455,0.01475296,0.5510386,0.00057792914,0.00019193406],"about_ca_topic_score_codex":0.0000024042715,"about_ca_topic_score_gemma":0.000012908858,"teacher_disagreement_score":0.468064,"about_ca_system_score_codex":0.00003816578,"about_ca_system_score_gemma":0.0002272291,"threshold_uncertainty_score":0.56285346},"labels":[],"label_agreement":null},{"id":"W2091819253","doi":"10.1002/cjs.11226","title":"A semiparametric inverse‐Gaussian model and inference for survival data with a cured proportion","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Inverse Gaussian distribution; Mathematics; Statistics; Applied mathematics; Estimator; Inference; Mixture model; Semiparametric regression; Gaussian process; Econometrics; Gaussian; Distribution (mathematics); Computer science; Artificial intelligence; Physics","score_opus":0.23510729598083807,"score_gpt":0.42598170705924726,"score_spread":0.1908744110784092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091819253","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":[0.011529066,0.000065039865,0.98681426,0.00016911379,0.0001822914,0.00016151762,0.00065228215,0.000002390899,0.00042402826],"genre_scores_gemma":[0.44525775,0.000004808727,0.554504,0.00006965835,0.000034019984,0.0000013356324,0.000010524516,0.000009098296,0.00010882682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981647,0.00017410328,0.0005585787,0.0002507152,0.00060401147,0.00024786108],"domain_scores_gemma":[0.9963509,0.0013647813,0.000507025,0.000404007,0.00065752974,0.0007157528],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003545156,0.00011895,0.0003085103,0.0004779584,0.00013301967,0.00029427616,0.00064364437,0.000053336702,0.000030689],"category_scores_gemma":[0.012031563,0.00008492464,0.00001899635,0.0004601142,0.00021848323,0.00042671183,0.000035654954,0.00015100457,0.0000030448696],"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":[0.00091445254,0.00015126196,0.085741065,0.0001436441,0.00026648186,0.00038107432,0.005080806,0.039820235,0.0017044066,0.21582526,0.10396596,0.54600537],"study_design_scores_gemma":[0.0009062952,0.0007830155,0.0039677266,0.000042437794,0.000059447415,0.0001120123,0.00055880944,0.92367727,0.000071005445,0.065356605,0.004242077,0.0002232776],"about_ca_topic_score_codex":0.0006282701,"about_ca_topic_score_gemma":0.010064948,"teacher_disagreement_score":0.8838571,"about_ca_system_score_codex":0.00007317578,"about_ca_system_score_gemma":0.0015223164,"threshold_uncertainty_score":0.9962905},"labels":[],"label_agreement":null},{"id":"W2093310116","doi":"10.1111/j.1744-7976.2001.tb00308.x","title":"Bayesian Inference and Posterior Simulators","year":2001,"lang":"fr","type":"article","venue":"Canadian Journal of Agricultural Economics/Revue canadienne d agroeconomie","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Bayesian inference; Presentation (obstetrics); Computer science; Inference; Operations research; Library science; Artificial intelligence; Engineering","score_opus":0.07104408598911092,"score_gpt":0.2828379342917801,"score_spread":0.21179384830266917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093310116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96480066,0.008575162,0.00016929576,0.014860981,0.007647547,0.0003811376,0.0002712,0.0000081817725,0.0032858653],"genre_scores_gemma":[0.9824823,0.0007183082,0.0040764315,0.0010021091,0.0013684265,0.000005513597,0.000010607555,0.00005005669,0.01028626],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944344,0.0003763058,0.0025301606,0.0008874008,0.00005871327,0.0017129772],"domain_scores_gemma":[0.9898743,0.0011963978,0.001618095,0.00054588396,0.00060713425,0.006158192],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002112811,0.00073147507,0.0014818341,0.00093095837,0.00050822593,0.0013890053,0.0015064977,0.0004800243,0.0034702863],"category_scores_gemma":[0.0012489627,0.00065137295,0.00051763526,0.0005200663,0.0007972748,0.00275396,0.00011427014,0.0007311915,0.00023470081],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071399345,0.00020673456,0.15929933,0.0002199019,0.001670031,0.004559297,0.023511935,0.06644396,0.001838743,0.092453256,0.016399356,0.63268346],"study_design_scores_gemma":[0.0048584426,0.0050779176,0.308934,0.0011557217,0.00060343277,0.036352612,0.029937925,0.010156712,0.00084856385,0.048347685,0.5490286,0.004698422],"about_ca_topic_score_codex":0.072546236,"about_ca_topic_score_gemma":0.7154187,"teacher_disagreement_score":0.64287245,"about_ca_system_score_codex":0.0033984615,"about_ca_system_score_gemma":0.002462298,"threshold_uncertainty_score":0.9996477},"labels":[],"label_agreement":null},{"id":"W2093651162","doi":"10.1080/03610920008832539","title":"A multiple comparisons procedure for detecting differences between treatments and a control in two-factor experiments","year":2000,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Statistics; Confidence interval; Statistic; Test (biology); Mathematics; Control (management); Test statistic; Multiple comparisons problem; Factor (programming language); Computer science; Statistical hypothesis testing; Artificial intelligence","score_opus":0.22477659479494264,"score_gpt":0.5454785967800768,"score_spread":0.3207020019851342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093651162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3777692,0.0016909728,0.6193166,0.000036167407,0.000028879953,0.000742187,0.00014832306,0.000015916128,0.00025178987],"genre_scores_gemma":[0.5392069,0.000055297016,0.46036664,0.000038985334,0.000006469567,0.00020333329,0.000005312232,0.000008843279,0.00010816576],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99131083,0.006986878,0.00078655063,0.00043822342,0.00021722086,0.00026026764],"domain_scores_gemma":[0.96794945,0.031125078,0.00022315231,0.0005223642,0.00006895882,0.00011096832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077260365,0.00020815353,0.0005671656,0.00020518474,0.00026072047,0.00016051947,0.0004620987,0.00007383447,0.0001140554],"category_scores_gemma":[0.0053024087,0.00017073043,0.00003377541,0.0002792553,0.00030542194,0.00021317741,0.000104802195,0.00019569563,0.0000027568533],"study_design_candidate":"observational","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":[0.0007868507,0.00018122829,0.24177572,0.000015310274,0.00004173998,7.7957435e-7,0.0078049605,0.000026175114,0.0059148725,0.006775728,0.000018300601,0.73665833],"study_design_scores_gemma":[0.009467198,0.00042193825,0.58429223,0.00016788565,0.00006765735,0.000006458292,0.0077061052,0.052080937,0.00907866,0.33555272,0.0005985328,0.0005596764],"about_ca_topic_score_codex":0.00008743672,"about_ca_topic_score_gemma":0.00005822104,"teacher_disagreement_score":0.73609865,"about_ca_system_score_codex":0.000055225144,"about_ca_system_score_gemma":0.00003212558,"threshold_uncertainty_score":0.6962185},"labels":[],"label_agreement":null},{"id":"W2093931098","doi":"10.1007/s00184-011-0373-5","title":"An information theoretical algorithm for analyzing supersaturated designs for a binary response","year":2011,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Bernoulli's principle; Entropy (arrow of time); Binary number; Measure (data warehouse); Algorithm; Binary data; Information theory; Statistics; Type I and type II errors; Applied mathematics; Mathematical optimization; Computer science; Data mining","score_opus":0.24055488432725156,"score_gpt":0.4568054255120258,"score_spread":0.21625054118477424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093931098","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":[0.06704495,0.00007822056,0.93105054,0.00006453394,0.00029327738,0.0009014246,0.000091103335,0.00007678652,0.0003991612],"genre_scores_gemma":[0.3325186,0.0000013799837,0.6670806,0.00014835242,0.00003351416,0.000113706395,0.000018193816,0.000013802236,0.00007183221],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996929,0.0010216151,0.0006958692,0.00037635127,0.00059820985,0.00037896205],"domain_scores_gemma":[0.99327743,0.0051979367,0.0001740266,0.0005454048,0.0005962408,0.00020896336],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014118853,0.00018369507,0.00032542343,0.0009940831,0.00022829058,0.00028172726,0.00070629444,0.0001510296,0.00043958038],"category_scores_gemma":[0.010960015,0.00013640254,0.00019975015,0.0016271903,0.00020241048,0.001604183,0.0000568422,0.0000957525,0.0000756941],"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":[0.011459767,0.00034665846,0.00029946715,0.000008650315,0.00007312114,0.0000062245103,0.007957794,0.00004132967,0.14022447,0.031050107,0.0022911453,0.8062413],"study_design_scores_gemma":[0.0027539702,0.0061726444,0.003364693,0.000016022903,0.000079986276,0.000016531205,0.0064657433,0.4998658,0.41903555,0.054366417,0.007204305,0.0006583404],"about_ca_topic_score_codex":0.000009574099,"about_ca_topic_score_gemma":2.7172422e-7,"teacher_disagreement_score":0.80558294,"about_ca_system_score_codex":0.00008073886,"about_ca_system_score_gemma":0.0001013713,"threshold_uncertainty_score":0.9973711},"labels":[],"label_agreement":null},{"id":"W2093956415","doi":"10.1080/08982110601093679","title":"Imputation of Censored Response Data in a Bivariate Designed Experiment","year":2006,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"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":"Pacific Institute for the Mathematical Sciences; National Science Foundation","keywords":"Bivariate analysis; Imputation (statistics); Bivariate data; Statistics; Multivariate statistics; Econometrics; Multivariate normal distribution; Computer science; Missing data; Data mining; Mathematics","score_opus":0.20312244162418633,"score_gpt":0.4854396034963143,"score_spread":0.28231716187212796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093956415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65787125,0.00016267246,0.3414022,0.00006233584,0.0001302825,0.00016666473,0.000022757436,0.000040948824,0.0001409228],"genre_scores_gemma":[0.7375166,6.873069e-7,0.2623602,0.000008765025,0.000021541466,0.000009823047,0.000010299255,0.000012458027,0.000059669837],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99614125,0.0010507796,0.0011606573,0.00050052296,0.0008955085,0.0002512697],"domain_scores_gemma":[0.9958129,0.0027667675,0.00021279698,0.0010710247,0.00008065447,0.00005588724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0130902,0.00014939136,0.0003583413,0.00039114078,0.000023447754,0.000073849675,0.0007686605,0.00007223862,0.00006016047],"category_scores_gemma":[0.0046087326,0.00013840587,0.00005324897,0.00085982814,0.000034317207,0.00040904916,0.00023022985,0.000086266504,0.00001979226],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004006543,0.00008914447,0.0005352629,0.0000076638835,0.000005135543,0.000008190025,0.0005970316,0.044779226,0.94971293,0.0026002731,0.000055728073,0.0012087326],"study_design_scores_gemma":[0.0013792658,0.00014748625,0.1409229,0.000048588503,0.00000627621,0.0000056743834,0.0010641428,0.2574253,0.59513277,0.0029198867,0.00051553466,0.0004321256],"about_ca_topic_score_codex":0.0005328704,"about_ca_topic_score_gemma":0.0000063891725,"teacher_disagreement_score":0.35458016,"about_ca_system_score_codex":0.00010452414,"about_ca_system_score_gemma":0.000049536582,"threshold_uncertainty_score":0.56440276},"labels":[],"label_agreement":null},{"id":"W2095502480","doi":"10.1111/j.0006-341x.2000.00824.x","title":"Fractional Simplex Designs for Interaction Screening in Complex Mixtures","year":2000,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Saskatchewan; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Centroid; Simplex; Simplex algorithm; Computer science; Component (thermodynamics); Factor (programming language); Design of experiments; Mathematical optimization; Mathematics; Statistics; Combinatorics; Linear programming; Artificial intelligence","score_opus":0.5679953532525431,"score_gpt":0.54489968650042,"score_spread":0.023095666752123156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095502480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09362973,0.00029386109,0.8972394,0.00035629736,0.00044507574,0.0005862556,0.00007322458,0.000068185196,0.0073079895],"genre_scores_gemma":[0.59455943,0.000011458469,0.40382656,0.00033966984,0.00012767295,0.000033507076,0.00002854159,0.000016076627,0.0010570629],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99723715,0.00029210217,0.00064990827,0.0004922677,0.0010124244,0.00031613105],"domain_scores_gemma":[0.9941377,0.0051063662,0.00016260313,0.00029464706,0.0001837201,0.000114936985],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030212656,0.00015650652,0.00027245763,0.002703595,0.00014809109,0.00028618163,0.00048401344,0.00011626334,0.0049267095],"category_scores_gemma":[0.003934832,0.00013487991,0.00014417434,0.0069255144,0.00006718122,0.0005242438,0.000047061872,0.00013349777,0.00023377308],"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":[0.00038066876,0.00019545792,0.002880599,0.0000031587326,0.000013924229,0.0000049036144,0.00013236694,0.0018091759,0.033228636,0.00037345727,0.021697145,0.9392805],"study_design_scores_gemma":[0.0016490043,0.0004369767,0.077005535,0.000013595055,0.000011278171,0.000027475773,0.0006780348,0.11602998,0.018335609,0.009881669,0.77542603,0.0005048159],"about_ca_topic_score_codex":0.00007723742,"about_ca_topic_score_gemma":0.000004605977,"teacher_disagreement_score":0.9387757,"about_ca_system_score_codex":0.000100640005,"about_ca_system_score_gemma":0.000033210163,"threshold_uncertainty_score":0.99598294},"labels":[],"label_agreement":null},{"id":"W2095999217","doi":"","title":"Variational bounds for mixed-data factor analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Binary data; Categorical variable; Exponential family; Algorithm; Quadratic equation; Simple (philosophy); Binary number; Missing data; Upper and lower bounds; Exponential function; Mathematics; Applied mathematics; Factor (programming language); Computer science; Mathematical optimization; Statistics","score_opus":0.30993787660183986,"score_gpt":0.5271086331942241,"score_spread":0.21717075659238422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095999217","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":[0.03246647,0.000012267067,0.955208,0.0004972268,0.0009755783,0.000184112,0.00038310688,0.0000436965,0.010229567],"genre_scores_gemma":[0.3828995,2.492294e-7,0.61093426,0.00017101032,0.00011310931,0.0000133200765,0.00006128782,0.000006314308,0.005800977],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975029,0.00013096097,0.00046755694,0.0006629793,0.0010298633,0.00020569398],"domain_scores_gemma":[0.9945728,0.003382404,0.0001394986,0.0014949632,0.0002764905,0.00013385034],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0038691433,0.000113293965,0.00027045017,0.0003541739,0.0001545656,0.00049803243,0.0017398982,0.00009237334,0.011158431],"category_scores_gemma":[0.005429323,0.00007795621,0.00018627004,0.0012450636,0.00007574261,0.0006247672,0.0003225453,0.00010422281,0.00028437134],"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":[0.00020053734,0.00057746394,0.07185369,0.0000037895263,0.0012554108,0.0000029914704,0.00054413127,0.000323999,0.36064938,0.26799044,0.13155788,0.1650403],"study_design_scores_gemma":[0.00074503256,0.00012853288,0.15911546,6.6785054e-7,0.00024452296,0.0000039283195,0.00035953536,0.52513236,0.024797847,0.07655941,0.2123967,0.0005159956],"about_ca_topic_score_codex":0.00004540874,"about_ca_topic_score_gemma":0.00017504129,"teacher_disagreement_score":0.52480835,"about_ca_system_score_codex":0.000013444453,"about_ca_system_score_gemma":0.0000860927,"threshold_uncertainty_score":0.9897455},"labels":[],"label_agreement":null},{"id":"W2097184733","doi":"10.3758/brm.41.3.664","title":"Randomization tests and the unequal-N/unequal-variance problem","year":2009,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Variance (accounting); Statistics; Value (mathematics); Analysis of variance; Type I and type II errors; Word error rate; Mathematics; Computer science; Econometrics; Algorithm; Speech recognition; Economics","score_opus":0.5240400499227096,"score_gpt":0.6774203494091746,"score_spread":0.15338029948646503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097184733","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":[0.04472293,0.0030830153,0.9244085,0.004957608,0.00039946364,0.005131354,0.000011757029,0.00015291444,0.017132461],"genre_scores_gemma":[0.14535812,0.00010386743,0.8496844,0.00020780778,0.00010563886,0.00046979843,0.0000023842085,0.00002678597,0.004041207],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9625892,0.03114322,0.0011257926,0.0010342356,0.0032829049,0.0008246419],"domain_scores_gemma":[0.9751085,0.021994848,0.00026080693,0.0013175973,0.0009873183,0.0003309188],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.13020396,0.00026840589,0.000701457,0.000547941,0.0007807363,0.0010812175,0.0014340441,0.00018160902,0.0003485864],"category_scores_gemma":[0.0295601,0.00015472759,0.00017883966,0.0028094912,0.001104982,0.0006152187,0.00040340933,0.0006181231,0.0001083054],"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":[0.0014267263,0.00020918448,0.0004520642,0.000003453317,0.00000771756,0.000022739427,0.0015587636,0.000093309754,0.11126951,0.03581606,0.0008696936,0.8482708],"study_design_scores_gemma":[0.042202737,0.0024294558,0.05318082,0.0001642974,0.00014610137,0.00012495957,0.005244955,0.04489345,0.16514705,0.6534899,0.031327635,0.0016486772],"about_ca_topic_score_codex":0.00009767659,"about_ca_topic_score_gemma":0.0000046097744,"teacher_disagreement_score":0.8466221,"about_ca_system_score_codex":0.00010251862,"about_ca_system_score_gemma":0.0001754634,"threshold_uncertainty_score":0.9999558},"labels":[],"label_agreement":null},{"id":"W2099320460","doi":"10.1002/sim.6523","title":"Adaptive sampling in two‐phase designs: a biomarker study for progression in arthritis","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Waterloo; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Estimator; Computer science; Sampling (signal processing); Exploit; Adaptive sampling; Optimal design; Sample size determination; Phase (matter); Biomarker; Resource allocation; Statistics; Data mining; Machine learning; Mathematics; Monte Carlo method","score_opus":0.5332255921247048,"score_gpt":0.6223730082861888,"score_spread":0.08914741616148403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099320460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10435087,0.001606732,0.8900298,0.00014574482,0.0005935245,0.0025681832,0.00008724842,0.00001852951,0.00059936923],"genre_scores_gemma":[0.54259527,0.000013570113,0.45702443,0.000056036955,0.000041821706,0.00021196465,0.000009784405,0.000015157138,0.000031960946],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99500746,0.0011788368,0.0013589697,0.00065969955,0.001375219,0.00041979586],"domain_scores_gemma":[0.9933094,0.005585012,0.00024665028,0.0003700105,0.00029061647,0.00019830574],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016815467,0.00021898646,0.0006904062,0.000936709,0.00004038219,0.000045620338,0.00041571507,0.000057154262,0.00009064612],"category_scores_gemma":[0.02275156,0.00016454009,0.000020632138,0.0014458012,0.00025596944,0.00017887207,0.00011397751,0.00023512641,0.000013437149],"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":[0.003311131,0.0021822636,0.052194852,0.00001034054,0.000014028209,0.0007217414,0.019721461,0.0004948592,0.0029379453,0.0040778834,0.0036327867,0.9107007],"study_design_scores_gemma":[0.08519574,0.015920449,0.049154263,0.001959624,0.000028867158,0.00003102818,0.11763887,0.25054425,0.00072109804,0.47708663,0.00081121945,0.00090797787],"about_ca_topic_score_codex":0.00042048443,"about_ca_topic_score_gemma":0.0013707507,"teacher_disagreement_score":0.9097927,"about_ca_system_score_codex":0.00024398451,"about_ca_system_score_gemma":0.00016733735,"threshold_uncertainty_score":0.9854802},"labels":[],"label_agreement":null},{"id":"W2101423867","doi":"10.1016/j.spl.2014.07.032","title":"Space-filling Latin hypercube designs based on randomization restrictions in factorial experiments","year":2014,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Latin hypercube sampling; Fractional factorial design; Randomization; Factorial; Factorial experiment; Space (punctuation); Statistics; Combinatorics; Econometrics; Monte Carlo method; Computer science; Mathematical analysis; Randomized controlled trial","score_opus":0.16819135320109907,"score_gpt":0.4025455546473535,"score_spread":0.2343542014462544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101423867","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":[0.10250259,0.0000058273777,0.8942771,0.000548644,0.0010592815,0.00084878074,0.00006567523,0.000056999314,0.00063508784],"genre_scores_gemma":[0.38812527,8.415645e-7,0.6111013,0.00051064574,0.00014105064,0.000051943658,0.000020960179,0.000021249016,0.000026744268],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935264,0.0027173252,0.001021133,0.0008623316,0.0014440201,0.00042880158],"domain_scores_gemma":[0.99090856,0.0076882937,0.00027751987,0.00078919623,0.00017380853,0.00016263207],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0050279354,0.0002882843,0.00049271237,0.00040111266,0.00021185279,0.00027356768,0.00045591086,0.00011407933,0.00019800378],"category_scores_gemma":[0.0150236115,0.00025133722,0.00009966964,0.00082775654,0.00020502845,0.00022687613,0.000060757728,0.00028807952,0.00010124402],"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":[0.007222747,0.0022788616,0.08683908,0.00006814973,0.000060926443,0.000039638926,0.0063776975,0.21641313,0.4577365,0.18015154,0.01948351,0.023328237],"study_design_scores_gemma":[0.014160668,0.0009209149,0.02605143,0.000091242706,0.000044416807,0.0000017954436,0.00012749352,0.47166124,0.032234978,0.4512912,0.002187924,0.0012267025],"about_ca_topic_score_codex":0.00019321227,"about_ca_topic_score_gemma":0.00003561577,"teacher_disagreement_score":0.42550153,"about_ca_system_score_codex":0.00038304646,"about_ca_system_score_gemma":0.00008939449,"threshold_uncertainty_score":0.99999386},"labels":[],"label_agreement":null},{"id":"W2102267569","doi":"10.1111/j.1467-9469.2005.00417.x","title":"Marginal Regression for Binary Longitudinal Data in Adaptive Clinical Trials","year":2005,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Statistics; Longitudinal data; Regression; Binary data; Marginal model; Binary number; Regression analysis; Econometrics; Data mining; Computer science; Arithmetic","score_opus":0.7341740624007648,"score_gpt":0.640240670026331,"score_spread":0.09393339237443377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102267569","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":[0.03043833,0.001629288,0.9625901,0.0008760996,0.0019197129,0.0005418389,0.0015823432,0.000007360015,0.00041495002],"genre_scores_gemma":[0.27103972,0.00012922994,0.72756195,0.00006930674,0.000766431,0.0000031480736,0.000017655448,0.000018384277,0.00039415015],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9900595,0.0033146318,0.004155597,0.00053961924,0.0015463259,0.00038431183],"domain_scores_gemma":[0.9785849,0.016979162,0.0026520095,0.0007423148,0.0006782586,0.00036336906],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.059929106,0.00023096353,0.0013471752,0.00052973634,0.000116978954,0.00019401523,0.0017318807,0.00014074636,0.0004656554],"category_scores_gemma":[0.034617316,0.00015266324,0.00023237149,0.0005028825,0.0002828306,0.0008690299,0.00027956945,0.0005203272,0.000038535814],"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":[0.008806418,0.00069435395,0.06422234,0.000013538677,0.0001117359,0.0005068442,0.00040485142,0.0003472785,0.0007438287,0.0047349543,0.14404222,0.7753716],"study_design_scores_gemma":[0.030800838,0.020152941,0.48369628,0.00231592,0.0007211374,0.001605642,0.0067008217,0.2132192,0.0019365026,0.15897113,0.077927314,0.0019522738],"about_ca_topic_score_codex":0.0000061432615,"about_ca_topic_score_gemma":0.000013630544,"teacher_disagreement_score":0.7734194,"about_ca_system_score_codex":0.00015852542,"about_ca_system_score_gemma":0.00033146917,"threshold_uncertainty_score":0.9735145},"labels":[],"label_agreement":null},{"id":"W2103104637","doi":"10.1081/sta-120029833","title":"Bayesian Minimally Supported <i>D</i> -Optimal Designs for an Exponential Regression Model","year":2004,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Heteroscedasticity; Applied mathematics; Prior probability; Optimal design; Exponential function; Bayesian probability; Nonlinear regression; Polynomial regression; Polynomial; Bayesian information criterion; Variance (accounting); Regression analysis; Bayesian linear regression; Variance function; Function (biology); Regression; Mathematical optimization; Statistics; Bayesian inference; Mathematical analysis","score_opus":0.23821428598294192,"score_gpt":0.5421395109583911,"score_spread":0.30392522497544916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103104637","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":[0.010051273,0.0005754876,0.9874326,0.00010430602,0.00012604536,0.0006401917,0.00012528333,0.000049188588,0.0008956085],"genre_scores_gemma":[0.21585675,0.00008274011,0.78333724,0.00018356123,0.000016741267,0.00015187456,0.000055832417,0.00003060639,0.00028467618],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98936707,0.008098779,0.0010733984,0.00064216234,0.00044964155,0.00036893334],"domain_scores_gemma":[0.98837256,0.009212276,0.00040972294,0.001455675,0.00030497066,0.00024478103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027602063,0.0002729368,0.0004953308,0.00030947317,0.00042255974,0.00024176124,0.0011818166,0.00017857221,0.00009378769],"category_scores_gemma":[0.0071091005,0.00023151853,0.00007745474,0.00038064487,0.00053375505,0.000610776,0.00031122295,0.00027488018,0.000005410999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018954375,0.0003783934,0.000071961316,0.000017185004,0.000018335115,0.0000029928672,0.006903846,0.009595578,0.14801992,0.6181943,0.00028779433,0.21461426],"study_design_scores_gemma":[0.0013948325,0.00032336547,0.00023945398,0.000047083624,0.000027868238,0.000010094803,0.0018052491,0.14266543,0.032624163,0.8202443,0.00033473238,0.00028344814],"about_ca_topic_score_codex":0.000018338977,"about_ca_topic_score_gemma":0.00001558793,"teacher_disagreement_score":0.2143308,"about_ca_system_score_codex":0.00008613781,"about_ca_system_score_gemma":0.0001935024,"threshold_uncertainty_score":0.956638},"labels":[],"label_agreement":null},{"id":"W2104082640","doi":"10.1002/pst.483","title":"Additional results for ‘Sequential design approaches for bioequivalence studies with crossover designs’","year":2011,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Theratechnologies (Canada)","funders":"","keywords":"Bioequivalence; Crossover; Sample size determination; Statistics; Crossover study; Type I and type II errors; Econometrics; Mathematics; Computer science; Medicine; Pharmacology; Artificial intelligence; Pharmacokinetics","score_opus":0.8859019112477156,"score_gpt":0.5762477195149607,"score_spread":0.3096541917327549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104082640","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":[0.000041124415,0.00022889684,0.947814,0.00008327632,0.00030205172,0.0014982423,0.049121056,0.00005460373,0.0008567678],"genre_scores_gemma":[0.011617737,0.000018825462,0.9854012,0.0003040605,0.00018025986,0.0010994328,0.00034650706,0.000040383304,0.0009915975],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9960055,0.00048028035,0.00087025214,0.00090151094,0.001092489,0.0006500178],"domain_scores_gemma":[0.9745771,0.023612859,0.0003139712,0.00035075255,0.0008409287,0.00030438832],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0035724074,0.0003287175,0.00047184608,0.00012075977,0.00039450158,0.00019030871,0.00065211975,0.00009237118,0.0029652745],"category_scores_gemma":[0.013711357,0.00023119374,0.00011594361,0.00033847045,0.0010551861,0.0002676624,0.00012992612,0.00015446232,0.000087145476],"study_design_candidate":"not_applicable","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":[0.026129985,0.00076285127,0.0000266126,0.00010571208,0.00046588888,0.000040898565,0.0018691474,0.0007008105,0.0017470265,0.115948185,0.76507163,0.087131254],"study_design_scores_gemma":[0.0102002425,0.0043306295,0.00023010538,0.00009376231,0.0004897055,0.0000561542,0.0013001869,0.22872546,0.116059996,0.48999488,0.14705797,0.0014608923],"about_ca_topic_score_codex":0.0000021880103,"about_ca_topic_score_gemma":0.0000017839324,"teacher_disagreement_score":0.6180137,"about_ca_system_score_codex":0.00010590116,"about_ca_system_score_gemma":0.00017714516,"threshold_uncertainty_score":0.99794614},"labels":[],"label_agreement":null},{"id":"W2104919849","doi":"10.1007/s10463-006-0033-0","title":"Classification of Three-word Indicator Functions of Two-level Factorial Designs","year":2006,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island; McMaster University","funders":"","keywords":"Fractional factorial design; Mathematics; Factorial experiment; Factorial; Function (biology); Statistics; Word (group theory); Mathematical analysis; Geometry","score_opus":0.44937214145692117,"score_gpt":0.4780368278204825,"score_spread":0.028664686363561342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104919849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14912385,0.000038107253,0.8459705,0.00013146245,0.0005445405,0.0003307547,0.000716012,0.000008182311,0.0031365985],"genre_scores_gemma":[0.6323515,0.000002016016,0.36750123,0.00000787628,0.000036963673,0.00000571695,0.0000050724507,0.000008579758,0.000080995225],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9959872,0.00019287782,0.0018329088,0.00023793434,0.0015668358,0.00018228972],"domain_scores_gemma":[0.9942888,0.002670191,0.0015532102,0.0007848296,0.0006326008,0.000070343485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002150216,0.00016815358,0.00065754354,0.00019520795,0.00007061287,0.0000207593,0.00090204837,0.00009968897,0.00017645578],"category_scores_gemma":[0.004875929,0.00010948857,0.00020290699,0.00063378527,0.0012194656,0.00021089533,0.00015999451,0.00011961719,0.000016913156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000098135664,0.0009350921,0.0014622081,0.00012442985,0.00006523931,6.493009e-7,0.00024263213,0.0006199382,0.14261404,0.84660053,0.0028147637,0.0044223503],"study_design_scores_gemma":[0.0004450086,0.00021855459,0.0286284,0.0001220967,0.000075661555,0.0000028387556,0.00020586421,0.0036262113,0.17724495,0.78903204,0.000248584,0.00014980507],"about_ca_topic_score_codex":0.00016981018,"about_ca_topic_score_gemma":0.000034292934,"teacher_disagreement_score":0.4832277,"about_ca_system_score_codex":0.000016265114,"about_ca_system_score_gemma":0.00018599494,"threshold_uncertainty_score":0.5837295},"labels":[],"label_agreement":null},{"id":"W2104944547","doi":"10.1111/j.1751-5823.2007.00015_11.x","title":"Modern Experimental Design by Thomas P. Ryan","year":2007,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Library science; Citation; Statistics; Computer science; Mathematics; History","score_opus":0.23279022890008683,"score_gpt":0.547922591138043,"score_spread":0.31513236223795615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104944547","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":[0.00006483364,0.036627,0.92731375,0.0006276426,0.0005415611,0.0003847887,0.00010053825,0.000041527863,0.034298338],"genre_scores_gemma":[0.1126439,0.0024766016,0.87188566,0.0062161726,0.00019361346,0.00007772281,0.000070042835,0.00004944055,0.0063868747],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949888,0.0004972162,0.0011207568,0.0006176763,0.002423179,0.00035237792],"domain_scores_gemma":[0.9942924,0.0045502475,0.00022520493,0.00036462166,0.00025880898,0.00030875998],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.006593883,0.00022221352,0.00041980454,0.00009837745,0.00008496703,0.0001846596,0.0010875243,0.00006169553,0.0075868363],"category_scores_gemma":[0.0068682553,0.00016803385,0.00012572379,0.0002924963,0.00017671904,0.00028643676,0.00019006831,0.00017283135,0.0013358854],"study_design_candidate":"not_applicable","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":[0.00015197495,0.0004170617,0.0001194768,0.000024520788,0.000049741728,0.000112538684,0.00008769169,0.00001030298,0.03340487,0.0865593,0.39913264,0.4799299],"study_design_scores_gemma":[0.0008024618,0.00043089796,0.00065134774,0.00054299994,0.00003711666,0.00013029262,0.00011936033,0.009771049,0.053929966,0.09246085,0.84037095,0.0007527152],"about_ca_topic_score_codex":0.00001618016,"about_ca_topic_score_gemma":7.8616364e-7,"teacher_disagreement_score":0.47917718,"about_ca_system_score_codex":0.00019210522,"about_ca_system_score_gemma":0.000044581084,"threshold_uncertainty_score":0.9994417},"labels":[],"label_agreement":null},{"id":"W2105936640","doi":"10.1016/j.jspi.2007.05.025","title":"Robust prediction and extrapolation designs for misspecified generalized linear regression models","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Victoria","keywords":"Extrapolation; Mathematics; Regression; Linear regression; Nonlinear regression; Regression analysis; Minimax; Robust regression; Linear model; Applied mathematics; Statistics; Proper linear model; Nonlinear system; Econometrics; Bayesian multivariate linear regression; Mathematical optimization","score_opus":0.47448533544856053,"score_gpt":0.5029415616939009,"score_spread":0.028456226245340388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105936640","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":[0.06913182,0.000548011,0.92965037,0.000048937472,0.00014532673,0.0000945948,0.000028347482,0.000007242284,0.00034537155],"genre_scores_gemma":[0.4774169,0.00002704574,0.5224038,0.000028865596,0.000070155904,6.929203e-7,0.0000022846482,0.000004485492,0.00004581638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814373,0.00015075249,0.000779573,0.00020055202,0.0005544239,0.00017099512],"domain_scores_gemma":[0.9945848,0.004393363,0.00037176526,0.00008411218,0.00034909978,0.0002168712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004271141,0.00011089619,0.0002800971,0.00019665883,0.00014809314,0.00013860453,0.00011418565,0.000088330875,0.000028044451],"category_scores_gemma":[0.0031548152,0.00007390929,0.00003217106,0.00012844316,0.00011162157,0.00048677146,0.000026517202,0.00017240543,6.156636e-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":[0.0117437085,0.00040286835,0.035899185,0.000099040124,0.00013295567,0.00021249424,0.0075705065,0.19704962,0.13549218,0.15708968,0.014423338,0.43988445],"study_design_scores_gemma":[0.0011884967,0.0009809583,0.021398675,0.00014954197,0.000028718805,0.00009874376,0.0005545599,0.81187636,0.0016987607,0.16149975,0.000382082,0.00014335236],"about_ca_topic_score_codex":0.000003487827,"about_ca_topic_score_gemma":2.0943638e-7,"teacher_disagreement_score":0.61482674,"about_ca_system_score_codex":0.000023056247,"about_ca_system_score_gemma":0.00003972725,"threshold_uncertainty_score":0.37768364},"labels":[],"label_agreement":null},{"id":"W2107733460","doi":"10.1080/10691898.2007.11889458","title":"A Bubble Mixture Experiment Project for Use in an Advanced Design of Experiments Class","year":2007,"lang":"en","type":"article","venue":"Journal of Statistics Education","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Camosun College; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Procter and Gamble","keywords":"Class (philosophy); Design of experiments; Computer science; Focus (optics); Soap bubble; Ideal (ethics); Mathematics education; Mathematics; Statistics; Artificial intelligence","score_opus":0.27217178740500675,"score_gpt":0.5405196164625709,"score_spread":0.2683478290575641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107733460","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":[0.18365534,0.00033057606,0.8143634,0.000019641708,0.00091186486,0.00062670384,0.000024802051,0.0000029910157,0.00006463821],"genre_scores_gemma":[0.2609982,0.000011542094,0.7386604,0.00005673278,0.00007688388,0.000023024477,0.0000048564048,0.000015336127,0.00015301225],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966396,0.000393821,0.0014950401,0.00025011544,0.0009802841,0.00024113504],"domain_scores_gemma":[0.9946267,0.0023365226,0.0012707348,0.00029479314,0.0013319644,0.00013927785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005127849,0.00015524412,0.00039466814,0.000684651,0.000050081668,0.00011966305,0.00041623492,0.00008210827,0.00003935681],"category_scores_gemma":[0.0029387572,0.00012632846,0.00006696394,0.0004929711,0.00006822192,0.00085327885,0.000025838997,0.00013396307,0.0000017203388],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031918613,0.0028720007,0.0027127457,0.000024765139,0.000031892014,0.000011279999,0.012211299,0.0020281558,0.8329338,0.0050467597,0.010749034,0.1281864],"study_design_scores_gemma":[0.0034751643,0.005852642,0.01046924,0.00021948719,0.000046487774,0.000062132895,0.04262545,0.009512677,0.8988744,0.024249341,0.004099277,0.0005137116],"about_ca_topic_score_codex":0.000023676415,"about_ca_topic_score_gemma":0.000007515749,"teacher_disagreement_score":0.12767269,"about_ca_system_score_codex":0.000285468,"about_ca_system_score_gemma":0.0007856093,"threshold_uncertainty_score":0.5151525},"labels":[],"label_agreement":null},{"id":"W2108484033","doi":"10.1287/ited.1080.0008","title":"An Interactive Spreadsheet-Based Tool to Support Teaching Design of Experiments","year":2008,"lang":"en","type":"article","venue":"INFORMS Transactions on Education","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Factor (programming language); Set (abstract data type); Replication (statistics); Software; Simple (philosophy); Product (mathematics); Programming language; Statistics; Mathematics","score_opus":0.14078575005690017,"score_gpt":0.470903571447055,"score_spread":0.3301178213901549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108484033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20293024,0.0000029007142,0.79185736,0.00007012246,0.00059986184,0.00054976624,0.000015137184,0.00005006566,0.003924515],"genre_scores_gemma":[0.72197974,6.227373e-7,0.27678415,0.00045535853,0.00002145906,0.00016057395,0.000010489639,0.0000147932205,0.0005728359],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973846,0.0002696565,0.0007816158,0.0003921033,0.0009482636,0.00022374169],"domain_scores_gemma":[0.9979435,0.00059827795,0.00023764228,0.0007343207,0.00028557592,0.00020064945],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014583573,0.00020006427,0.00026076456,0.00079446426,0.0002867166,0.00009401622,0.00055244647,0.00008629615,0.0009984225],"category_scores_gemma":[0.00033161242,0.00016614173,0.00011862548,0.0005621652,0.00008097765,0.0016901424,0.0000042091488,0.00021728223,0.0003355068],"study_design_candidate":"bench_or_experimental","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":[0.0012182923,0.0037797526,0.0003580237,0.000006194571,0.000036815432,0.000001901041,0.021293268,0.09572184,0.11686472,0.00029870673,0.0015908601,0.75882965],"study_design_scores_gemma":[0.0007713478,0.0034515688,0.0053374427,0.00005132045,0.000017543438,0.000028588387,0.0099830935,0.008939733,0.9671454,0.00050981547,0.0032856544,0.00047850615],"about_ca_topic_score_codex":0.00011554942,"about_ca_topic_score_gemma":0.0000017372281,"teacher_disagreement_score":0.85028064,"about_ca_system_score_codex":0.00023750441,"about_ca_system_score_gemma":0.0006136254,"threshold_uncertainty_score":0.9999148},"labels":[],"label_agreement":null},{"id":"W2108875588","doi":"10.1504/ijpd.2008.016369","title":"Design-for-six-sigma for multiple response systems","year":2007,"lang":"en","type":"article","venue":"International Journal of Product Development","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Six Sigma; Reliability engineering; Engineering; Sigma; Design for Six Sigma; Mathematical optimization; Control theory (sociology); Computer science; Control engineering; Control (management); Manufacturing engineering; Mathematics","score_opus":0.24449466712966555,"score_gpt":0.4820971252253927,"score_spread":0.23760245809572716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108875588","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":[0.06786534,0.000561864,0.9231128,0.000988108,0.006595608,0.0007631832,0.000008327287,0.000013368857,0.00009138968],"genre_scores_gemma":[0.42611226,0.0000030716528,0.5716941,0.00009573287,0.00055241864,0.000028191425,0.0000018098393,0.00001704144,0.0014953794],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947192,0.00037119366,0.0019131884,0.00039309775,0.00228305,0.000320276],"domain_scores_gemma":[0.9876874,0.006871957,0.0010890418,0.00024156952,0.0039395383,0.00017050652],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04113451,0.00018773602,0.00036388525,0.0007770073,0.00012494838,0.00031603486,0.0014367624,0.000060621267,0.000036398284],"category_scores_gemma":[0.019301036,0.00014077783,0.00017218257,0.0002550736,0.00005762664,0.00044029148,0.00010123899,0.00012238504,0.000023838207],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.04028279,0.00058446074,0.0028253929,0.000019191817,0.00067852903,0.0001378996,0.0052416064,0.011354281,0.6227718,0.0009896175,0.02784271,0.2872717],"study_design_scores_gemma":[0.0024498333,0.0004776414,0.007683799,0.00008943071,0.000012984241,0.00034879355,0.0009948397,0.0016400232,0.65504354,0.0015448066,0.3294058,0.0003084609],"about_ca_topic_score_codex":0.0000023996272,"about_ca_topic_score_gemma":0.0000014175183,"teacher_disagreement_score":0.35824692,"about_ca_system_score_codex":0.0005400893,"about_ca_system_score_gemma":0.0006539044,"threshold_uncertainty_score":0.9889598},"labels":[],"label_agreement":null},{"id":"W2110690304","doi":"","title":"Identification of correlated characteristics in a linear statistical tolerance design","year":2005,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Allowance (engineering); Computer science; Variance (accounting); Probabilistic logic; Simple (philosophy); Design of experiments; Component (thermodynamics); Identification (biology); Statistical model; Mechanical engineering; Biological system; Mathematics; Engineering; Artificial intelligence; Statistics","score_opus":0.13028778071775277,"score_gpt":0.45177660681909426,"score_spread":0.3214888261013415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110690304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10872933,0.000034200653,0.88990366,0.00009553807,0.0002002151,0.00020720412,0.000015457534,0.000019794374,0.0007945665],"genre_scores_gemma":[0.670984,0.000004333718,0.32767388,0.00006113161,0.000020343874,0.0000072576127,0.0000020885766,0.0000059025747,0.0012410352],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707234,0.00050194806,0.0012440515,0.00032237225,0.00069454394,0.00016474452],"domain_scores_gemma":[0.9973713,0.0017489262,0.0002701798,0.0003523512,0.00018964267,0.00006758391],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0039217183,0.00009711639,0.00029085705,0.00019093849,0.000025498162,0.000052488635,0.0003620254,0.000072864874,0.001013446],"category_scores_gemma":[0.0033671318,0.00007789275,0.00003291309,0.0006212871,0.00010560817,0.0002531514,0.000045181077,0.00010959011,0.0005866648],"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":[0.000615894,0.0007952465,0.011496619,0.00000840701,0.000012418456,0.00001908116,0.0016231021,0.009872219,0.6932569,0.015289751,0.0032625643,0.26374775],"study_design_scores_gemma":[0.0003967438,0.00009164143,0.050380576,0.000010609499,0.0000035321725,0.0000047037956,0.00014250945,0.82808876,0.118024446,0.0021982691,0.0005226985,0.00013553293],"about_ca_topic_score_codex":0.000012271744,"about_ca_topic_score_gemma":0.0000023933574,"teacher_disagreement_score":0.8182165,"about_ca_system_score_codex":0.00004696725,"about_ca_system_score_gemma":0.00005003946,"threshold_uncertainty_score":0.99989974},"labels":[],"label_agreement":null},{"id":"W2110742637","doi":"10.1046/j.1467-9876.2003.05029.x","title":"Designing Fractional Factorial Split-Plot Experiments with Few Whole-Plot Factors","year":2004,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":63,"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":"Fractional factorial design; Split plot; Restricted randomization; Plot (graphics); Factorial; Factorial experiment; Mathematics; Design of experiments; Main effect; Statistics; Table (database); Computer science; Algorithm; Arithmetic; Data mining; Randomization","score_opus":0.07178356171609879,"score_gpt":0.369274802522208,"score_spread":0.2974912408061092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110742637","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":[0.015219664,0.000063259235,0.9805551,0.00028694855,0.0017482049,0.00040465,0.00084505574,0.000038561877,0.0008385159],"genre_scores_gemma":[0.36840835,0.0000050838426,0.6304008,0.0002183407,0.00041610663,0.0000127547955,0.000014426265,0.00006271343,0.00046145808],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9920789,0.00044504247,0.0017528787,0.00062970875,0.0043498976,0.0007435993],"domain_scores_gemma":[0.99303645,0.0036817684,0.0014497355,0.00054681033,0.00070869736,0.0005765255],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0020867307,0.0005805499,0.000996276,0.00008290734,0.00091324496,0.00073277793,0.0013706874,0.0002273125,0.0009899703],"category_scores_gemma":[0.002065538,0.00033946382,0.0003575091,0.0005653996,0.0010417403,0.0004840009,0.00030354495,0.0010638215,0.00008379001],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009952045,0.002856746,0.005503826,0.000115390714,0.0024377995,0.00029687627,0.020132909,0.09731606,0.09114518,0.6441902,0.11553942,0.010513585],"study_design_scores_gemma":[0.01660175,0.0074014533,0.07162046,0.00034226023,0.0010031816,0.00032414138,0.04097831,0.0027846512,0.11194513,0.70089954,0.042462196,0.003636928],"about_ca_topic_score_codex":0.00007352287,"about_ca_topic_score_gemma":0.000008106988,"teacher_disagreement_score":0.3531887,"about_ca_system_score_codex":0.0007312308,"about_ca_system_score_gemma":0.0006384557,"threshold_uncertainty_score":0.9999233},"labels":[],"label_agreement":null},{"id":"W2111060966","doi":"10.1111/j.1467-9868.2010.00763.x","title":"Robustness of Design in Dose–Response Studies","year":2011,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":19,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Mean squared error; Mathematics; Quadratic equation; Simulated annealing; Statistics; Mean value; Population; Mathematical optimization; Applied mathematics; Medicine","score_opus":0.5021927126025718,"score_gpt":0.4926546344217619,"score_spread":0.009538078180809928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111060966","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":[0.016884556,0.00056670606,0.9799684,0.00050790247,0.0014722553,0.0003178758,0.00010225077,0.000010945847,0.00016908783],"genre_scores_gemma":[0.067206286,0.00004079601,0.9321209,0.00019381958,0.00008582021,0.000011436335,3.7961752e-7,0.000028950935,0.00031158724],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97406805,0.020128444,0.0028760685,0.0005076648,0.0017839855,0.0006357749],"domain_scores_gemma":[0.91960055,0.07721955,0.0013360183,0.0005699144,0.0009692904,0.0003046649],"candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.040632024,0.0003846646,0.0017561106,0.00015416025,0.00019209986,0.00005894422,0.0016747904,0.00028075135,0.0011453105],"category_scores_gemma":[0.13838531,0.00022302382,0.00047264056,0.00096215884,0.0031269356,0.00028230602,0.0005694019,0.00092134636,0.000010797055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.124630444,0.004329958,0.0125370165,0.00048670563,0.0033751696,0.0013756414,0.04875433,0.09255163,0.027212761,0.4269002,0.11073557,0.14711055],"study_design_scores_gemma":[0.0037831082,0.006499895,0.1840842,0.00023773937,0.000513621,0.0004110404,0.023986416,0.03702628,0.021167526,0.72039247,0.00091551244,0.00098219],"about_ca_topic_score_codex":0.000037454865,"about_ca_topic_score_gemma":0.000005544846,"teacher_disagreement_score":0.29349226,"about_ca_system_score_codex":0.00023097308,"about_ca_system_score_gemma":0.00034782075,"threshold_uncertainty_score":0.9997678},"labels":[],"label_agreement":null},{"id":"W2111591386","doi":"10.1002/mren.201400048","title":"Design of Optimal Experiments for Terpolymerization Reactivity Ratio Estimation","year":2015,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"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","keywords":"Reactivity (psychology); Heuristics; Chemistry; Mathematics; Optimal design; Composition (language); Thermodynamics; Statistics; Mathematical optimization; Physics","score_opus":0.1416078855675469,"score_gpt":0.40260992708605464,"score_spread":0.26100204151850775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111591386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061807513,0.00008530036,0.93684876,0.000025090352,0.00045015218,0.00054689107,0.000004588542,0.000082250415,0.00014944791],"genre_scores_gemma":[0.61636746,9.2787803e-7,0.38341334,0.000009846363,0.000027466938,0.00008407344,0.000012928165,0.000025970128,0.00005799038],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978208,0.00019032918,0.00057061145,0.0003905498,0.0008119697,0.00021571832],"domain_scores_gemma":[0.99852043,0.00038153873,0.0002928581,0.0004006801,0.00026734054,0.00013714188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021241955,0.0001893308,0.00029482873,0.00030599063,0.000051156338,0.00010129218,0.00024581165,0.00009649723,0.00001239708],"category_scores_gemma":[0.0022382713,0.00018719515,0.00009650467,0.00050959614,0.000029742107,0.0006545818,0.00004980368,0.00007973691,0.000016378664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008726558,0.000045200784,0.0000061013,0.0000037935995,0.000012855646,0.0000015989295,0.00030407822,0.27146372,0.721862,0.00023241002,0.000040088107,0.0059408667],"study_design_scores_gemma":[0.00031557248,0.00011674104,0.000035660774,0.000007619866,0.0000077648765,0.000009829817,0.00009356228,0.47051853,0.5284651,0.00011917151,0.00021004069,0.00010041432],"about_ca_topic_score_codex":0.000015900683,"about_ca_topic_score_gemma":5.5916757e-8,"teacher_disagreement_score":0.55455995,"about_ca_system_score_codex":0.00014486416,"about_ca_system_score_gemma":0.00006737548,"threshold_uncertainty_score":0.76335967},"labels":[],"label_agreement":null},{"id":"W2124694383","doi":"10.1002/sim.2329","title":"Interval estimation of the mean response in a log‐regression model","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Cancer Institute; American Lebanese Syrian Associated Charities","keywords":"Statistics; Regression analysis; Mathematics; Sample size determination; Prediction interval; Confidence interval; Interval estimation; Interval (graph theory); Regression; Inference; Econometrics; Computer science","score_opus":0.15493030895431098,"score_gpt":0.5154423581120995,"score_spread":0.3605120491577885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124694383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23627348,0.00013363846,0.7603413,0.0016247475,0.00022170562,0.00022165233,0.000029823912,0.0000064499654,0.0011472178],"genre_scores_gemma":[0.63584673,0.000004755362,0.36363265,0.00016082822,0.000016038488,0.0000051426523,0.0000013753187,0.0000056428876,0.00032681244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99640995,0.0010834669,0.0009134441,0.00025763924,0.0011740571,0.0001614119],"domain_scores_gemma":[0.9958268,0.0033100995,0.00025680044,0.0004519528,0.000104334795,0.000050044015],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008791205,0.00011234831,0.00032792334,0.00033799847,0.00002979448,0.0000109093,0.0005401251,0.000056400408,0.00020624005],"category_scores_gemma":[0.017589597,0.00006110896,0.000023904899,0.0006915811,0.0003545062,0.00011073975,0.00013763312,0.00021381557,0.000014061406],"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":[0.004163803,0.00035706421,0.0037644408,0.000027062732,0.000008763695,0.00003722065,0.034433216,0.44312364,0.06311374,0.037574437,0.015015998,0.39838064],"study_design_scores_gemma":[0.00079195644,0.00017121782,0.010577067,0.000206797,0.0000038220824,0.0000034105456,0.0008916745,0.91059595,0.0029683015,0.07366726,0.00006123903,0.00006127483],"about_ca_topic_score_codex":0.00005170473,"about_ca_topic_score_gemma":0.00010874539,"teacher_disagreement_score":0.46747234,"about_ca_system_score_codex":0.00013350222,"about_ca_system_score_gemma":0.000082092854,"threshold_uncertainty_score":0.99068564},"labels":[],"label_agreement":null},{"id":"W2124704652","doi":"10.1115/1.1829728","title":"A Model-Based Formulation of Robust Design","year":2005,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Design matrix; Probabilistic design; Computer science; Design of experiments; Norm (philosophy); Isotropy; Mathematical optimization; Mathematics; Engineering design process; Engineering; Machine learning; Regression analysis; Statistics","score_opus":0.384120982725377,"score_gpt":0.4468542260209646,"score_spread":0.0627332432955876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124704652","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":[0.0027429387,0.0001979544,0.9958397,0.0004918811,0.00019301099,0.0002823183,0.0000020317282,0.0000134334205,0.00023669994],"genre_scores_gemma":[0.4598762,0.000004631338,0.53979516,0.00015875953,0.000075646494,0.000002287918,1.0345701e-7,0.000013302108,0.00007391717],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993429,0.0015918799,0.0020379492,0.00028698903,0.0023586862,0.00029551735],"domain_scores_gemma":[0.992832,0.004057931,0.001446632,0.00044129704,0.0009421377,0.0002800209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01747728,0.00020905382,0.0006886659,0.0005030229,0.00007528679,0.00009775893,0.0010343444,0.000192049,0.00037122733],"category_scores_gemma":[0.004999444,0.00014706049,0.0004046662,0.00068323896,0.00005207468,0.00077916466,0.000060841863,0.00030789868,0.000047136935],"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":[0.0008343055,0.00021408724,0.000003454876,0.0000021825851,0.000017155688,0.000007148589,0.0000642645,0.7568233,0.21219528,0.0025366573,0.0017173223,0.025584875],"study_design_scores_gemma":[0.0008145041,0.0008155486,0.000012592895,0.000029065537,0.000026609767,0.000025618361,0.0000344459,0.6698631,0.29483223,0.033346817,0.000094118026,0.00010532294],"about_ca_topic_score_codex":0.0000011260536,"about_ca_topic_score_gemma":3.5552372e-7,"teacher_disagreement_score":0.45713326,"about_ca_system_score_codex":0.00015711699,"about_ca_system_score_gemma":0.00038402027,"threshold_uncertainty_score":0.60573107},"labels":[],"label_agreement":null},{"id":"W2125878398","doi":"10.1002/mren.200900033","title":"Design of Optimal Sequential Experiments to Improve Model Predictions from a Polyethylene Molecular Weight Distribution Model","year":2009,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Maxima and minima; Design of experiments; Optimal design; Estimation theory; Computer science; Experimental data; Work (physics); Mathematical optimization; Algorithm; Mathematics; Statistics; Machine learning; Engineering","score_opus":0.050816237668299624,"score_gpt":0.3529426931396546,"score_spread":0.30212645547135497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125878398","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14448874,0.00014693289,0.8540338,0.00009051946,0.00029416467,0.0004978487,0.00013137149,0.00019350088,0.00012309509],"genre_scores_gemma":[0.63859427,0.000004549134,0.36112228,0.000060474755,0.000036427733,0.00005201943,0.000042208907,0.00003156392,0.000056193614],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963244,0.00016558701,0.00087258505,0.0008392861,0.0013315503,0.00046656732],"domain_scores_gemma":[0.99827385,0.000107177504,0.0002264367,0.00082970964,0.00023902467,0.00032380503],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00091322616,0.00037493475,0.00046131693,0.00033731735,0.0001030016,0.00013584283,0.0005614098,0.00019771246,0.000030192548],"category_scores_gemma":[0.0004533233,0.00038598964,0.00023773762,0.0007458029,0.000033930195,0.0004790534,0.00012972261,0.00025185058,0.000030484554],"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":[0.00006978166,0.000069868416,4.5980127e-7,8.890063e-7,0.000023458007,0.00000931701,0.00014783065,0.48967993,0.5087999,0.00027327577,0.00005081167,0.0008744574],"study_design_scores_gemma":[0.00026030416,0.00011710087,0.000016495935,0.000012818946,0.00002215756,0.000005366116,0.000023324183,0.5140201,0.48478064,0.00053907547,0.000025737907,0.00017689313],"about_ca_topic_score_codex":0.000037688926,"about_ca_topic_score_gemma":1.0404626e-7,"teacher_disagreement_score":0.49410552,"about_ca_system_score_codex":0.00031772468,"about_ca_system_score_gemma":0.00008957992,"threshold_uncertainty_score":0.9998592},"labels":[],"label_agreement":null},{"id":"W2129917462","doi":"10.4141/a03-123","title":"The use of MIXED models in the analysis of animal experiments with repeated measures data","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":534,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Repeated measures design; Covariance; Akaike information criterion; Mixed model; Statistics; Univariate; Mathematics; Missing data; Linear model; Random effects model; Analysis of covariance; Analysis of variance; Generalized linear mixed model; Bayesian information criterion; Bayesian probability; Multivariate statistics; Econometrics; Medicine","score_opus":0.4737523180852924,"score_gpt":0.44749795533439074,"score_spread":0.02625436275090165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129917462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99560153,0.0004879629,0.0029775684,0.00041171775,0.00007294287,0.00013486383,0.000038789,0.0000013638129,0.00027326235],"genre_scores_gemma":[0.9781027,0.000010758441,0.021794565,0.00006760234,0.000011678295,0.0000010704357,5.4255275e-7,0.0000055403066,0.000005552475],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9950409,0.00049495295,0.001058393,0.0003964567,0.0025761581,0.0004331265],"domain_scores_gemma":[0.9959512,0.0007632936,0.0008366937,0.0011195759,0.00093115325,0.00039810443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012473327,0.00014293072,0.00043810598,0.0011143911,0.00033836576,0.00047148002,0.0049215727,0.000040505907,0.000023412713],"category_scores_gemma":[0.0034618434,0.000069325695,0.0001176464,0.0070520267,0.0019342094,0.0020008937,0.00012777379,0.00020057299,9.720089e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017320791,0.00019276637,0.023185948,0.0000026245411,0.00032613176,0.0003051204,0.011945392,0.07227905,0.87414414,0.00800758,0.00057781977,0.0073013688],"study_design_scores_gemma":[0.0017417648,0.005056032,0.77861094,0.00017947084,0.00045278016,0.0002839058,0.0231326,0.055586267,0.12994647,0.00311772,0.0013054679,0.00058659865],"about_ca_topic_score_codex":0.011339521,"about_ca_topic_score_gemma":0.017089395,"teacher_disagreement_score":0.755425,"about_ca_system_score_codex":0.0001954616,"about_ca_system_score_gemma":0.0026858887,"threshold_uncertainty_score":0.995244},"labels":[],"label_agreement":null},{"id":"W2131073835","doi":"10.1002/cjs.10041","title":"Testing for order among <i>K</i> populations: theory and examples","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nonparametric statistics; Statistical hypothesis testing; Statistic; Stochastic ordering; Test statistic; Statistics; Econometrics; Test (biology); Mathematics; Limiting; Observational study; Order (exchange); Order statistic; Computer science; Engineering; Economics","score_opus":0.23722008250568363,"score_gpt":0.4240020428956637,"score_spread":0.18678196038998005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131073835","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":[0.049674567,0.0006551296,0.9479119,0.00015755533,0.00032115428,0.00013852264,0.00019407237,0.0000033410936,0.0009437239],"genre_scores_gemma":[0.33446506,0.0000012126621,0.66504717,0.00026817218,0.00006226114,6.5116916e-7,0.0000014045888,0.0000062133886,0.00014784094],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985933,0.0002214895,0.00056332,0.00012615576,0.00028708566,0.00020868861],"domain_scores_gemma":[0.9945251,0.0037100064,0.00033865529,0.00012449663,0.00083383644,0.00046790479],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0035643762,0.000088655375,0.00020667148,0.00027812467,0.00021404051,0.0002646031,0.00023998236,0.000039757862,0.00008398444],"category_scores_gemma":[0.027658485,0.000071452094,0.00002723576,0.0003285702,0.00014556084,0.00023766253,0.000005890299,0.00009819533,0.0000023441225],"study_design_candidate":"theoretical_or_conceptual","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":[0.000060838,0.000018159686,0.05875825,0.0000056224703,0.000018689892,0.000117050164,0.001401841,0.00058285205,0.0018461801,0.38537717,0.025746789,0.52606654],"study_design_scores_gemma":[0.00030541216,0.00043160876,0.20743652,0.000030684474,0.00002363981,0.000092797374,0.00093456596,0.0013808996,0.00011347718,0.7860139,0.003104323,0.00013217845],"about_ca_topic_score_codex":0.00036085234,"about_ca_topic_score_gemma":0.0014181139,"teacher_disagreement_score":0.5259344,"about_ca_system_score_codex":0.00005155415,"about_ca_system_score_gemma":0.0004626159,"threshold_uncertainty_score":0.980532},"labels":[],"label_agreement":null},{"id":"W2132337369","doi":"10.5267/j.msl.2011.11.001","title":"A Semi parametric approach to dual modeling","year":2012,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Parametric statistics; Variance (accounting); Nonparametric statistics; Dual (grammatical number); Computer science; Parametric model; Regression analysis; Mathematical optimization; Econometrics; Mathematics; Statistics; Machine learning","score_opus":0.1952088514838664,"score_gpt":0.4235144844043999,"score_spread":0.22830563292053352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132337369","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2517832,0.000031755117,0.7001532,0.0011242602,0.0006274381,0.00044907306,8.1130753e-7,0.00006622271,0.045764104],"genre_scores_gemma":[0.68516254,6.9874426e-7,0.30824232,0.005974856,0.000076803146,0.00004920326,3.490585e-7,0.000009969694,0.0004832734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936316,0.00019783647,0.00050497137,0.00095171016,0.0036289878,0.0010849162],"domain_scores_gemma":[0.9981052,0.00025872266,0.00010160332,0.0009992571,0.000041818766,0.00049334555],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01286337,0.00021251937,0.00023980608,0.001865143,0.00040917203,0.0007704921,0.0019564584,0.000029565346,0.00006437231],"category_scores_gemma":[0.0010005289,0.00016689945,0.0000946036,0.008320507,0.00029466857,0.0017119644,0.0010491478,0.0001293167,0.0011537931],"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":[0.00008369245,0.000985192,0.016188966,0.000028096836,0.000056676272,0.000028025655,0.0068977955,0.6540946,0.13725284,0.06324137,0.034944274,0.086198516],"study_design_scores_gemma":[0.00056873227,0.00009151399,0.009725977,0.000019853922,0.000035852205,0.000019070505,0.006921894,0.9635645,0.0063595898,0.0013955949,0.010205425,0.0010919877],"about_ca_topic_score_codex":0.000016322068,"about_ca_topic_score_gemma":7.048125e-8,"teacher_disagreement_score":0.43337932,"about_ca_system_score_codex":0.00023768621,"about_ca_system_score_gemma":0.0000075706917,"threshold_uncertainty_score":0.9996239},"labels":[],"label_agreement":null},{"id":"W2132714854","doi":"10.1139/x00-138","title":"Variance and efficiency of the combined estimator in incomplete block designs of use in forest genetics: a numerical study","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Efficiency; Bias of an estimator; Mathematics; Efficient estimator; Minimum-variance unbiased estimator; Stein's unbiased risk estimate; Mean squared error; Variance (accounting); Monte Carlo method","score_opus":0.31459587934654504,"score_gpt":0.47413797042589395,"score_spread":0.1595420910793489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132714854","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9973666,0.00032853815,0.001213897,0.00024444936,0.000089000074,0.0005503702,0.0000066571383,7.3629315e-7,0.00019974378],"genre_scores_gemma":[0.992627,0.000007818914,0.0072646826,0.0000149252655,0.000015076379,0.0000063139096,8.34902e-8,0.000012055792,0.000052042982],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99443865,0.0019889935,0.0011943573,0.000267824,0.0015792156,0.0005309897],"domain_scores_gemma":[0.9949754,0.0029738205,0.00033069527,0.0005069879,0.0007206348,0.000492494],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010405794,0.00012717421,0.0004967188,0.001422562,0.00012737964,0.00013861824,0.0014248153,0.0000709599,0.000048135153],"category_scores_gemma":[0.008763894,0.00008647681,0.000078018136,0.00295619,0.0007182786,0.00022488339,0.00015030177,0.0005511886,0.000002625153],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016923188,0.00020073533,0.9898662,0.000004317642,0.0000078380335,0.00024720567,0.0011387834,0.0062126857,0.0009865661,0.0005072647,0.00007378021,0.0005853864],"study_design_scores_gemma":[0.0012291882,0.001417276,0.98134196,0.00009388149,0.0000037395976,0.00008940232,0.0013997108,0.009469681,0.00033521221,0.00447341,0.000067454836,0.000079082885],"about_ca_topic_score_codex":0.016628575,"about_ca_topic_score_gemma":0.095002666,"teacher_disagreement_score":0.07837409,"about_ca_system_score_codex":0.00020652979,"about_ca_system_score_gemma":0.0017037607,"threshold_uncertainty_score":0.9995857},"labels":[],"label_agreement":null},{"id":"W2132912577","doi":"10.1111/0272-4332.214140","title":"Limitations to Empirical Extrapolation Studies: The Case of BMD Ratios","year":2001,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":9,"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 Ottawa","funders":"","keywords":"Extrapolation; Benchmark (surveying); Computer science; Process (computing); Econometrics; Data mining; Statistics; Mathematics","score_opus":0.5544835754753674,"score_gpt":0.5725686033294992,"score_spread":0.018085027854131797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132912577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6630787,0.000566587,0.33420166,0.0009347913,0.00005337664,0.00015220421,0.000011729927,0.000013212846,0.0009877178],"genre_scores_gemma":[0.9410579,0.00015631194,0.05800205,0.00017357597,0.000029353438,0.000022802515,0.0000015472807,0.0000049295786,0.0005515225],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705774,0.0011318709,0.00070938794,0.00034382808,0.00061484764,0.00014233858],"domain_scores_gemma":[0.9920908,0.0063653565,0.0002919191,0.0006766909,0.00048254398,0.00009266244],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0040343595,0.000104873645,0.0003657156,0.00065064174,0.00030426387,0.000087134,0.00032437546,0.000038445723,0.00032632987],"category_scores_gemma":[0.009407835,0.000060961964,0.0003391042,0.0072917845,0.000107370906,0.00017731632,0.00007719293,0.00008266396,0.00014077398],"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":[0.0001783583,0.00040998642,0.46019736,0.0000024586047,0.0036851568,0.00025234607,0.048454165,0.16276407,0.0063934666,0.0019794994,0.014619222,0.3010639],"study_design_scores_gemma":[0.000692371,0.0004598705,0.21207248,0.000010189008,0.005753224,0.00014214996,0.15503179,0.5710515,0.005606502,0.035465002,0.013014149,0.00070081657],"about_ca_topic_score_codex":0.0002891131,"about_ca_topic_score_gemma":0.0011152131,"teacher_disagreement_score":0.40828738,"about_ca_system_score_codex":0.000045877692,"about_ca_system_score_gemma":0.000024652472,"threshold_uncertainty_score":0.99893636},"labels":[],"label_agreement":null},{"id":"W2133763020","doi":"10.1002/asmb.504","title":"Efficiency measure, modelling and estimation in combined array designs","year":2003,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Weighting; Measure (data warehouse); Mathematics; Variance (accounting); Statistics; Parametric statistics; Minification; Computer science; Mathematical optimization; Data mining","score_opus":0.1822536033017244,"score_gpt":0.3617884531755646,"score_spread":0.17953484987384016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133763020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23219909,0.00018395366,0.7637331,0.000044838576,0.00007529457,0.00036891576,0.0000015128345,0.000015394518,0.0033778995],"genre_scores_gemma":[0.9200503,0.0000060963644,0.07975093,0.000054120934,0.000008706981,0.00007745287,0.0000012570772,0.000018731309,0.00003236128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739194,0.00017235403,0.00069759844,0.00068843033,0.00068577856,0.00036390524],"domain_scores_gemma":[0.99860173,0.0006869639,0.00015154653,0.0003152243,0.000118919656,0.00012561122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030170148,0.0002562588,0.00044479908,0.00044828054,0.00012447037,0.00018295816,0.0002290959,0.00037548368,0.000024967047],"category_scores_gemma":[0.0005725549,0.00022329821,0.00001805694,0.0013859877,0.00018914064,0.0003986255,0.000057049245,0.0004773847,0.0000038387693],"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":[0.0000688999,0.000095474985,0.000110599365,0.0000061852334,0.000002118249,0.000002370373,0.0005092396,0.93753517,0.0016971419,0.05570717,0.0000047814174,0.00426086],"study_design_scores_gemma":[0.0012085078,0.000026628888,0.00035839053,0.000060332037,0.0000054168186,0.0000087247945,0.0005955238,0.82831025,0.0005631211,0.16861835,0.0000019387116,0.00024283618],"about_ca_topic_score_codex":0.000058888345,"about_ca_topic_score_gemma":0.0000041672733,"teacher_disagreement_score":0.68785125,"about_ca_system_score_codex":0.00005932982,"about_ca_system_score_gemma":0.000106382686,"threshold_uncertainty_score":0.9105836},"labels":[],"label_agreement":null},{"id":"W2134387151","doi":"10.1111/j.1541-0420.2009.01229.x","title":"On the Role of Baseline Measurements for Crossover Designs under the Self and Mixed Carryover Effects Model","year":2009,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crossover; Baseline (sea); Optimal design; Crossover study; Design of experiments; Lagrange multiplier; Statistics; Computer science; Mathematics; Econometrics; Mathematical optimization; Medicine; Machine learning","score_opus":0.2673972802127009,"score_gpt":0.4347266216222311,"score_spread":0.16732934140953015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134387151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45223674,0.0026144646,0.5404026,0.0007286993,0.00037475702,0.001616178,0.000058099453,0.000041309107,0.0019271787],"genre_scores_gemma":[0.9427237,0.000010130827,0.05623636,0.0008165256,0.000027037235,0.000020041545,8.6107076e-7,0.000011752941,0.0001535971],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970083,0.0004903231,0.00038627101,0.00035926988,0.0015074809,0.00024835288],"domain_scores_gemma":[0.9894493,0.009374247,0.00020430579,0.00056390977,0.00032615932,0.00008207754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007769553,0.00016729235,0.0002467212,0.0005361489,0.00021183724,0.00018302319,0.00062023854,0.00009009523,0.000015825815],"category_scores_gemma":[0.0069338735,0.000080695216,0.00012348652,0.0030628666,0.00010676465,0.00012396874,0.000071196526,0.000082897415,0.000011166099],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066260004,0.0008822673,0.0006880287,0.0000148296485,0.00012472465,9.439335e-7,0.00085054524,0.0053122337,0.79282403,0.04072654,0.012365866,0.14554742],"study_design_scores_gemma":[0.0014559684,0.00082862994,0.007070724,0.00001608976,0.000064361004,0.000001863379,0.00037317857,0.19767252,0.6479823,0.14301768,0.0012421728,0.00027449525],"about_ca_topic_score_codex":0.000006426938,"about_ca_topic_score_gemma":5.566179e-7,"teacher_disagreement_score":0.49048695,"about_ca_system_score_codex":0.00006542032,"about_ca_system_score_gemma":0.00005932195,"threshold_uncertainty_score":0.8300995},"labels":[],"label_agreement":null},{"id":"W2134800676","doi":"10.1002/pst.1721","title":"Optimal adaptive sequential designs for crossover bioequivalence studies","year":2015,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Bioequivalence; Sequential analysis; Crossover; Sample size determination; Statistics; Mathematics; Crossover study; Adaptive design; Type I and type II errors; Nominal level; Computer science; Mathematical optimization; Confidence interval; Medicine; Machine learning; Clinical trial","score_opus":0.8262700275298006,"score_gpt":0.64127193821718,"score_spread":0.18499808931262063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134800676","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":[0.003036842,0.0014179198,0.9911149,0.00025823552,0.0014579303,0.0006916271,0.000976675,0.00007369261,0.0009721798],"genre_scores_gemma":[0.15324175,0.000043730626,0.8444121,0.0006857688,0.00025512726,0.000098356315,0.000009271241,0.000034224446,0.0012196695],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952837,0.0006494634,0.00084663834,0.00076433166,0.0017553548,0.00070047134],"domain_scores_gemma":[0.9903796,0.0066965236,0.00022025063,0.0003751384,0.001470114,0.0008584304],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0046629147,0.00031501416,0.00054078235,0.00012447646,0.00023489862,0.0003199013,0.0007730286,0.00009518306,0.00048355557],"category_scores_gemma":[0.01868592,0.00024660392,0.0001255622,0.00046570908,0.00094272156,0.00040230164,0.0003885938,0.00023190753,0.00045623854],"study_design_candidate":"theoretical_or_conceptual","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":[0.010446934,0.0010783764,0.0007848894,0.00009035785,0.00078086636,0.0003267597,0.0068744607,0.013821514,0.03570293,0.3807734,0.41784886,0.13147065],"study_design_scores_gemma":[0.0067622936,0.002414656,0.00013857184,0.0000324527,0.00036281408,0.000060835013,0.004822372,0.4878848,0.12064022,0.2744123,0.1011669,0.0013017947],"about_ca_topic_score_codex":0.000007343349,"about_ca_topic_score_gemma":0.0000013953122,"teacher_disagreement_score":0.47406328,"about_ca_system_score_codex":0.0002686265,"about_ca_system_score_gemma":0.0002874721,"threshold_uncertainty_score":0.9999986},"labels":[],"label_agreement":null},{"id":"W2135683075","doi":"10.5267/j.ijiec.2013.04.003","title":"Application of desirability function for optimizing the performance characteristics of carbonitrided bushes","year":2013,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Response surface methodology; Function (biology); Variable (mathematics); Product (mathematics); Mathematical optimization; Hardness; Surface (topology); Materials science; Computer science; Process engineering; Mathematics; Statistics; Composite material; Engineering","score_opus":0.11648766625191335,"score_gpt":0.3698494016880249,"score_spread":0.2533617354361115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135683075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47686303,0.00001705132,0.5218862,0.00017142821,0.00083623943,0.00019866467,0.000009024811,0.0000043970767,0.000013972328],"genre_scores_gemma":[0.9384704,0.000003231207,0.061163455,0.000011443961,0.00031623087,0.000016888187,0.000003866309,0.000007966268,0.000006485578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775255,0.00008054062,0.0012186843,0.00010823945,0.00075168436,0.000088308225],"domain_scores_gemma":[0.99404037,0.002384183,0.0010449764,0.00014840628,0.002337718,0.000044347842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018881517,0.00009157026,0.00025797103,0.00028918276,0.000040120136,0.00007557871,0.00060748146,0.00006476491,0.00001759307],"category_scores_gemma":[0.0028782403,0.00006570921,0.00015252779,0.00031952257,0.00006202488,0.00035810392,0.000045911092,0.00016785433,0.0000019921517],"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":[0.00018359118,0.00010894349,0.0063741473,0.000007963679,0.00015639632,2.2180974e-7,0.00028883552,0.8060001,0.07588068,0.0013476124,0.00013937271,0.10951213],"study_design_scores_gemma":[0.0010344418,0.0003509828,0.041677624,0.000079894526,0.00004621177,0.000018311512,0.00028126466,0.920156,0.034469083,0.0014387391,0.0003351512,0.00011227063],"about_ca_topic_score_codex":0.000020917318,"about_ca_topic_score_gemma":1.4779253e-7,"teacher_disagreement_score":0.46160743,"about_ca_system_score_codex":0.00007728515,"about_ca_system_score_gemma":0.00010562716,"threshold_uncertainty_score":0.34457302},"labels":[],"label_agreement":null},{"id":"W2135843367","doi":"10.1007/s00184-014-0514-8","title":"Blocked semifoldovers of two-level orthogonal designs","year":2014,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Fractional factorial design; Factorial experiment; Fraction (chemistry); Mathematics; Block (permutation group theory); Blocking (statistics); Factor (programming language); Mathematical optimization; Design of experiments; Econometrics; Computer science; Statistics; Combinatorics","score_opus":0.37527729619071154,"score_gpt":0.4833293118572496,"score_spread":0.10805201566653805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135843367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4543784,0.00026947775,0.50601923,0.00011563785,0.0006441883,0.00023488795,0.0000298866,0.00005729061,0.03825101],"genre_scores_gemma":[0.6848846,0.0000023832613,0.3129402,0.00014902012,0.00006205802,0.00000582887,0.0000012997021,0.000013807327,0.0019407712],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99565256,0.0009499584,0.00076058396,0.0005099947,0.0018086171,0.00031829474],"domain_scores_gemma":[0.99393123,0.004551451,0.0003324896,0.00072136236,0.00028405897,0.0001794247],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007986927,0.00018749706,0.00049219356,0.0006211272,0.000086846936,0.00008890627,0.00091393734,0.00008466536,0.0013804634],"category_scores_gemma":[0.00890927,0.00014222483,0.00022469295,0.0020146442,0.0001920477,0.00022621019,0.00018203403,0.00012894842,0.00036751106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019819636,0.00028788278,0.034456544,0.000009249624,0.00007714237,0.000008028149,0.00065868645,0.000781576,0.6739311,0.037401482,0.00677264,0.24541746],"study_design_scores_gemma":[0.0018607487,0.00056240446,0.022835813,0.000021441996,0.000038490984,0.000015629723,0.00045631302,0.009432342,0.9115029,0.03832646,0.014479727,0.00046771762],"about_ca_topic_score_codex":0.000040563405,"about_ca_topic_score_gemma":0.0000058779524,"teacher_disagreement_score":0.24494974,"about_ca_system_score_codex":0.00004227752,"about_ca_system_score_gemma":0.000065219756,"threshold_uncertainty_score":0.9995324},"labels":[],"label_agreement":null},{"id":"W2136011484","doi":"10.1111/1539-6924.00283","title":"Optimal Designs for Estimating the Effective Dose in Developmental Toxicity Experiments","year":2002,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Population and Public Health","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Toxicity; Developmental toxicity; Toxicology; Computer science; Reliability engineering; Engineering; Biology; Medicine; Genetics; Internal medicine","score_opus":0.2050355976052273,"score_gpt":0.44561177045277023,"score_spread":0.24057617284754293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136011484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5567488,0.00022954911,0.44150665,0.00004179837,0.00008101087,0.0005680988,0.000015187063,0.00002300516,0.0007858776],"genre_scores_gemma":[0.5533753,0.000004197928,0.44607928,0.000050407874,0.000028313658,0.00022062895,0.0000018829576,0.000010095022,0.00022988013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99569684,0.0013015612,0.00081322057,0.00075577706,0.0009905891,0.0004420008],"domain_scores_gemma":[0.9943144,0.0045824135,0.00036497563,0.00049222866,0.00012395052,0.00012207816],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005663777,0.00025849187,0.0005925889,0.0006257165,0.0004706902,0.00030035706,0.00085754634,0.000089960005,0.00093583454],"category_scores_gemma":[0.0039054488,0.00016734721,0.00047624187,0.0034338003,0.00014127938,0.00034372843,0.0001894088,0.00018886228,0.00019287632],"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":[0.0003607828,0.0010262008,0.13543928,0.000005931032,0.0019411424,0.000027274007,0.024756184,0.402354,0.033196695,0.00017932573,0.0023102767,0.3984029],"study_design_scores_gemma":[0.0006743204,0.000109042834,0.01777184,0.0000056982067,0.00024725744,0.0000025460095,0.0024928404,0.944232,0.033472527,0.00059080264,0.00013898504,0.00026217147],"about_ca_topic_score_codex":0.00019993923,"about_ca_topic_score_gemma":0.0000609279,"teacher_disagreement_score":0.541878,"about_ca_system_score_codex":0.00028107048,"about_ca_system_score_gemma":0.000020529505,"threshold_uncertainty_score":0.99997747},"labels":[],"label_agreement":null},{"id":"W2136520695","doi":"10.1109/iscas.1998.705280","title":"Statistical design of integrated circuits using maximum likelihood estimation of the covariance matrix","year":2002,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Ellipsoid; Covariance matrix; Mathematics; Covariance; Ellipsoid method; Mathematical optimization; Polyhedron; Estimation of covariance matrices; Algorithm; Semidefinite programming; Matrix (chemical analysis); Computer science; Statistics; Convex optimization; Geometry","score_opus":0.20873537854818264,"score_gpt":0.4310086727187494,"score_spread":0.22227329417056674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136520695","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":[0.011543039,0.00011286447,0.98642725,0.000053223954,0.00019971105,0.00031610072,0.000051246963,0.00001714377,0.0012793933],"genre_scores_gemma":[0.4736803,0.0000015377944,0.52609307,0.000025703726,0.0000045457728,0.000002171947,4.1378598e-7,0.0000066708194,0.00018562409],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966201,0.000928726,0.0008509293,0.00029590313,0.0011069166,0.00019740239],"domain_scores_gemma":[0.9967579,0.0019606764,0.00037792005,0.0005262246,0.00031063645,0.00006663275],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025566842,0.0001319031,0.0003273863,0.0001300999,0.00007384004,0.000047961294,0.0006374566,0.000078851735,0.003385011],"category_scores_gemma":[0.0037714439,0.0000780567,0.000072378374,0.0010068563,0.00026772037,0.00024007131,0.000095754665,0.00010779396,0.000086157044],"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":[0.00008488258,0.00048196997,0.0010291177,0.000023734836,0.00004617932,0.000006571193,0.0010166127,0.15285513,0.5478775,0.019553827,0.003601858,0.2734226],"study_design_scores_gemma":[0.0002545212,0.00010018027,0.0007307787,0.000024022678,0.000015104594,0.000012134487,0.00022779517,0.8458026,0.106863685,0.045849215,0.000027308863,0.00009265538],"about_ca_topic_score_codex":0.00007666012,"about_ca_topic_score_gemma":6.543975e-7,"teacher_disagreement_score":0.69294745,"about_ca_system_score_codex":0.000059514758,"about_ca_system_score_gemma":0.00007868023,"threshold_uncertainty_score":0.99752605},"labels":[],"label_agreement":null},{"id":"W2137109403","doi":"10.1002/qre.1790","title":"A Prediction Region‐based Approach to Model Uncertainty for Multi‐response Optimization","year":2015,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Calgary","funders":"National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Minimax; Mathematical optimization; Function (biology); Set (abstract data type); Computer science; Dispersion (optics); Variable (mathematics); Process (computing); Mathematics","score_opus":0.3965922080803353,"score_gpt":0.46378172639864923,"score_spread":0.06718951831831393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137109403","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":[0.024232857,0.000016971684,0.973325,0.0010404581,0.0004918372,0.00049406855,0.00009349446,0.000097612436,0.00020768658],"genre_scores_gemma":[0.28869507,7.839662e-7,0.7105678,0.00018644506,0.000050770057,0.00012844504,0.000026842708,0.000011192221,0.00033269887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975469,0.00030962913,0.0006197232,0.0005571176,0.0007995622,0.00016704483],"domain_scores_gemma":[0.99750495,0.0010980652,0.00010531755,0.00035131493,0.0006986129,0.00024171677],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008487799,0.00015005675,0.0002175367,0.00023348854,0.00006276203,0.00015067802,0.00035509627,0.000113868635,0.000006815584],"category_scores_gemma":[0.0157616,0.00012843934,0.00009630685,0.00026218168,0.000047082816,0.00028208236,0.000083885076,0.00010326059,0.0000034269626],"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":[0.0009959625,0.00015535038,0.00030552587,0.000009893459,0.0000074944173,1.2869754e-7,0.0004941424,0.99445873,0.00048264966,0.0022751146,0.0003783671,0.0004366505],"study_design_scores_gemma":[0.00086157775,0.000092667695,0.0010170344,0.0000102264285,0.000004340672,0.0000024137134,0.00018592228,0.99519867,0.0002918588,0.0008046088,0.0013889645,0.00014168976],"about_ca_topic_score_codex":0.000023400515,"about_ca_topic_score_gemma":3.83732e-7,"teacher_disagreement_score":0.2644622,"about_ca_system_score_codex":0.000262678,"about_ca_system_score_gemma":0.000104337145,"threshold_uncertainty_score":0.99252903},"labels":[],"label_agreement":null},{"id":"W2137679700","doi":"10.2307/3315999","title":"Theory of optimal blocking of nonregular factorial designs","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fractional factorial design; Factorial experiment; Mathematics; Factorial; Blocking (statistics); Block (permutation group theory); Coding theory; Type (biology); Coding (social sciences); Orthogonal array; Block design; Plackett–Burman design; Designtheory; Arithmetic; Mathematical optimization; Computer science; Discrete mathematics; Combinatorics; Statistics; Mathematical analysis","score_opus":0.18644542258703284,"score_gpt":0.3933985522755768,"score_spread":0.20695312968854396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137679700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087120526,0.00033974388,0.91050965,0.00003032135,0.0010748483,0.00008089328,0.00032413722,0.0000014334988,0.0005184679],"genre_scores_gemma":[0.5922801,0.0000036673346,0.4075503,0.00001692643,0.000094765506,2.1851957e-7,7.5529056e-7,0.000010321014,0.000042940537],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970365,0.0003846519,0.0012103962,0.00014656365,0.0009456806,0.00027621965],"domain_scores_gemma":[0.99558055,0.001575634,0.0009899933,0.00026916259,0.0010310801,0.000553572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038728223,0.00013672424,0.0005004619,0.0005365635,0.00007715916,0.00006726994,0.00073045806,0.00008608562,0.00056702446],"category_scores_gemma":[0.0058418396,0.00011292877,0.00012965142,0.00041035158,0.00043189738,0.00019029487,0.000022584376,0.0001965737,0.000007126215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008953742,0.00023696528,0.005413784,0.000058508467,0.00043944645,0.0015820488,0.01973847,0.07687231,0.16681375,0.61697483,0.0069455435,0.10402897],"study_design_scores_gemma":[0.0036914682,0.0031256003,0.010188166,0.00031445888,0.00020569909,0.00042885198,0.0075273244,0.00060960645,0.28522557,0.68421537,0.003839712,0.00062815816],"about_ca_topic_score_codex":0.00076514896,"about_ca_topic_score_gemma":0.00042450376,"teacher_disagreement_score":0.5051596,"about_ca_system_score_codex":0.00017757739,"about_ca_system_score_gemma":0.0024786082,"threshold_uncertainty_score":0.69936496},"labels":[],"label_agreement":null},{"id":"W2138688019","doi":"10.1007/s11336-013-9373-x","title":"Generalized Functional Extended Redundancy Analysis","year":2013,"lang":"en","type":"article","venue":"Psychometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Universidade de Santiago de Compostela; National Research Foundation of Korea","keywords":"Generalized linear model; Redundancy (engineering); Computer science; Component (thermodynamics); Exponential family; Algorithm; Function (biology); Data mining; Mathematics; Machine learning","score_opus":0.19418635098283296,"score_gpt":0.452341991218989,"score_spread":0.258155640236156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138688019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6090467,0.0023142353,0.3099356,0.0014988201,0.0027514987,0.00070329447,0.000022071741,0.00022943824,0.073498346],"genre_scores_gemma":[0.7862081,0.000026280872,0.18989117,0.0006626668,0.00020494426,0.000118394164,0.00001303428,0.000022366023,0.022853017],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99505424,0.00056695525,0.00089181383,0.0009121192,0.0021494273,0.0004254215],"domain_scores_gemma":[0.9959892,0.0017402194,0.00028233023,0.0011632146,0.0005016482,0.00032340235],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0028281396,0.00023394694,0.0005629021,0.003152951,0.00018330118,0.0006199484,0.00084839104,0.00011568068,0.05671374],"category_scores_gemma":[0.0036040284,0.00017460473,0.0005616044,0.01564379,0.00012850047,0.00063409813,0.00010919989,0.00016658251,0.0074183457],"study_design_candidate":"observational","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":[0.00026913802,0.0009749392,0.08931499,0.0000039141996,0.0015621346,0.000008572096,0.00041867408,0.00074688246,0.10853258,0.012618609,0.29578406,0.4897655],"study_design_scores_gemma":[0.0012583933,0.00016382925,0.89760095,0.0000028732481,0.0001565355,0.000009328528,0.00034119017,0.0057097427,0.0057479907,0.052076932,0.036425672,0.0005065521],"about_ca_topic_score_codex":0.000104100975,"about_ca_topic_score_gemma":0.0000032630999,"teacher_disagreement_score":0.80828595,"about_ca_system_score_codex":0.000074356074,"about_ca_system_score_gemma":0.000034960623,"threshold_uncertainty_score":0.9933545},"labels":[],"label_agreement":null},{"id":"W2138725277","doi":"10.1080/08982110701241293","title":"Probability Constrained Optimization as a Tool for Functional Design for Six Sigma","year":2007,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Robustness (evolution); Six Sigma; Reliability engineering; Probabilistic design; Mathematical optimization; Design of experiments; Engineering; Computer science; Mathematics; Engineering design process; Manufacturing engineering; Statistics","score_opus":0.3031025173730075,"score_gpt":0.45388115307334037,"score_spread":0.1507786357003329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138725277","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":[0.014166461,0.000032415122,0.9830229,0.00012494749,0.0005434609,0.0016827777,0.00003160736,0.00014938114,0.00024599797],"genre_scores_gemma":[0.20305601,2.8544753e-7,0.7962627,0.00008643164,0.0001369294,0.00019906451,0.000014767746,0.0000220649,0.00022178006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971839,0.00017370017,0.000977977,0.0005626589,0.0006970627,0.0004046778],"domain_scores_gemma":[0.9877397,0.01110239,0.00017900777,0.00038759128,0.00045848955,0.00013283236],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.019162282,0.00020175395,0.00033620378,0.00016693564,0.0001370229,0.00015732045,0.0002766003,0.0001349187,0.00016514704],"category_scores_gemma":[0.021252008,0.00018550143,0.00020964474,0.00041895412,0.00006456667,0.00034681897,0.000039883907,0.000088644934,0.000010807048],"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":[0.00050933636,0.000074274,0.00009301143,0.000034327997,0.000021743283,5.096748e-7,0.00017430977,0.9285816,0.036907203,0.02664303,0.00023373023,0.0067269118],"study_design_scores_gemma":[0.001486201,0.00039106756,0.001116208,0.00001657318,0.000017013204,0.00000845777,0.00028066634,0.91558224,0.063163616,0.015192358,0.0022782602,0.00046731395],"about_ca_topic_score_codex":0.000005799678,"about_ca_topic_score_gemma":8.23141e-7,"teacher_disagreement_score":0.18888955,"about_ca_system_score_codex":0.00016086583,"about_ca_system_score_gemma":0.00010574165,"threshold_uncertainty_score":0.9869924},"labels":[],"label_agreement":null},{"id":"W2140217706","doi":"10.1348/000711003321645412","title":"Pairwise multiple comparisons: A model comparison approach versus stepwise procedures","year":2003,"lang":"en","type":"article","venue":"British Journal of Mathematical and Statistical Psychology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":28,"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 Manitoba; York University","funders":"","keywords":"Pairwise comparison; Selection (genetic algorithm); Normality; Model selection; Multiple comparisons problem; Set (abstract data type); Variance (accounting); Mathematics; Statistics; Type I and type II errors; Computer science; Econometrics; Machine learning","score_opus":0.2715206547783738,"score_gpt":0.48669607144257343,"score_spread":0.21517541666419965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140217706","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":[0.028122235,0.0015235452,0.9566978,0.00011539469,0.00021804622,0.00021389665,0.000038493148,0.000015262229,0.013055278],"genre_scores_gemma":[0.47808227,0.000034999783,0.5216356,0.00012067054,0.000022265634,0.0000068218937,9.648272e-7,0.000012725816,0.00008370102],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9951512,0.0011026035,0.0017765943,0.00046508,0.001087768,0.00041674203],"domain_scores_gemma":[0.99301815,0.0054168473,0.00048180268,0.00025637346,0.00031839786,0.0005084319],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0042140326,0.0002199207,0.0010337683,0.00014946816,0.00015211935,0.00027214686,0.00048683543,0.00016895177,0.0004544082],"category_scores_gemma":[0.01908389,0.0001808818,0.00014035661,0.00025665044,0.0006746527,0.00020785097,0.0000668162,0.00055385975,0.000053193857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0048413826,0.0106235165,0.00290718,0.0002743962,0.0003540334,0.00087535416,0.0018538808,0.0019499502,0.0016479086,0.6336246,0.20537844,0.1356693],"study_design_scores_gemma":[0.008575572,0.0017710557,0.0036648384,0.00014943413,0.00012912121,0.006980786,0.0024244352,0.28272554,0.000121089746,0.6909167,0.0019491663,0.000592271],"about_ca_topic_score_codex":0.0000020922635,"about_ca_topic_score_gemma":0.000002842764,"teacher_disagreement_score":0.44996002,"about_ca_system_score_codex":0.000026031008,"about_ca_system_score_gemma":0.00008966122,"threshold_uncertainty_score":0.9891788},"labels":[],"label_agreement":null},{"id":"W2141313754","doi":"10.1007/s11222-012-9339-3","title":"Robust model-based sampling designs","year":2012,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Australian Research Council; Australian National University","keywords":"Mathematics; Minimax; Robustness (evolution); Population; Mean squared error; Sample size determination; Neighbourhood (mathematics); Statistics; Optimal design; Linear model; Mathematical optimization; Population variance; Algorithm","score_opus":0.4559566454812878,"score_gpt":0.4775928154674137,"score_spread":0.02163616998612594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141313754","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":[0.01793959,0.00023466503,0.9799839,0.00002039641,0.0002408226,0.00007908244,0.00002797671,0.000027962982,0.001445554],"genre_scores_gemma":[0.4755082,6.62387e-7,0.52427816,0.000112041365,0.000047355592,6.95702e-7,0.0000017977973,0.000007263812,0.000043787597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983573,0.0001786257,0.00036837024,0.000259303,0.00049127074,0.0003451595],"domain_scores_gemma":[0.99709904,0.0022621152,0.00013405213,0.00020675505,0.00011245431,0.00018556417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002801111,0.00012131435,0.00019339488,0.00009139572,0.00026268902,0.00022450842,0.00020296668,0.000038827708,0.000054815675],"category_scores_gemma":[0.00097240636,0.0001010499,0.00002788286,0.00020203086,0.000079152705,0.00011099788,0.00012050252,0.0001021748,0.000028700853],"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":[0.00003310499,0.00012554349,0.010295222,0.000015550178,0.000014709339,0.000004166036,0.0016423176,0.45601857,0.0075259544,0.135443,0.0021230597,0.3867588],"study_design_scores_gemma":[0.00014090839,0.000032624088,0.001540184,0.000008947227,0.0000063164534,0.0000030867411,0.00020816294,0.98112744,0.00069352076,0.015925433,0.00017556631,0.00013781153],"about_ca_topic_score_codex":0.000007721799,"about_ca_topic_score_gemma":4.6780207e-7,"teacher_disagreement_score":0.5251089,"about_ca_system_score_codex":0.000023190194,"about_ca_system_score_gemma":0.00003866682,"threshold_uncertainty_score":0.41206953},"labels":[],"label_agreement":null},{"id":"W2141848375","doi":"10.1002/sim.5951","title":"Multiple‐objective response‐adaptive repeated measurement designs in clinical trials for binary responses","year":2013,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"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 Alberta","funders":"","keywords":"Binary number; Computer science; Basis (linear algebra); Binary data; Function (biology); Mathematical optimization; Algorithm; Mathematics","score_opus":0.716823768290405,"score_gpt":0.608469569222505,"score_spread":0.1083541990679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141848375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085150905,0.0006621059,0.90496975,0.0014882252,0.001558305,0.005174044,0.00028996158,0.00004999827,0.0006566739],"genre_scores_gemma":[0.54507834,0.000036747322,0.4530936,0.0003899835,0.00016613938,0.0005381291,0.000009724306,0.000033014396,0.00065430836],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9575358,0.03251019,0.0053455085,0.0012350599,0.0027214882,0.00065193477],"domain_scores_gemma":[0.82434934,0.17206478,0.0009832422,0.00077272044,0.0015524083,0.00027751407],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.19958463,0.00034457853,0.0017787791,0.0011354641,0.000107546584,0.00006690883,0.00069578853,0.00023111455,0.00063349004],"category_scores_gemma":[0.64270383,0.00023973633,0.00013882447,0.0012604293,0.0007535795,0.00021735048,0.00013270228,0.000498704,0.000100780926],"study_design_candidate":"observational","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":[0.15854746,0.0018598071,0.04052957,0.00004303313,0.00028172383,0.00060298044,0.012494338,0.00065030827,0.33172762,0.008625367,0.2008538,0.24378401],"study_design_scores_gemma":[0.024491249,0.015803413,0.6512699,0.00072330073,0.00010708318,0.0000151836,0.02248097,0.08723338,0.009445006,0.18529008,0.0021657639,0.0009746684],"about_ca_topic_score_codex":0.00053209666,"about_ca_topic_score_gemma":0.00018100713,"teacher_disagreement_score":0.6107403,"about_ca_system_score_codex":0.0005192709,"about_ca_system_score_gemma":0.00052657374,"threshold_uncertainty_score":0.97761637},"labels":[],"label_agreement":null},{"id":"W2142334108","doi":"10.1016/j.disc.2005.02.022","title":"Detailed wordlength pattern of regular fractional factorial split-plot designs in terms of complementary sets","year":2006,"lang":"en","type":"article","venue":"Discrete Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Mathematics; Fractional factorial design; Factorial; Split plot; Factorial experiment; Plot (graphics); Arithmetic; Discrete mathematics; Combinatorics; Statistics; Mathematical analysis","score_opus":0.14529886469096281,"score_gpt":0.4207809977068823,"score_spread":0.2754821330159195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142334108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7064618,0.000034697765,0.28840822,0.000050617335,0.00032378707,0.0005029819,0.00018029692,0.000021470407,0.0040161675],"genre_scores_gemma":[0.8052124,0.0000014162483,0.19455184,0.000014846329,0.000058648682,0.000013982303,0.000026651196,0.000021966209,0.000098249766],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957625,0.00035402217,0.0016491134,0.00036037306,0.0015971015,0.00027684233],"domain_scores_gemma":[0.99668914,0.0017446423,0.00076008006,0.00063067,0.000110702545,0.000064748325],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021988044,0.00022831709,0.0006736601,0.00029439473,0.000045908317,0.00005311815,0.0006472701,0.00009057588,0.0010034073],"category_scores_gemma":[0.00041125034,0.00017647263,0.00019326837,0.0004702564,0.00018502313,0.0002633553,0.00017192283,0.00013792382,0.00003232756],"study_design_candidate":"bench_or_experimental","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":[0.0007296462,0.0044266805,0.36009023,0.00058895326,0.00030529796,0.000071140545,0.013613887,0.0035845528,0.50925964,0.07852597,0.006415184,0.02238884],"study_design_scores_gemma":[0.0033030978,0.0005323166,0.08069436,0.00033081675,0.00007879337,0.000018138253,0.004518082,0.04224767,0.21898894,0.6480331,0.00047496604,0.000779732],"about_ca_topic_score_codex":0.00012332853,"about_ca_topic_score_gemma":0.00007020712,"teacher_disagreement_score":0.5695071,"about_ca_system_score_codex":0.000069721405,"about_ca_system_score_gemma":0.000029803487,"threshold_uncertainty_score":0.9999098},"labels":[],"label_agreement":null},{"id":"W2143379509","doi":"10.1016/j.fertnstert.2009.10.053","title":"Cross-over design trials in infertility—How much multiplicity is too much?","year":2009,"lang":"en","type":"letter","venue":"Fertility and Sterility","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Infertility; Multiplicity (mathematics); Andrology; Gynecology; Medicine; Biology; Mathematics; Pregnancy; Genetics","score_opus":0.4644380845236428,"score_gpt":0.51416713564632,"score_spread":0.04972905112267717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143379509","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73666203,0.0021323354,0.03580114,0.2132097,0.0017403119,0.005887649,0.0016273279,0.00030831236,0.0026311802],"genre_scores_gemma":[0.61615986,0.000038610793,0.027861526,0.34267294,0.00068859616,0.00027437825,0.00011751948,0.00009864714,0.01208793],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.96668434,0.019712122,0.004773817,0.003964634,0.0033872526,0.0014778083],"domain_scores_gemma":[0.9771616,0.016698515,0.0013958534,0.0036884923,0.00064458355,0.00041099385],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","metaepi_narrow","research_integrity"],"category_scores_codex":[0.055760585,0.0013202309,0.004591026,0.00064639683,0.00040653016,0.0024868553,0.002156022,0.0027116323,0.0023051945],"category_scores_gemma":[0.03717477,0.001044665,0.0010820975,0.0011659435,0.0011312623,0.0014871014,0.0008607642,0.0030009307,0.00011700663],"study_design_candidate":"not_applicable","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":[0.04775618,0.001119648,0.0516388,0.00043527866,0.00018478515,0.00035936464,0.0032066815,0.000014006668,0.0047358978,0.0000059408353,0.81097686,0.07956657],"study_design_scores_gemma":[0.0017907243,0.00034122806,0.8540056,0.00006992152,0.000044743952,0.0000063512325,0.00009703786,0.0018860832,0.0058602537,0.025578368,0.10934747,0.00097221945],"about_ca_topic_score_codex":0.00077509927,"about_ca_topic_score_gemma":0.000078680496,"teacher_disagreement_score":0.8023668,"about_ca_system_score_codex":0.00049768784,"about_ca_system_score_gemma":0.00034446557,"threshold_uncertainty_score":0.9999549},"labels":[],"label_agreement":null},{"id":"W2144125453","doi":"10.1016/j.jmp.2015.03.003","title":"Bayesian alternatives to null-hypothesis significance testing for repeated-measures designs","year":2015,"lang":"en","type":"article","venue":"Journal of Mathematical Psychology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bayes factor; Statistics; Bayesian probability; Sample size determination; Null hypothesis; Statistical hypothesis testing; Mathematics; Posterior probability; Bayesian inference; Repeated measures design; Bayesian average; Econometrics; Bayesian hierarchical modeling","score_opus":0.6247835408792345,"score_gpt":0.5520142874432188,"score_spread":0.0727692534360157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144125453","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":[0.024946677,0.00013071041,0.9575857,0.0025604893,0.0005530745,0.00041906006,0.0000049517475,0.00002314515,0.01377623],"genre_scores_gemma":[0.27612376,0.0000011745851,0.7222739,0.00087567634,0.00024377294,0.000021267806,5.5527018e-8,0.000027097298,0.00043328683],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9950693,0.0009657572,0.0017472092,0.00044302354,0.001364699,0.0004099869],"domain_scores_gemma":[0.9863609,0.010206716,0.00091855845,0.00053715036,0.0013525126,0.00062416325],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013810514,0.000227919,0.0008219573,0.00045750142,0.00007655067,0.00015649873,0.0012785251,0.00012867444,0.00016990503],"category_scores_gemma":[0.07202473,0.00015397237,0.00024301621,0.0006664257,0.00018859337,0.00027163973,0.000071201386,0.00024009655,0.00022100855],"study_design_candidate":"theoretical_or_conceptual","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":[0.0038119573,0.0021507824,0.0022315362,0.000050629507,0.00033154423,0.00031220826,0.0076708705,0.0019192696,0.50716794,0.011461662,0.13923769,0.3236539],"study_design_scores_gemma":[0.0017905039,0.0045038126,0.001064026,0.00013504755,0.000054885524,0.00084200926,0.0017381378,0.00250052,0.024471382,0.95710015,0.0054124575,0.00038707777],"about_ca_topic_score_codex":0.0000012845794,"about_ca_topic_score_gemma":3.9708635e-7,"teacher_disagreement_score":0.9456385,"about_ca_system_score_codex":0.00009081581,"about_ca_system_score_gemma":0.00014225833,"threshold_uncertainty_score":0.93579197},"labels":[],"label_agreement":null},{"id":"W2149119822","doi":"10.1002/asmb.455","title":"Minimizing a general loss function in off‐line quality control","year":2002,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Heteroscedasticity; Parametric statistics; Metric (unit); Variance (accounting); Transformation (genetics); Dependency (UML); Computer science; Function (biology); Mathematics; Statistics; Univariate; Mathematical optimization; Econometrics; Multivariate statistics","score_opus":0.20299004800203027,"score_gpt":0.3886543633076745,"score_spread":0.18566431530564423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149119822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48039836,0.0003498058,0.51594806,0.00027389405,0.00019864798,0.00031754217,0.0000105198715,0.000019919395,0.002483247],"genre_scores_gemma":[0.99185133,0.000011060494,0.0071795243,0.0004269211,0.00013294582,0.000115963434,0.0000024823125,0.000020234575,0.00025953105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968652,0.00020735731,0.0010156018,0.00074083195,0.0007561085,0.00041487272],"domain_scores_gemma":[0.9982879,0.0008141903,0.00022740182,0.0004130192,0.00013112288,0.00012632519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024475278,0.0002653039,0.00059235434,0.00038568443,0.00009300667,0.00014604544,0.00031187438,0.00047254094,0.0002690406],"category_scores_gemma":[0.0005203862,0.00022714323,0.00003874962,0.0012496061,0.00018748747,0.00040247102,0.00012485332,0.0005963502,0.00001906882],"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":[0.0007461985,0.0004693628,0.0014915636,0.00001733285,0.000016565617,0.000022558708,0.0008688025,0.821632,0.006945917,0.048047796,0.00024413245,0.11949775],"study_design_scores_gemma":[0.00430633,0.00004996763,0.014077171,0.000052130636,0.000011533794,0.000012197759,0.0007364991,0.9355789,0.00008038819,0.044630934,0.000052345687,0.0004116459],"about_ca_topic_score_codex":0.0001713634,"about_ca_topic_score_gemma":0.000020174277,"teacher_disagreement_score":0.511453,"about_ca_system_score_codex":0.0000790454,"about_ca_system_score_gemma":0.00003485687,"threshold_uncertainty_score":0.9262632},"labels":[],"label_agreement":null},{"id":"W2149957882","doi":"10.1002/asmb.2013","title":"A journey of discovery with George Box","year":2014,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Queen's University","funders":"University of Wisconsin-Madison","keywords":"George (robot); Computer science; Mathematical economics; Data science; Philosophy; Artificial intelligence; Mathematics","score_opus":0.09360265058677056,"score_gpt":0.35044180036241046,"score_spread":0.25683914977563993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149957882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34101665,0.000040903804,0.6508713,0.00009343675,0.00005856479,0.00015758199,0.0000047165972,0.000008394058,0.0077484814],"genre_scores_gemma":[0.979765,0.0000024243045,0.019760318,0.000107845124,0.00005285714,0.000037388658,0.0000013900055,0.000019304858,0.00025346247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997802,0.00008378917,0.0005736449,0.0004929662,0.0007692861,0.00027827325],"domain_scores_gemma":[0.9984066,0.0006396505,0.00024967504,0.00045040643,0.00014777126,0.00010592115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016190622,0.00020709049,0.00049335504,0.00026187833,0.00006873071,0.00014549868,0.0003804992,0.0002610134,0.000046877936],"category_scores_gemma":[0.00031993116,0.00014091798,0.00002491426,0.00091803505,0.0002893939,0.00050736254,0.00017877357,0.00038101978,0.0000037594416],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008383142,0.00036169632,0.0011365681,0.000040580886,0.000028970384,0.0000073544984,0.001240538,0.46789533,0.011563783,0.47854143,0.0002602141,0.03808521],"study_design_scores_gemma":[0.007351237,0.0004295441,0.025829583,0.00055935845,0.00006943313,0.00009769473,0.0055779554,0.44864914,0.005192457,0.5045283,0.00024646014,0.0014688204],"about_ca_topic_score_codex":0.00009140492,"about_ca_topic_score_gemma":0.000006339529,"teacher_disagreement_score":0.63874835,"about_ca_system_score_codex":0.000022107966,"about_ca_system_score_gemma":0.00008361013,"threshold_uncertainty_score":0.5746469},"labels":[],"label_agreement":null},{"id":"W2150605328","doi":"10.1243/1748006xjrr210","title":"Physical programming and conjoint analysis-based redundancy allocation in multistate systems: A Taguchi embedded algorithm selection and control (TAS&amp;C) approach","year":2009,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"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 Alberta","funders":"","keywords":"Computer science; Particle swarm optimization; Taguchi methods; Redundancy (engineering); Mathematical optimization; Algorithm; Machine learning; Mathematics","score_opus":0.024271052201691665,"score_gpt":0.3266420823009661,"score_spread":0.3023710300992744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150605328","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69882596,0.00027538044,0.30037332,0.00009171636,0.00008048715,0.00032917847,0.0000072079865,0.000007075872,0.000009686934],"genre_scores_gemma":[0.89073676,0.00005684095,0.10916088,0.000006317069,0.000027725226,0.0000054831494,4.499166e-7,0.0000034409495,0.0000021200767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757403,0.00017112342,0.0010725312,0.00030502887,0.00072297786,0.00015428751],"domain_scores_gemma":[0.99766225,0.00039725535,0.0009852699,0.00010293041,0.0007223389,0.0001299372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006200801,0.00016031622,0.00069309183,0.00030070276,0.00009506733,0.00007624728,0.00018837118,0.00010794793,6.6854943e-7],"category_scores_gemma":[0.0032338158,0.00010034666,0.00017135,0.0009147041,0.00024075073,0.00034482646,0.000030270468,0.00034348233,6.207189e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043418747,0.0045750784,0.02476011,0.00068233104,0.0007301879,0.0000025890367,0.007092819,0.48847803,0.25149116,0.026597058,0.00005915907,0.1911896],"study_design_scores_gemma":[0.0020356947,0.0006066755,0.0073462585,0.00014127293,0.00036428007,0.00003267031,0.000956948,0.9677417,0.01641271,0.004146653,0.00006020362,0.00015488811],"about_ca_topic_score_codex":0.000035350233,"about_ca_topic_score_gemma":0.0000010310162,"teacher_disagreement_score":0.47926372,"about_ca_system_score_codex":0.00007178884,"about_ca_system_score_gemma":0.00005713566,"threshold_uncertainty_score":0.4092018},"labels":[],"label_agreement":null},{"id":"W2151911004","doi":"10.1002/cjs.5550350303","title":"Optimal designs for calibration of orientations","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Western University; Queen's University; University of Ottawa; Health Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optimal design; Rotation (mathematics); Mathematics; Calibration; Point (geometry); Matrix (chemical analysis); Algorithm; Applied mathematics; Computer science; Mathematical optimization; Statistics; Geometry","score_opus":0.23018122271465527,"score_gpt":0.45164021760137874,"score_spread":0.22145899488672346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151911004","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":[0.014283675,0.00009172969,0.9839699,0.000073302566,0.00059846824,0.00012997385,0.00043926906,0.0000013497605,0.00041229223],"genre_scores_gemma":[0.3312603,0.0000011872573,0.66842115,0.00005433777,0.00006457963,7.0784466e-7,0.0000043502355,0.000007815418,0.0001855592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998096,0.00010150672,0.0009766564,0.000105636835,0.0004920182,0.00022820763],"domain_scores_gemma":[0.99514943,0.0024569589,0.00059201196,0.0001390926,0.0011460795,0.0005163999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003723009,0.00007882974,0.00022653521,0.00050959643,0.000116197836,0.000089485206,0.00033284124,0.000051017116,0.0002897825],"category_scores_gemma":[0.0053663403,0.000068129746,0.00007212115,0.00040690132,0.0001612161,0.00025160375,0.0000060874786,0.000086254615,0.0000032312746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073924643,0.0001704118,0.021042543,0.000045689107,0.00019893161,0.0004795039,0.01383342,0.028679924,0.05536511,0.52066314,0.19494973,0.16383235],"study_design_scores_gemma":[0.007206929,0.009190549,0.05805606,0.0002129948,0.00045617225,0.0008183083,0.0408402,0.0920221,0.33102283,0.37742963,0.08109091,0.0016533346],"about_ca_topic_score_codex":0.00035334096,"about_ca_topic_score_gemma":0.0022725095,"teacher_disagreement_score":0.3169766,"about_ca_system_score_codex":0.00011131712,"about_ca_system_score_gemma":0.0012224,"threshold_uncertainty_score":0.64243984},"labels":[],"label_agreement":null},{"id":"W2156197192","doi":"10.1111/j.1467-9469.2004.02_101.x","title":"Goodness‐of‐fit Tests for Mixed Models","year":2004,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Ottawa Mental Health Centre","funders":"","keywords":"Goodness of fit; Mathematics; Allowance (engineering); Normality; Econometrics; Statistics; Statistical hypothesis testing; Engineering","score_opus":0.2841348147446454,"score_gpt":0.47828194534106994,"score_spread":0.19414713059642452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156197192","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":[0.032126155,0.00037594239,0.96530163,0.00011736092,0.000901749,0.00018470341,0.00038439553,0.000004923348,0.00060314365],"genre_scores_gemma":[0.48637363,0.000015141792,0.5133697,0.000025066747,0.000062505205,0.000001953935,0.0000012237703,0.000012902925,0.00013792224],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967848,0.00015517086,0.0013448051,0.00020694803,0.0012355004,0.00027274564],"domain_scores_gemma":[0.9953761,0.0017136628,0.0011860254,0.00028943803,0.001178542,0.00025622913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028710044,0.00016524576,0.0005671565,0.00032603653,0.0000968808,0.000112921116,0.00073385553,0.00007164495,0.00008416528],"category_scores_gemma":[0.002953797,0.00012411078,0.00017668938,0.00046139467,0.00021060121,0.00042757828,0.000053075117,0.00015975126,0.000012114443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024702044,0.0014047012,0.0060366993,0.00015206926,0.00034193162,0.00044043697,0.0064668516,0.09793715,0.054235775,0.54835325,0.033841845,0.24831907],"study_design_scores_gemma":[0.0023446514,0.0017208059,0.002740211,0.00015927352,0.000059467053,0.00018289872,0.0012542824,0.0027474982,0.018888243,0.9692949,0.00039502862,0.00021276125],"about_ca_topic_score_codex":0.000010026847,"about_ca_topic_score_gemma":0.0000039968986,"teacher_disagreement_score":0.45424747,"about_ca_system_score_codex":0.00012309213,"about_ca_system_score_gemma":0.00025033046,"threshold_uncertainty_score":0.50610906},"labels":[],"label_agreement":null},{"id":"W2158731372","doi":"10.1093/biomet/ass061","title":"Blocked two-level regular factorial designs with weak minimum aberration","year":2012,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematics; Factorial experiment; Fractional factorial design; Block (permutation group theory); Block design; Value (mathematics); Arithmetic; Combinatorics; Statistics","score_opus":0.3637872844944415,"score_gpt":0.4581214626095582,"score_spread":0.09433417811511674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158731372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49632433,0.0010569172,0.48040184,0.0002121739,0.0054933955,0.0009126868,0.000059557387,0.00018510481,0.015353986],"genre_scores_gemma":[0.74707,0.0000028924605,0.24791259,0.000061392035,0.00082979555,0.000024773028,0.00000885492,0.0000293296,0.004060374],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9951493,0.0006624273,0.000669341,0.0005855281,0.0022945907,0.00063881185],"domain_scores_gemma":[0.99663717,0.0014660951,0.0003366185,0.0008337241,0.00031210692,0.0004142806],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0050920555,0.00029419296,0.0004274074,0.0014741288,0.00020575507,0.00032680418,0.0007369457,0.000167837,0.0009850941],"category_scores_gemma":[0.0026814921,0.00020108871,0.00013397975,0.005433444,0.0002013919,0.00096077577,0.00013574837,0.00014002626,0.00090461544],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005560922,0.00046054015,0.01563698,0.0000039737233,0.00007698535,0.000005346436,0.0011703438,0.000012388663,0.91717356,0.0028713066,0.014100647,0.04793183],"study_design_scores_gemma":[0.004062373,0.0013034289,0.036837995,0.000025275353,0.0000741284,0.000055021068,0.001733161,0.00068125624,0.84781784,0.0026786204,0.10363315,0.0010977639],"about_ca_topic_score_codex":0.000048388123,"about_ca_topic_score_gemma":0.0000035571804,"teacher_disagreement_score":0.25074568,"about_ca_system_score_codex":0.00015273078,"about_ca_system_score_gemma":0.00010364069,"threshold_uncertainty_score":0.9999281},"labels":[],"label_agreement":null},{"id":"W2159016562","doi":"","title":"OPTIMAL FRACTIONAL FACTORIAL SPLIT-PLOT DESIGNS FOR MODEL SELECTION","year":2009,"lang":"en","type":"dissertation","venue":"Summit (Simon Fraser University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fractional factorial design; Mathematics; Factorial experiment; Split plot; Selection (genetic algorithm); Statistics; Factorial; Plot (graphics); Mathematical optimization; Applied mathematics; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.10435496523265804,"score_gpt":0.37380206426952545,"score_spread":0.2694470990368674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159016562","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":[0.13939027,0.00013091217,0.8168155,0.00008309416,0.00474251,0.0019890997,0.0004451493,0.00038101248,0.036022455],"genre_scores_gemma":[0.26412842,0.00008481314,0.42251953,0.0002302729,0.0020378549,0.000033092725,0.002473486,0.00026064235,0.3082319],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950369,0.00041038514,0.00069527334,0.0014577508,0.0017873237,0.0006123811],"domain_scores_gemma":[0.99619013,0.0011470935,0.000705584,0.0005421653,0.0010833486,0.00033168396],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011502252,0.0006238471,0.0007809868,0.0015623436,0.00064968073,0.00035434216,0.001331235,0.0009390407,0.00058923673],"category_scores_gemma":[0.0008286329,0.00066792313,0.0006665555,0.0015802426,0.00007622509,0.0012514181,0.00006152758,0.00065682555,0.00019151827],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.031182675,0.002193149,0.011428473,0.00013666264,0.0009099944,0.00014724149,0.0005957439,0.3362945,0.009483464,0.036385696,0.512158,0.05908443],"study_design_scores_gemma":[0.007584597,0.002840847,0.0015376852,0.00017783674,0.0009364152,9.945251e-8,0.046179276,0.28262684,0.08688437,0.036373008,0.5306596,0.004199433],"about_ca_topic_score_codex":0.000090674395,"about_ca_topic_score_gemma":0.0046241535,"teacher_disagreement_score":0.39429596,"about_ca_system_score_codex":0.0007319138,"about_ca_system_score_gemma":0.00075897394,"threshold_uncertainty_score":0.9995772},"labels":[],"label_agreement":null},{"id":"W2159697589","doi":"10.1016/j.jspi.2007.05.020","title":"Robust estimators and designs for field experiments","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Alberta","funders":"","keywords":"Mathematics; Estimator; Robustness (evolution); Variance (accounting); Kriging; Minimax; Statistics; Correlation; Contrast (vision); Field (mathematics); Regression; Econometrics; Mathematical optimization; Artificial intelligence; Computer science; Geometry","score_opus":0.4041987539618632,"score_gpt":0.5366567002772741,"score_spread":0.13245794631541086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159697589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08815104,0.0004573805,0.9104531,0.000059991813,0.00017151645,0.00006352005,0.000007688515,0.000004246569,0.0006315469],"genre_scores_gemma":[0.53202707,0.0000048737797,0.4677865,0.000107543034,0.000032622225,6.9527664e-7,2.541261e-7,0.0000034557079,0.000037017693],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984268,0.00007840714,0.0006464212,0.0001752897,0.00047075344,0.00020231033],"domain_scores_gemma":[0.9875875,0.011616332,0.00026265584,0.00008185619,0.00019778435,0.00025384635],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0033444015,0.00010566075,0.00028757146,0.00015451302,0.00011255373,0.00018814218,0.00016748843,0.000063466956,0.00005918185],"category_scores_gemma":[0.0096969195,0.0000736547,0.000028672202,0.00010423317,0.00012471656,0.0002676011,0.000048108843,0.00015930226,0.000001827322],"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":[0.0041105044,0.00039359785,0.18058677,0.00007855621,0.00014853942,0.0005579197,0.007346076,0.0013127777,0.07217548,0.10699767,0.027240135,0.59905195],"study_design_scores_gemma":[0.007252037,0.017669002,0.35211748,0.0009834565,0.00019305057,0.0013913929,0.016242156,0.104440376,0.11381364,0.37731668,0.0068705534,0.001710171],"about_ca_topic_score_codex":0.0000046881996,"about_ca_topic_score_gemma":1.77863e-7,"teacher_disagreement_score":0.5973418,"about_ca_system_score_codex":0.000015053695,"about_ca_system_score_gemma":0.000040384046,"threshold_uncertainty_score":0.9986448},"labels":[],"label_agreement":null},{"id":"W2167286520","doi":"10.1177/1471082x0700700204","title":"A measure of partial association for generalized estimating equations","year":2007,"lang":"en","type":"article","venue":"Statistical Modelling","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Stephen's University; St. Michael's Hospital","funders":"National Institutes of Health","keywords":"Mathematics; Generalized estimating equation; Statistics; Measure (data warehouse); Estimating equations; Linear regression; Covariate; Regression analysis; Partial correlation; Ordinary least squares; Outcome (game theory); Applied mathematics; Correlation; Estimator","score_opus":0.3184001615592276,"score_gpt":0.5005314592625004,"score_spread":0.1821312977032728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167286520","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":[0.0024713161,0.000037846057,0.99528134,0.00005766098,0.00033854417,0.00029166634,0.00009994711,0.000024945288,0.001396741],"genre_scores_gemma":[0.4096393,2.2466375e-7,0.5901337,0.000021182139,0.00007963147,0.0000115149005,0.000007319363,0.000008456437,0.00009867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969178,0.00023490605,0.0009528924,0.00032338616,0.0012417,0.0003293039],"domain_scores_gemma":[0.9844333,0.014310517,0.00036187406,0.0001957919,0.0005795184,0.00011903007],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.010461778,0.00010642024,0.00030254127,0.000119857825,0.00014628068,0.00008213563,0.00020655553,0.00009060969,0.00011953582],"category_scores_gemma":[0.017727658,0.000092079754,0.00009190915,0.0003146858,0.000049406073,0.00014508658,0.00003168338,0.000093612674,0.000020460675],"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":[0.00020346123,0.00008421636,0.00019174976,0.000011413054,0.000028114111,0.0000014898152,0.00062153686,0.4657313,0.011793467,0.49197343,0.00035123923,0.029008573],"study_design_scores_gemma":[0.00032411417,0.00006025087,0.000018866771,0.000008437252,0.000023838582,2.6529483e-7,0.000096464355,0.81146795,0.0061169243,0.18171713,0.000082778635,0.00008300427],"about_ca_topic_score_codex":0.000035888035,"about_ca_topic_score_gemma":0.000002851628,"teacher_disagreement_score":0.40716797,"about_ca_system_score_codex":0.00011066752,"about_ca_system_score_gemma":0.00006016572,"threshold_uncertainty_score":0.99054646},"labels":[],"label_agreement":null},{"id":"W2168158999","doi":"10.1002/sim.3356","title":"Two‐stage design for dose‐finding that accounts for both efficacy and safety","year":2008,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women's Health Research Institute","funders":"","keywords":"Stage (stratigraphy); Computer science; Statistics; Econometrics; Mathematics; Biology","score_opus":0.34766234225019504,"score_gpt":0.5197216396087865,"score_spread":0.17205929735859143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168158999","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":[0.0054853815,0.00040600367,0.9904995,0.00020267218,0.0005897167,0.0014799676,0.0003226353,0.000020932112,0.0009931651],"genre_scores_gemma":[0.14294478,0.00012857337,0.8547421,0.0003415147,0.0001540768,0.00008471996,0.000024249466,0.000030108666,0.0015498715],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970332,0.00029022762,0.0007582186,0.0005775079,0.0009267807,0.00041410985],"domain_scores_gemma":[0.97842014,0.020642078,0.00026068927,0.00035765045,0.00016109436,0.00015835777],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0071358774,0.00022096992,0.0005895959,0.0003118919,0.00028410877,0.000044937693,0.0004151735,0.00007513678,0.00020356917],"category_scores_gemma":[0.013864223,0.00016267218,0.00003125757,0.00035931286,0.00049276365,0.00016332834,0.00007675168,0.00014147756,0.000008149917],"study_design_candidate":"theoretical_or_conceptual","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":[0.0139706135,0.00074481004,0.028584423,0.00030428966,0.00019560521,0.00043894377,0.029368056,0.004161478,0.038716946,0.26820025,0.21339303,0.40192157],"study_design_scores_gemma":[0.086433426,0.009719736,0.11798875,0.00078356185,0.00023546649,0.00021976286,0.013810877,0.23003048,0.015088809,0.43333387,0.08980787,0.0025474033],"about_ca_topic_score_codex":0.000040578096,"about_ca_topic_score_gemma":0.000011653987,"teacher_disagreement_score":0.39937416,"about_ca_system_score_codex":0.00009646926,"about_ca_system_score_gemma":0.00009244185,"threshold_uncertainty_score":0.9944424},"labels":[],"label_agreement":null},{"id":"W2169366955","doi":"10.4103/0971-6203.94742","title":"Impact of edema and seed movement on the dosimetry of prostate seed implants","year":2012,"lang":"en","type":"article","venue":"Journal of Medical Physics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"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 Alberta; Alberta Health Services","funders":"","keywords":"Edema; Medicine; Implant; Prostate; Dosimetry; Nuclear medicine; Surgery; Internal medicine","score_opus":0.11360448094696199,"score_gpt":0.468628439410112,"score_spread":0.35502395846315005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169366955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9958708,0.00031077673,0.0025841345,0.00039275704,0.00028105394,0.000116126816,0.0000122526735,0.0000017653257,0.00043028712],"genre_scores_gemma":[0.9977945,0.00004985939,0.0016209563,0.00025638996,0.0002415767,7.291153e-7,2.0973569e-7,0.000008207319,0.000027585276],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9947166,0.00049309724,0.0009126757,0.00010303878,0.0035492359,0.00022535864],"domain_scores_gemma":[0.99576986,0.0024293985,0.0010189192,0.00024062408,0.00020270068,0.000338499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008742049,0.00012706502,0.00048837456,0.000088538676,0.000039763985,0.00002863907,0.00056907145,0.00007437698,0.00017518134],"category_scores_gemma":[0.0023763624,0.000057831916,0.00023253448,0.00036407085,0.00024514014,0.00026079582,0.00014183937,0.00031324715,0.00000692836],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018955369,0.0031960737,0.46098924,0.000048545757,0.000722365,0.000029428287,0.0065626004,0.0001954409,0.3744832,0.0047788713,0.01001719,0.13708152],"study_design_scores_gemma":[0.0021319569,0.0027266317,0.68307084,0.0002734592,0.000062813466,0.00006993692,0.0013876539,0.0013445208,0.28742528,0.02121996,0.0000972978,0.00018965435],"about_ca_topic_score_codex":0.000017605089,"about_ca_topic_score_gemma":9.235943e-8,"teacher_disagreement_score":0.2220816,"about_ca_system_score_codex":0.00004391609,"about_ca_system_score_gemma":0.00014948405,"threshold_uncertainty_score":0.30298373},"labels":[],"label_agreement":null},{"id":"W2188071017","doi":"10.1080/16843703.2009.11673206","title":"Finding Design Space and a Reliable Operating Region Using a Multivariate Bayesian Approach with Experimental Design","year":2009,"lang":"en","type":"article","venue":"Quality Technology & Quantitative Management","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":38,"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 Regina","keywords":"Frequentist inference; Computer science; Quality (philosophy); Bayesian probability; Reliability (semiconductor); Process (computing); Engineering design process; Product (mathematics); Multivariate statistics; Quality by Design; Grid; Bayesian optimization; Data mining; Reliability engineering; Machine learning; New product development; Bayesian inference; Mathematics; Artificial intelligence; Engineering; Power (physics)","score_opus":0.38139625710453073,"score_gpt":0.49673563259608566,"score_spread":0.11533937549155493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188071017","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":[0.048453186,0.0005910464,0.9450485,0.00075456407,0.000059329108,0.0017344337,0.0000014473113,0.0002727298,0.0030847804],"genre_scores_gemma":[0.4487439,0.0000068988943,0.55081993,0.00012873206,0.000005932493,0.00006417266,8.3976005e-7,0.000020588948,0.00020897623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938081,0.001976452,0.00097549916,0.001544607,0.0010067346,0.0006886133],"domain_scores_gemma":[0.997278,0.00088697975,0.000597022,0.0009167083,0.00018676485,0.00013456862],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0074456283,0.00049800455,0.00077494676,0.0010973557,0.0007045935,0.00041906632,0.00080579676,0.00022306746,0.00002049015],"category_scores_gemma":[0.00077513384,0.00039529963,0.00007889771,0.0022053013,0.00058688474,0.00068981975,0.00037612033,0.00035238554,0.000017399165],"study_design_candidate":"theoretical_or_conceptual","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":[0.0010740688,0.0009318994,0.0009846959,0.00003376008,0.00025531277,0.00023071532,0.006332727,0.024105564,0.1640888,0.78905123,0.00033792935,0.0125733055],"study_design_scores_gemma":[0.00447131,0.0055341832,0.0016623619,0.00034080286,0.00014231354,0.00017375534,0.1372188,0.57336086,0.1870428,0.08803095,0.00020749164,0.0018143757],"about_ca_topic_score_codex":0.00006253905,"about_ca_topic_score_gemma":6.123677e-7,"teacher_disagreement_score":0.7010203,"about_ca_system_score_codex":0.00026663434,"about_ca_system_score_gemma":0.00004502749,"threshold_uncertainty_score":0.9998499},"labels":[],"label_agreement":null},{"id":"W2204833299","doi":"10.1016/j.jspi.2015.12.007","title":"A note on the construction of blocked two-level designs with general minimum lower order confounding","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematics; Confounding; Order (exchange); Statistics; Combinatorics; Econometrics","score_opus":0.2133840983652915,"score_gpt":0.46344630997915415,"score_spread":0.25006221161386266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204833299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26850897,0.000019676221,0.7302467,0.00028772795,0.00016081556,0.000045228924,0.000032207165,0.000002488214,0.0006961779],"genre_scores_gemma":[0.6562793,0.000003763587,0.3435229,0.00007645911,0.000039950086,7.2660833e-7,1.4919128e-7,0.0000040908108,0.00007263424],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99789906,0.0003277336,0.00061835203,0.00016897687,0.0008179175,0.00016798187],"domain_scores_gemma":[0.98888856,0.009848147,0.0005123246,0.00013912187,0.0004873078,0.0001245318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002000886,0.00012257413,0.00031509963,0.00013008987,0.000100714366,0.00010519195,0.0002287538,0.000042274984,0.00027938184],"category_scores_gemma":[0.0062160306,0.000050140858,0.00002962363,0.00020115267,0.0005762395,0.00020012785,0.000034396784,0.00018004903,0.000005926204],"study_design_candidate":"theoretical_or_conceptual","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":[0.0058422633,0.00027187038,0.038378898,0.000017729713,0.00018783286,0.00031203747,0.002169645,0.00078484754,0.3903696,0.38479352,0.0030648187,0.17380695],"study_design_scores_gemma":[0.01804245,0.031486396,0.16993603,0.0054952526,0.0004473461,0.0035983033,0.008391859,0.056113474,0.18262915,0.51760185,0.0039108037,0.0023471022],"about_ca_topic_score_codex":0.0000062863164,"about_ca_topic_score_gemma":5.1352094e-7,"teacher_disagreement_score":0.38777035,"about_ca_system_score_codex":0.000026072776,"about_ca_system_score_gemma":0.00014118574,"threshold_uncertainty_score":0.74416184},"labels":[],"label_agreement":null},{"id":"W2218446173","doi":"10.1080/00401706.2015.1114024","title":"Joint Identification of Location and Dispersion Effects in Unreplicated Two-Level Factorials","year":2015,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Dispersion (optics); Identification (biology); Statistics; Variance (accounting); Selection (genetic algorithm); Mathematics; Model selection; Computer science; Artificial intelligence","score_opus":0.35327163915902265,"score_gpt":0.46525957885420266,"score_spread":0.11198793969518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2218446173","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6426324,0.00056563545,0.35570747,0.00005472963,0.00027440657,0.0003967679,0.000006906926,0.000048360685,0.00031330262],"genre_scores_gemma":[0.9680533,0.000012870264,0.031814706,0.000009419494,0.000016726386,0.00001476975,0.0000041108287,0.000009895971,0.00006421025],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99745226,0.0002896665,0.00080322655,0.00040284346,0.00091503555,0.0001369764],"domain_scores_gemma":[0.99756426,0.0009626906,0.0004183611,0.0005706553,0.00038637477,0.00009767889],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009079034,0.00010882007,0.00029031327,0.0021478832,0.000028165898,0.00008081805,0.00036863043,0.00008898093,0.000005837351],"category_scores_gemma":[0.02485898,0.00008948321,0.000032107877,0.008054658,0.00010147524,0.00029431246,0.00017304956,0.00009329919,0.000047924947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050508566,0.00020245617,0.018482542,0.000023872472,0.0000052390506,0.0000020059795,0.00048511353,0.00020449465,0.8157003,0.006530242,0.00021817541,0.15809508],"study_design_scores_gemma":[0.0011523095,0.00020537524,0.1948925,0.00003298463,0.000009944731,0.0000031247625,0.00061549334,0.0076191435,0.7599382,0.03521657,0.000109898785,0.00020445736],"about_ca_topic_score_codex":0.00006680791,"about_ca_topic_score_gemma":0.0000019200743,"teacher_disagreement_score":0.32542086,"about_ca_system_score_codex":0.00016193635,"about_ca_system_score_gemma":0.000036290905,"threshold_uncertainty_score":0.98335505},"labels":[],"label_agreement":null},{"id":"W2290566946","doi":"","title":"A general criterion for factorial designs under model uncertainty","year":2010,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"","keywords":"Generalization; Computer science; Mathematical optimization; Limiting; Factorial experiment; Mathematics; Machine learning; Engineering","score_opus":0.3655784518244831,"score_gpt":0.5189059344658788,"score_spread":0.15332748264139573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290566946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25911248,0.000012133507,0.7383338,0.00008488952,0.0018655465,0.00022657543,0.00003166141,0.000095167314,0.00023769993],"genre_scores_gemma":[0.57679504,4.2275337e-7,0.4222872,0.00008295012,0.00041019905,0.000044434113,0.0000050040044,0.000021939946,0.00035284404],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976517,0.0001294406,0.0006292968,0.00048432793,0.0007306889,0.0003745735],"domain_scores_gemma":[0.9974782,0.0015005255,0.000103764454,0.0005525489,0.00018331228,0.00018167669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004373758,0.00020569899,0.00033306264,0.0001455865,0.00010239369,0.0002574251,0.0005249008,0.00016352005,0.00015398761],"category_scores_gemma":[0.0031244075,0.0001750327,0.00019598656,0.00024312393,0.000041739902,0.00033736735,0.00008586525,0.00024319439,0.000023402044],"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":[0.00004533423,0.000022021255,0.00001972138,0.000005840955,0.000007327179,2.8446613e-7,0.00028289505,0.27567306,0.6845289,0.036751516,0.00026210226,0.002401011],"study_design_scores_gemma":[0.00042733783,0.00005018194,0.0003726364,0.0000029466748,0.0000053507174,0.0000018625531,0.00007880815,0.93396574,0.040544875,0.022174317,0.0020973478,0.00027857185],"about_ca_topic_score_codex":0.000054545053,"about_ca_topic_score_gemma":0.000010575089,"teacher_disagreement_score":0.6582927,"about_ca_system_score_codex":0.000069985756,"about_ca_system_score_gemma":0.00006657761,"threshold_uncertainty_score":0.71376264},"labels":[],"label_agreement":null},{"id":"W2295999734","doi":"10.1080/03610926.2014.935433","title":"Construction of minimum aberration blocked two-level regular factorial designs","year":2015,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"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":"Fractional factorial design; Factorial experiment; Mathematics; Algorithm; Computer science; Arithmetic; Mathematical optimization; Combinatorics; Statistics","score_opus":0.38133606300950473,"score_gpt":0.5472235196899693,"score_spread":0.16588745668046456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295999734","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":[0.015511859,0.00070241943,0.9802734,0.000044948294,0.0005970956,0.00028854216,0.000060128394,0.000018888051,0.0025026863],"genre_scores_gemma":[0.18338357,0.000045727487,0.8162001,0.000027196134,0.000030123292,0.000024400277,0.000017250257,0.0000115236,0.00026014532],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9811131,0.016796947,0.0010456446,0.00033574065,0.00053639273,0.00017218472],"domain_scores_gemma":[0.98561776,0.0120862685,0.0005524215,0.0010374706,0.0005642086,0.00014187876],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03144261,0.00016472649,0.00043103314,0.00029031417,0.00012339182,0.0000948335,0.00062528223,0.000120243494,0.00010449867],"category_scores_gemma":[0.019680826,0.0001468687,0.0000402546,0.00052541174,0.00077645294,0.00031623078,0.00023073853,0.00021094564,0.000007497943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056374894,0.000073137824,0.00063381944,0.0000064140604,0.000015675681,6.4788935e-7,0.004823097,0.00010855979,0.038127664,0.7493701,0.00027099685,0.20600614],"study_design_scores_gemma":[0.0011854863,0.00013371208,0.0011458518,0.000025902058,0.00002219833,0.000010110374,0.004523731,0.004307814,0.03773374,0.949866,0.0008804731,0.00016501297],"about_ca_topic_score_codex":0.000044774388,"about_ca_topic_score_gemma":0.000010416208,"teacher_disagreement_score":0.20584112,"about_ca_system_score_codex":0.00007197836,"about_ca_system_score_gemma":0.00014355454,"threshold_uncertainty_score":0.99733365},"labels":[],"label_agreement":null},{"id":"W2313674309","doi":"10.5705/ss.2013.322t","title":"Robust sampling designs for a possibly misspecified stochastic process","year":2014,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Computer science; Sampling (signal processing); Process (computing); Econometrics; Statistics; Mathematics","score_opus":0.5440202437885654,"score_gpt":0.5280115648360327,"score_spread":0.016008678952532662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2313674309","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":[0.0012588047,0.0000594652,0.99042493,0.00029798874,0.00045842765,0.0007290292,0.00017581102,0.000098472774,0.006497054],"genre_scores_gemma":[0.36567777,6.56477e-7,0.63308597,0.000245681,0.0001493876,0.00009991457,0.000011179103,0.000038066453,0.00069138873],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99562705,0.0005209262,0.0010659301,0.0010033074,0.0011784398,0.00060438074],"domain_scores_gemma":[0.9783534,0.01956356,0.00036578046,0.00078511523,0.00058587745,0.00034628823],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0049237055,0.00029120204,0.0005946619,0.00022353648,0.00036168133,0.00048192678,0.0010397835,0.00011692236,0.00072556635],"category_scores_gemma":[0.033656117,0.00023644893,0.00014339443,0.00061708636,0.0003013194,0.00022922807,0.000098109,0.00018104845,0.00031320588],"study_design_candidate":"theoretical_or_conceptual","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":[0.003215898,0.0010229626,0.00017566304,0.00014983735,0.0001727881,0.000017544167,0.004437647,0.041540734,0.042993605,0.41506124,0.040457953,0.45075414],"study_design_scores_gemma":[0.0027333242,0.0019258311,0.0019731869,0.00010390196,0.00010370505,0.000029798923,0.0016913469,0.29048666,0.0032313643,0.68617237,0.010436455,0.0011120426],"about_ca_topic_score_codex":0.0000067962433,"about_ca_topic_score_gemma":0.0000041706626,"teacher_disagreement_score":0.4496421,"about_ca_system_score_codex":0.00006534148,"about_ca_system_score_gemma":0.00019082127,"threshold_uncertainty_score":0.9744838},"labels":[],"label_agreement":null},{"id":"W2316484965","doi":"10.2514/6.2004-2139","title":"Application of DOE for Optimal Turbomachinery Design","year":2004,"lang":"en","type":"article","venue":"34th AIAA Fluid Dynamics Conference and Exhibit","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Polytechnique Montréal","funders":"U.S. Department of Energy","keywords":"Turbomachinery; Computer science; Design of experiments; Systems engineering; Engineering; Mechanical engineering; Mathematics","score_opus":0.08617590373675947,"score_gpt":0.38370269422514053,"score_spread":0.29752679048838104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2316484965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1100859,0.00023292219,0.88746035,0.00023744215,0.00011767349,0.0006425333,0.00004426911,0.000036127814,0.0011427947],"genre_scores_gemma":[0.70472187,0.000032627322,0.29480115,0.00008836664,0.000030102354,0.00007658048,0.000019307208,0.000015573723,0.00021439421],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975799,0.00013851175,0.00071333535,0.00064674445,0.00060238765,0.00031915403],"domain_scores_gemma":[0.99780846,0.00080699567,0.00025704037,0.00054848,0.00041266554,0.00016636716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020770747,0.00023947071,0.00045507154,0.00021722159,0.00014175667,0.00011823059,0.0005648017,0.00015360706,0.000036730824],"category_scores_gemma":[0.0006360271,0.00019320866,0.0001254674,0.0003875779,0.00025428899,0.0003632256,0.00013446563,0.00011835048,0.000029211267],"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":[0.00082154747,0.00031343344,0.0017071491,0.000043775955,0.000055116932,0.000005087271,0.0016761903,0.048613243,0.3629916,0.3219274,0.00029070876,0.26155472],"study_design_scores_gemma":[0.0012103547,0.0005132765,0.00078817667,0.00002652993,0.00002413959,0.0000126813175,0.0007039327,0.8699113,0.04176628,0.084507786,0.00022393843,0.0003115669],"about_ca_topic_score_codex":0.00008824712,"about_ca_topic_score_gemma":0.000017304192,"teacher_disagreement_score":0.8212981,"about_ca_system_score_codex":0.00007597961,"about_ca_system_score_gemma":0.00017302569,"threshold_uncertainty_score":0.78788203},"labels":[],"label_agreement":null},{"id":"W2321293294","doi":"10.1080/00207543.2016.1154212","title":"Robust optimisation of Nd: YLF laser beam micro-drilling process using Bayesian probabilistic approach","year":2016,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Machining; Robustness (evolution); Probabilistic logic; Bayesian probability; Laser beam machining; Process (computing); Computer science; Engineering; Mathematical optimization; Laser; Mechanical engineering; Mathematics; Laser beams; Artificial intelligence; Optics; Physics","score_opus":0.4508261617594121,"score_gpt":0.5319701852666353,"score_spread":0.08114402350722322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2321293294","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76719904,0.00014043972,0.22782043,0.0020498794,0.0015906683,0.00040597614,0.0000123718055,0.0000113376,0.0007698643],"genre_scores_gemma":[0.8248813,0.000022142802,0.17356989,0.000010928032,0.0008465703,0.0000077568275,0.000001154,0.000019528525,0.0006406998],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99194807,0.0009434807,0.0013813678,0.00049957854,0.0049329353,0.00029453993],"domain_scores_gemma":[0.9884038,0.00095298095,0.00093087816,0.00035413724,0.009211579,0.00014665541],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016126782,0.00014477716,0.0003177138,0.0014621273,0.00013352204,0.00021339407,0.0013695088,0.000093064715,0.00032190603],"category_scores_gemma":[0.014907575,0.000091692105,0.00015847574,0.0009746109,0.0004384984,0.0012474154,0.00015586772,0.0003532786,0.00003581682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009964034,0.0006418432,0.0042578774,0.000029997664,0.00013362626,0.00001617017,0.0010769062,0.16951542,0.79645157,0.0002705105,0.0012414039,0.025368296],"study_design_scores_gemma":[0.00095173286,0.00035751102,0.0010091354,0.0003241976,0.00001810072,0.00054873404,0.002544856,0.009005152,0.9664884,0.017858796,0.0006793686,0.00021398578],"about_ca_topic_score_codex":0.00001265018,"about_ca_topic_score_gemma":8.767814e-7,"teacher_disagreement_score":0.17003688,"about_ca_system_score_codex":0.0004393399,"about_ca_system_score_gemma":0.00045549794,"threshold_uncertainty_score":0.99339026},"labels":[],"label_agreement":null},{"id":"W2368506875","doi":"","title":"Adjusted empirical likelihood in Cox proportional hazard model","year":2009,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Proportional hazards model; Hazard ratio; Statistics; Confidence interval; Mathematics; Monte Carlo method; Empirical likelihood; Hazard; Econometrics; Maximum likelihood; Regression analysis; Applied mathematics","score_opus":0.26424894235224244,"score_gpt":0.49778225132281684,"score_spread":0.2335333089705744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2368506875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43887398,0.00013652041,0.37989268,0.0068151527,0.00019798073,0.0005326284,0.000006824947,0.00015751034,0.17338674],"genre_scores_gemma":[0.72001475,0.000001616071,0.27490404,0.0020759082,0.000026993894,0.000008794311,0.0000018649692,0.000005941296,0.0029601215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964646,0.0002678645,0.0007866246,0.0005732901,0.0015527348,0.0003548841],"domain_scores_gemma":[0.998776,0.0003314504,0.000106797976,0.00042328498,0.00017132203,0.0001911385],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027073156,0.00015803061,0.0003038217,0.00030293083,0.000056498364,0.00013234925,0.0005438488,0.00011734358,0.0010661178],"category_scores_gemma":[0.0011420224,0.00011001902,0.00011057815,0.00094057014,0.00007173154,0.00039520152,0.000076574026,0.00018446092,0.00054614653],"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":[0.0011159877,0.0036106918,0.090229735,0.000004499957,0.000023394421,0.00021789975,0.0032557836,0.0342313,0.09519852,0.045523725,0.14194511,0.58464336],"study_design_scores_gemma":[0.00087257737,0.0003355144,0.101112604,0.000007931214,0.0000036056192,0.00002254712,0.00040560984,0.6800549,0.011207495,0.2048212,0.0008403358,0.0003156739],"about_ca_topic_score_codex":0.000009066539,"about_ca_topic_score_gemma":0.000019189034,"teacher_disagreement_score":0.6458236,"about_ca_system_score_codex":0.00009555648,"about_ca_system_score_gemma":0.00019524696,"threshold_uncertainty_score":0.99984705},"labels":[],"label_agreement":null},{"id":"W2374120850","doi":"","title":"D-optimal Orthogonal Block Designs with Parameter Estimation for the Additive Mixture Models","year":2009,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Science North","funders":"","keywords":"Orthogonal array; Block (permutation group theory); Mathematics; Block design; Orthogonal matrix; Orthogonal transformation; Applied mathematics; Optimal design; Mathematical optimization; Orthogonal basis; Combinatorics; Algorithm; Statistics; Taguchi methods","score_opus":0.16519074933164782,"score_gpt":0.4211642392842557,"score_spread":0.25597348995260794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2374120850","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":[0.010188867,0.000088625806,0.98266083,0.0012163605,0.00008361018,0.00091082195,0.00003655106,0.00006435761,0.004750003],"genre_scores_gemma":[0.40313455,0.0000017587671,0.5943022,0.00091593334,0.000042726773,0.000073045136,0.0000051117963,0.000010117805,0.0015145693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727076,0.000250446,0.00044054684,0.00056875154,0.0011180353,0.00035146065],"domain_scores_gemma":[0.9928462,0.005952179,0.00018173532,0.0005293478,0.00036992953,0.00012062614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020066006,0.00023843558,0.0002810246,0.00011446984,0.00029925877,0.00037234873,0.00063879875,0.00010284713,0.0003815345],"category_scores_gemma":[0.0009886975,0.000113402384,0.0001591306,0.0005056994,0.00015290582,0.00069456623,0.000039348968,0.0001527863,0.000059934577],"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":[0.0016023535,0.00026627784,0.000029361272,0.0000016864851,0.000087820525,0.0000100161915,0.0013963031,0.63029844,0.004729246,0.05128223,0.028486127,0.28181016],"study_design_scores_gemma":[0.0005455562,0.0009749262,0.00047259664,0.0000082814895,0.000034873472,0.000034507862,0.00054676144,0.9164619,0.0157862,0.06406015,0.00084004976,0.0002341986],"about_ca_topic_score_codex":0.0000042163956,"about_ca_topic_score_gemma":0.000003233048,"teacher_disagreement_score":0.3929457,"about_ca_system_score_codex":0.00003820587,"about_ca_system_score_gemma":0.00008866176,"threshold_uncertainty_score":0.4624415},"labels":[],"label_agreement":null},{"id":"W2396913950","doi":"10.1080/03610926.2014.942434","title":"Robust designs for experiments with blocks","year":2015,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematics; Algorithm","score_opus":0.5196230109503861,"score_gpt":0.5699451367973264,"score_spread":0.05032212584694029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396913950","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":[0.001592951,0.0023611537,0.9907136,0.000064770546,0.00011340875,0.00051924813,0.000034829813,0.000026035881,0.0045739864],"genre_scores_gemma":[0.044246662,0.000047724243,0.9539907,0.00016786836,0.000013502628,0.00023965274,0.000012199281,0.000021675021,0.0012600187],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99007404,0.008302672,0.00060306856,0.0003972979,0.00038588297,0.00023701254],"domain_scores_gemma":[0.9814278,0.01664761,0.00025227346,0.0010906587,0.00038463937,0.00019700151],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.031154031,0.00017858105,0.00036484748,0.00020354977,0.0001865308,0.0001801041,0.0008205583,0.000083707535,0.000090143716],"category_scores_gemma":[0.010448914,0.00013571858,0.000027925886,0.00038939243,0.00045810017,0.0002503126,0.00024831132,0.00016599875,0.000007084572],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00166953,0.00019867835,0.0007939274,0.000008751152,0.000030006922,0.0000023716377,0.008981454,0.0008492306,0.004429271,0.71317625,0.0020612353,0.2677993],"study_design_scores_gemma":[0.001842803,0.00044519253,0.00043689262,0.00004927674,0.00002468707,0.000013472964,0.010245684,0.010207447,0.016522702,0.95220417,0.00770249,0.0003051982],"about_ca_topic_score_codex":0.000015289232,"about_ca_topic_score_gemma":0.000006090651,"teacher_disagreement_score":0.26749408,"about_ca_system_score_codex":0.00006817099,"about_ca_system_score_gemma":0.00010342337,"threshold_uncertainty_score":0.9978865},"labels":[],"label_agreement":null},{"id":"W2399998015","doi":"","title":"First Order Rotatable Designs Incorporating Neighbour Effects.","year":2013,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Order (exchange); Business","score_opus":0.06978905910689037,"score_gpt":0.36284157691097285,"score_spread":0.2930525178040825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399998015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93733203,0.0005213775,0.015631147,0.000814935,0.0055460883,0.0018724113,0.0000039298347,0.00030909726,0.037969004],"genre_scores_gemma":[0.8847686,0.000002894021,0.11292202,0.00034476057,0.000019167734,0.00019666924,0.0000022769054,0.000042654647,0.0017009277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958652,0.0007275331,0.0007590566,0.0007487468,0.0013388905,0.00056051783],"domain_scores_gemma":[0.99390423,0.0038604445,0.00035895704,0.0010070904,0.00055232225,0.0003169421],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0027040725,0.00031684418,0.0005058081,0.0002280102,0.00044367713,0.0008221669,0.0012002938,0.00014869178,0.002054944],"category_scores_gemma":[0.0052570496,0.0002532598,0.00012489528,0.001705988,0.000159025,0.0013150113,0.00040336014,0.00027956223,0.0055019087],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057464684,0.00066365465,0.03495907,0.00004269357,0.00008243558,0.00007160083,0.0010815993,0.00051468227,0.11129296,0.75573546,0.06625628,0.02924209],"study_design_scores_gemma":[0.0010377347,0.0003517111,0.008791636,0.000035049212,0.0000109351495,0.000010851007,0.00031818287,0.014445937,0.031531032,0.94144315,0.0015910962,0.0004326807],"about_ca_topic_score_codex":0.00040788602,"about_ca_topic_score_gemma":0.0000059855083,"teacher_disagreement_score":0.18570769,"about_ca_system_score_codex":0.00011993265,"about_ca_system_score_gemma":0.00010528621,"threshold_uncertainty_score":0.99999195},"labels":[],"label_agreement":null},{"id":"W2409250823","doi":"10.5539/ijsp.v5n4p22","title":"Alternative Second-Order N-Point Spherical Response Surface Methodology Design and Their Efficiencies","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Central composite design; Composite number; Inscribed figure; Point (geometry); Response surface methodology; Mathematics; Optimal design; Design of experiments; RADIUS; Mathematical optimization; Computer science; Geometry; Algorithm; Statistics","score_opus":0.20338887857460763,"score_gpt":0.4453104486077617,"score_spread":0.24192157003315407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409250823","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":[0.2826559,0.00016640013,0.715256,0.0012289865,0.00041819696,0.00010346619,0.00010568513,0.00000359459,0.00006181468],"genre_scores_gemma":[0.377981,0.00004749787,0.6217162,0.00010163666,0.00003609846,0.0000011982376,1.6523872e-7,0.0000054058355,0.00011078961],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99446446,0.0033789626,0.0008815347,0.0003312589,0.0007755339,0.00016823866],"domain_scores_gemma":[0.96824455,0.029137988,0.00057091186,0.00016650172,0.0017209712,0.00015905053],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.017288836,0.00015913016,0.00038031134,0.00010544725,0.000064659274,0.00016974796,0.0005347233,0.000060163,0.0005670393],"category_scores_gemma":[0.021492017,0.00008262319,0.000055790682,0.00013169693,0.00063430826,0.000305657,0.00021623024,0.00014366898,0.000006023722],"study_design_candidate":"theoretical_or_conceptual","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":[0.016746834,0.00048791952,0.006510351,0.000012367809,0.00040661095,0.0001878256,0.0071128067,0.0023157615,0.45394206,0.055040188,0.0024518443,0.45478544],"study_design_scores_gemma":[0.0013072607,0.0013003798,0.012264233,0.000035095207,0.000011503707,0.00038731916,0.0005808825,0.0058248243,0.04828736,0.9263924,0.0033936934,0.00021505772],"about_ca_topic_score_codex":0.000010671508,"about_ca_topic_score_gemma":0.0000030716146,"teacher_disagreement_score":0.8713522,"about_ca_system_score_codex":0.000110052635,"about_ca_system_score_gemma":0.00017238432,"threshold_uncertainty_score":0.98675036},"labels":[],"label_agreement":null},{"id":"W2410674143","doi":"10.1002/qre.2022","title":"Using Bayesian Variable Selection to Analyze Regular Resolution IV Two‐level Fractional Factorial Designs","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractional factorial design; Selection (genetic algorithm); Bayesian probability; Mathematics; Factorial experiment; Variable (mathematics); Statistics; Aliasing; Feature selection; Computer science; Artificial intelligence","score_opus":0.21593583600767408,"score_gpt":0.4599711975242283,"score_spread":0.24403536151655425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2410674143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0882054,0.000010008558,0.90842396,0.00070670655,0.0020235295,0.00018418416,0.0000741594,0.000074811534,0.0002972433],"genre_scores_gemma":[0.5112982,0.0000016789463,0.48760262,0.000061604675,0.00051723706,0.000013404456,0.0000046109035,0.000013521183,0.00048714413],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638397,0.00045712758,0.0008371534,0.00071702997,0.0013234222,0.0002812848],"domain_scores_gemma":[0.9971184,0.0016195636,0.00017619466,0.0003337415,0.00053475786,0.00021730037],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0062382095,0.00020410231,0.00028695303,0.00033230294,0.00016408289,0.00019652056,0.00036785653,0.0001491547,0.0006261737],"category_scores_gemma":[0.0088327415,0.00015272002,0.00011012878,0.00051684596,0.00006347709,0.0007133406,0.00013811942,0.0001698019,0.000030810857],"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":[0.0005541006,0.0001981158,0.01279453,0.00001384787,0.00009057586,0.0000022211877,0.00026374712,0.22931795,0.6559542,0.09682862,0.0005786059,0.003403497],"study_design_scores_gemma":[0.0019735857,0.00024923528,0.07733469,0.00013787937,0.00003567706,0.00006457731,0.00010192578,0.7922534,0.029638167,0.07275795,0.024493909,0.0009590469],"about_ca_topic_score_codex":0.00030926714,"about_ca_topic_score_gemma":0.000005663172,"teacher_disagreement_score":0.626316,"about_ca_system_score_codex":0.0005896489,"about_ca_system_score_gemma":0.0001006318,"threshold_uncertainty_score":0.9995163},"labels":[],"label_agreement":null},{"id":"W2471086566","doi":"10.1007/s10985-016-9371-2","title":"Association measures for bivariate failure times in the presence of a cure fraction","year":2016,"lang":"en","type":"article","venue":"Lifetime Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Estimator; Fraction (chemistry); Flexibility (engineering); Mathematics; Applied mathematics; Range (aeronautics); Computer science; Econometrics; Statistics; Materials science","score_opus":0.1340657016613935,"score_gpt":0.44417221815478936,"score_spread":0.3101065164933958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2471086566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009301752,0.00027299658,0.9733252,0.013303299,0.00013467082,0.00065265317,0.0020636735,0.000030006491,0.00091576594],"genre_scores_gemma":[0.9017079,0.000043708136,0.09537723,0.00017026872,0.000117852855,0.000057585297,0.00017971307,0.000012047066,0.0023337328],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99551636,0.001387316,0.0006859762,0.00059138756,0.0016088756,0.00021008978],"domain_scores_gemma":[0.9889074,0.00842122,0.0006393937,0.0016988802,0.0002919686,0.0000411699],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.013704161,0.00012213131,0.00042736586,0.00049517275,0.00008070999,0.00015713008,0.0020611528,0.000109057306,0.00060800614],"category_scores_gemma":[0.017585224,0.000060765324,0.00019780578,0.0026522856,0.00005042785,0.0009908428,0.00021608846,0.00007913857,0.00007927697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005645077,0.00068629766,0.35792872,0.000015744314,0.0035541228,0.0000048269185,0.0021358079,0.001531684,0.1400212,0.0031095857,0.39391366,0.096533835],"study_design_scores_gemma":[0.0040019974,0.00058832526,0.24320136,0.00014752822,0.006756913,0.0000046368464,0.0068111164,0.23123501,0.043309156,0.07592168,0.38655123,0.0014710552],"about_ca_topic_score_codex":0.0002496661,"about_ca_topic_score_gemma":0.00020910922,"teacher_disagreement_score":0.8924061,"about_ca_system_score_codex":0.00005968203,"about_ca_system_score_gemma":0.000043204265,"threshold_uncertainty_score":0.99069005},"labels":[],"label_agreement":null},{"id":"W2472752992","doi":"10.1080/03610918.2015.1030414","title":"Computing A-optimal and E-optimal designs for regression models via semidefinite programming","year":2015,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Semidefinite programming; Optimal design; Mathematical optimization; MATLAB; Linear programming; Computer science; Mathematics; Convergence (economics); Semidefinite embedding; Construct (python library); Quadratically constrained quadratic program; Machine learning","score_opus":0.5693965277082369,"score_gpt":0.5724371946419906,"score_spread":0.0030406669337537107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2472752992","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":[0.024386656,0.0006052486,0.9738052,0.00013007033,0.00008690985,0.00068720034,0.00003533562,0.000055623226,0.00020774906],"genre_scores_gemma":[0.4972548,0.0000116863675,0.50255674,0.000036093537,0.000010252721,0.00002079944,0.00007952311,0.0000119057395,0.000018234743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718446,0.0007546454,0.0008979399,0.00043144508,0.00050554523,0.00022597963],"domain_scores_gemma":[0.9910648,0.007144559,0.0004004439,0.00052269764,0.0007043116,0.00016319852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033609627,0.00019351601,0.00031045062,0.0003580237,0.0003901436,0.00039417148,0.00038381605,0.00010335249,0.000002539532],"category_scores_gemma":[0.0017661065,0.00018418327,0.00003066442,0.0005280619,0.00025218635,0.0005250482,0.00037554983,0.00017833039,0.000004420927],"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":[0.00006834347,0.00006567303,0.00057433423,0.000006985148,0.0000052693777,5.2285776e-7,0.0029929187,0.7134866,0.000062156956,0.010879405,0.00007601486,0.2717818],"study_design_scores_gemma":[0.0009385921,0.00015574039,0.00077052496,0.000036335266,0.000013691788,0.0000053806443,0.0014927031,0.9204538,0.0000210252,0.07548784,0.00043869397,0.00018569567],"about_ca_topic_score_codex":0.000025863792,"about_ca_topic_score_gemma":0.000011023632,"teacher_disagreement_score":0.4728681,"about_ca_system_score_codex":0.00009330558,"about_ca_system_score_gemma":0.00007517182,"threshold_uncertainty_score":0.7510776},"labels":[],"label_agreement":null},{"id":"W2474739796","doi":"10.5539/ijsp.v5n4p84","title":"On the Existence Conditions for Balanced Fractional $2^{m}$ Factorial Designs of Resolution $\\mathrm{R}^{\\ast}(\\{1\\}|\\mathrm{\\Omega}_{\\ell})$ with $N&lt;\\nu_{\\ell}(m)$","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fractional factorial design; Mathematics; Omega; Resolution (logic); Factorial; Combinatorics; Factorial experiment; Order (exchange); Physics; Mathematical analysis; Statistics; Computer science; Quantum mechanics","score_opus":0.1622880970431732,"score_gpt":0.43520305136846815,"score_spread":0.27291495432529495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2474739796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11428538,0.0000403428,0.8803106,0.0016968553,0.0011537208,0.00038850517,0.0016547941,0.000006372518,0.00046340565],"genre_scores_gemma":[0.8373392,0.000030360612,0.16213046,0.000105949366,0.00022549753,0.00002159175,0.0000070244437,0.000010887934,0.00012899889],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962526,0.00039336903,0.0010688507,0.0003162058,0.001779895,0.00018907594],"domain_scores_gemma":[0.98358023,0.012273678,0.0011618162,0.00026116043,0.0025993024,0.00012379991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00374165,0.00017762485,0.00034943942,0.00016867883,0.0001590519,0.00014322101,0.0006371535,0.00007375462,0.00029532108],"category_scores_gemma":[0.0076753157,0.00008856536,0.00012208523,0.00015422265,0.0005685564,0.0003776523,0.00006594859,0.00016669594,0.0000072979387],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005120202,0.00053802575,0.0031498282,0.00001691162,0.00023689111,0.000014146319,0.00048634296,0.0006288706,0.036401633,0.93408185,0.0067868163,0.012538488],"study_design_scores_gemma":[0.0018106692,0.0015569153,0.013465103,0.000143395,0.000032635875,0.00007518768,0.00014017233,0.0017860908,0.005613621,0.9723913,0.0028124666,0.00017242564],"about_ca_topic_score_codex":0.000010036938,"about_ca_topic_score_gemma":0.000008307625,"teacher_disagreement_score":0.7230539,"about_ca_system_score_codex":0.00018561997,"about_ca_system_score_gemma":0.0002642625,"threshold_uncertainty_score":0.9188624},"labels":[],"label_agreement":null},{"id":"W2483827338","doi":"10.5539/jmr.v8n4p40","title":"Useful Numerical Statistics of Some Response Surface Methodology Designs","year":2016,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Plackett–Burman design; Fractional factorial design; Response surface methodology; Optimality criterion; Factorial experiment; Order (exchange); Optimal design; Mathematical optimization; Surface (topology); Factorial; Hypercube; Statistics; Combinatorics; Geometry; Mathematical analysis","score_opus":0.7972484735417239,"score_gpt":0.6384052529672962,"score_spread":0.15884322057442768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483827338","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":[0.30254394,0.00022079723,0.6957763,0.0009094996,0.00018968256,0.00014712776,0.000026108602,0.000004522546,0.00018205337],"genre_scores_gemma":[0.18295632,0.00006256538,0.81497866,0.000018112614,0.000060017355,0.0000015508019,5.749881e-8,0.000024808704,0.0018978891],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9762204,0.014916907,0.0023873346,0.00030345618,0.005586289,0.00058561156],"domain_scores_gemma":[0.8813098,0.1140967,0.0010166514,0.0006795098,0.0025906265,0.00030671433],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.11860434,0.00016276329,0.00092590397,0.00084988517,0.000102308884,0.00010429568,0.0017740192,0.00014995264,0.0011168296],"category_scores_gemma":[0.12614648,0.00008651276,0.00020557121,0.0009392386,0.0006860956,0.00043176088,0.00037537544,0.0004922372,0.00026143497],"study_design_candidate":"bench_or_experimental","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":[0.0017986151,0.00052026566,0.00043975576,0.000026657919,0.00007094226,0.00012983823,0.0016646667,0.00008776464,0.9575796,0.018942649,0.011465921,0.0072733057],"study_design_scores_gemma":[0.0011300076,0.0027546925,0.0011802155,0.0001595652,0.000020267338,0.0003371086,0.0030663596,0.001280028,0.4443863,0.54362905,0.0018727306,0.00018370658],"about_ca_topic_score_codex":0.0000043935765,"about_ca_topic_score_gemma":3.3253522e-7,"teacher_disagreement_score":0.5246864,"about_ca_system_score_codex":0.000192751,"about_ca_system_score_gemma":0.00063846866,"threshold_uncertainty_score":0.9997963},"labels":[],"label_agreement":null},{"id":"W2492791888","doi":"10.1037/10693-007","title":"Effect Size Estimation in Multifactor Designs.","year":2006,"lang":"en","type":"book-chapter","venue":"American Psychological Association eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Estimation; Econometrics; Computer science; Statistics; Mathematics; Economics","score_opus":0.0937258832690499,"score_gpt":0.4519220721500317,"score_spread":0.3581961888809818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2492791888","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017378999,0.000047860347,0.006731048,0.00023751105,0.0006489823,0.0012334969,0.000064493295,0.00022822715,0.9734294],"genre_scores_gemma":[0.34650624,0.0000058225132,0.04522108,0.0010230545,0.00017142831,0.00012370397,0.000029787418,0.00009402151,0.6068249],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99207056,0.0016838702,0.001682646,0.0014957153,0.0024678684,0.00059932494],"domain_scores_gemma":[0.97989845,0.016086215,0.002516803,0.0010722815,0.00023905843,0.00018717087],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0068615465,0.00067585305,0.0014968929,0.00044887525,0.0001235722,0.0002783258,0.0012450829,0.00074930367,0.0011262366],"category_scores_gemma":[0.011201689,0.0005255938,0.0005083609,0.00022578388,0.0003766598,0.00013033467,0.00019552428,0.0009401049,0.0016246755],"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":[0.0006411404,0.00029350084,0.010942154,0.0000096885415,0.00010964443,0.000115175964,0.00024466973,0.0004122972,0.0036971543,0.0045998427,0.045800842,0.9331339],"study_design_scores_gemma":[0.011544457,0.01968781,0.2754871,0.0005265107,0.00047156386,0.000057780326,0.00026921995,0.0074034035,0.005968634,0.5087417,0.16076912,0.009072736],"about_ca_topic_score_codex":0.00014017627,"about_ca_topic_score_gemma":0.00002066359,"teacher_disagreement_score":0.9240612,"about_ca_system_score_codex":0.0012166952,"about_ca_system_score_gemma":0.000040962892,"threshold_uncertainty_score":0.99978685},"labels":[],"label_agreement":null},{"id":"W2507592422","doi":"10.1177/0008068320020515","title":"Split Block Designs","year":2002,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Block (permutation group theory); Mathematics; Class (philosophy); Arithmetic; Computer science; Mathematical optimization; Statistics; Algorithm; Combinatorics; Artificial intelligence","score_opus":0.1838751938998208,"score_gpt":0.4101778376903155,"score_spread":0.22630264379049472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507592422","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":[0.0075617153,0.00047425978,0.7649153,0.025839511,0.0015646643,0.0008960583,0.0005789714,0.000397876,0.19777164],"genre_scores_gemma":[0.41030243,0.000024041727,0.46097556,0.0033020538,0.00040136575,0.0000753405,0.000021788308,0.00006310599,0.124834314],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99391043,0.001342578,0.0009985968,0.0007305144,0.0024022972,0.00061556714],"domain_scores_gemma":[0.9877415,0.010589681,0.00040244494,0.000483101,0.0004284652,0.00035483172],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0043427465,0.00024112935,0.00047065405,0.00015552786,0.0002514979,0.00045356748,0.00061667827,0.00022259173,0.053169955],"category_scores_gemma":[0.030640312,0.00020678208,0.00014073384,0.0006119025,0.00011053802,0.000097972494,0.00014234688,0.00031957266,0.03213017],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021006827,0.00020656883,0.00234818,0.0000024000365,0.000029558341,0.000032732365,0.00020960225,0.00003656605,0.0010481541,0.039051168,0.93342674,0.023587309],"study_design_scores_gemma":[0.0010000305,0.00026990386,0.014490423,0.000011051709,0.00004450359,0.000015765409,0.00024315229,0.014386406,0.0013611444,0.022362452,0.9452142,0.00060095085],"about_ca_topic_score_codex":0.000016532274,"about_ca_topic_score_gemma":0.0000011995534,"teacher_disagreement_score":0.40274072,"about_ca_system_score_codex":0.00048815634,"about_ca_system_score_gemma":0.000021401644,"threshold_uncertainty_score":0.977525},"labels":[],"label_agreement":null},{"id":"W2508257146","doi":"10.1016/j.jco.2014.10.004","title":"On construction of blocked general minimum lower-order confounding <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si18.gif\" display=\"inline\" overflow=\"scroll\"><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>−</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:msup><mml:mo>:</mml:mo><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msup></mml:math> designs with <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si19.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>N</mml:mi><mml:mo>/</mml:mo><mml:mn>4</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>n</mml:mi><mml:mo>≤</mml:mo><mml:mn>5</mml:mn><mml:mi>N</mml:mi><mml:mo>/</mml:mo><mml:mn>16</mml:mn></mml:math>","year":2014,"lang":"lv","type":"article","venue":"Journal of Complexity","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Order (exchange); Algorithm; Combinatorics; Statistics","score_opus":0.034737542100621216,"score_gpt":0.285277251390713,"score_spread":0.25053970929009184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508257146","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5910146,0.006268,0.007464156,0.0027037403,0.016478686,0.0004893424,0.005066238,0.0018353895,0.36867982],"genre_scores_gemma":[0.9281347,0.0062759556,0.023365298,0.0065775244,0.012656401,0.006697295,0.008478846,0.006559316,0.001254676],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9348077,0.0055319997,0.014288248,0.01172028,0.018739723,0.014912011],"domain_scores_gemma":[0.9449865,0.013227199,0.018289097,0.011707518,0.0025538555,0.00923582],"candidate_categories":["metaresearch","metaepi_narrow","metaepi_broad","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"category_scores_codex":[0.016307684,0.007322169,0.0033233145,0.005589905,0.011693506,0.012092945,0.016786221,0.015166331,0.4688826],"category_scores_gemma":[0.014685944,0.013764804,0.013935131,0.009734044,0.015134277,0.010843817,0.013169177,0.013402169,0.009781474],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014069808,0.0026061723,0.00006314305,0.0038701089,0.009952321,0.0071696267,0.0046856604,0.008162143,0.010565727,0.51288617,0.4202269,0.005742216],"study_design_scores_gemma":[0.013213623,0.012433648,0.00018323056,0.0067073954,0.010616304,0.017451085,0.009049667,0.071615666,0.8229293,0.0037217396,0.02080331,0.011274993],"about_ca_topic_score_codex":0.010077901,"about_ca_topic_score_gemma":0.007042088,"teacher_disagreement_score":0.8123636,"about_ca_system_score_codex":0.0003001196,"about_ca_system_score_gemma":0.012091218,"threshold_uncertainty_score":0.9965141},"labels":[],"label_agreement":null},{"id":"W2508662586","doi":"10.1002/qre.2047","title":"Planning and Analyzing Experiments with Models that Distinguish Between Replicates and Repeats","year":2016,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Computer science; Variation (astronomy); Binary number; Value (mathematics); Artificial intelligence; Data mining; Mathematics; Machine learning","score_opus":0.24185350200226333,"score_gpt":0.45400472597203956,"score_spread":0.21215122396977623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508662586","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79304034,0.00028237526,0.2053119,0.00065194676,0.0000929891,0.00009768897,0.000021504713,0.000052959884,0.00044831572],"genre_scores_gemma":[0.9582521,0.000019332947,0.041455537,0.000025296207,0.00006172563,0.000012924389,0.0000025178683,0.00001099302,0.00015958615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99799937,0.00013214975,0.0004538497,0.00066482177,0.0005775668,0.0001722492],"domain_scores_gemma":[0.9971971,0.0020751546,0.00014171418,0.00032116706,0.00011894246,0.00014593756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029508907,0.00016413901,0.0002783442,0.00011492736,0.00009325008,0.00019843395,0.00021377565,0.00007004079,0.000016475959],"category_scores_gemma":[0.0021124713,0.0001000782,0.00003247869,0.00009753284,0.00016103515,0.00047789188,0.00021853563,0.00009983973,0.0000010710031],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010499459,0.00004398849,0.9548357,0.000030543088,0.00008288611,0.0000055195037,0.0016622212,0.0015877752,0.012794713,0.005667706,0.000038247505,0.023145672],"study_design_scores_gemma":[0.0012134142,0.00017905464,0.9233617,0.00033303007,0.000023393963,0.000034689572,0.0006108823,0.031998605,0.019495463,0.020989968,0.0011382335,0.00062154926],"about_ca_topic_score_codex":0.00004600105,"about_ca_topic_score_gemma":3.8832133e-7,"teacher_disagreement_score":0.16521177,"about_ca_system_score_codex":0.000050169245,"about_ca_system_score_gemma":0.000011236149,"threshold_uncertainty_score":0.40810704},"labels":[],"label_agreement":null},{"id":"W2509252776","doi":"10.4153/cmb-2015-073-7","title":"An Existence Theory for Incomplete Designs","year":2015,"lang":"en","type":"article","venue":"Canadian Mathematical Bulletin","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"California Institute of Technology","keywords":"Mathematics; Pairwise comparison; Block (permutation group theory); Combinatorics; Congruence (geometry); Block design; Discrete mathematics; Pure mathematics; Geometry; Statistics","score_opus":0.4663675317154439,"score_gpt":0.48140845073535726,"score_spread":0.01504091901991339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509252776","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":[0.022954877,0.00012910058,0.8792169,0.002906395,0.00036906492,0.000977141,0.000068288835,0.000093213624,0.09328503],"genre_scores_gemma":[0.45191044,2.9184008e-7,0.5414397,0.0020159618,0.0001429229,0.00013673905,0.0000058128385,0.000042942473,0.0043051825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966548,0.0008039211,0.00061272824,0.00055466226,0.0007817538,0.00059214525],"domain_scores_gemma":[0.9921763,0.0044191508,0.00011121887,0.0009012757,0.00035862564,0.0020334506],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.010332261,0.00020674773,0.00039939678,0.00025275096,0.00017192567,0.0004619926,0.0012773077,0.00012269101,0.009677942],"category_scores_gemma":[0.017640593,0.00016107254,0.00012185989,0.00031980028,0.00028045653,0.00016038289,0.000052763568,0.0001222646,0.015083168],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011083262,0.00009969138,0.000048288806,0.000014131206,0.000014552909,0.00004303889,0.001566068,0.00004889135,0.0017473088,0.91624767,0.0673095,0.012750056],"study_design_scores_gemma":[0.00034859127,0.00031311082,0.00004589051,0.000016526557,0.000009834009,0.000028654287,0.0027206636,0.003913143,0.0010367628,0.8810025,0.110293314,0.00027102735],"about_ca_topic_score_codex":0.0002605873,"about_ca_topic_score_gemma":0.00018109995,"teacher_disagreement_score":0.42895555,"about_ca_system_score_codex":0.00022730457,"about_ca_system_score_gemma":0.00045560213,"threshold_uncertainty_score":0.9912273},"labels":[],"label_agreement":null},{"id":"W2509946706","doi":"10.1177/0008068320050508","title":"Estimation of Residual Effects in Repeated Measurements Designs","year":2005,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Residual; Optimal design; Mathematics; Mathematical optimization; Design of experiments; Applied mathematics; Computer science; Algorithm; Statistics","score_opus":0.12819508368051005,"score_gpt":0.4314059461789958,"score_spread":0.3032108624984857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509946706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08509124,0.00014066428,0.9011088,0.0037028466,0.00034674257,0.000921947,0.00010823435,0.00008726717,0.008492269],"genre_scores_gemma":[0.6636111,0.0000014877318,0.33492047,0.00022142117,0.000045927103,0.000031470237,0.000020480964,0.000015111253,0.001132497],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9932424,0.0021327203,0.0013269698,0.00052150304,0.0023838538,0.00039258448],"domain_scores_gemma":[0.9898569,0.008705445,0.00059420895,0.0003251146,0.00037398096,0.00014436812],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.008626998,0.000186603,0.0005017004,0.0002774235,0.00007491861,0.00010541485,0.00035811434,0.0001890248,0.0014206603],"category_scores_gemma":[0.053448454,0.00016842973,0.00006934474,0.00066530716,0.000073913885,0.00013673014,0.00008024917,0.00021609924,0.000956186],"study_design_candidate":"observational","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":[0.0008550511,0.001875024,0.0800856,0.00007435962,0.00016284114,0.00005219263,0.0019578089,0.025625026,0.050387487,0.03778622,0.26902664,0.53211176],"study_design_scores_gemma":[0.008241866,0.0012040928,0.58709484,0.00025042385,0.0001483827,0.00000903114,0.00038741095,0.14734088,0.19653812,0.041290235,0.01612681,0.0013679239],"about_ca_topic_score_codex":0.000050481827,"about_ca_topic_score_gemma":0.0000115086505,"teacher_disagreement_score":0.5785199,"about_ca_system_score_codex":0.00076237397,"about_ca_system_score_gemma":0.000078032506,"threshold_uncertainty_score":0.99982166},"labels":[],"label_agreement":null},{"id":"W2512287401","doi":"10.1177/0008068320130104","title":"A Unified Approach to Factorial Designs with Randomization Restrictions","year":2013,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Factorial experiment; Randomization; Factorial; Mathematics; Restricted randomization; Fractional factorial design; Isomorphism (crystallography); Linear subspace; Contrast (vision); Rank (graph theory); Plackett–Burman design; Mathematical optimization; Computer science; Arithmetic; Algorithm; Statistics; Combinatorics; Artificial intelligence; Pure mathematics; Clinical trial","score_opus":0.09177858634421168,"score_gpt":0.3715417699575644,"score_spread":0.27976318361335273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2512287401","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":[0.0022809685,0.0000065932745,0.9683448,0.0022048394,0.00039538552,0.0012025787,0.000090504596,0.0001025769,0.025371734],"genre_scores_gemma":[0.29991585,0.0000019922982,0.68669087,0.0007589152,0.0003177984,0.00047775602,0.00007282222,0.000041132487,0.011722842],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99443287,0.0015489281,0.00083944574,0.0006992929,0.0019956226,0.0004838284],"domain_scores_gemma":[0.9901452,0.0077466834,0.00035948688,0.0003889756,0.00093315804,0.00042650927],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0028702444,0.00023919587,0.0004895005,0.00024880588,0.00029167827,0.00072386186,0.00042440038,0.00019109654,0.0037202737],"category_scores_gemma":[0.022855822,0.00017504786,0.00007645684,0.0011439838,0.00006580781,0.00017929939,0.000085540356,0.0002509845,0.0042181574],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008888562,0.0005767417,0.003923627,0.0000054301354,0.000104037994,0.0000047018343,0.0008571562,0.00259518,0.0034641996,0.08708046,0.89299035,0.0075092763],"study_design_scores_gemma":[0.03446379,0.003350506,0.16212144,0.00007399081,0.0004396097,0.00003703848,0.0048710625,0.065691024,0.0052218633,0.098721005,0.6209722,0.0040364605],"about_ca_topic_score_codex":0.00023658013,"about_ca_topic_score_gemma":0.0000030072792,"teacher_disagreement_score":0.2976349,"about_ca_system_score_codex":0.0005188979,"about_ca_system_score_gemma":0.00010324836,"threshold_uncertainty_score":0.9971905},"labels":[],"label_agreement":null},{"id":"W2515506909","doi":"10.1177/0008068320060305","title":"Optimal Crossover Designs for Comparing Mixed Carryover Effects","year":2006,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Actua; University of Waterloo","funders":"","keywords":"Crossover; Mixed model; Design of experiments; Point (geometry); Treatment effect; Optimal design; Estimation; Mathematics; Unit (ring theory); Identification (biology); Computer science; Mathematical optimization; Statistics; Medicine; Machine learning; Engineering","score_opus":0.08002191894145441,"score_gpt":0.4032220343776169,"score_spread":0.32320011543616245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2515506909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030754047,0.000091629474,0.95962316,0.00091598154,0.0009175411,0.0007873739,0.00030185506,0.00011030032,0.0064981114],"genre_scores_gemma":[0.5253507,7.584505e-7,0.46121964,0.00040898356,0.00035188883,0.00015625838,0.00008843023,0.000045929733,0.012377412],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99461305,0.000936886,0.0010618415,0.00082424964,0.0017894745,0.00077446876],"domain_scores_gemma":[0.9771619,0.02117511,0.0005098776,0.000368289,0.0005671505,0.00021768441],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004324927,0.00031066663,0.0007100156,0.00014021415,0.0003737405,0.000696551,0.0005106148,0.0002554249,0.0012600428],"category_scores_gemma":[0.018835913,0.00027691427,0.00022746329,0.00038257038,0.00013076302,0.00013040748,0.00014179361,0.0002425563,0.0013440134],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036753,0.0003156518,0.0137047,0.000023610824,0.0000627925,0.000021429516,0.00010547842,0.0026050918,0.0075161527,0.08822992,0.88403994,0.0030077035],"study_design_scores_gemma":[0.009803657,0.0009104609,0.21208687,0.000059222497,0.00024675703,0.000012802454,0.00030154586,0.083623566,0.041267876,0.06534225,0.58446217,0.0018828193],"about_ca_topic_score_codex":0.00011777878,"about_ca_topic_score_gemma":0.000009914547,"teacher_disagreement_score":0.49840352,"about_ca_system_score_codex":0.00070416724,"about_ca_system_score_gemma":0.000071898394,"threshold_uncertainty_score":0.9999683},"labels":[],"label_agreement":null},{"id":"W2524850296","doi":"10.1080/00224065.2006.11918614","title":"Blocked Fractional Factorial Split-Plot Experiments for Robust Parameter Design","year":2006,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Manitoba; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Fractional factorial design; Split plot; Factorial experiment; Mathematics; Plot (graphics); Ranking (information retrieval); Factorial; Statistics; Design of experiments; Plackett–Burman design; Restricted randomization; Mathematical optimization; Computer science; Response surface methodology; Artificial intelligence","score_opus":0.4162652108112608,"score_gpt":0.520613587271537,"score_spread":0.10434837646027617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2524850296","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":[0.15007065,0.00027887788,0.8453553,0.0016645472,0.0019874666,0.00034522134,0.000014858384,0.00005681138,0.00022630677],"genre_scores_gemma":[0.3239589,0.0000031064144,0.67494094,0.00015316991,0.0005396017,0.000026446005,0.0000012441101,0.000021666425,0.00035490174],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99462384,0.0008257027,0.002179254,0.00043624087,0.0015172466,0.00041771895],"domain_scores_gemma":[0.99186134,0.004848457,0.0017069987,0.0005269855,0.00094724935,0.00010899585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007994698,0.00024652923,0.00080253405,0.0008838782,0.00017384857,0.00015868038,0.0011253348,0.00044305722,0.00031730675],"category_scores_gemma":[0.00829123,0.00018745905,0.00038539857,0.000729466,0.0003224412,0.0004670676,0.00012150873,0.0004370349,0.0000430169],"study_design_candidate":"bench_or_experimental","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":[0.005032284,0.0024197255,0.006423539,0.000015092096,0.00033925826,0.00006719181,0.00037345034,0.00511115,0.77124786,0.09579766,0.059782065,0.053390723],"study_design_scores_gemma":[0.003166108,0.0016206534,0.002192878,0.000018663855,0.00003937984,0.00018285081,0.0009828777,0.0016071135,0.42943597,0.53467077,0.025673376,0.00040933499],"about_ca_topic_score_codex":0.00002530107,"about_ca_topic_score_gemma":0.0000017967781,"teacher_disagreement_score":0.43887314,"about_ca_system_score_codex":0.00022289451,"about_ca_system_score_gemma":0.00017778645,"threshold_uncertainty_score":0.9925975},"labels":[],"label_agreement":null},{"id":"W2526674685","doi":"10.1007/s00184-016-0595-7","title":"Blocked factor aliased effect-number pattern and column rank of blocked regular designs","year":2016,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Column (typography); Ranking (information retrieval); Rank (graph theory); Factor (programming language); Combinatorics; Statistics; Selection (genetic algorithm); Fractional factorial design; Factorial experiment; Geometry; Computer science; Artificial intelligence","score_opus":0.13119729395925922,"score_gpt":0.4245310810891063,"score_spread":0.2933337871298471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2526674685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95290565,0.00028975215,0.044910952,0.00018562225,0.00033558818,0.0005751982,0.0000604641,0.000054510296,0.0006822296],"genre_scores_gemma":[0.9749336,0.000015723006,0.02232553,0.00010361938,0.00005458532,0.000031557913,8.801397e-7,0.000032938202,0.0025015634],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947487,0.0016758987,0.0008496238,0.000754683,0.0015491043,0.00042197626],"domain_scores_gemma":[0.9908096,0.0073789363,0.00037681538,0.00091339933,0.0002485384,0.00027266188],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004439486,0.00029825838,0.00079626235,0.00041250954,0.00010168666,0.00011958615,0.0006979012,0.00018087716,0.002986459],"category_scores_gemma":[0.006077186,0.0001785013,0.00021347895,0.00125468,0.00035859057,0.00026337575,0.00021934698,0.00010235728,0.0003822023],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021951771,0.000078094374,0.07483106,0.000009854433,0.0000539892,0.00001638457,0.00020752553,5.3144436e-7,0.6693814,0.000026047395,0.0023946972,0.2527809],"study_design_scores_gemma":[0.0027180782,0.0005425186,0.035990994,0.000049492708,0.000036796195,0.000015037444,0.000082212726,0.00012287425,0.95573056,0.00090141996,0.0035065503,0.0003034641],"about_ca_topic_score_codex":0.00008956648,"about_ca_topic_score_gemma":0.0000062591876,"teacher_disagreement_score":0.28634918,"about_ca_system_score_codex":0.000069227324,"about_ca_system_score_gemma":0.00004819607,"threshold_uncertainty_score":0.9979249},"labels":[],"label_agreement":null},{"id":"W2545991974","doi":"10.1109/nsspw.2006.4378829","title":"SMC Samplers for Bayesian Optimal Nonlinear Design","year":2006,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Software deployment; Monte Carlo method; Bayesian probability; Nonlinear system; Mathematical optimization; Bayesian experimental design; Bayesian network; Machine learning; Bayesian inference; Bayesian statistics; Artificial intelligence; Mathematics; Software engineering","score_opus":0.21792370910297099,"score_gpt":0.4595369197990576,"score_spread":0.2416132106960866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545991974","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":[0.0024153735,0.00007817276,0.9786049,0.00037025177,0.00024978822,0.0006417815,0.00002206877,0.00011194473,0.017505726],"genre_scores_gemma":[0.0362156,7.8712003e-7,0.95169365,0.00034290474,0.00022485763,0.00006313076,0.00000611158,0.000029730085,0.011423239],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968417,0.00034073537,0.0007170385,0.00067634246,0.000951009,0.0004731543],"domain_scores_gemma":[0.9952734,0.0037364177,0.00014936706,0.00046375467,0.00022897751,0.00014806609],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0041761277,0.00021813212,0.00034619326,0.00023209552,0.00019570714,0.00035563883,0.0007955991,0.000110554145,0.0018250447],"category_scores_gemma":[0.0012408278,0.00015971003,0.00022802177,0.00058581564,0.0001352161,0.00037006655,0.000093752875,0.000081986786,0.00034508252],"study_design_candidate":"bench_or_experimental","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":[0.001110754,0.0008712325,0.0014783384,0.00001115912,0.000065205226,0.00003039464,0.0004427195,0.07576691,0.34940132,0.06311375,0.38723943,0.120468795],"study_design_scores_gemma":[0.0016280806,0.0007566039,0.0008570757,0.000006899577,0.000021309654,0.000021554994,0.0009121842,0.5345176,0.30878997,0.0533081,0.098475374,0.00070524006],"about_ca_topic_score_codex":0.000120775,"about_ca_topic_score_gemma":0.0000088185225,"teacher_disagreement_score":0.4587507,"about_ca_system_score_codex":0.000058928385,"about_ca_system_score_gemma":0.0000881466,"threshold_uncertainty_score":0.9990874},"labels":[],"label_agreement":null},{"id":"W2566938964","doi":"10.1016/j.jspi.2016.11.005","title":"Optimal designs for spline wavelet regression models","year":2016,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Wavelet; Mathematics; Optimal design; Spline (mechanical); Haar wavelet; Mathematical optimization; Quadratic equation; Regression; Haar; Regression analysis; Set (abstract data type); Algorithm; Construct (python library); Applied mathematics; Statistics; Wavelet transform; Discrete wavelet transform; Computer science; Artificial intelligence","score_opus":0.3312389479618946,"score_gpt":0.5110017454229613,"score_spread":0.17976279746106666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2566938964","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":[0.03492299,0.00024680048,0.96383333,0.00030583068,0.00016929679,0.00007345121,0.00006437559,0.000006403249,0.0003775272],"genre_scores_gemma":[0.4991366,0.000017409378,0.50057787,0.00004715396,0.000059857026,0.0000015414162,4.6306127e-7,0.000005360462,0.00015374391],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978271,0.00019607891,0.000805403,0.00023148103,0.00070725987,0.00023269186],"domain_scores_gemma":[0.988659,0.0100682825,0.00042808085,0.00014131602,0.0004465371,0.0002567301],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0029791873,0.00013676797,0.0003960168,0.0001617311,0.00010659919,0.00014158014,0.00030687955,0.000074027965,0.00010371549],"category_scores_gemma":[0.0087350635,0.00006538494,0.00005869283,0.00011113565,0.00018208822,0.0005680262,0.00006892971,0.00013824468,0.0000073608544],"study_design_candidate":"theoretical_or_conceptual","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":[0.0038162714,0.00025948955,0.00432442,0.00003290302,0.00008002138,0.0002475937,0.0014054994,0.0045413477,0.13094892,0.14271559,0.02971465,0.6819133],"study_design_scores_gemma":[0.0037538926,0.0050917454,0.006472813,0.0011256709,0.00006830448,0.00038506227,0.0012058348,0.2374451,0.018083079,0.7226878,0.003069808,0.000610909],"about_ca_topic_score_codex":0.000001240143,"about_ca_topic_score_gemma":4.8553034e-8,"teacher_disagreement_score":0.68130237,"about_ca_system_score_codex":0.0000281601,"about_ca_system_score_gemma":0.00009038872,"threshold_uncertainty_score":0.9996148},"labels":[],"label_agreement":null},{"id":"W2584413285","doi":"10.1111/anzs.12178","title":"Optimal split‐plot orthogonal arrays","year":2017,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Council","keywords":"Split plot; Mathematics; Plot (graphics); Restricted randomization; Statistics","score_opus":0.18047291145836383,"score_gpt":0.45181526529757227,"score_spread":0.27134235383920846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584413285","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":[0.4608782,0.00016040592,0.5177988,0.009473733,0.0055351947,0.00040460197,0.00072116253,0.00003679931,0.0049911467],"genre_scores_gemma":[0.1898598,0.000042016414,0.7685252,0.00010373033,0.00077244785,6.6041855e-7,0.0000037031145,0.000028619483,0.04066384],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9956628,0.0002462216,0.0014067242,0.00032391964,0.0018964307,0.000463875],"domain_scores_gemma":[0.9950673,0.0008744235,0.0019685556,0.00077026343,0.0005952262,0.00072423613],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031446111,0.000262488,0.0006364017,0.00022431836,0.00038181953,0.001207252,0.0018020037,0.000120892546,0.0018022645],"category_scores_gemma":[0.0033325427,0.00019226376,0.00020021347,0.00013833545,0.00032313677,0.00079968723,0.00014563953,0.00048368858,0.0002550395],"study_design_candidate":"not_applicable","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":[0.0008749091,0.00023531141,0.036053944,0.000007736672,0.0001548189,0.0012058765,0.00092072133,0.0012837336,0.0057280622,0.013147741,0.85682946,0.08355765],"study_design_scores_gemma":[0.005830544,0.0047023604,0.4345248,0.00019450298,0.00024597006,0.0020689815,0.0012053704,0.0009684089,0.009464297,0.17931642,0.36038318,0.0010951562],"about_ca_topic_score_codex":0.00007507416,"about_ca_topic_score_gemma":0.000012993314,"teacher_disagreement_score":0.4964463,"about_ca_system_score_codex":0.00005942934,"about_ca_system_score_gemma":0.00037642606,"threshold_uncertainty_score":0.9998296},"labels":[],"label_agreement":null},{"id":"W2586054978","doi":"10.22237/jmasm/1130803440","title":"Estimation of Process Variances in Robust Parameter Designs","year":2005,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Fractional factorial design; Mathematics; Robustness (evolution); Orthogonal array; Taguchi methods; Design of experiments; Statistics; Noise (video); Factorial experiment; Mathematical optimization; Algorithm; Computer science; Artificial intelligence","score_opus":0.2510232612297184,"score_gpt":0.5285006607066095,"score_spread":0.2774773994768911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586054978","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":[0.007434651,0.0002306939,0.98915833,0.00013517863,0.0001428799,0.00022855798,0.000012759928,0.000007855131,0.0026490958],"genre_scores_gemma":[0.43747425,0.00000431561,0.56238496,0.00005862196,0.000041708183,0.000007809935,4.1014678e-7,0.000011840848,0.000016056172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99358916,0.0017298984,0.0023302443,0.000380772,0.0016426182,0.0003272826],"domain_scores_gemma":[0.98428977,0.013673254,0.001180009,0.00031885284,0.00032235228,0.00021574832],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.014674827,0.00022241772,0.00094044785,0.00050042826,0.000047544352,0.00011438629,0.00076374545,0.00014569054,0.00040779117],"category_scores_gemma":[0.010819702,0.00016172281,0.00011436413,0.0007231078,0.0002441193,0.00044255063,0.000066507484,0.00044277418,0.000014976575],"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":[0.0004283265,0.00023726223,0.000069128815,0.000014026956,0.000019758463,0.000008570209,0.00075844215,0.19132008,0.0183659,0.015115629,0.000095942356,0.77356696],"study_design_scores_gemma":[0.0005750401,0.00019081758,0.0011480091,0.000025576108,0.000025423356,0.00002873689,0.00017228427,0.5252419,0.033901535,0.43850067,0.000057409492,0.00013258237],"about_ca_topic_score_codex":0.0000030904582,"about_ca_topic_score_gemma":0.0000015008881,"teacher_disagreement_score":0.77343434,"about_ca_system_score_codex":0.00011185131,"about_ca_system_score_gemma":0.0001872196,"threshold_uncertainty_score":0.9975126},"labels":[],"label_agreement":null},{"id":"W2586752023","doi":"10.1017/s0001924000009027","title":"A multidisciplinary robust optimisation framework for UAV conceptual design","year":2014,"lang":"en","type":"article","venue":"The Aeronautical Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Toronto Metropolitan University","funders":"National Research Foundation of Korea","keywords":"Probabilistic logic; Computer science; Conceptual design; Probabilistic design; Reliability engineering; Monte Carlo method; Engineering design process; Engineering; Artificial intelligence","score_opus":0.34113082493421654,"score_gpt":0.4665918349306681,"score_spread":0.12546100999645154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586752023","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":[0.0064925374,0.00012375382,0.9868705,0.0030918717,0.0005216485,0.00039140752,0.0000019397883,0.000038178183,0.002468157],"genre_scores_gemma":[0.2959766,0.0000034962516,0.702118,0.0003362648,0.000431242,0.000019403496,3.5159684e-7,0.000029753925,0.0010849149],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956433,0.0017747635,0.00067697454,0.00034567606,0.0011219978,0.00043728287],"domain_scores_gemma":[0.98684764,0.011898344,0.00024687639,0.000505045,0.00020269904,0.00029941136],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0111108655,0.00019096812,0.00031710305,0.00009592768,0.00060003746,0.00038367484,0.0011242542,0.00014596213,0.0017445141],"category_scores_gemma":[0.010925191,0.000105204475,0.00020539967,0.00028650355,0.0006008354,0.00024260912,0.0001847489,0.00047059002,0.0004922512],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014820206,0.000365451,0.00023897241,0.0000031429813,0.00008991418,0.0000110329765,0.0039802277,0.09743663,0.014985668,0.7297379,0.014383517,0.13728555],"study_design_scores_gemma":[0.0010452277,0.0010278907,0.0015346638,0.000036473048,0.00004243643,0.0002010697,0.0022255613,0.33326083,0.00422546,0.6524744,0.0036512497,0.00027470104],"about_ca_topic_score_codex":0.0000010036879,"about_ca_topic_score_gemma":2.696342e-7,"teacher_disagreement_score":0.28948408,"about_ca_system_score_codex":0.00007104469,"about_ca_system_score_gemma":0.00007180436,"threshold_uncertainty_score":0.99916804},"labels":[],"label_agreement":null},{"id":"W2592561635","doi":"10.1007/s00362-017-0887-7","title":"Using SeDuMi to find various optimal designs for regression models","year":2017,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Institute of General Medical Sciences","keywords":"Mathematical optimization; Optimal design; Semidefinite programming; Mathematics; Estimator; Discretization; Computer science; Focus (optics); Linear programming; Nonlinear programming; Equivalence (formal languages); Nonlinear system; Machine learning","score_opus":0.4964396165725281,"score_gpt":0.5570098859689532,"score_spread":0.06057026939642507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592561635","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":[0.01037971,0.000025236615,0.9729118,0.00027268258,0.0004931853,0.0005869574,0.00025654232,0.000029973418,0.015043931],"genre_scores_gemma":[0.34394953,8.376957e-7,0.65442115,0.00023426951,0.000061570034,0.000027096936,0.0000040069535,0.00002330102,0.0012782764],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679494,0.0002819761,0.00053709524,0.0008003489,0.0010473859,0.0005382461],"domain_scores_gemma":[0.995019,0.0030789073,0.00021783393,0.0009971384,0.00020962511,0.00047749773],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0023104486,0.00024327108,0.00042807785,0.000119825134,0.0009815631,0.00076515146,0.0010955109,0.00012471748,0.00063556345],"category_scores_gemma":[0.009216588,0.00017813411,0.00010238266,0.00011472868,0.00030417874,0.00041446908,0.00030723718,0.00014153625,0.00014317827],"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":[0.0017086272,0.00023548504,0.0002327643,0.000021541067,0.00006208587,0.000117257674,0.0017032588,0.04910829,0.5227506,0.18483178,0.016179688,0.22304863],"study_design_scores_gemma":[0.0016133904,0.0010308732,0.003343775,0.000081583144,0.00006889814,0.000026936066,0.0009970346,0.7808322,0.01129725,0.1942298,0.0055712406,0.0009070693],"about_ca_topic_score_codex":0.00009409669,"about_ca_topic_score_gemma":0.0000055935084,"teacher_disagreement_score":0.73172385,"about_ca_system_score_codex":0.000108761764,"about_ca_system_score_gemma":0.00014388344,"threshold_uncertainty_score":0.9991292},"labels":[],"label_agreement":null},{"id":"W2593819367","doi":"10.1142/s0219691317500254","title":"Minimax robust designs for wavelet estimation of nonparametric regression models with autocorrelated errors","year":2017,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Minimax; Autocorrelation; Ordinary least squares; Statistics; Wavelet; Generalized least squares; Least-squares function approximation; Mean squared error; Nonparametric statistics; Mathematical optimization; Applied mathematics; Algorithm; Computer science; Estimator; Artificial intelligence","score_opus":0.16130177020043582,"score_gpt":0.4319048563023143,"score_spread":0.27060308610187844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593819367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08159923,0.000118523254,0.9160862,0.0005106387,0.0004027145,0.00032175102,0.0000206543,0.000012795788,0.0009275033],"genre_scores_gemma":[0.65678436,0.000024489911,0.34302643,0.00006117246,0.000032719494,0.000006700494,0.000008337936,0.000006984687,0.00004881757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964526,0.00009443519,0.0015246783,0.00016541651,0.0015893,0.00017353233],"domain_scores_gemma":[0.9917013,0.00040676698,0.004289921,0.00022218232,0.0032608933,0.00011892589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027102411,0.0001809189,0.00035752336,0.0010045577,0.00039498022,0.0007768568,0.00079529017,0.0001249381,0.000018950295],"category_scores_gemma":[0.0041218717,0.00012261217,0.0001124748,0.0002364347,0.0002250762,0.01123468,0.00010220407,0.00017607122,0.0000030531594],"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":[0.0015755198,0.000107557884,0.00033482225,0.000040572406,0.000062140134,0.0000037692012,0.0021865603,0.12475498,0.0015885355,0.001761653,0.0003311785,0.8672527],"study_design_scores_gemma":[0.0023209967,0.00031872955,0.0044166422,0.0004423508,0.000024493025,0.0001303836,0.0006814381,0.9832727,0.005825521,0.002042301,0.0003735987,0.00015087081],"about_ca_topic_score_codex":0.000016091735,"about_ca_topic_score_gemma":9.068719e-7,"teacher_disagreement_score":0.86710185,"about_ca_system_score_codex":0.00012646703,"about_ca_system_score_gemma":0.00023421767,"threshold_uncertainty_score":0.8144872},"labels":[],"label_agreement":null},{"id":"W2605718405","doi":"10.5539/ijsp.v6n3p198","title":"Linear Contrasts Based on an Extension of the Wilcoxon--Mann--Whitney Approach","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Statistics; Extension (predicate logic); Contrast (vision); Generalization; Measure (data warehouse); Random variable; Wilcoxon signed-rank test; Simple (philosophy); Mann–Whitney U test; Artificial intelligence; Computer science","score_opus":0.14184048002497512,"score_gpt":0.45139218140162,"score_spread":0.3095517013766449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605718405","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5942008,0.000044562712,0.4005736,0.0010537115,0.0014248294,0.0002644533,0.00033794143,0.000003316722,0.0020967892],"genre_scores_gemma":[0.779252,0.0000040923537,0.22052348,0.00010777644,0.00007729709,9.916093e-7,0.0000012907371,0.00000433734,0.000028763487],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99698573,0.00042319915,0.00072453887,0.00022295931,0.001551942,0.000091601],"domain_scores_gemma":[0.99555296,0.0010227753,0.0011590958,0.0005126728,0.00164842,0.00010409045],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00486866,0.00010208612,0.00025388147,0.0000795701,0.00015826139,0.00021066215,0.0012331675,0.00004779029,0.000059257687],"category_scores_gemma":[0.008646272,0.00005780065,0.00008689676,0.000043231514,0.00035839298,0.00026705023,0.0001367308,0.00018848164,0.0000013115558],"study_design_candidate":"observational","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":[0.007939405,0.005529578,0.26016423,0.00008047446,0.00032513432,0.00011236874,0.0021656496,0.02724168,0.03390532,0.20935473,0.0035066665,0.44967476],"study_design_scores_gemma":[0.0012931738,0.0007547359,0.48870656,0.00007498329,0.000021232057,0.00004287282,0.00009144866,0.26824042,0.004776247,0.23537955,0.00048429333,0.00013447778],"about_ca_topic_score_codex":0.000028987406,"about_ca_topic_score_gemma":0.000005061223,"teacher_disagreement_score":0.4495403,"about_ca_system_score_codex":0.000041045154,"about_ca_system_score_gemma":0.00012330891,"threshold_uncertainty_score":0.9997043},"labels":[],"label_agreement":null},{"id":"W2618160794","doi":"10.1002/cjs.11324","title":"Estimation of a generalized linear mixed model for response‐adaptive designs in multi‐centre clinical trials","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Hessian matrix; Estimator; Generalized linear mixed model; Mathematics; Generalized linear model; Generalized estimating equation; Function (biology); Statistics; Applied mathematics; Computer science; Mathematical optimization","score_opus":0.7676137574481702,"score_gpt":0.6015678473685178,"score_spread":0.16604591007965241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618160794","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":[0.061163984,0.0000937976,0.9364685,0.00025813037,0.00058761507,0.00036846334,0.0010416203,0.0000012202571,0.00001670037],"genre_scores_gemma":[0.3890071,0.0000058783216,0.6106661,0.00003457567,0.000035428464,0.0000021567866,0.0000020228929,0.000010773878,0.0002359555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9930605,0.003107381,0.002803742,0.00022297412,0.00052572927,0.0002796636],"domain_scores_gemma":[0.9829936,0.012099901,0.002850608,0.0004316323,0.0010139447,0.00061031204],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.037818637,0.00014387807,0.0009894015,0.00048085715,0.00018899153,0.00017679541,0.0008308785,0.00014208659,0.000059351016],"category_scores_gemma":[0.1885769,0.00011645038,0.00023003371,0.00011887682,0.00033421436,0.0002874622,0.000024753488,0.0002097881,0.000005505321],"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":[0.020886427,0.00039793368,0.010474691,0.000034291068,0.00031669356,0.0005715078,0.008455363,0.48435563,0.010232028,0.022494355,0.03653352,0.40524754],"study_design_scores_gemma":[0.0030018587,0.00032534785,0.0049878056,0.000055536166,0.000043158165,0.000007855543,0.00038953172,0.96733576,0.0014238011,0.022182016,0.00012922152,0.000118109274],"about_ca_topic_score_codex":0.0007196397,"about_ca_topic_score_gemma":0.005959668,"teacher_disagreement_score":0.48298013,"about_ca_system_score_codex":0.00016932246,"about_ca_system_score_gemma":0.002962972,"threshold_uncertainty_score":0.9907682},"labels":[],"label_agreement":null},{"id":"W2618479281","doi":"","title":"An Approximate Design Effect for Unequal Weighting When Measurements May Correlate with Selection Probabilities","year":2000,"lang":"en","type":"article","venue":"Survey methodology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Weighting; Statistics; Mathematics; Computer science; Artificial intelligence; Physics","score_opus":0.6404714675082755,"score_gpt":0.5156032129815941,"score_spread":0.12486825452668149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618479281","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":[0.25035864,0.0000780136,0.7465387,0.000024278193,0.00037697118,0.0015543107,0.000021139394,0.0001291338,0.0009188364],"genre_scores_gemma":[0.16219676,0.0000015455298,0.8358517,0.00007189196,0.00007144724,0.0003505485,0.000022603474,0.0000510908,0.0013823935],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9249417,0.07100408,0.00095328107,0.0012823114,0.0010453943,0.000773208],"domain_scores_gemma":[0.9584668,0.039705217,0.00035780953,0.0006851662,0.0005804582,0.00020453642],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.1411556,0.00042574987,0.0010342028,0.00030521277,0.00038865968,0.0002275762,0.00080785085,0.00028561574,0.0009832893],"category_scores_gemma":[0.012244051,0.0002939175,0.00013727359,0.0007900334,0.00030640006,0.0005859789,0.00004463154,0.00027721992,0.00008585351],"study_design_candidate":"bench_or_experimental","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":[0.041142963,0.00067277736,0.10279235,0.00010169689,0.00049223274,0.00001007151,0.009266234,0.06138249,0.21646984,0.0010542908,0.0011935881,0.56542146],"study_design_scores_gemma":[0.008704267,0.029703602,0.07245251,0.00009944568,0.00028451093,0.00018743784,0.001189755,0.1512279,0.6366047,0.095089585,0.0018215256,0.0026347155],"about_ca_topic_score_codex":0.0004897322,"about_ca_topic_score_gemma":0.00015359002,"teacher_disagreement_score":0.56278676,"about_ca_system_score_codex":0.0001367479,"about_ca_system_score_gemma":0.0001518569,"threshold_uncertainty_score":0.9999513},"labels":[],"label_agreement":null},{"id":"W2621215411","doi":"10.22237/jmasm/1493597400","title":"Analysis of robust parameter designs","year":2017,"lang":"fa","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Mathematics; Statistics; Variance (accounting); Regression analysis; Robust regression; Econometrics; Regression; One-way analysis of variance; Analysis of variance","score_opus":0.35670994284087887,"score_gpt":0.5404252361768767,"score_spread":0.1837152933359978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621215411","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":[0.0028207742,0.0010010302,0.9853304,0.00019008404,0.0010485053,0.00034200095,0.000282218,0.000008618056,0.008976374],"genre_scores_gemma":[0.31330714,0.000078434634,0.6861267,0.00009562414,0.00012322623,0.0000060388584,0.0000021425712,0.000042099364,0.00021856667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98546135,0.004861578,0.0045315097,0.0009664768,0.0033456087,0.0008334455],"domain_scores_gemma":[0.95809436,0.03095483,0.006573177,0.0022928533,0.001165878,0.00091888814],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032409675,0.00064995943,0.0039008667,0.0015333896,0.0005706336,0.0010778734,0.0037843522,0.00050165324,0.0028806543],"category_scores_gemma":[0.030335838,0.00050005096,0.0012428926,0.0010854533,0.0018314983,0.0005777185,0.0006879904,0.0011627052,0.000039806575],"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":[0.0017297277,0.00069085095,0.0009193166,0.00004072497,0.0039228126,0.00012570243,0.0010457386,0.011763849,0.08847965,0.03081918,0.0005110659,0.8599514],"study_design_scores_gemma":[0.0019351522,0.0011689366,0.030421106,0.00009377883,0.008996478,0.000049308917,0.00061871076,0.61785245,0.030304816,0.30715546,0.00060328434,0.0008005135],"about_ca_topic_score_codex":0.00003461624,"about_ca_topic_score_gemma":0.0000035890437,"teacher_disagreement_score":0.8591509,"about_ca_system_score_codex":0.00021507872,"about_ca_system_score_gemma":0.00045350433,"threshold_uncertainty_score":0.9999591},"labels":[],"label_agreement":null},{"id":"W2622157825","doi":"10.22237/jmasm/1177992300","title":"Application of a New Procedure for Power Analysis and Comparison of the Adjusted Univariate and Multivariate Tests in Repeated Measures Designs","year":2007,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of British Columbia","funders":"","keywords":"Univariate; Multivariate statistics; Statistics; Mathematics; Multivariate analysis; Repeated measures design; Multivariate analysis of variance; Econometrics","score_opus":0.20997651935340306,"score_gpt":0.5204642173940497,"score_spread":0.31048769804064663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622157825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038110983,0.00028817248,0.96078587,0.000057526235,0.000037187638,0.0005507179,0.000021664568,0.0000040655877,0.00014382643],"genre_scores_gemma":[0.50012016,0.0000018627819,0.49983886,0.000012922458,0.0000068025115,0.000003285334,4.3343815e-7,0.0000075923294,0.000008080255],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9956113,0.0009306986,0.0019679149,0.00036334136,0.00089603936,0.00023069525],"domain_scores_gemma":[0.98722106,0.010162695,0.0015831394,0.00032596735,0.0005022524,0.00020486736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01512898,0.00018592678,0.0010601018,0.00055100815,0.000058192523,0.00004205827,0.00039956073,0.00015014027,0.000010099053],"category_scores_gemma":[0.008024457,0.00011778829,0.000120964374,0.0014499513,0.0002320319,0.00009192166,0.000101265105,0.00026904288,1.1016864e-7],"study_design_candidate":"bench_or_experimental","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":[0.0017635113,0.00020556965,0.014258154,0.000022942504,0.00019539491,0.0000010363656,0.0022695553,0.0014227187,0.6177834,0.01159456,0.000019663525,0.3504635],"study_design_scores_gemma":[0.0019743755,0.00036607322,0.4996613,0.000033861936,0.00051974953,0.0000111779855,0.00092706847,0.18690182,0.114534855,0.19485684,0.000033593402,0.00017927993],"about_ca_topic_score_codex":0.000057934718,"about_ca_topic_score_gemma":0.000031629115,"teacher_disagreement_score":0.5032485,"about_ca_system_score_codex":0.000052913412,"about_ca_system_score_gemma":0.00013498531,"threshold_uncertainty_score":0.96066034},"labels":[],"label_agreement":null},{"id":"W2624739645","doi":"10.1016/j.csda.2017.05.012","title":"Special issue on Design of Experiments","year":2017,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robustness (evolution); Computer science; Covariate; Event (particle physics); Event data; Observational error; Data mining; Econometrics; Statistics; Algorithm; Machine learning; Mathematics","score_opus":0.41748951280374996,"score_gpt":0.5449631452611435,"score_spread":0.12747363245739357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2624739645","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":[0.00032522305,0.000021194726,0.9912878,0.00008277175,0.00043151734,0.00013913731,0.0034190423,0.0000089297855,0.0042843707],"genre_scores_gemma":[0.04715719,0.0000070998817,0.95012015,0.000090193964,0.00089989614,0.000004871891,0.00089839613,0.000012597118,0.000809582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99549,0.0005124508,0.000841746,0.00078835426,0.0021721562,0.00019530037],"domain_scores_gemma":[0.99281794,0.0032175686,0.0008881834,0.002477089,0.0004657951,0.00013341452],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0027355067,0.00018300154,0.0005553711,0.00045602236,0.00046694244,0.0005835916,0.0029680266,0.000050917195,0.004388982],"category_scores_gemma":[0.0055683334,0.00015825979,0.00010226989,0.0005484496,0.0003248312,0.00046909723,0.0007780845,0.00009079808,0.0005434009],"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":[0.00028087682,0.0004815145,0.0070377765,0.0000044547614,0.0013653452,0.000046797835,0.00040057916,0.46017215,0.00025550427,0.026450135,0.35809422,0.14541067],"study_design_scores_gemma":[0.00043203542,0.00014568515,0.06112413,0.000006518209,0.00038335982,9.607085e-7,0.000077666504,0.8736442,0.0004708424,0.05257071,0.010895384,0.00024851278],"about_ca_topic_score_codex":0.0001536759,"about_ca_topic_score_gemma":0.000010329122,"teacher_disagreement_score":0.41347206,"about_ca_system_score_codex":0.000045931607,"about_ca_system_score_gemma":0.00009934498,"threshold_uncertainty_score":0.9965211},"labels":[],"label_agreement":null},{"id":"W263390582","doi":"10.24200/jams.vol5iss2pp97-106","title":"Neural Network Assisted Experimental Designs for Food Research","year":2000,"lang":"en","type":"article","venue":"Journal of Agricultural and Marine Sciences [JAMS]","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"","keywords":"Fractional factorial design; Factorial experiment; Factorial; Artificial neural network; Box–Behnken design; Design of experiments; Mathematics; Experimental data; Statistics; Computer science; Artificial intelligence; Response surface methodology","score_opus":0.3936017160934224,"score_gpt":0.4944129017924242,"score_spread":0.10081118569900177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W263390582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98855764,0.0010974902,0.00020260643,0.0014731166,0.00060424197,0.0003603024,0.0000062418417,0.00001376375,0.0076846215],"genre_scores_gemma":[0.91265965,0.00005277382,0.082616,0.00015744938,0.0007716332,0.00001472299,0.0000013726711,0.000007151454,0.0037192488],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9950151,0.0007364584,0.00095803844,0.00047382797,0.0021719588,0.0006446118],"domain_scores_gemma":[0.99655974,0.0018757305,0.00036862394,0.00016229022,0.00067958515,0.00035404327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008857691,0.00021708751,0.00046732248,0.00021709337,0.00093967875,0.0008499005,0.0011378521,0.00008397406,0.0008157881],"category_scores_gemma":[0.00070085825,0.00010532159,0.00024847547,0.0017542968,0.00068209635,0.0012431276,0.00026321638,0.0003099345,0.000016791035],"study_design_candidate":"observational","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":[0.0016414095,0.00090785127,0.0071599805,0.000017916875,0.00014427536,0.000040448846,0.0022475438,0.0099013,0.22559658,0.006107393,0.14043939,0.6057959],"study_design_scores_gemma":[0.0063837897,0.043743517,0.71148384,0.00019760663,0.00010025326,0.0049153278,0.024014864,0.0062867575,0.09264064,0.046451416,0.06201727,0.0017647524],"about_ca_topic_score_codex":0.000022474214,"about_ca_topic_score_gemma":0.000016282931,"teacher_disagreement_score":0.7043238,"about_ca_system_score_codex":0.00007192698,"about_ca_system_score_gemma":0.00008768766,"threshold_uncertainty_score":0.8932308},"labels":[],"label_agreement":null},{"id":"W2676863892","doi":"10.22237/jmasm/1241137800","title":"A Heteroscedastic, Rank-Based Approach for Analyzing 2 x 2 Independent Groups Designs","year":2009,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"York University","funders":"","keywords":"Mathematics; Statistics; Type I and type II errors; Heteroscedasticity; Normality; Statistic; Econometrics; Homogeneity (statistics); Monte Carlo method; Rank (graph theory); Analysis of variance; Variance (accounting); Truncated mean; Repeated measures design; Combinatorics","score_opus":0.2227208620892771,"score_gpt":0.4985135099474539,"score_spread":0.2757926478581768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2676863892","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":[0.00047949783,0.00027194788,0.99629,0.00012418425,0.00027026108,0.0006354183,0.000046858262,0.000029027498,0.0018527614],"genre_scores_gemma":[0.3543883,0.0000022383747,0.6450153,0.00037761463,0.00014130685,0.000021758231,0.0000036549984,0.000027299828,0.000022515638],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99180037,0.0021898076,0.00238336,0.0007630716,0.002184222,0.00067917915],"domain_scores_gemma":[0.9831333,0.013856649,0.0012129179,0.0005813163,0.0005926931,0.0006230983],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.022465385,0.00042948173,0.0014163228,0.0006586215,0.00022747804,0.00045647027,0.001256914,0.00024314276,0.00014835695],"category_scores_gemma":[0.008236326,0.00031570368,0.00045346955,0.0006412905,0.00020395855,0.000254858,0.000074338735,0.0006628065,0.000007877538],"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":[0.0023892825,0.00061466463,0.000037903337,0.000017148559,0.00007467962,0.000021680054,0.00027029173,0.020215863,0.2694033,0.039157975,0.000523197,0.667274],"study_design_scores_gemma":[0.0027521169,0.00129353,0.0008883314,0.000016422937,0.00015413953,0.000056462664,0.00015877961,0.44367117,0.02853325,0.5218659,0.00020666426,0.00040324705],"about_ca_topic_score_codex":8.1056197e-7,"about_ca_topic_score_gemma":1.13737336e-7,"teacher_disagreement_score":0.6668708,"about_ca_system_score_codex":0.00021030671,"about_ca_system_score_gemma":0.000225613,"threshold_uncertainty_score":0.9999295},"labels":[],"label_agreement":null},{"id":"W2734623983","doi":"10.5709/acp-0214-z","title":"Varieties of Confidence Intervals","year":2017,"lang":"en","type":"article","venue":"Advances in Cognitive Psychology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":72,"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 Ottawa","funders":"","keywords":"Multiplicative function; Statistics; Confidence interval; Mathematics; Measure (data warehouse); Population mean; Robust confidence intervals; Sampling (signal processing); Population; Computer science; Econometrics; Data mining; Demography","score_opus":0.31209798918670284,"score_gpt":0.6219433147655263,"score_spread":0.3098453255788235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2734623983","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20056076,0.0069335774,0.31630477,0.0007606355,0.003055855,0.00047814034,0.00004116349,0.000027113361,0.47183797],"genre_scores_gemma":[0.9812736,0.00045955382,0.01717798,0.0005375323,0.000033984707,0.000031586318,6.456901e-7,0.000008586726,0.00047653302],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974088,0.0005480014,0.0006995586,0.0006074065,0.00048225935,0.00025395074],"domain_scores_gemma":[0.99531156,0.002885566,0.0005614447,0.000796527,0.00038954063,0.000055371882],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0029619515,0.00014455832,0.00045948793,0.00022639142,0.0001172759,0.000081883096,0.0014300143,0.000094909316,0.0008816834],"category_scores_gemma":[0.013591987,0.00011939627,0.000086049666,0.00018032899,0.0017039406,0.001081835,0.00026940738,0.00018690921,0.00015549027],"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":[0.0009820504,0.0003201122,0.11898832,0.000009846815,0.000025357107,0.000070009184,0.0017595745,0.000015922657,0.015981857,0.041302677,0.00027874802,0.82026553],"study_design_scores_gemma":[0.0016628222,0.0007262184,0.19976997,0.00013370934,0.0000091638885,0.000031926058,0.002363893,0.00011893797,0.04233975,0.7460121,0.0065387716,0.00029275182],"about_ca_topic_score_codex":0.000025665602,"about_ca_topic_score_gemma":0.00005051364,"teacher_disagreement_score":0.81997275,"about_ca_system_score_codex":0.000011717317,"about_ca_system_score_gemma":0.000023326877,"threshold_uncertainty_score":0.99471694},"labels":[],"label_agreement":null},{"id":"W274425175","doi":"10.1002/cjs.5550330401","title":"A nonparametric procedure for the analysis of balanced crossover designs","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Mathematics; Nonparametric statistics; Combinatorics; Permutation (music); Estimator; Crossover; Statistics; Limiting; Computer science; Engineering; Artificial intelligence; Philosophy","score_opus":0.16500893793671878,"score_gpt":0.4316667355078029,"score_spread":0.2666577975710841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W274425175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015895924,0.0010142169,0.9810505,0.00038436253,0.00025506213,0.0002126593,0.0009006554,0.0000015005653,0.00028511698],"genre_scores_gemma":[0.60440755,0.000009185991,0.3948986,0.0002256025,0.000064805616,0.0000028186441,0.0000016525848,0.000008150951,0.00038168306],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781764,0.00012788735,0.00092462596,0.00015301233,0.00069913885,0.00027771757],"domain_scores_gemma":[0.9920995,0.0052048042,0.0007809906,0.0002836603,0.0012321421,0.00039885606],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0028707925,0.00011244858,0.0004636281,0.0011356205,0.00016405252,0.00020015021,0.0007924558,0.000057656933,0.00059949886],"category_scores_gemma":[0.011263494,0.000070908776,0.00022189242,0.0027221192,0.00022968547,0.0001728624,0.000011325894,0.00014077952,0.0000080488],"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":[0.0007246692,0.00018171254,0.048270028,0.00003714072,0.0036884996,0.000112087495,0.007480572,0.22054122,0.005629468,0.035836928,0.27620468,0.401293],"study_design_scores_gemma":[0.0035301573,0.0015408248,0.29533443,0.00006608591,0.004252352,0.00014770702,0.0045810533,0.54354537,0.009878039,0.03137822,0.10488192,0.0008638366],"about_ca_topic_score_codex":0.00038509877,"about_ca_topic_score_gemma":0.005461044,"teacher_disagreement_score":0.5885116,"about_ca_system_score_codex":0.00016205959,"about_ca_system_score_gemma":0.0012807141,"threshold_uncertainty_score":0.99706507},"labels":[],"label_agreement":null},{"id":"W2754529490","doi":"10.1016/j.csda.2017.08.012","title":"Optimizing two-level orthogonal arrays for simultaneously estimating main effects and pre-specified two-factor interactions","year":2017,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Mathematics; Factor (programming language); Orthogonal array; Set (abstract data type); Algorithm; Matrix (chemical analysis); Mathematical optimization; Upper and lower bounds; Applied mathematics; Computer science; Statistics; Taguchi methods; Mathematical analysis","score_opus":0.28302554846404343,"score_gpt":0.5167663804318552,"score_spread":0.23374083196781176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754529490","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":[0.008434566,0.000045540866,0.9776845,0.000106036045,0.00041494792,0.00039821325,0.012756569,0.000031986252,0.00012766395],"genre_scores_gemma":[0.28407758,0.0000020996542,0.71367264,0.00006493122,0.00014908212,0.000023306258,0.001811292,0.000020844918,0.00017823253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99591374,0.0003830946,0.00096063624,0.0011846913,0.0012000533,0.00035780546],"domain_scores_gemma":[0.97651607,0.020257095,0.00094195904,0.0014374183,0.00059620215,0.00025126478],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0021054659,0.0003114646,0.00068261824,0.00044487568,0.0014895279,0.0020469988,0.001551705,0.000048148282,0.00023755869],"category_scores_gemma":[0.015177713,0.00028578256,0.00014891203,0.0004463021,0.00028717524,0.0009844508,0.0008822556,0.00020353323,0.000029112147],"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":[0.00019473972,0.000115250165,0.0018477184,0.000024392493,0.00093043374,0.00004439446,0.00055537967,0.87469125,0.0004637374,0.017027803,0.0013184221,0.102786504],"study_design_scores_gemma":[0.00076806516,0.000054850985,0.021577865,0.000018869505,0.00050803414,0.0000124895905,0.000056025332,0.92454004,0.000042703345,0.051934555,0.00019716554,0.0002893413],"about_ca_topic_score_codex":0.00019700939,"about_ca_topic_score_gemma":0.00038813555,"teacher_disagreement_score":0.27564302,"about_ca_system_score_codex":0.00009349147,"about_ca_system_score_gemma":0.00012792535,"threshold_uncertainty_score":0.9999594},"labels":[],"label_agreement":null},{"id":"W2756886357","doi":"10.1111/anzs.12195","title":"Optimal designs for minimising covariances among parameter estimators in a linear model","year":2017,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Calgary; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Mathematics; Covariance; Applied mathematics; Linear model; Linear regression; Mathematical optimization; Uncorrelated; Statistics","score_opus":0.29343508942618224,"score_gpt":0.48164265004626494,"score_spread":0.1882075606200827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2756886357","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":[0.20456147,0.00002285762,0.7937958,0.0006172566,0.0005804517,0.00023012323,0.00012684762,0.0000052190017,0.0000599522],"genre_scores_gemma":[0.22678508,0.000009921894,0.7674253,0.00003822451,0.00014021203,0.000002593809,0.0000019110935,0.000020792124,0.005575981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967254,0.00017646236,0.0014048269,0.00032640944,0.0009161589,0.0004507644],"domain_scores_gemma":[0.99492943,0.0021982084,0.0016148757,0.00047460967,0.00037782724,0.00040503527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032787365,0.00024407949,0.0006711075,0.0002862239,0.00024153522,0.0007992877,0.0011357514,0.00013848273,0.00008527347],"category_scores_gemma":[0.0066550956,0.00018776937,0.00015845467,0.00013497713,0.0002883618,0.0009064124,0.00007185231,0.00031821855,0.000010313079],"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":[0.0026382857,0.00051132386,0.12616894,0.000043127522,0.00021880792,0.00082736,0.004521296,0.47926006,0.0048237387,0.002305994,0.31104472,0.06763635],"study_design_scores_gemma":[0.0064931265,0.0021172133,0.07047917,0.000380984,0.00017916482,0.00017542807,0.0008563013,0.79542106,0.0067750886,0.11212371,0.004177685,0.0008210652],"about_ca_topic_score_codex":0.00012023251,"about_ca_topic_score_gemma":0.000027515238,"teacher_disagreement_score":0.316161,"about_ca_system_score_codex":0.00006875346,"about_ca_system_score_gemma":0.00035043297,"threshold_uncertainty_score":0.79672515},"labels":[],"label_agreement":null},{"id":"W2771581515","doi":"10.22237/jmasm/1509494760","title":"Robust Measures of Variable Importance for Multivariate Group Designs","year":2017,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"Multivariate statistics; Multivariate analysis of variance; Mathematics; Statistics; Multivariate analysis; Variable (mathematics); Estimator; Linear discriminant analysis; Econometrics","score_opus":0.5161443066563941,"score_gpt":0.5322163049779595,"score_spread":0.01607199832156536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771581515","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":[0.00034254006,0.0002518662,0.992852,0.00007800311,0.00057568285,0.00046421823,0.000111479145,0.000010133467,0.005314124],"genre_scores_gemma":[0.13967611,0.000008472517,0.85993475,0.00006855554,0.00014824109,0.00002614452,0.0000011870762,0.000037249905,0.000099259734],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99361354,0.0013255801,0.002314038,0.0005454135,0.0017281439,0.000473261],"domain_scores_gemma":[0.9798903,0.014634617,0.0030984206,0.0010527954,0.0009455152,0.00037838492],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032297928,0.00030249418,0.0013463704,0.00023739166,0.00039522647,0.00036873997,0.002004599,0.0001988885,0.0002547083],"category_scores_gemma":[0.032582954,0.00021798194,0.00025997165,0.00017643788,0.00047797014,0.00042112087,0.00022297405,0.00040162218,0.0000047445315],"study_design_candidate":"theoretical_or_conceptual","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":[0.0013806886,0.00022419504,0.00018828691,0.000021847916,0.00011328334,0.000010727204,0.00020097147,0.0017380419,0.46036342,0.20045641,0.00069696194,0.33460516],"study_design_scores_gemma":[0.0018960837,0.0005073858,0.003911738,0.000033500066,0.00012612002,0.000029305633,0.00013959037,0.06969451,0.036134183,0.8861561,0.0010960846,0.00027539098],"about_ca_topic_score_codex":0.000015205392,"about_ca_topic_score_gemma":0.0000015107129,"teacher_disagreement_score":0.6856997,"about_ca_system_score_codex":0.00009498548,"about_ca_system_score_gemma":0.00021159666,"threshold_uncertainty_score":0.9964529},"labels":[],"label_agreement":null},{"id":"W2788034503","doi":"10.1016/j.jmp.2018.07.005","title":"A better (Bayesian) interval estimate for within-subject designs","year":2018,"lang":"en","type":"preprint","venue":"Journal of Mathematical Psychology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credible interval; Bayesian probability; Normalization (sociology); Mathematics; Heteroscedasticity; Interval estimation; Statistics; Confidence interval; Subject (documents); Interval (graph theory); Point estimation; Prior probability; Prediction interval; Tolerance interval; Econometrics; Computer science; Combinatorics","score_opus":0.34467201804378583,"score_gpt":0.5614297589589514,"score_spread":0.2167577409151656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788034503","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":[0.04000939,0.0002095439,0.94622016,0.0029627874,0.004206044,0.0006545382,0.000024707233,0.00002882353,0.005684007],"genre_scores_gemma":[0.06444078,0.000005784612,0.93214935,0.001811729,0.0010552121,0.000047028425,0.0000015797343,0.000080494814,0.00040806417],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99140227,0.0014628371,0.004010031,0.0008696213,0.0016755944,0.00057966495],"domain_scores_gemma":[0.98803306,0.005631852,0.0034167748,0.0013413052,0.0011786773,0.00039833417],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.019163288,0.00055186165,0.0021106058,0.0007906914,0.00009933374,0.00039619155,0.003014611,0.00080601854,0.0021251538],"category_scores_gemma":[0.01238506,0.00036629563,0.0011995798,0.00029947897,0.0006821703,0.00025004914,0.00069931237,0.0013545121,0.00042853184],"study_design_candidate":"theoretical_or_conceptual","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":[0.012522932,0.0062981676,0.0010835486,0.0012261339,0.0033865692,0.0011163426,0.013897225,0.0008982012,0.051359773,0.02763585,0.7806614,0.09991386],"study_design_scores_gemma":[0.0011937913,0.0020311025,0.00019292187,0.0004070546,0.00017594825,0.0012487187,0.00012843484,0.007709333,0.0036013294,0.9816994,0.0012222284,0.00038972037],"about_ca_topic_score_codex":5.009643e-7,"about_ca_topic_score_gemma":4.802455e-7,"teacher_disagreement_score":0.9540636,"about_ca_system_score_codex":0.00012568159,"about_ca_system_score_gemma":0.00022042239,"threshold_uncertainty_score":0.9998789},"labels":[],"label_agreement":null},{"id":"W2794752316","doi":"10.1111/anzs.12227","title":"Optimal robust parameter designs with qualitative and quantitative factors","year":2018,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Mathematical optimization; Variance (accounting); Design of experiments; Optimal design; Statistics","score_opus":0.341034175515843,"score_gpt":0.48452145975238553,"score_spread":0.14348728423654256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794752316","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":[0.4377254,0.000038817438,0.56113416,0.00045921348,0.00024496,0.000133723,0.0001398972,0.000007327706,0.00011653573],"genre_scores_gemma":[0.22402003,0.000012844677,0.77039385,0.000052954623,0.00010265482,6.1259635e-7,0.0000027174897,0.000020214951,0.0053941417],"study_design_codex":"not_applicable","study_design_gemma":"qualitative","domain_scores_codex":[0.99618423,0.0008573167,0.001020612,0.0003328348,0.0012306864,0.00037430378],"domain_scores_gemma":[0.99181104,0.0055062617,0.0009971556,0.00022740681,0.00093290186,0.0005252208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026115496,0.0002803863,0.0006143353,0.0003035451,0.00014722491,0.00038095587,0.00041918986,0.000091063324,0.00055332936],"category_scores_gemma":[0.002650865,0.00017102262,0.00007126168,0.00045417494,0.00076299824,0.0005966215,0.000048066857,0.00031646804,0.00003815397],"study_design_candidate":"not_applicable","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":[0.007055491,0.0006554102,0.048182387,0.000029630995,0.0011737793,0.0008267482,0.25004935,0.0029753158,0.011684814,0.02209649,0.6335574,0.021713167],"study_design_scores_gemma":[0.01630742,0.10383211,0.20844008,0.000762136,0.001135797,0.0023572724,0.3401896,0.0046276916,0.07229665,0.2015863,0.0447186,0.003746344],"about_ca_topic_score_codex":0.00011714561,"about_ca_topic_score_gemma":0.0000302525,"teacher_disagreement_score":0.5888388,"about_ca_system_score_codex":0.00005058881,"about_ca_system_score_gemma":0.00022583519,"threshold_uncertainty_score":0.69741},"labels":[],"label_agreement":null},{"id":"W2795596766","doi":"10.1016/j.cor.2018.04.001","title":"","year":2018,"lang":"en","type":"article","venue":"Anet (University of Antwerp)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Integer programming; Blocking (statistics); Symmetry (geometry); Orthogonal array; Integer (computer science); Mathematics; Computer science; Linear programming; Mathematical optimization; Algorithm; Product (mathematics); Symmetry breaking; Statistics; Geometry","score_opus":0.16652821751323044,"score_gpt":0.4122434320750788,"score_spread":0.24571521456184836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795596766","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92716765,0.000058918078,0.059718356,0.000844218,0.00025879487,0.00017541113,0.00002041496,0.000060961647,0.011695261],"genre_scores_gemma":[0.8528921,0.000013547713,0.1450223,0.00020716868,0.000065742344,4.857974e-8,0.0000022658091,0.000008048079,0.0017888058],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99604964,0.000671834,0.0003959759,0.00079886214,0.0016346572,0.000449004],"domain_scores_gemma":[0.99626976,0.0011245034,0.00045017558,0.0010796587,0.000781428,0.00029444898],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0036352132,0.00020308302,0.0005511324,0.00050039677,0.00057346147,0.00007885477,0.0022998594,0.00014326413,0.0018692788],"category_scores_gemma":[0.00074783544,0.00020875807,0.00024162367,0.0016958109,0.0018908812,0.0014171441,0.00064792606,0.00013842256,0.0004214966],"study_design_candidate":"bench_or_experimental","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":[0.003109979,0.00075819757,0.084520705,0.000011841501,0.00016738348,0.00041417932,0.037810646,0.00008889227,0.6660453,0.013634679,0.02759356,0.16584462],"study_design_scores_gemma":[0.0054092472,0.0077309683,0.5442859,0.000072201234,0.00011160697,0.00015133363,0.15568739,0.01705207,0.15564609,0.04232343,0.070034906,0.0014948696],"about_ca_topic_score_codex":0.0012935151,"about_ca_topic_score_gemma":0.00029250755,"teacher_disagreement_score":0.5103992,"about_ca_system_score_codex":0.000046777113,"about_ca_system_score_gemma":0.00008384524,"threshold_uncertainty_score":0.99904317},"labels":[],"label_agreement":null},{"id":"W2802818241","doi":"10.1002/cjs.11355","title":"Locally optimal designs for binary dose‐response models","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institutes of Health; Louisiana Clinical and Translational Science Center","keywords":"Binary number; Optimal design; Nonlinear system; Mathematical optimization; Algebraic number; Computer science; Mathematics; Algorithm; Applied mathematics; Statistics; Arithmetic; Physics","score_opus":0.26585290794277,"score_gpt":0.4335272839070256,"score_spread":0.1676743759642556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802818241","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":[0.027614836,0.0001796567,0.9694266,0.000339789,0.0009814417,0.00020293464,0.0006265689,0.0000038930343,0.00062431424],"genre_scores_gemma":[0.34937555,0.0000019948036,0.64920694,0.00034574664,0.00019760594,0.0000026202501,0.0000016951748,0.000022394634,0.00084545143],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646014,0.0007203264,0.0011967772,0.00026998715,0.0008516392,0.00050111586],"domain_scores_gemma":[0.99151695,0.003981402,0.0005935942,0.00037581168,0.0023099345,0.0012223358],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008715302,0.00018784757,0.00044045688,0.0006899647,0.00031524946,0.00038298388,0.0010452236,0.00010423799,0.0009508726],"category_scores_gemma":[0.009002369,0.00015225308,0.00014043963,0.00045356582,0.0005612501,0.00044664493,0.000028523122,0.0001856966,0.0001050765],"study_design_candidate":"not_applicable","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":[0.008055554,0.00012418187,0.00061764434,0.000015307285,0.00018623563,0.0019359445,0.0070229606,0.033406008,0.026554171,0.063800514,0.74726397,0.11101754],"study_design_scores_gemma":[0.004046489,0.015362372,0.0057623736,0.0001277517,0.00014232179,0.0011498135,0.0045781108,0.49007007,0.010478164,0.35250032,0.11461353,0.0011686864],"about_ca_topic_score_codex":0.0002529281,"about_ca_topic_score_gemma":0.00069689046,"teacher_disagreement_score":0.63265043,"about_ca_system_score_codex":0.00026476817,"about_ca_system_score_gemma":0.0036997146,"threshold_uncertainty_score":0.9999624},"labels":[],"label_agreement":null},{"id":"W2804134309","doi":"10.1080/10618600.2018.1476250","title":"Optimal Designs for Multi-Response Nonlinear Regression Models With Several Factors via Semidefinite Programming","year":2018,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"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 Victoria","funders":"National Institute of General Medical Sciences","keywords":"Semidefinite programming; Optimal design; Mathematical optimization; Mathematics; Bivariate analysis; Design matrix; Linear model; Nonlinear system; Computation; Linear programming; Computer science; Algorithm; Statistics","score_opus":0.19381600922201345,"score_gpt":0.44673970487015846,"score_spread":0.252923695648145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804134309","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":[0.24217366,0.000050218176,0.75729597,0.00010606594,0.000108310844,0.00015133973,0.0001043328,0.000007186588,0.0000029220696],"genre_scores_gemma":[0.31733647,0.0000026207065,0.6824673,0.000074002746,0.00007038446,0.000002400078,0.0000072711887,0.000011987421,0.000027564169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969905,0.00043646403,0.0008433437,0.0002658185,0.0012243316,0.00023951776],"domain_scores_gemma":[0.99153674,0.0057797804,0.0006167866,0.00009448654,0.0016844228,0.0002877924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024966062,0.00020144758,0.00040039272,0.0003117153,0.0002948001,0.00022194404,0.00025379285,0.000086957225,0.00002138564],"category_scores_gemma":[0.0013172975,0.00011791905,0.0001067212,0.0004225798,0.0004663178,0.0003634457,0.000052901538,0.00021269356,0.0000017680677],"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":[0.05962659,0.0025478306,0.014894756,0.000083209794,0.0007400046,0.00034503557,0.00806698,0.67387563,0.006850563,0.044907592,0.004035939,0.18402585],"study_design_scores_gemma":[0.0015790194,0.0033009017,0.008169041,0.00004808635,0.000037062342,0.00016065601,0.00022320735,0.9000634,0.00036543657,0.08538292,0.00047179507,0.00019845528],"about_ca_topic_score_codex":0.000003409093,"about_ca_topic_score_gemma":0.0000016877374,"teacher_disagreement_score":0.22618777,"about_ca_system_score_codex":0.000027694146,"about_ca_system_score_gemma":0.00014779103,"threshold_uncertainty_score":0.48085994},"labels":[],"label_agreement":null},{"id":"W2808679788","doi":"10.1109/icccbda.2018.8386592","title":"Constrained optimal designs for step-stress accelerated life testing experiments","year":2018,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Brock University","funders":"","keywords":"Censoring (clinical trials); Stress (linguistics); Accelerated life testing; Optimal design; Computer science; Stress testing (software); Maximum likelihood; Fisher information; Mathematical optimization; Mathematics; Statistics","score_opus":0.654074768207559,"score_gpt":0.5387384974516848,"score_spread":0.11533627075587427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808679788","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":[0.13130485,0.00007074554,0.81464386,0.0001355384,0.00073152076,0.0010750592,0.000050929317,0.00025594182,0.051731586],"genre_scores_gemma":[0.44313005,2.5949228e-7,0.5538889,0.00044553113,0.00020753573,0.000086913766,0.0000041690564,0.00002596411,0.0022106671],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959012,0.00041871436,0.0010048384,0.00094173435,0.0010683024,0.00066516025],"domain_scores_gemma":[0.9935354,0.0038514053,0.00030837997,0.00066809374,0.0011915128,0.00044519932],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029911383,0.00032903676,0.0004863225,0.00025355507,0.00045549122,0.00070669345,0.0011154701,0.00015170252,0.0032664288],"category_scores_gemma":[0.010308383,0.00024932166,0.00014168513,0.0010577338,0.00053854246,0.00060680555,0.0002464284,0.00010850714,0.00041627378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005551177,0.00040806714,0.0030142255,0.000007168697,0.000089071575,0.000011598991,0.0011061428,0.00020603463,0.9156242,0.0036799375,0.024486529,0.050811924],"study_design_scores_gemma":[0.0018170228,0.0013653367,0.0005098529,0.00002441594,0.000014985196,0.000012889278,0.004297671,0.113409825,0.8747224,0.0006695825,0.0026450055,0.0005109992],"about_ca_topic_score_codex":0.000031545373,"about_ca_topic_score_gemma":0.000002556642,"teacher_disagreement_score":0.31182522,"about_ca_system_score_codex":0.00006234764,"about_ca_system_score_gemma":0.00026213165,"threshold_uncertainty_score":0.9999959},"labels":[],"label_agreement":null},{"id":"W2810629198","doi":"10.5267/j.ijiec.2018.2.001","title":"Trade-off in robustness, cost and performance by a multi-objective robust production optimization method","year":2018,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Robustness (evolution); Robust optimization; Mathematical optimization; Multi-objective optimization; Computer science; Reliability engineering; Production (economics); Engineering; Economics; Mathematics; Microeconomics; Biology","score_opus":0.11246284113617322,"score_gpt":0.3936420668807403,"score_spread":0.2811792257445671,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810629198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10197046,0.000091600654,0.89431435,0.0007771495,0.0025788276,0.0002051204,0.000010119798,0.00001647676,0.000035879617],"genre_scores_gemma":[0.55450207,0.000022451715,0.444781,0.000025009816,0.00061438093,0.00000490117,0.0000047090684,0.000014415077,0.00003106202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976263,0.00022481545,0.00089188,0.00025919915,0.0008423928,0.00015542236],"domain_scores_gemma":[0.9979368,0.0006664991,0.00045375066,0.00009998609,0.000745095,0.000097902564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002528698,0.00014904074,0.00026052105,0.00078437483,0.00007041736,0.00021486497,0.0004243856,0.000110925255,0.00003825338],"category_scores_gemma":[0.0034622843,0.00013506539,0.00006057931,0.00072878343,0.00007565058,0.0009169425,0.000064525426,0.00035980495,0.0000024398103],"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":[0.000109602595,0.00008518948,0.00075722084,7.334578e-7,0.000037963135,0.000004320552,0.00046667742,0.9458471,0.0011632114,0.000032419874,0.0005460407,0.05094954],"study_design_scores_gemma":[0.0012836443,0.00019060884,0.0018418975,0.000089330635,0.000010138107,0.00016609252,0.0002954128,0.9913653,0.0042057973,0.000021754417,0.00039958293,0.00013039898],"about_ca_topic_score_codex":0.000011237666,"about_ca_topic_score_gemma":0.0000019834192,"teacher_disagreement_score":0.4525316,"about_ca_system_score_codex":0.00024517346,"about_ca_system_score_gemma":0.00012213342,"threshold_uncertainty_score":0.55078065},"labels":[],"label_agreement":null},{"id":"W2883022650","doi":"10.1016/j.jspi.2018.07.007","title":"Maximin power designs in testing lack of fit","year":2018,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Minimax; Cardinality (data modeling); Lebesgue measure; Power (physics); Property (philosophy); Space (punctuation); Measure (data warehouse); Class (philosophy); Discrete mathematics; Mathematical optimization; Lebesgue integration; Computer science","score_opus":0.5897006106323665,"score_gpt":0.5586507683827052,"score_spread":0.03104984224966123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883022650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47786716,0.00017542341,0.5163673,0.000051437662,0.00014649454,0.00004129365,0.000012981014,0.00000336986,0.0053345207],"genre_scores_gemma":[0.6546827,0.0000016456493,0.34522375,0.00004363423,0.000023078524,2.255754e-7,8.35597e-8,0.0000030181595,0.000021876145],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9978596,0.00025763587,0.0009166167,0.00015729344,0.00063253124,0.00017635015],"domain_scores_gemma":[0.9912009,0.007648486,0.0004433778,0.000111481066,0.00045920975,0.00013651694],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0032534935,0.00009449641,0.00035999587,0.00023387358,0.000044119643,0.00008749883,0.00027047595,0.000053030417,0.00027617466],"category_scores_gemma":[0.022044612,0.00006599304,0.000023360806,0.00037114846,0.00034552848,0.0002539204,0.0000691636,0.00022117133,0.0000132812],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001347444,0.00047501267,0.6525981,0.000042627365,0.00004953438,0.00066217955,0.0093629295,0.0009201171,0.09329016,0.023970524,0.008077852,0.20920356],"study_design_scores_gemma":[0.0014500796,0.006715425,0.7752383,0.0008610087,0.000025589945,0.00031565534,0.0036852306,0.035299808,0.010710948,0.16467603,0.00060918217,0.0004127732],"about_ca_topic_score_codex":0.000014934705,"about_ca_topic_score_gemma":6.199219e-7,"teacher_disagreement_score":0.2087908,"about_ca_system_score_codex":0.000018621029,"about_ca_system_score_gemma":0.00009329472,"threshold_uncertainty_score":0.9861931},"labels":[],"label_agreement":null},{"id":"W2884763632","doi":"10.1002/cjce.23296","title":"Using normal probability plots to determine parameters for higher‐level factorial experiments with orthogonal and orthonormal bases","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Orthonormal basis; Factorial; Mathematics; Factorial experiment; Basis (linear algebra); Fractional factorial design; Monte Carlo method; Statistics; Design of experiments; Matrix (chemical analysis); Orthogonal array; Applied mathematics; Taguchi methods; Mathematical analysis; Geometry","score_opus":0.27254984586572456,"score_gpt":0.3809935461924838,"score_spread":0.10844370032675926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884763632","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96229655,0.000039996507,0.03659738,0.00012454424,0.0006358834,0.00024066304,0.000026487964,0.000006586201,0.000031893265],"genre_scores_gemma":[0.7892042,4.892912e-8,0.21029156,0.000081864164,0.00038465956,0.000006298721,3.9734505e-7,0.000018530556,0.000012444893],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981855,0.00006026418,0.000550324,0.00023499144,0.0005405635,0.00042833164],"domain_scores_gemma":[0.99774194,0.00068956264,0.000173411,0.00023014564,0.0003587606,0.00080619403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013782902,0.0001959014,0.00033064437,0.00018637936,0.00014129421,0.0002150255,0.0004936524,0.000072333474,0.00006075015],"category_scores_gemma":[0.0014388014,0.00012414045,0.000090691516,0.00029889488,0.0002164618,0.0003224013,0.000050162264,0.00017850973,0.0000016057916],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013115135,0.000040270323,0.0044235135,0.000020467953,0.00013370697,0.000044984437,0.0019358873,0.0131561365,0.9725201,0.00054526475,0.0002652113,0.0056029204],"study_design_scores_gemma":[0.0014186163,0.001027497,0.002058872,0.00010895888,0.000055430537,0.00035179697,0.000067084235,0.017509162,0.97425574,0.0007906672,0.0018635265,0.00049267337],"about_ca_topic_score_codex":0.00035175754,"about_ca_topic_score_gemma":0.00014639762,"teacher_disagreement_score":0.17369418,"about_ca_system_score_codex":0.00021009428,"about_ca_system_score_gemma":0.00040771213,"threshold_uncertainty_score":0.50623006},"labels":[],"label_agreement":null},{"id":"W2884878604","doi":"10.1038/s41592-018-0083-2","title":"Optimal experimental design","year":2018,"lang":"en","type":"article","venue":"Nature Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":140,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Computer science; Computational biology; Design of experiments; Biology; Mathematics; Statistics","score_opus":0.22516560416053172,"score_gpt":0.5845820471417642,"score_spread":0.3594164429812325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884878604","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":[0.0092822295,0.002816162,0.94466245,0.00022481967,0.0032394438,0.00039763513,0.0000040824416,0.00016911265,0.03920407],"genre_scores_gemma":[0.12599267,0.0000022601685,0.868608,0.0011696684,0.00071770494,0.000036398123,0.000001013902,0.000043263888,0.0034290154],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9900789,0.005639257,0.00077664043,0.0011559549,0.0017367619,0.0006124742],"domain_scores_gemma":[0.9929113,0.0046957536,0.0002628601,0.001309058,0.000500216,0.0003208305],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.019650089,0.00037388637,0.0005671358,0.0004298831,0.00035354274,0.00039816016,0.0017705648,0.0006294202,0.004690396],"category_scores_gemma":[0.008689243,0.00027470395,0.0002629244,0.0016247635,0.0005870594,0.0005372126,0.00045375625,0.00081057387,0.001204592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039473217,0.00017479893,0.00007130169,0.0000010323023,0.0000338065,0.000020053709,0.0012812602,0.000093823415,0.82576936,0.00564539,0.030346772,0.13616765],"study_design_scores_gemma":[0.00040525975,0.0006484679,0.00027414344,0.000005871701,0.00000945488,0.00004924504,0.0007076598,0.0072740326,0.9056861,0.0065763583,0.07802934,0.00033408075],"about_ca_topic_score_codex":0.000005235876,"about_ca_topic_score_gemma":2.5962566e-7,"teacher_disagreement_score":0.13583356,"about_ca_system_score_codex":0.00012913618,"about_ca_system_score_gemma":0.000109004126,"threshold_uncertainty_score":0.9999705},"labels":[],"label_agreement":null},{"id":"W2890155095","doi":"10.1177/0962280218797145","title":"Eliminating systematic bias from case-crossover designs","year":2018,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta; Government of Alberta; Alberta Health Services","funders":"Health Research Board","keywords":"Crossover; Statistics; Confounding; Calibration; Crossover study; Computer science; Econometrics; Confidence interval; Meta-analysis; Publication bias; Medicine; Mathematics","score_opus":0.7550744409644745,"score_gpt":0.7229459907852953,"score_spread":0.0321284501791792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890155095","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":[0.008436683,0.00041198402,0.97363675,0.00047123808,0.0007372853,0.0008995623,0.00006567203,0.000048812137,0.01529202],"genre_scores_gemma":[0.16646382,0.000009323682,0.8321209,0.0002874515,0.00034411182,0.00016070138,0.0000030549681,0.000041093106,0.00056953897],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9206273,0.06157307,0.0027594515,0.0016682474,0.011723217,0.0016487059],"domain_scores_gemma":[0.62481576,0.3704985,0.0002550768,0.0015137399,0.0014661757,0.0014507273],"candidate_categories":["metaresearch","sts","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.19606236,0.00032665124,0.0012660925,0.0009191419,0.00053759286,0.0006885874,0.002219333,0.0004846388,0.02719697],"category_scores_gemma":[0.72692835,0.00022623158,0.00012478503,0.003496544,0.0038160635,0.00027344722,0.0012592258,0.0019897828,0.0019376832],"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":[0.00049600267,0.0005061928,0.0007454312,0.0006701403,0.00006680835,0.014514359,0.0040727607,0.0000027218932,0.008685268,0.08475431,0.0068907244,0.8785953],"study_design_scores_gemma":[0.001633283,0.0012723422,0.0016419713,0.002499367,0.00003041674,0.0007567715,0.01073465,0.24139817,0.013873408,0.72443175,0.0010760506,0.00065178605],"about_ca_topic_score_codex":0.0010942464,"about_ca_topic_score_gemma":0.0001475893,"teacher_disagreement_score":0.8779435,"about_ca_system_score_codex":0.00035942261,"about_ca_system_score_gemma":0.00060386705,"threshold_uncertainty_score":0.998895},"labels":[],"label_agreement":null},{"id":"W2891884894","doi":"10.1016/b978-0-08-100693-1.15002-7","title":"Experimental statistical design","year":2015,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science","score_opus":0.27183209914397594,"score_gpt":0.44923442044987344,"score_spread":0.1774023213058975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891884894","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000023814098,0.003733229,0.01606061,0.000027247683,0.0011134823,0.0009330132,0.00010674007,0.00011424527,0.977909],"genre_scores_gemma":[0.00035456644,0.0000044987078,0.18078183,0.00024564023,0.00031990395,0.0000575407,0.0000131370825,0.00013988174,0.818083],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9921227,0.0006455714,0.0014730304,0.0014502082,0.0037351185,0.00057337474],"domain_scores_gemma":[0.9947752,0.001970055,0.0005620266,0.0015542844,0.00043519321,0.00070329366],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0055578044,0.00079968444,0.0012762676,0.00041696423,0.00016703599,0.00042623313,0.001583796,0.0005628191,0.00883026],"category_scores_gemma":[0.00091151346,0.00064235687,0.00033760804,0.000043320448,0.0006608782,0.0001540423,0.00062598416,0.00066302984,0.008388213],"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":[0.00013512559,0.000028571978,4.2434434e-7,0.000003402841,0.000052089934,0.00017576234,0.00034309083,0.000007653319,0.001369765,0.052062508,0.019988207,0.9258334],"study_design_scores_gemma":[0.00037748934,0.00042648934,0.0000011193969,0.000051191288,0.00003708607,0.000052669962,0.000082896484,0.00012554626,0.0033438012,0.18196665,0.8128928,0.00064228766],"about_ca_topic_score_codex":5.257142e-7,"about_ca_topic_score_gemma":5.455893e-7,"teacher_disagreement_score":0.9251911,"about_ca_system_score_codex":0.0004030476,"about_ca_system_score_gemma":0.00047785597,"threshold_uncertainty_score":0.9996028},"labels":[],"label_agreement":null},{"id":"W2905188466","doi":"10.1007/s00184-018-0702-z","title":"Some properties of foldover designs with column permutations","year":2018,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Column (typography); Mathematics; Column generation; Optimal design; Mathematical optimization; Function (biology); Applied mathematics; Statistics; Geometry","score_opus":0.3358189822527115,"score_gpt":0.4444268068932322,"score_spread":0.10860782464052071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905188466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9356642,0.0013231107,0.054572422,0.00017413104,0.00032631037,0.0004380278,0.000012407741,0.000055162505,0.0074342205],"genre_scores_gemma":[0.8956702,0.000003963974,0.100704506,0.0001239389,0.00010540812,0.000021294485,3.8673235e-7,0.000014707005,0.003355595],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974621,0.00034773463,0.00043474717,0.00035075934,0.0011878781,0.00021679915],"domain_scores_gemma":[0.99810606,0.000575536,0.00019280236,0.00048729577,0.00054781366,0.00009050179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018961336,0.00012612679,0.00029579713,0.00042977405,0.00015015533,0.00013408014,0.0005176616,0.000051335017,0.0005547834],"category_scores_gemma":[0.0023782407,0.00007781876,0.00007490203,0.0016663437,0.00056817563,0.0005312785,0.00008945886,0.00006337914,0.00033651045],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004418535,0.00031689604,0.009063251,0.00001171054,0.000088418295,0.000007638058,0.0041450183,0.00008024626,0.95536894,0.010099988,0.004885552,0.015490499],"study_design_scores_gemma":[0.00047639594,0.001093597,0.006659205,0.00002606392,0.000022712307,0.000009112856,0.0017475076,0.00092857087,0.9803903,0.00528811,0.0031690737,0.00018932694],"about_ca_topic_score_codex":0.000063739324,"about_ca_topic_score_gemma":0.00001454795,"teacher_disagreement_score":0.046132084,"about_ca_system_score_codex":0.00003879783,"about_ca_system_score_gemma":0.00010665853,"threshold_uncertainty_score":0.6074489},"labels":[],"label_agreement":null},{"id":"W2908775770","doi":"","title":"Robust Model-Based Stratified Sampling Designs.","year":2014,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; MacEwan University","funders":"","keywords":"Computer science; Stratified sampling; Sampling (signal processing); Econometrics; Statistics; Mathematics; Filter (signal processing)","score_opus":0.5366446408774386,"score_gpt":0.4769825374543436,"score_spread":0.05966210342309497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908775770","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":[0.009224366,0.000017414517,0.90826106,0.00019412089,0.0001613481,0.00015770346,0.0000017274206,0.00011051172,0.081871726],"genre_scores_gemma":[0.45739964,1.621621e-7,0.54036075,0.00057344366,0.00002955138,0.000008563462,9.310341e-7,0.000010994097,0.0016159526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968002,0.00051316235,0.00058943126,0.0006194655,0.0011536208,0.0003241259],"domain_scores_gemma":[0.99628466,0.0023937551,0.00013207392,0.000800252,0.00018941291,0.00019985343],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005819754,0.00018309297,0.00029552323,0.00019485515,0.00016688087,0.0004210709,0.00081299123,0.000097389646,0.0013724259],"category_scores_gemma":[0.0022819918,0.00013065233,0.00012870443,0.00049156067,0.00010341134,0.00030253798,0.00007336468,0.00012400781,0.000553773],"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":[0.00005156956,0.00007187132,0.00020440982,0.0000018562159,0.000004436265,7.831554e-7,0.00008266332,0.80996835,0.11438863,0.03175868,0.002316754,0.041149992],"study_design_scores_gemma":[0.00027612815,0.00008442641,0.00012933863,0.000003852546,0.000003542841,8.4709916e-7,0.00011018799,0.8945573,0.069011614,0.035112508,0.00053147966,0.00017880053],"about_ca_topic_score_codex":0.00001553815,"about_ca_topic_score_gemma":0.000005822257,"teacher_disagreement_score":0.44817528,"about_ca_system_score_codex":0.00003753195,"about_ca_system_score_gemma":0.00009388722,"threshold_uncertainty_score":0.99954045},"labels":[],"label_agreement":null},{"id":"W2908858115","doi":"10.1007/s00362-018-01076-6","title":"Properties of optimal regression designs under the second-order least squares estimator","year":2019,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Applied mathematics; Polynomial regression; Equivalence (formal languages); Estimator; Optimal design; Generalized least squares; Nonlinear regression; Non-linear least squares; Mathematical optimization; Least-squares function approximation; Linear regression; Regression analysis; Statistics; Discrete mathematics","score_opus":0.14110002371706523,"score_gpt":0.41578880930191625,"score_spread":0.274688785584851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908858115","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64753336,0.0009772574,0.27260393,0.0014880309,0.00125005,0.0015985332,0.00022755896,0.00011891277,0.07420238],"genre_scores_gemma":[0.85007674,0.0000020639495,0.14420916,0.00026877812,0.0000239337,0.000019286985,0.00000254512,0.000023920122,0.0053735822],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961487,0.0008130399,0.00067614915,0.00055057585,0.0014353483,0.00037617807],"domain_scores_gemma":[0.9958734,0.0028435045,0.00020530229,0.00068001554,0.00023619311,0.00016160132],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018191902,0.00023912043,0.00043520296,0.00009448561,0.00017455718,0.00015837011,0.0007496838,0.00009937084,0.011720067],"category_scores_gemma":[0.0020351193,0.00011782664,0.00009907894,0.00043241086,0.00074028753,0.00021789584,0.00019780426,0.0002345568,0.0007738888],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007832453,0.00019760313,0.0014728548,0.00006379253,0.00006910028,0.000012854375,0.0012729568,0.0031071396,0.8539827,0.11559288,0.0064470978,0.016997805],"study_design_scores_gemma":[0.0064081238,0.005061688,0.07211877,0.00085661863,0.00021852968,0.00016639961,0.10644298,0.1341312,0.56195325,0.073081054,0.036178388,0.0033829752],"about_ca_topic_score_codex":0.000026799427,"about_ca_topic_score_gemma":0.0000073358747,"teacher_disagreement_score":0.29202938,"about_ca_system_score_codex":0.000045774883,"about_ca_system_score_gemma":0.00016657676,"threshold_uncertainty_score":0.9947033},"labels":[],"label_agreement":null},{"id":"W2911726748","doi":"10.1002/9781118445112.stat06100","title":"Job‐Exposure Matrices","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec","funders":"","keywords":"Encyclopedia; Library science; Citation; Information retrieval; Computer science; Operations research; World Wide Web; Mathematics","score_opus":0.1437139828260724,"score_gpt":0.451835767916533,"score_spread":0.3081217850904606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911726748","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":[0.000046420184,0.006190404,0.5682337,0.0001451549,0.0021939764,0.0009302665,0.04250245,0.00056299043,0.37919462],"genre_scores_gemma":[0.00013378504,0.0016046847,0.55302155,0.0003548237,0.0005371905,0.00002974961,0.0012568214,0.0005500623,0.4425113],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9899536,0.0013291821,0.0019455568,0.0019506788,0.003817533,0.0010034743],"domain_scores_gemma":[0.99196786,0.002774523,0.0017555383,0.0023502477,0.000635955,0.00051585137],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0023325596,0.0010949207,0.0018096125,0.0012781447,0.00018221537,0.0006300886,0.0028683362,0.00082672935,0.0272228],"category_scores_gemma":[0.0039356393,0.0008551503,0.00018448262,0.001143224,0.0005644955,0.00017355285,0.0005909502,0.0010141343,0.0062128925],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055020504,0.00019589112,0.00032044822,0.000066022934,0.00008204828,0.00006831478,0.00007193975,0.000018022012,0.00019515943,0.014470624,0.8985237,0.085932806],"study_design_scores_gemma":[0.00067558605,0.0005829743,0.0004390953,0.0003442785,0.00009338671,0.000017306,0.00019084608,0.0011585987,0.000036019363,0.02429466,0.97106767,0.0010995737],"about_ca_topic_score_codex":0.0005050461,"about_ca_topic_score_gemma":0.00078991713,"teacher_disagreement_score":0.08483323,"about_ca_system_score_codex":0.00015474808,"about_ca_system_score_gemma":0.00041734314,"threshold_uncertainty_score":0.99938995},"labels":[],"label_agreement":null},{"id":"W2913000119","doi":"10.1007/s42519-019-0064-5","title":"Isomorphism Check for $$2^{n}$$ Factorial Designs with Randomization Restrictions","year":2019,"lang":"en","type":"preprint","venue":"Journal of Statistical Theory and Practice","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Randomization; Isomorphism (crystallography); Restricted randomization; Factorial; Factorial experiment; Fractional factorial design; Construct (python library); Mathematics; Computer science; Block (permutation group theory); Arithmetic; Statistics; Combinatorics; Randomized controlled trial; Programming language","score_opus":0.22603537522416198,"score_gpt":0.501908425204389,"score_spread":0.2758730499802271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913000119","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":[0.0005964786,0.00048917,0.99145913,0.0005569264,0.0024891505,0.00073077227,0.00020528493,0.000008133024,0.00346493],"genre_scores_gemma":[0.13444777,0.00031010323,0.86315256,0.00028606137,0.0009163214,0.000023846535,0.000012300232,0.000037605136,0.0008134393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9903949,0.0064546443,0.0012658397,0.00042012968,0.0012480104,0.0002164984],"domain_scores_gemma":[0.8640732,0.13211183,0.0019714863,0.0003096664,0.0013276307,0.00020614688],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.028706996,0.00026329985,0.0008597363,0.00027067726,0.00017919848,0.0005965478,0.00043974374,0.00027071522,0.00036514318],"category_scores_gemma":[0.120110065,0.0001665143,0.00015126322,0.00020977091,0.0002436617,0.00081030634,0.00015846576,0.00090468006,0.000023884753],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.24038318,0.00053467817,0.000057867885,0.00008994307,0.0007359931,0.00012000719,0.0014970306,0.0070954626,0.0023204763,0.7140236,0.014771441,0.01837035],"study_design_scores_gemma":[0.0077322144,0.0032275459,0.00020904838,0.00013781179,0.0009778002,0.0007168028,0.0020954374,0.0020184417,0.0005425137,0.94707364,0.03485892,0.00040979777],"about_ca_topic_score_codex":0.0000055795704,"about_ca_topic_score_gemma":2.1957469e-7,"teacher_disagreement_score":0.2330501,"about_ca_system_score_codex":0.0000780526,"about_ca_system_score_gemma":0.00051208056,"threshold_uncertainty_score":0.9949329},"labels":[],"label_agreement":null},{"id":"W2914546816","doi":"10.1016/b978-0-44-463782-6.00003-3","title":"Experimental Planning","year":2017,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multivariate statistics; Variables; Econometrics; Statistics; Set (abstract data type); Regression analysis; Regression; Computer science; Mathematics; Geography","score_opus":0.19076963495043917,"score_gpt":0.4499048192428757,"score_spread":0.25913518429243654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914546816","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000047561327,0.004843439,0.00008700298,0.000029098317,0.0016827486,0.0005392571,0.00003052229,0.000097963464,0.9926424],"genre_scores_gemma":[0.0029929637,0.0000051035186,0.013212813,0.00029808524,0.00059637695,0.000040855743,0.0000068588784,0.00013970416,0.98270726],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99453783,0.00015856409,0.0011203656,0.0013404657,0.0023485448,0.00049420376],"domain_scores_gemma":[0.99513227,0.00062095124,0.0011006017,0.002623051,0.00018204305,0.00034107507],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0025312041,0.0007203337,0.0011366997,0.0004136602,0.00055904046,0.0008965625,0.0026513638,0.0005441772,0.0032949874],"category_scores_gemma":[0.000449938,0.00059166417,0.00061128417,0.000010545867,0.0005695139,0.0002347614,0.00090914965,0.0006757354,0.0038964578],"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":[0.000046543228,0.0000113989945,0.0000055716973,0.0000041013072,0.00005297012,0.00019110733,0.00076052366,0.0000017641283,0.00307595,0.0074967975,0.0018533668,0.9864999],"study_design_scores_gemma":[0.00028699086,0.0001499718,0.000013706963,0.00021562018,0.000026014613,0.000047380774,0.00008467362,0.000022096716,0.008753237,0.042155005,0.94759,0.0006552855],"about_ca_topic_score_codex":5.121171e-7,"about_ca_topic_score_gemma":6.441584e-7,"teacher_disagreement_score":0.9858446,"about_ca_system_score_codex":0.00017458969,"about_ca_system_score_gemma":0.00016526795,"threshold_uncertainty_score":0.99965346},"labels":[],"label_agreement":null},{"id":"W2916947021","doi":"10.1038/s41592-019-0335-9","title":"Two-level factorial experiments","year":2019,"lang":"en","type":"article","venue":"Nature Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Factorial experiment; Factorial; Computational biology; Fractional factorial design; Biology; Computer science; Biological system; Mathematics; Statistics","score_opus":0.25865839145345765,"score_gpt":0.5964617912588637,"score_spread":0.337803399805406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916947021","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":[0.07289374,0.0029548625,0.64189535,0.00023681266,0.03566495,0.0011211974,0.000042702195,0.00023639761,0.244954],"genre_scores_gemma":[0.15540881,0.000002520755,0.83250123,0.000685289,0.00056936574,0.000019916875,0.0000035124094,0.000038350576,0.010770993],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9918267,0.003486002,0.00078456925,0.0011286251,0.0022370063,0.00053710834],"domain_scores_gemma":[0.99292684,0.0046783346,0.0003033336,0.0015029848,0.00032897005,0.00025955253],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.011896915,0.00035976758,0.00068830274,0.00037266273,0.00011881381,0.00033843023,0.0016764598,0.0005823202,0.005572913],"category_scores_gemma":[0.006514198,0.00025913512,0.00032530222,0.0011718996,0.00009332828,0.0005574741,0.0004438134,0.0010532237,0.001959524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021550049,0.000101410194,0.0022115875,0.0000028019176,0.000039702965,0.000007167139,0.0006789486,0.000033452878,0.8367106,0.015926266,0.004280488,0.13979207],"study_design_scores_gemma":[0.0016906048,0.00020973737,0.0031791737,0.0000118955295,0.000012291145,0.0000150082205,0.00065573276,0.0009063847,0.80637616,0.036307078,0.15010814,0.000527809],"about_ca_topic_score_codex":0.000021549957,"about_ca_topic_score_gemma":8.023536e-7,"teacher_disagreement_score":0.23418301,"about_ca_system_score_codex":0.00013759568,"about_ca_system_score_gemma":0.0001014065,"threshold_uncertainty_score":0.9999861},"labels":[],"label_agreement":null},{"id":"W2928591628","doi":"10.1080/03610918.2019.1588311","title":"Controlling individual and experimentwise error rates in replicated regular two-level factorial experiments","year":2019,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Variance (accounting); Monte Carlo method; Factorial experiment; Statistics; Analysis of variance; Factorial; Type I and type II errors; Fractional factorial design; Main effect; Mathematics; Word error rate; Computer science; Algorithm; Artificial intelligence","score_opus":0.4922343568183236,"score_gpt":0.5798727002424177,"score_spread":0.08763834342409405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928591628","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5649966,0.00076615525,0.4324645,0.00009549307,0.00021919126,0.0008916012,0.000080652266,0.000032521326,0.0004532823],"genre_scores_gemma":[0.77916735,0.000019829298,0.220503,0.00006262441,0.000011973487,0.00004142633,0.00013192346,0.000014114611,0.00004777086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671495,0.0010139563,0.0010133869,0.00049946416,0.00056241284,0.00019584577],"domain_scores_gemma":[0.99424815,0.004331207,0.00034919154,0.00075471046,0.00023327146,0.00008348475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019713298,0.00018949434,0.000363111,0.00046279706,0.00015914824,0.0003292118,0.00046285553,0.000092250644,0.000048856986],"category_scores_gemma":[0.0010021287,0.00019132969,0.000023501645,0.0005889727,0.00017571395,0.00042911863,0.00034934664,0.00019679747,0.000021593734],"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":[0.0007059268,0.0008321131,0.123803675,0.000022782213,0.000064775406,0.0000055210885,0.024233857,0.63163376,0.022076331,0.06584684,0.00012462618,0.1306498],"study_design_scores_gemma":[0.0030420786,0.00008222082,0.04585695,0.00002847924,0.0000069655175,0.000001264397,0.001790436,0.9210107,0.0005144733,0.027310917,0.00015249824,0.00020307269],"about_ca_topic_score_codex":0.00009710758,"about_ca_topic_score_gemma":0.000028758895,"teacher_disagreement_score":0.28937688,"about_ca_system_score_codex":0.000098498116,"about_ca_system_score_gemma":0.00004851818,"threshold_uncertainty_score":0.7802198},"labels":[],"label_agreement":null},{"id":"W2940123489","doi":"10.1002/cjs.11494","title":"Design selection for strong orthogonal arrays","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Class (philosophy); Space (punctuation); Computer science; Focus (optics); Design of experiments; Theoretical computer science; Artificial intelligence; Mathematics; Statistics; Physics; Optics","score_opus":0.1751092719048859,"score_gpt":0.40161405568027486,"score_spread":0.22650478377538896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940123489","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":[0.007841905,0.00008612248,0.9898916,0.00007104285,0.0010646465,0.00021973666,0.00014778571,0.0000024232318,0.0006747442],"genre_scores_gemma":[0.24447393,0.0000015679699,0.7543864,0.00011148878,0.00011790752,0.0000019313586,0.000002211007,0.00001498424,0.00088957156],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99807507,0.00024434936,0.000642391,0.00016691397,0.0005638133,0.00030749044],"domain_scores_gemma":[0.9960983,0.0018629967,0.00043255035,0.0001362328,0.00092892273,0.00054102537],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031562906,0.00010933792,0.00027356023,0.00039549274,0.00012385748,0.00022667655,0.0003288963,0.000060631468,0.0013312467],"category_scores_gemma":[0.0023506547,0.00009187084,0.00007505506,0.00030520887,0.00006516215,0.00025532497,0.0000064350324,0.00016686734,0.000092656075],"study_design_candidate":"theoretical_or_conceptual","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":[0.0007864325,0.00008531461,0.05194485,0.000038163875,0.00025903145,0.00021684855,0.002562433,0.09176095,0.032210406,0.30376872,0.35011667,0.1662502],"study_design_scores_gemma":[0.004841354,0.0076957257,0.018884817,0.00014280737,0.00017983619,0.0013868965,0.0043329806,0.24364448,0.019410653,0.5743596,0.12376288,0.0013579971],"about_ca_topic_score_codex":0.00016565382,"about_ca_topic_score_gemma":0.00090536004,"teacher_disagreement_score":0.27059084,"about_ca_system_score_codex":0.00020687575,"about_ca_system_score_gemma":0.001914062,"threshold_uncertainty_score":0.9995817},"labels":[],"label_agreement":null},{"id":"W2940292305","doi":"10.1038/s41592-019-0405-z","title":"Author Correction: Two-level factorial experiments","year":2019,"lang":"en","type":"erratum","venue":"Nature Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"","keywords":"Column (typography); Table (database); Factorial experiment; Factorial; Fractional factorial design; Computer science; Statistics; Error detection and correction; Mathematics; Algorithm; Data mining; Telecommunications","score_opus":0.2813980481515689,"score_gpt":0.5840002347499562,"score_spread":0.30260218659838733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940292305","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000070245496,0.0068457252,0.23914224,0.00018361174,0.5258143,0.0008138062,0.00013419674,0.00018647313,0.22687264],"genre_scores_gemma":[0.0000567718,0.00001772427,0.47307622,0.00049223047,0.009329642,0.00006611907,0.00010639478,0.0001422315,0.51671267],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9770538,0.010287285,0.0021909676,0.0032453237,0.0060439347,0.0011786562],"domain_scores_gemma":[0.9840663,0.008641799,0.0016918114,0.003758549,0.0012626773,0.0005788716],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.02083597,0.0014384117,0.0026793114,0.0013875837,0.0004150801,0.0012027967,0.0043614013,0.0060140262,0.006892056],"category_scores_gemma":[0.029513229,0.0011166139,0.0012682443,0.0025418461,0.00028424268,0.00069870654,0.0012094432,0.009549423,0.0021594933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000202471,0.00010514987,0.000021089345,0.000012715247,0.000121344456,0.000019568386,0.00046505345,0.000014370017,0.008100854,0.000483305,0.8879462,0.10250789],"study_design_scores_gemma":[0.00086985156,0.00028245756,0.00020705728,0.00010640267,0.0000837839,0.000046659985,0.00037594946,0.0015017685,0.029355194,0.0052614072,0.9607216,0.0011878682],"about_ca_topic_score_codex":0.000077714256,"about_ca_topic_score_gemma":0.00000687165,"teacher_disagreement_score":0.5164847,"about_ca_system_score_codex":0.00079750235,"about_ca_system_score_gemma":0.0010962351,"threshold_uncertainty_score":0.99983656},"labels":[],"label_agreement":null},{"id":"W2941373075","doi":"10.1016/j.csda.2019.03.007","title":"Data-driven multistratum designs with the generalized Bayesian <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\" id=\"d1e3469\" altimg=\"si339.gif\"><mml:mi>D</mml:mi></mml:math>-<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\" id=\"d1e3474\" altimg=\"si339.gif\"><mml:mi>D</mml:mi></mml:math> criterion for highly uncertain models","year":2019,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Ministry of Science and Technology, Taiwan","keywords":"Bayesian probability; Bayesian information criterion; Optimal design; Computer science; Algorithm; Class (philosophy); Mathematics; Plot (graphics); Data mining; Artificial intelligence; Statistics; Machine learning","score_opus":0.06620302271394407,"score_gpt":0.33799095240569316,"score_spread":0.2717879296917491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941373075","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49757606,0.00039685488,0.48181772,0.00041842845,0.0009045084,0.0003075843,0.01814648,0.00019324201,0.00023911752],"genre_scores_gemma":[0.61881924,0.00024238757,0.34922206,0.0012179271,0.0007021631,0.0005042469,0.028682714,0.00037913697,0.00023011855],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9849316,0.0010631541,0.0032170136,0.0037186863,0.0050561866,0.0020133874],"domain_scores_gemma":[0.9831095,0.0068213735,0.0030516817,0.0053069373,0.00072972424,0.0009807958],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0051803915,0.0015058158,0.0013509628,0.0007274118,0.0022265639,0.0031812442,0.0054732705,0.0010871605,0.00030417743],"category_scores_gemma":[0.0025124142,0.001525032,0.0011641172,0.002265005,0.0013796577,0.0032513598,0.003300195,0.0012975191,0.0013805908],"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":[0.0013664012,0.0004986141,0.000040349812,0.0002578367,0.0022799263,0.00043429743,0.0008113458,0.19516544,0.0011762055,0.79000837,0.0065453933,0.0014158011],"study_design_scores_gemma":[0.0024898634,0.0010077254,0.0002958336,0.00030753348,0.003037845,0.00024676794,0.0008813384,0.9813035,0.0005854141,0.0045186346,0.0038722495,0.0014533087],"about_ca_topic_score_codex":0.0023052911,"about_ca_topic_score_gemma":0.0013350236,"teacher_disagreement_score":0.78613806,"about_ca_system_score_codex":0.00012211832,"about_ca_system_score_gemma":0.0018503729,"threshold_uncertainty_score":0.9999076},"labels":[],"label_agreement":null},{"id":"W2943137452","doi":"10.1002/cjs.11499","title":"CVX‐based algorithms for constructing various optimal regression designs","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of Victoria","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Optimal design; MATLAB; Flexibility (engineering); Mathematical optimization; Computer science; Nonlinear system; Convex optimization; Regular polygon; Algorithm; Mathematics; Machine learning; Statistics","score_opus":0.20324947407372299,"score_gpt":0.4264114287170358,"score_spread":0.22316195464331282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943137452","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":[0.016876118,0.00021148953,0.97939336,0.00013738737,0.0018623164,0.0002753119,0.00044452725,0.000004781746,0.0007946768],"genre_scores_gemma":[0.16097227,0.000001384652,0.83826584,0.00019453042,0.00010981164,0.0000019947424,0.0000045513984,0.000023129753,0.00042651157],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99730486,0.00028620157,0.0009563572,0.00026038213,0.0007534169,0.0004387738],"domain_scores_gemma":[0.99349576,0.0034932704,0.0007977995,0.0002952103,0.0010964632,0.0008214839],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032951015,0.00017929148,0.00046339855,0.00053204916,0.00018869917,0.00033909062,0.0006618508,0.000105439256,0.00096910895],"category_scores_gemma":[0.0049673235,0.00014002662,0.00012762361,0.0003453118,0.0001871153,0.0002492318,0.000016910211,0.00025884472,0.00005822803],"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":[0.00083548046,0.0001266368,0.034082435,0.000100659,0.00024380944,0.001955132,0.00405446,0.031465888,0.038379844,0.049949054,0.13092993,0.7078767],"study_design_scores_gemma":[0.015672894,0.010061066,0.00709658,0.0011329759,0.0004033611,0.0032617408,0.022371767,0.5748285,0.08026196,0.12802877,0.1537516,0.0031287703],"about_ca_topic_score_codex":0.00030974406,"about_ca_topic_score_gemma":0.00024331266,"teacher_disagreement_score":0.7047479,"about_ca_system_score_codex":0.00024030797,"about_ca_system_score_gemma":0.0026153969,"threshold_uncertainty_score":0.99994415},"labels":[],"label_agreement":null},{"id":"W2947185180","doi":"10.5203/pmuser.201841670","title":"Construction of A-optimal Designs for Linear Models","year":2018,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Estimator; Mathematical optimization; Optimal design; Convergence (economics); Variance (accounting); Context (archaeology); Mathematics; Function (biology); Optimality criterion; Efficiency; Design of experiments; Computer science; Applied mathematics; Statistics","score_opus":0.3952469154709028,"score_gpt":0.5051451164533173,"score_spread":0.10989820098241448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947185180","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":[0.04692517,0.00001938239,0.92838824,0.00006204521,0.00033284843,0.00026504428,0.000010807629,0.00003165747,0.023964804],"genre_scores_gemma":[0.28538284,8.669933e-7,0.7131574,0.000060868773,0.00009366797,0.000010942628,5.8193535e-7,0.0000074610143,0.0012853622],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982519,0.0001445711,0.00052452524,0.0003351559,0.0005621087,0.00018169869],"domain_scores_gemma":[0.9977823,0.00096355536,0.00016944353,0.0003694875,0.00063772127,0.000077527075],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022867005,0.00009827024,0.00023825633,0.00015924218,0.0000876293,0.000042594722,0.00038067144,0.00007375253,0.0009167228],"category_scores_gemma":[0.00087124686,0.00007067931,0.000119042874,0.00037051007,0.0004287152,0.00039439715,0.00006830319,0.000036177167,0.00010389541],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009490551,0.0001831949,0.00036727468,0.000008650545,0.000047682926,7.989464e-7,0.0014441498,0.004061669,0.5002987,0.35914096,0.009790227,0.12370767],"study_design_scores_gemma":[0.00033258763,0.0005807872,0.000021918942,0.0000034242178,0.0000061064934,0.000006581815,0.00079858355,0.34572262,0.5635502,0.087582655,0.0012979257,0.00009666776],"about_ca_topic_score_codex":0.0000129826085,"about_ca_topic_score_gemma":0.0000013311864,"teacher_disagreement_score":0.34166095,"about_ca_system_score_codex":0.000017733717,"about_ca_system_score_gemma":0.00005724803,"threshold_uncertainty_score":0.9999966},"labels":[],"label_agreement":null},{"id":"W2950867586","doi":"10.48550/arxiv.1605.08473","title":"Robust designs for experiments with blocks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Estimator; Covariance matrix; Algorithm; Simulated annealing; Covariance; Construct (python library); Mathematical optimization; Mathematics; Design matrix; Matrix (chemical analysis); Computer science; Least-squares function approximation; Design of experiments; Generalized least squares; Minimax estimator; Applied mathematics; Linear model; Statistics; Minimum-variance unbiased estimator","score_opus":0.5308108886623619,"score_gpt":0.340477567155548,"score_spread":0.19033332150681392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950867586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08299574,0.00011806335,0.90149945,0.000066584726,0.00066301686,0.0011434343,0.00009776812,0.00013611723,0.013279824],"genre_scores_gemma":[0.90659314,0.000022058357,0.070170164,0.000101887854,0.00014492968,0.000016780821,0.000009474876,0.00006456069,0.02287702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956665,0.00051199296,0.0004991253,0.002219446,0.00047697886,0.0006259817],"domain_scores_gemma":[0.9949781,0.0015585823,0.00061648607,0.001901476,0.00057463325,0.00037072753],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018073602,0.00056515087,0.00075125793,0.00051243376,0.0002806406,0.00027455066,0.0024804764,0.00043499257,0.0007226186],"category_scores_gemma":[0.00046637616,0.0004194623,0.00040617792,0.0006271692,0.0003885236,0.0004166899,0.0012628302,0.00033894237,0.00021599027],"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":[0.011739407,0.002285539,0.02232849,0.00017608653,0.0020288914,0.0016004515,0.0031960306,0.6414355,0.048423633,0.20444106,0.046957325,0.015387611],"study_design_scores_gemma":[0.012006733,0.002873851,0.0010210614,0.0010431499,0.00069847214,0.000044617434,0.00521869,0.21898812,0.14672683,0.5893358,0.016433073,0.0056096055],"about_ca_topic_score_codex":0.00003019765,"about_ca_topic_score_gemma":0.0000074226095,"teacher_disagreement_score":0.8313293,"about_ca_system_score_codex":0.00037018192,"about_ca_system_score_gemma":0.00032060777,"threshold_uncertainty_score":0.9998257},"labels":[],"label_agreement":null},{"id":"W2952791551","doi":"10.48550/arxiv.1305.0182","title":"Space-filling Latin Hypercube Designs based on Randomization Restrictions in Factorial Experiments","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University","keywords":"Orthogonality; Latin hypercube sampling; Class (philosophy); Minimax; Space (punctuation); Computer science; Factorial experiment; Factorial; Mathematics; Combinatorics; Discrete mathematics; Algorithm; Mathematical optimization; Geometry; Statistics; Artificial intelligence; Mathematical analysis","score_opus":0.3911098360711547,"score_gpt":0.33307722437880205,"score_spread":0.05803261169235263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952791551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50939393,0.000040146613,0.48064336,0.000044131488,0.0023089754,0.0011872213,0.000026380661,0.00011468082,0.0062411665],"genre_scores_gemma":[0.9819972,0.000041375286,0.015952349,0.000058002188,0.00022973993,0.0000087903045,0.000024707626,0.000043398082,0.0016444299],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945937,0.0019778865,0.0007222042,0.0016442966,0.00060469063,0.00045721757],"domain_scores_gemma":[0.9949025,0.0026784586,0.0005546989,0.0013152307,0.00029410273,0.00025504138],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019078245,0.00049326173,0.00075933634,0.0014029901,0.0002312559,0.0003528594,0.0012321563,0.0005619143,0.0006842916],"category_scores_gemma":[0.0019761329,0.00048641837,0.00034988683,0.0016569138,0.0001404796,0.00045741387,0.0005049157,0.0007594917,0.00043941315],"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":[0.0010073825,0.00028046005,0.0058799554,0.0000059406675,0.000025127221,0.00006057021,0.00039225697,0.9823444,0.0036587701,0.0055856192,0.00056150474,0.00019803959],"study_design_scores_gemma":[0.005853995,0.00017722411,0.0021487048,0.00012204266,0.000045415458,5.626814e-7,0.0007692575,0.95617515,0.006722979,0.027010014,0.00027456097,0.00070007856],"about_ca_topic_score_codex":0.0007785904,"about_ca_topic_score_gemma":0.000024411705,"teacher_disagreement_score":0.47260326,"about_ca_system_score_codex":0.0007259849,"about_ca_system_score_gemma":0.00028448712,"threshold_uncertainty_score":0.9997587},"labels":[],"label_agreement":null},{"id":"W2961162542","doi":"10.1002/sim.8316","title":"Bayesian consensus‐based sample size criteria for binomial proportions","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"McGill University; McGill University Health Centre","funders":"","keywords":"Prior probability; Frequentist inference; Sample size determination; Bayesian probability; Statistics; Econometrics; Credible interval; Sample (material); Mathematics; Point estimation; Bayes' theorem; Computer science; Bayesian inference","score_opus":0.14867597247685566,"score_gpt":0.5096998485410166,"score_spread":0.3610238760641609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961162542","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":[0.004378801,0.00003273658,0.98435307,0.0017347976,0.0015349215,0.0011719284,0.0033324847,0.000032013933,0.0034292398],"genre_scores_gemma":[0.17122051,0.0000011413039,0.82647276,0.0006797071,0.0001429389,0.00007188835,0.00009035887,0.00002108202,0.0012996077],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99690145,0.00036179228,0.000980063,0.0005252103,0.00089348515,0.00033802498],"domain_scores_gemma":[0.96624047,0.032491848,0.0002536614,0.0005356783,0.00032864572,0.00014969119],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003995045,0.00017695382,0.000523061,0.00025811672,0.000078762874,0.000051263425,0.00038808602,0.00007896664,0.023830386],"category_scores_gemma":[0.06313896,0.00013175327,0.0000441126,0.00051058445,0.00037345343,0.00004836914,0.000046192115,0.00014602064,0.0001136383],"study_design_candidate":"not_applicable","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":[0.0012190522,0.00036288128,0.0161245,0.000120609635,0.000034882247,0.00007744051,0.0016199584,0.00040435718,0.06679549,0.060087457,0.81616586,0.036987484],"study_design_scores_gemma":[0.008298786,0.0027552312,0.013703851,0.00020690418,0.00005068413,0.000014831959,0.0031497169,0.3117243,0.0031270967,0.5702257,0.08605554,0.000687376],"about_ca_topic_score_codex":0.00013053072,"about_ca_topic_score_gemma":0.000048887592,"teacher_disagreement_score":0.73011035,"about_ca_system_score_codex":0.000087015665,"about_ca_system_score_gemma":0.00021115164,"threshold_uncertainty_score":0.977062},"labels":[],"label_agreement":null},{"id":"W2973746593","doi":"10.1093/biomet/asz043","title":"Column-orthogonal strong orthogonal arrays of strength two plus and three minus","year":2019,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":43,"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","keywords":"Orthogonality; Orthogonal array; Mathematics; Column (typography); Property (philosophy); Orthogonal functions; Combinatorics; Orthogonal basis; Space (punctuation); Algorithm; Mathematical analysis; Geometry; Statistics; Computer science; Connection (principal bundle); Taguchi methods","score_opus":0.11845592230210818,"score_gpt":0.4147318128645278,"score_spread":0.29627589056241965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973746593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9685297,0.001149861,0.020088805,0.000035727004,0.00068022165,0.00041889006,0.00014255631,0.000032386284,0.008921871],"genre_scores_gemma":[0.9013624,0.000009104752,0.09714448,0.000038594735,0.00008477409,0.000009152992,0.0000088117795,0.0000213779,0.0013212751],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99563277,0.0002689927,0.00086941064,0.0007859154,0.002014645,0.00042825087],"domain_scores_gemma":[0.9962161,0.0020975822,0.00042440952,0.00072844955,0.00027380692,0.0002596293],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0032170627,0.00025901292,0.0006463635,0.0013807671,0.00008516681,0.00018907194,0.00075907534,0.00012666562,0.002075538],"category_scores_gemma":[0.0012285633,0.00021210493,0.00018692303,0.0035212853,0.00032369723,0.00037548738,0.0003663177,0.00016857336,0.00028120042],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007488611,0.00044489498,0.42494178,0.00004157002,0.00018319144,0.000036294547,0.00047672953,0.00021600764,0.24496843,0.035226207,0.001988664,0.29072738],"study_design_scores_gemma":[0.015362577,0.004872262,0.7012211,0.00018163306,0.000148187,0.00019661289,0.004191662,0.043884482,0.17157124,0.026660956,0.029097497,0.00261175],"about_ca_topic_score_codex":0.00007399344,"about_ca_topic_score_gemma":0.00003904091,"teacher_disagreement_score":0.28811562,"about_ca_system_score_codex":0.00007671911,"about_ca_system_score_gemma":0.00018856484,"threshold_uncertainty_score":0.9988367},"labels":[],"label_agreement":null},{"id":"W2979956400","doi":"10.1177/0962280219881032","title":"Time-stratified case-crossover design applied with conditional logistic regression is not free from overlap bias","year":2019,"lang":"en","type":"letter","venue":"Statistical Methods in Medical Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Health Services","funders":"","keywords":"Logistic regression; Crossover; Statistics; Crossover study; Econometrics; Mathematics; Computer science; Medicine; Artificial intelligence","score_opus":0.6160523859351099,"score_gpt":0.6265387992108831,"score_spread":0.010486413275773154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979956400","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":[0.000043674063,0.00013488084,0.9069193,0.07574439,0.0005809629,0.0019053301,0.003839249,0.000062549894,0.010769662],"genre_scores_gemma":[0.0003572729,0.000018578316,0.8894079,0.101768136,0.0013392336,0.00037837317,0.00051399536,0.00016445912,0.006052091],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.92868316,0.038372297,0.0028869633,0.0038618955,0.02381375,0.0023819522],"domain_scores_gemma":[0.6528362,0.34205654,0.0005170947,0.0027880336,0.0009662966,0.0008358356],"candidate_categories":["metaresearch","metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaresearch","research_integrity","insufficient_payload"],"category_scores_codex":[0.07022276,0.0010439579,0.002636788,0.0013907749,0.00046347087,0.0010135083,0.0046148747,0.0033163044,0.078732006],"category_scores_gemma":[0.25353375,0.00067885517,0.00023477079,0.0023477364,0.0069348733,0.0002468314,0.001803815,0.013241078,0.0036343231],"study_design_candidate":"not_applicable","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":[0.0019014094,0.00017619514,0.000014945158,0.00006895838,0.000101601916,0.029746618,0.00022490759,0.000023664867,0.0015159053,0.0051061804,0.89659166,0.06452795],"study_design_scores_gemma":[0.0046371766,0.0013703275,0.0002238591,0.0007094414,0.00008380569,0.0006881342,0.00054689357,0.053243496,0.00464159,0.80112404,0.13117811,0.0015531544],"about_ca_topic_score_codex":0.00046527444,"about_ca_topic_score_gemma":0.000011879671,"teacher_disagreement_score":0.7960178,"about_ca_system_score_codex":0.00069640315,"about_ca_system_score_gemma":0.0029865177,"threshold_uncertainty_score":0.99956626},"labels":[],"label_agreement":null},{"id":"W2986495668","doi":"10.1002/qre.2589","title":"Integrated multiresponse parameter and tolerance design with model parameter uncertainty","year":2019,"lang":"en","type":"article","venue":"Quality and Reliability Engineering International","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Robustness (evolution); Mathematical optimization; Tolerance analysis; Computer science; Quality (philosophy); Reliability engineering; Mathematics; Engineering","score_opus":0.10957677726956226,"score_gpt":0.3960173430410653,"score_spread":0.286440565771503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986495668","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6246834,0.00003466201,0.3744031,0.00029722147,0.000187372,0.00023426567,0.000020582098,0.000042953314,0.000096454954],"genre_scores_gemma":[0.67996246,0.000006650927,0.3192826,0.00015423963,0.000012974348,0.000023246586,0.0000034681943,0.000010895452,0.00054348196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726975,0.0004336903,0.0005947599,0.00069938967,0.0007828385,0.00021954993],"domain_scores_gemma":[0.9933667,0.005656544,0.00012270536,0.00044767355,0.00026930525,0.00013704028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004577665,0.00022890019,0.00036009555,0.00014684141,0.000048401183,0.00024118359,0.00034779552,0.000118333046,0.00011385731],"category_scores_gemma":[0.0048435796,0.00015624621,0.000065417764,0.0002064144,0.00014498309,0.00040038876,0.00011492213,0.0002646407,0.00002107291],"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":[0.0013756139,0.00010495293,0.012672579,0.000022666218,0.00004006884,0.0000027502983,0.0007543154,0.9661066,0.0099679185,0.002200216,0.00006264742,0.0066896756],"study_design_scores_gemma":[0.0005988972,0.00011986072,0.022901414,0.000034584868,0.0000045102174,0.0000111505,0.00011917173,0.9708247,0.0015875897,0.0029881485,0.0005725518,0.00023741784],"about_ca_topic_score_codex":0.00007031471,"about_ca_topic_score_gemma":0.0000012313403,"teacher_disagreement_score":0.055279057,"about_ca_system_score_codex":0.00008780967,"about_ca_system_score_gemma":0.000050803865,"threshold_uncertainty_score":0.63715357},"labels":[],"label_agreement":null},{"id":"W2988095348","doi":"10.1007/s42519-019-0071-6","title":"Power Considerations in Designed Experiments","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Power (physics); Design of experiments; Statistical power; Mathematics; Power analysis; Distribution (mathematics); Reliability engineering; Statistics; Computer science; Algorithm; Engineering","score_opus":0.13381475264786238,"score_gpt":0.5078732818791895,"score_spread":0.3740585292313271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988095348","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15712848,0.0017313963,0.7218047,0.0014275262,0.0009934016,0.00034101465,0.000018007651,0.0000073823253,0.11654812],"genre_scores_gemma":[0.73501784,0.000014060446,0.26386967,0.000780566,0.00001659436,0.0000010176943,1.10090845e-7,0.0000052096198,0.00029494817],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.993,0.0051817903,0.0007837255,0.00017871633,0.0007119136,0.00014389007],"domain_scores_gemma":[0.9218221,0.07722325,0.00040375238,0.00015259352,0.00026457963,0.00013375696],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.020642767,0.00009410534,0.00030096844,0.00017039286,0.000055429453,0.00021930502,0.00014647965,0.00005339549,0.006859429],"category_scores_gemma":[0.06836014,0.00006602048,0.000037082275,0.00018291852,0.0001303342,0.0011583137,0.000050157672,0.00025869417,0.00022493282],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0032248625,0.00022962893,0.0004101917,0.0000019640406,0.00003090599,0.00025655256,0.0015497344,0.000040088675,0.039267257,0.95114505,0.0011849015,0.0026588668],"study_design_scores_gemma":[0.0016389554,0.0016728927,0.004815069,0.000032576296,0.000028079497,0.0014552067,0.0137426965,0.00008895863,0.0049088355,0.9640266,0.007395277,0.00019487621],"about_ca_topic_score_codex":0.000001566815,"about_ca_topic_score_gemma":8.229881e-8,"teacher_disagreement_score":0.5778893,"about_ca_system_score_codex":0.000029955816,"about_ca_system_score_gemma":0.000091783964,"threshold_uncertainty_score":0.9940484},"labels":[],"label_agreement":null},{"id":"W2992636254","doi":"10.1002/sim.8414","title":"Optimizing interim analysis timing for Bayesian adaptive commensurate designs","year":2019,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Xenon Pharmaceuticals (Canada)","funders":"Sanofi","keywords":"Interim; Bayesian probability; Interim analysis; Computer science; Adaptive design; Econometrics; Statistics; Artificial intelligence; Mathematics; Medicine; Clinical trial; Internal medicine","score_opus":0.2877791635705335,"score_gpt":0.511439466930051,"score_spread":0.22366030335951753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992636254","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":[0.002559738,0.00013877079,0.9904547,0.00034390422,0.0005359823,0.00058229617,0.00012974505,0.00002125915,0.0052336594],"genre_scores_gemma":[0.35932592,0.000006831256,0.63938487,0.0004034993,0.000042985444,0.000026890984,0.00002690749,0.000017641056,0.0007644379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99636257,0.0005979744,0.0010529814,0.0006378673,0.0009445792,0.00040404897],"domain_scores_gemma":[0.9894605,0.0091143865,0.0003575578,0.00058020104,0.0003337502,0.00015356232],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0065411557,0.00023258758,0.0009122474,0.0009215321,0.00008528848,0.0000701718,0.00061790686,0.00007901041,0.0017609482],"category_scores_gemma":[0.004335207,0.00017639162,0.000094016475,0.0016956914,0.00022014628,0.00015569023,0.00011844481,0.0002144667,0.00006585135],"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":[0.0049123922,0.0006406633,0.049969606,0.00017683697,0.0027075114,0.0004038732,0.06291028,0.09809724,0.12059213,0.27659974,0.08787841,0.29511133],"study_design_scores_gemma":[0.0018504706,0.0013896783,0.0019065184,0.00008957319,0.0002319694,0.000003825767,0.010346517,0.94100004,0.0015593426,0.040284317,0.0009794036,0.00035832488],"about_ca_topic_score_codex":0.00010611101,"about_ca_topic_score_gemma":0.000056453686,"teacher_disagreement_score":0.84290284,"about_ca_system_score_codex":0.00014773497,"about_ca_system_score_gemma":0.00004974074,"threshold_uncertainty_score":0.9991516},"labels":[],"label_agreement":null},{"id":"W2993579411","doi":"10.21423/jrs-v07hannel","title":"An Approach for Predicting Mainstream Cigarette Smoke Harmful and Potentially Harmful Constituent (HPHC) Yields","year":2019,"lang":"en","type":"article","venue":"Journal of Regulatory Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Health Canada","keywords":"Consistency (knowledge bases); Repeatability; Quality (philosophy); Portfolio; Process engineering; Computer science; Biochemical engineering; Environmental science; Mathematics; Statistics; Engineering; Business; Artificial intelligence","score_opus":0.0760355844758024,"score_gpt":0.38523782247646976,"score_spread":0.3092022380006674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993579411","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89176226,0.00025514697,0.10400014,0.00017010354,0.0012633344,0.00039619833,0.000009492468,0.000018515428,0.002124828],"genre_scores_gemma":[0.8152858,0.0000056929252,0.18406329,0.00022646622,0.00015222203,0.0000032945375,3.8033758e-7,0.000012431645,0.0002503931],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99445826,0.00025632323,0.0010884984,0.00073503784,0.0029588894,0.00050298864],"domain_scores_gemma":[0.9959825,0.0005496726,0.0010473335,0.0007041366,0.001139615,0.0005767428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016695432,0.0002306227,0.0005235773,0.0006581772,0.00041101125,0.0008149153,0.0017941938,0.00011930791,0.00009816507],"category_scores_gemma":[0.00179048,0.00016834198,0.00019548241,0.0008674336,0.0012948092,0.0027784202,0.00021525695,0.00030741512,0.000004823769],"study_design_candidate":"bench_or_experimental","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":[0.0005417075,0.0004026848,0.16230036,0.000037536814,0.000071241855,0.00003453586,0.0016225085,0.00938053,0.6952615,0.0051117106,0.0009908762,0.1242448],"study_design_scores_gemma":[0.0065159784,0.005425054,0.39719898,0.0003185588,0.00019573305,0.0027003237,0.015681945,0.27170205,0.28404963,0.008598181,0.005984187,0.0016293882],"about_ca_topic_score_codex":0.0000052258465,"about_ca_topic_score_gemma":3.2547814e-7,"teacher_disagreement_score":0.41121188,"about_ca_system_score_codex":0.00013576644,"about_ca_system_score_gemma":0.0007086481,"threshold_uncertainty_score":0.78582466},"labels":[],"label_agreement":null},{"id":"W2995390831","doi":"","title":"The LP Relaxation Orthogonal Array Polytope and its Permutation Symmetries","year":2014,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Polytope; Birkhoff polytope; Permutation (music); Mathematics; Homogeneous space; Characterization (materials science); Combinatorics; Symmetry (geometry); Orthogonal array; Permutation group; Factorial; Relaxation (psychology); Geometry; Regular polygon; Mathematical analysis; Statistics","score_opus":0.04581368736529833,"score_gpt":0.35564317030539083,"score_spread":0.3098294829400925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995390831","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9341033,0.0012739877,0.045179375,0.0007437202,0.01605376,0.00032563542,0.0000013651884,0.00002652614,0.002292304],"genre_scores_gemma":[0.98869765,0.00005082984,0.010089906,0.000039513114,0.0010679666,0.0000015137474,3.6770047e-7,0.000022696848,0.000029567065],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953569,0.0007442884,0.0016407777,0.0002777985,0.0016838863,0.00029632382],"domain_scores_gemma":[0.98723334,0.008899039,0.0019532829,0.00026423854,0.0014346964,0.00021537772],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.013714043,0.00024986654,0.00064912305,0.00024864063,0.00089299656,0.0010694328,0.0005898299,0.00014138735,0.0000065836116],"category_scores_gemma":[0.01779307,0.0001615035,0.0001483383,0.00056588475,0.00017251432,0.00047789,0.00021020845,0.00042458533,0.0000061999363],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011880451,0.0001119484,0.0002440271,0.000019545707,0.00003912482,0.000002490117,0.0012088872,0.000023769087,0.0046159024,0.97999245,0.00013916956,0.013483857],"study_design_scores_gemma":[0.0021945115,0.000969417,0.0007482404,0.00010443958,0.000051155926,0.00012158536,0.0012852859,0.016315987,0.0052351984,0.97098595,0.0017523081,0.0002359074],"about_ca_topic_score_codex":0.0000019915406,"about_ca_topic_score_gemma":2.2286528e-7,"teacher_disagreement_score":0.054594316,"about_ca_system_score_codex":0.0000594251,"about_ca_system_score_gemma":0.00010097131,"threshold_uncertainty_score":0.9999676},"labels":[],"label_agreement":null},{"id":"W3004949560","doi":"10.1080/00949655.2020.1722669","title":"Beta approximation and its applications","year":2020,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"York University; Thompson Rivers University","funders":"","keywords":"Mathematics; Gumbel distribution; Beta distribution; Approximation theory; Distribution (mathematics); BETA (programming language); Applied mathematics; Matching (statistics); Statistics; Mathematical analysis; Extreme value theory","score_opus":0.19230507724041207,"score_gpt":0.47879685527151833,"score_spread":0.28649177803110626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004949560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031510156,0.00019774662,0.9669804,0.0008705334,0.00003485547,0.00016710335,0.000010134076,0.000008320516,0.00022076587],"genre_scores_gemma":[0.80266833,0.000007941374,0.19701809,0.00022859062,0.000062013685,0.0000012615372,0.0000033936983,0.0000042713523,0.0000060965353],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827456,0.00023899181,0.000664258,0.00016164524,0.00059243455,0.00006813101],"domain_scores_gemma":[0.99622583,0.0027282594,0.00037110702,0.00003676134,0.0004452076,0.00019281423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095524365,0.00007428594,0.00020313231,0.00010482666,0.000089931615,0.0001729942,0.00007697015,0.000032184274,0.000052356558],"category_scores_gemma":[0.0016652809,0.000058585396,0.000024626734,0.00025995527,0.000045489287,0.00040124063,0.0000317841,0.00010417196,0.000014234925],"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":[0.0002767192,0.00008753653,0.0007877507,0.00004085257,0.000026253354,0.000008239801,0.0017599816,0.40178508,0.0060657426,0.08033237,0.00032535897,0.50850415],"study_design_scores_gemma":[0.00043833046,0.00020434342,0.0059898132,0.000004531198,0.0000145743525,0.0000113361875,0.00020700225,0.95901984,0.00020579858,0.033142693,0.0006999639,0.00006175917],"about_ca_topic_score_codex":3.2756012e-7,"about_ca_topic_score_gemma":5.3492432e-8,"teacher_disagreement_score":0.77115816,"about_ca_system_score_codex":0.000014892862,"about_ca_system_score_gemma":0.00002326839,"threshold_uncertainty_score":0.23890431},"labels":[],"label_agreement":null},{"id":"W3006514018","doi":"10.1016/j.cie.2020.106357","title":"Bayesian modeling and optimization for multi-response surfaces","year":2020,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Bayesian probability; Robustness (evolution); Bayesian linear regression; Covariate; Bayesian inference; Multivariate statistics; Computer science; Posterior probability; Mathematics; Statistics","score_opus":0.30615563783757793,"score_gpt":0.3930521525893101,"score_spread":0.08689651475173216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3006514018","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":[0.047745492,0.00012219064,0.9504662,0.00050753885,0.0006578461,0.000370606,0.000008441182,0.00011818057,0.0000034620068],"genre_scores_gemma":[0.36160842,0.0000018506522,0.63805664,0.00009411264,0.00019749519,0.000009573599,0.0000020429745,0.000021451668,0.000008441807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834186,0.00015750658,0.0004632323,0.00046074056,0.00033881955,0.0002378442],"domain_scores_gemma":[0.9981815,0.001245887,0.000080798265,0.00016734617,0.00008343852,0.00024102369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016433046,0.00018294537,0.00030450037,0.00014186678,0.00008964245,0.0003124279,0.00035217707,0.00013537623,0.000016984557],"category_scores_gemma":[0.0034036615,0.00017311581,0.00007726965,0.0004243893,0.000022054306,0.00033063535,0.00014842456,0.0001612346,0.0000034688578],"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":[0.0002682831,0.000007666857,0.000020261305,0.0000030591054,0.000011339884,0.0000019796921,0.00039076642,0.9836815,0.006020303,0.00003999423,0.00012181257,0.009433033],"study_design_scores_gemma":[0.0012285145,0.0001403775,0.0000070050114,0.000019541105,0.0000070990623,0.000002234679,0.00012336782,0.99596643,0.0018181093,0.000009918892,0.0004900799,0.00018733609],"about_ca_topic_score_codex":0.0000044285553,"about_ca_topic_score_gemma":6.3361234e-8,"teacher_disagreement_score":0.31386292,"about_ca_system_score_codex":0.00003782059,"about_ca_system_score_gemma":0.00004149617,"threshold_uncertainty_score":0.7059458},"labels":[],"label_agreement":null},{"id":"W3007714545","doi":"10.5539/ijsp.v9n2p30","title":"D-Optimal Slope Design for Second Degree Kronecker Model Mixture Experiment With Three Ingredients","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kronecker delta; Kronecker product; Mathematics; Simplex; Degree (music); Matrix (chemical analysis); Centroid; Product (mathematics); Applied mathematics; Function (biology); Homogeneous space; Pure mathematics; Combinatorics; Geometry","score_opus":0.2158157521981118,"score_gpt":0.41038123483203026,"score_spread":0.19456548263391846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007714545","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":[0.09739114,0.00015159344,0.90059966,0.000767349,0.00024404695,0.0003339815,0.00032292964,0.0000041035582,0.00018518326],"genre_scores_gemma":[0.29749689,0.000004850647,0.702128,0.00021987755,0.0000917927,0.000009440903,0.0000030349318,0.000009875572,0.00003627103],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971537,0.00014802758,0.0008661727,0.00035041798,0.0013117959,0.00016990313],"domain_scores_gemma":[0.99615127,0.0011643765,0.0005851761,0.00014253167,0.001709263,0.0002473758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021647874,0.00017513888,0.00033974947,0.00008345581,0.00007317908,0.00027339597,0.00063496013,0.000059001217,0.0003116822],"category_scores_gemma":[0.0019935016,0.00011726027,0.00007720452,0.00010181016,0.00017005637,0.00034444444,0.00012791141,0.00018246753,0.0000037248744],"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":[0.04173271,0.0021559305,0.016946455,0.00015824696,0.0022217336,0.00040135207,0.027123649,0.3421899,0.06504048,0.05161488,0.057018247,0.3933964],"study_design_scores_gemma":[0.0040473007,0.0038124844,0.0029566134,0.0000667534,0.000069721915,0.00012849554,0.0005091062,0.7480718,0.020071153,0.21644214,0.0033508788,0.00047355954],"about_ca_topic_score_codex":0.0000018388071,"about_ca_topic_score_gemma":0.0000057526095,"teacher_disagreement_score":0.40588188,"about_ca_system_score_codex":0.00008645954,"about_ca_system_score_gemma":0.00020649181,"threshold_uncertainty_score":0.4781735},"labels":[],"label_agreement":null},{"id":"W3007895650","doi":"10.5539/ijsp.v9n2p7","title":"D-optimal Design in Linear Model With Different Heteroscedasticity Structures","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Heteroscedasticity; Optimal design; Mathematics; Function (biology); Applied mathematics; Mathematical optimization; Statistics","score_opus":0.1546272628039724,"score_gpt":0.4163377342000365,"score_spread":0.26171047139606407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007895650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33168724,0.000024467712,0.66756487,0.00043397848,0.00010445713,0.00008512153,0.000074780444,0.0000023836822,0.000022737588],"genre_scores_gemma":[0.5342378,0.0000064730266,0.46558684,0.00012385444,0.000035279045,9.768676e-7,6.931261e-7,0.0000038929097,0.0000041578537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973844,0.00030783744,0.0007559538,0.00024579838,0.0011860246,0.00011994805],"domain_scores_gemma":[0.9975356,0.0011425616,0.00037806464,0.000093583956,0.0006769106,0.00017327805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012464453,0.00013201349,0.0003065338,0.00009502323,0.00002779117,0.00015612297,0.00047846083,0.000039047045,0.0000791657],"category_scores_gemma":[0.0025944777,0.00008163612,0.000040108604,0.00010183903,0.0001566823,0.00020383153,0.000106857944,0.00023779148,0.0000011323654],"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":[0.0049744043,0.0003179238,0.03742624,0.000018786108,0.000105121406,0.00016150057,0.0024921852,0.909613,0.0062409057,0.013907428,0.0005378875,0.024204593],"study_design_scores_gemma":[0.0010184852,0.0008745165,0.024573298,0.000018852239,0.000011510166,0.000044785513,0.00009715609,0.8052713,0.002733474,0.16518585,0.000034869103,0.00013589195],"about_ca_topic_score_codex":0.0000062596246,"about_ca_topic_score_gemma":0.0000041835015,"teacher_disagreement_score":0.2025506,"about_ca_system_score_codex":0.00006349838,"about_ca_system_score_gemma":0.000097201024,"threshold_uncertainty_score":0.33290243},"labels":[],"label_agreement":null},{"id":"W3011367055","doi":"10.1002/bimj.201900005","title":"On a class of non‐linear transformation cure rate models","year":2020,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":8,"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":"Transformation (genetics); Mathematics; Inflation (cosmology); Applied mathematics; Maximum likelihood; Class (philosophy); Generalization; Cure rate; Binary data; Statistics; Binary number; Computer science; Econometrics; Artificial intelligence","score_opus":0.29297819451056173,"score_gpt":0.4656604519769179,"score_spread":0.17268225746635618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011367055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05973137,0.00016893602,0.9275067,0.003202385,0.00027142494,0.00014467718,0.000018276063,0.000015711175,0.008940527],"genre_scores_gemma":[0.9327718,0.00004232329,0.06605141,0.0009025436,0.00014226751,0.0000014919298,8.873242e-7,0.000011081874,0.00007621022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962983,0.0004985298,0.00096283713,0.00024254386,0.0017764638,0.00022130203],"domain_scores_gemma":[0.99747354,0.0012584545,0.0003533833,0.00016736539,0.00032631235,0.0004209539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032243195,0.0001362653,0.00037236037,0.0012878465,0.000094914896,0.00015104121,0.00066592824,0.00010805271,0.00036716167],"category_scores_gemma":[0.002694729,0.0000894758,0.0002558391,0.0065588783,0.00007533098,0.0005920081,0.000045892924,0.00034439503,0.00021146219],"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":[0.0021264567,0.0006639495,0.00015638376,0.000021456692,0.0000978275,0.00007851597,0.0031293756,0.028946452,0.44925934,0.008800864,0.02393361,0.4827858],"study_design_scores_gemma":[0.0018656112,0.0024772405,0.00065450685,0.000025331912,0.0000197308,0.000049944083,0.00057218823,0.86783123,0.101473905,0.017057722,0.0077102156,0.00026235043],"about_ca_topic_score_codex":0.0000012259961,"about_ca_topic_score_gemma":2.6846468e-8,"teacher_disagreement_score":0.87304044,"about_ca_system_score_codex":0.000054926568,"about_ca_system_score_gemma":0.00007270229,"threshold_uncertainty_score":0.40201628},"labels":[],"label_agreement":null},{"id":"W3016830834","doi":"10.1002/cjs.11549","title":"Optimal balanced block designs for correlated observations","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Correlation; Block (permutation group theory); Binary number; Mathematics; Value (mathematics); Optimal design; Constant (computer programming); Construct (python library); Block size; Statistics; Mathematical optimization; Computer science; Algorithm; Combinatorics; Arithmetic","score_opus":0.362038590917151,"score_gpt":0.40760848188996734,"score_spread":0.04556989097281633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016830834","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":[0.0116970735,0.00017402972,0.9840806,0.001997508,0.0006663372,0.00020762318,0.00089885463,0.00000616436,0.00027179136],"genre_scores_gemma":[0.22648336,0.0000035977187,0.77182925,0.0012494037,0.00013658192,0.0000027814237,0.000007953767,0.00001850495,0.00026858342],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979379,0.0001697783,0.00088977814,0.0001893464,0.0005010611,0.00031213462],"domain_scores_gemma":[0.9950658,0.0018769003,0.00048660723,0.00015782764,0.0011888646,0.0012239664],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0011666481,0.00013100167,0.0003468487,0.00019372642,0.00019719692,0.00023805049,0.00065262057,0.00007447068,0.000372611],"category_scores_gemma":[0.011849951,0.000114570525,0.00009957006,0.000578328,0.00012791819,0.00022141106,0.000013993432,0.00021122755,0.000044052867],"study_design_candidate":"not_applicable","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":[0.00032066228,0.000042128464,0.0077059628,0.00001818081,0.00015661983,0.0005551535,0.0052643833,0.065807536,0.017077604,0.028994665,0.85911185,0.014945277],"study_design_scores_gemma":[0.006089938,0.005687276,0.029373992,0.0001250151,0.00033945355,0.00047675194,0.007318281,0.5486391,0.008691489,0.04375976,0.3480563,0.0014426317],"about_ca_topic_score_codex":0.00014410642,"about_ca_topic_score_gemma":0.00030590655,"teacher_disagreement_score":0.5110555,"about_ca_system_score_codex":0.00011645684,"about_ca_system_score_gemma":0.0016512866,"threshold_uncertainty_score":0.99647367},"labels":[],"label_agreement":null},{"id":"W3022034773","doi":"10.1016/j.cam.2022.114676","title":"Power divergence approach for one-shot device testing under competing risks","year":2022,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Estimator; Robustness (evolution); Divergence (linguistics); Mathematics; One shot; Context (archaeology); Statistics; Engineering","score_opus":0.4421324131294275,"score_gpt":0.4589655974754331,"score_spread":0.01683318434600556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022034773","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":[0.12475571,0.000100461686,0.8693069,0.00012814227,0.00008599379,0.00020879746,0.000011613783,0.000008447593,0.0053939037],"genre_scores_gemma":[0.43172657,4.214602e-7,0.5680632,0.00013435958,0.000031040934,0.0000083656005,0.0000011454076,0.0000072769044,0.000027622542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974456,0.00008914557,0.0007953605,0.00018255309,0.0013405225,0.00014683734],"domain_scores_gemma":[0.9933782,0.0051213084,0.0009363356,0.00009296318,0.00038076928,0.00009041126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004346112,0.00011709012,0.00034432087,0.00015362343,0.0004346254,0.00013655853,0.00039776522,0.000023528546,0.000089431494],"category_scores_gemma":[0.0005857322,0.00009411503,0.00008381805,0.00039548625,0.000064928114,0.00011212438,0.00023487315,0.00019946616,0.0000023690925],"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":[0.00008874771,0.00050992117,0.00020643848,0.00003763151,0.00005980095,0.000001954139,0.0018238929,0.8945824,0.008521685,0.08890915,0.00047445067,0.004783922],"study_design_scores_gemma":[0.00086796447,0.00030552567,0.001168851,0.000019307708,0.000037084174,0.00022566614,0.013975252,0.4393986,0.0007869792,0.54254836,0.0004386666,0.000227778],"about_ca_topic_score_codex":3.8412838e-7,"about_ca_topic_score_gemma":1.7067366e-8,"teacher_disagreement_score":0.45518383,"about_ca_system_score_codex":0.00004451604,"about_ca_system_score_gemma":0.00007855828,"threshold_uncertainty_score":0.38378996},"labels":[],"label_agreement":null},{"id":"W3027736642","doi":"10.22237/jmasm/1571745720","title":"On Statistical Significance of Discriminant Function Coefficients","year":2020,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Manitoba; University of Calgary","funders":"","keywords":"Mathematics; Discriminant; Discriminant function analysis; Linear discriminant analysis; Statistics; Multivariate statistics; Statistical hypothesis testing; Optimal discriminant analysis; Statistical significance; Function (biology); Applied mathematics; Artificial intelligence","score_opus":0.19653101442433668,"score_gpt":0.48602676480000656,"score_spread":0.2894957503756699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027736642","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":[0.0015391144,0.0000934887,0.9930725,0.00025208213,0.00046889123,0.0002909338,0.00016484516,0.000014149395,0.004103983],"genre_scores_gemma":[0.4523569,0.000002768234,0.5471748,0.00035203758,0.00006859561,0.000004871981,0.00000180426,0.000020018368,0.00001816794],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.991891,0.0021587657,0.0022356773,0.0005972162,0.0027266783,0.00039062017],"domain_scores_gemma":[0.980056,0.017285168,0.0011609319,0.00038342454,0.00046706968,0.00064744055],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00786662,0.00030126394,0.0011624235,0.00022650378,0.00009490614,0.00011415916,0.000828848,0.00013173929,0.00090908504],"category_scores_gemma":[0.016167814,0.0002084852,0.00017232836,0.0006116178,0.0004427938,0.00013290739,0.00014364958,0.00061586074,0.00006718369],"study_design_candidate":"theoretical_or_conceptual","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":[0.003454174,0.00034394357,0.00001795983,0.000023862833,0.000048496306,0.000032851884,0.00051260606,0.0029915262,0.20371822,0.26380512,0.0023586561,0.52269256],"study_design_scores_gemma":[0.0017690938,0.0039961324,0.0027060371,0.00003561423,0.00017616559,0.00002116406,0.000700857,0.13809226,0.04661629,0.8037129,0.0017800463,0.0003934204],"about_ca_topic_score_codex":0.0000022764923,"about_ca_topic_score_gemma":1.2350003e-7,"teacher_disagreement_score":0.5399078,"about_ca_system_score_codex":0.0000854277,"about_ca_system_score_gemma":0.00017891942,"threshold_uncertainty_score":0.99538434},"labels":[],"label_agreement":null},{"id":"W3029583566","doi":"10.1016/j.jspi.2020.03.007","title":"Minimax D-optimal designs for multivariate regression models with multi-factors","year":2020,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Mathematics; Covariance; Covariance matrix; Mathematical optimization; Optimal design; Matrix (chemical analysis); Convex optimization; Applied mathematics; Regular polygon; Algorithm; Statistics","score_opus":0.47817906207246336,"score_gpt":0.5087787140387374,"score_spread":0.030599651966274066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3029583566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07032043,0.00015949813,0.92898494,0.00016626595,0.000074654716,0.00011836496,0.000063964704,0.000010065498,0.000101818485],"genre_scores_gemma":[0.50267136,0.000003009455,0.4971905,0.00007893288,0.000028025375,0.0000013472608,0.0000012867835,0.000007272882,0.000018268493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977356,0.00023582434,0.00074282795,0.00030484755,0.0007396815,0.00024118614],"domain_scores_gemma":[0.99242216,0.0061042504,0.00051455846,0.00010483123,0.00040809103,0.0004461107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012150466,0.00019247647,0.0005106728,0.00010888304,0.00013954514,0.00023298191,0.00032051056,0.00007623636,0.000048871043],"category_scores_gemma":[0.006650316,0.00010582792,0.000056590565,0.00018593745,0.00017323508,0.00058984646,0.0000647708,0.000282206,0.0000028786033],"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":[0.035560325,0.0015759768,0.10437029,0.0003199357,0.00070502225,0.0015456944,0.08140808,0.26682362,0.25130758,0.07827479,0.021999313,0.15610938],"study_design_scores_gemma":[0.0025633234,0.0045528742,0.013693243,0.00026366801,0.00006277312,0.00005294809,0.0038799895,0.95991534,0.0050945017,0.0092494385,0.00031016348,0.0003617111],"about_ca_topic_score_codex":0.0000061257174,"about_ca_topic_score_gemma":1.349155e-7,"teacher_disagreement_score":0.69309175,"about_ca_system_score_codex":0.000019501476,"about_ca_system_score_gemma":0.000114283845,"threshold_uncertainty_score":0.796153},"labels":[],"label_agreement":null},{"id":"W3042988590","doi":"10.1177/0962280220938418","title":"Random effects models for complex designs","year":2020,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Grains Research and Development Corporation","keywords":"Variety (cybernetics); Variance (accounting); Computer science; Set (abstract data type); Row; Table (database); Block (permutation group theory); Algorithm; Data mining; Statistics; Mathematics; Artificial intelligence","score_opus":0.7962302970634183,"score_gpt":0.7196117283011839,"score_spread":0.07661856876223438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042988590","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":[0.00007240582,0.00027707743,0.9819587,0.00624357,0.00022960152,0.0016497193,0.00005498449,0.00004611663,0.00946786],"genre_scores_gemma":[0.02037993,0.000026164746,0.9768267,0.0018881103,0.00021910213,0.000462135,0.000008225555,0.00004221276,0.00014739003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95328236,0.034249496,0.0015453921,0.0014513076,0.008071691,0.0013997675],"domain_scores_gemma":[0.6692301,0.3274498,0.00009053029,0.000584029,0.0006589679,0.001986556],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.108294636,0.00026070216,0.0011048993,0.00042644853,0.00024511127,0.0002660539,0.002188633,0.00032520294,0.0053314534],"category_scores_gemma":[0.55894893,0.00018935604,0.00016300775,0.0023453338,0.0015006056,0.00020311665,0.0007380665,0.0014444203,0.00023008547],"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":[0.0017415897,0.00016374499,0.000027355454,0.00008123762,0.00001691581,0.00017866847,0.0006469994,0.00014673578,0.015476811,0.17036843,0.02028323,0.7908683],"study_design_scores_gemma":[0.0021869962,0.000628507,0.00015298654,0.00002733665,0.0000040626414,0.000003062366,0.00024149925,0.5326016,0.002900265,0.45605767,0.005057306,0.00013870293],"about_ca_topic_score_codex":0.000039081653,"about_ca_topic_score_gemma":0.0000035775076,"teacher_disagreement_score":0.7907296,"about_ca_system_score_codex":0.00013237193,"about_ca_system_score_gemma":0.00055846333,"threshold_uncertainty_score":0.9955778},"labels":[],"label_agreement":null},{"id":"W3048504327","doi":"10.1002/aic.17021","title":"Using prior parameter knowledge in <scp>model‐based</scp> design of experiments for pharmaceutical production","year":2020,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Eli Lilly and Company","keywords":"Fisher information; Computation; Computer science; Bayesian probability; Selection (genetic algorithm); Bayesian information criterion; Invertible matrix; Prior information; Sequential analysis; Design of experiments; Algorithm; Process (computing); Mathematical optimization; Model selection; Matrix (chemical analysis); Mathematics; Machine learning; Artificial intelligence; Statistics","score_opus":0.6399688166531778,"score_gpt":0.5589323547005753,"score_spread":0.0810364619526025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048504327","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":[0.21984544,0.0007135333,0.7781219,0.00026253736,0.00041175945,0.0005139324,0.0000027129336,0.000012566227,0.000115604205],"genre_scores_gemma":[0.4216004,0.0000068846894,0.57789385,0.00023968607,0.00016592996,0.00001560012,2.4922778e-7,0.000020365223,0.000057009995],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622643,0.000988248,0.0010512011,0.0004768522,0.0008587127,0.0003985549],"domain_scores_gemma":[0.9961745,0.0024814417,0.0004288529,0.00023312133,0.00037295418,0.00030909549],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0054736733,0.00020512486,0.00044871692,0.00028513878,0.00013367349,0.00014839308,0.0005716475,0.00011238864,0.000056420973],"category_scores_gemma":[0.009402196,0.00016343089,0.00019359907,0.000731855,0.00012398182,0.0005525965,0.00009765608,0.00036223067,0.000021282985],"study_design_candidate":"bench_or_experimental","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":[0.00027706608,0.0002868451,0.00044999403,0.0000147795545,0.000025909629,0.0000064238434,0.0032626155,0.14291412,0.8391071,0.00004115005,0.0031956588,0.010418327],"study_design_scores_gemma":[0.0007529481,0.00021232918,0.000034597044,0.000023088636,0.000017401075,0.000017845328,0.0007228044,0.5435487,0.45304123,0.0011023205,0.00047535312,0.000051388313],"about_ca_topic_score_codex":8.7089916e-7,"about_ca_topic_score_gemma":1.2483352e-7,"teacher_disagreement_score":0.4006346,"about_ca_system_score_codex":0.00014398675,"about_ca_system_score_gemma":0.00035248473,"threshold_uncertainty_score":0.998942},"labels":[],"label_agreement":null},{"id":"W3067784497","doi":"10.1177/0962280220948159","title":"Model-robust designs for nonlinear quantile regression","year":2020,"lang":"en","type":"article","venue":"Statistical Methods in Medical Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Alberta","funders":"","keywords":"Quantile regression; Quantile; Statistics; Computer science; Nonlinear regression; Nonlinear system; Econometrics; Regression; Regression analysis; Mathematics","score_opus":0.7951379235756372,"score_gpt":0.7090320201533811,"score_spread":0.0861059034222561,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3067784497","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":[0.00028691744,0.00025896134,0.98637986,0.007417027,0.00021884932,0.0009185795,0.00014674914,0.000048758626,0.0043242867],"genre_scores_gemma":[0.0052181,0.00005335909,0.99265134,0.0011308487,0.0002549156,0.00022195515,0.000014145004,0.00005143934,0.0004038882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9719606,0.014924723,0.0017212112,0.0016390933,0.008360325,0.0013940642],"domain_scores_gemma":[0.88145715,0.11417745,0.00014888597,0.0008597267,0.0010108984,0.0023458672],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.084480844,0.0002800814,0.0009080352,0.0004637276,0.00028603678,0.00024882978,0.0022912773,0.0004431602,0.0057902667],"category_scores_gemma":[0.5042462,0.00019268191,0.00015260662,0.0024860867,0.0013575219,0.00020611913,0.00089887343,0.001857771,0.00027093448],"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":[0.0012057677,0.00027378043,0.00008651612,0.000059193586,0.000011133593,0.00016315164,0.0006796515,0.00063424936,0.014709645,0.07574068,0.03393458,0.8725017],"study_design_scores_gemma":[0.00074609247,0.0005838146,0.000049967417,0.00006396831,0.0000037540183,0.0000034849052,0.00058337464,0.8053923,0.004387172,0.17893496,0.0090683885,0.00018266535],"about_ca_topic_score_codex":0.000025376079,"about_ca_topic_score_gemma":0.0000061660658,"teacher_disagreement_score":0.872319,"about_ca_system_score_codex":0.00013945009,"about_ca_system_score_gemma":0.0009605286,"threshold_uncertainty_score":0.99511856},"labels":[],"label_agreement":null},{"id":"W3085503933","doi":"","title":"Sequential design for microarray experiments","year":2005,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Lunenfeld-Tanenbaum Research Institute","funders":"","keywords":"Computer science","score_opus":0.13647528988949462,"score_gpt":0.39069065083519466,"score_spread":0.2542153609457001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3085503933","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":[0.013570779,0.002200895,0.94734585,0.004225039,0.0010030399,0.0018758058,0.00015070317,0.00026370477,0.02936418],"genre_scores_gemma":[0.14088614,0.000118808464,0.8278551,0.00024079757,0.0001253098,0.00052045734,0.00019168877,0.00011162364,0.029950071],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9752695,0.01822655,0.0017031559,0.0022176038,0.0017956133,0.0007876051],"domain_scores_gemma":[0.98065484,0.009239316,0.0013133829,0.004263843,0.0040979725,0.0004306476],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03466921,0.00070829596,0.000942801,0.00054965937,0.0006206473,0.0018324219,0.0045663686,0.00063669105,0.0009285723],"category_scores_gemma":[0.010380673,0.00068778056,0.0007328722,0.00064060616,0.00049214636,0.00031699607,0.0026309635,0.0007245195,0.00028206792],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037723634,0.0026037928,0.00040811254,0.000120709265,0.00039964108,0.000015435755,0.032583863,0.0029888686,0.5199754,0.051143814,0.03290227,0.35648087],"study_design_scores_gemma":[0.0011452782,0.0000018058488,0.00016641231,0.0005097642,0.000056241865,0.000013245187,0.00037452666,0.035190117,0.8904601,0.022740573,0.04845514,0.00088683644],"about_ca_topic_score_codex":0.00029410695,"about_ca_topic_score_gemma":0.00010461551,"teacher_disagreement_score":0.37048465,"about_ca_system_score_codex":0.00038312117,"about_ca_system_score_gemma":0.00066262885,"threshold_uncertainty_score":0.99998474},"labels":[],"label_agreement":null},{"id":"W3092132750","doi":"10.1002/cjs.11571","title":"An efficient algorithm for Elastic I‐optimal design of generalized linear models","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"College of Science and Health; DePaul University; National Science Foundation","keywords":"Algorithm; Equivalence (formal languages); Mathematical optimization; Computer science; Multiplicative function; Generalized linear model; Convergence (economics); Set (abstract data type); Mathematics; Machine learning","score_opus":0.2169637399370339,"score_gpt":0.404329584293057,"score_spread":0.18736584435602308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092132750","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":[0.0025396643,0.00021003345,0.99526805,0.0001250449,0.00038122066,0.00024983942,0.0011906876,0.0000032943792,0.000032181426],"genre_scores_gemma":[0.08679118,0.0000037497846,0.9127187,0.0002532878,0.0001694134,0.0000025290242,0.0000055298597,0.000025328733,0.00003026828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966509,0.000515068,0.0013615133,0.0002595528,0.0008697813,0.0003432372],"domain_scores_gemma":[0.9944637,0.0016560834,0.00076501025,0.00024453312,0.0014531733,0.0014174992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002900917,0.00017206493,0.00058733334,0.0003119023,0.000115392984,0.00013411124,0.00083868025,0.000076466946,0.0002855618],"category_scores_gemma":[0.0034411359,0.0001407797,0.00012710778,0.000410125,0.00019142537,0.00018761583,0.00001551231,0.00016683439,0.000011438964],"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":[0.00010836988,0.00002801549,0.000014220667,0.0000052981095,0.00003102866,0.00009020663,0.0013407137,0.9496544,0.002775811,0.0037276556,0.004827549,0.037396703],"study_design_scores_gemma":[0.00075367664,0.0014662014,0.000023888077,0.000010983942,0.00004092202,0.000030708612,0.0004797069,0.9875754,0.0026011884,0.006277718,0.0005958475,0.00014377604],"about_ca_topic_score_codex":0.00017814265,"about_ca_topic_score_gemma":0.000035778197,"teacher_disagreement_score":0.084251516,"about_ca_system_score_codex":0.000097281314,"about_ca_system_score_gemma":0.0016802921,"threshold_uncertainty_score":0.574083},"labels":[],"label_agreement":null},{"id":"W3097065132","doi":"10.1093/biomet/asaa089","title":"A method of constructing maximin distance designs","year":2020,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":35,"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":"Minimax; Distance measures; Mathematical optimization; Mathematics; Measure (data warehouse); Construct (python library); Class (philosophy); Computer experiment; Optimal design; Computer science; Algorithm; Data mining; Artificial intelligence; Statistics","score_opus":0.41460356532027726,"score_gpt":0.49876673991261505,"score_spread":0.0841631745923378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097065132","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":[0.010518926,0.00051282067,0.9807139,0.00049186393,0.00018759297,0.00019668625,0.00003799001,0.000048229507,0.0072919596],"genre_scores_gemma":[0.2842655,0.0000033678525,0.71537715,0.00019464,0.00004331381,0.0000043044834,7.1607224e-7,0.000011452734,0.00009957046],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9963944,0.0006878523,0.0008317307,0.000574927,0.0012498088,0.00026129527],"domain_scores_gemma":[0.995199,0.003503475,0.00042747654,0.0004047065,0.00022290093,0.00024241715],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0038479494,0.00016064652,0.00049840834,0.000614778,0.000058906837,0.000107464824,0.00086943567,0.00008173326,0.000790291],"category_scores_gemma":[0.009872567,0.0001275223,0.00017236632,0.007507237,0.00021122853,0.00022426405,0.00020829072,0.0001087702,0.00016406196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000099252604,0.00003510528,0.0034089272,0.000010049009,0.000019675981,0.000009992129,0.00063854,0.000008542961,0.60272235,0.0037851853,0.0012685469,0.38799384],"study_design_scores_gemma":[0.0006440013,0.00039583462,0.00053773285,0.000015667458,0.000016971371,0.000022165248,0.0045139655,0.004306688,0.95714074,0.0030358243,0.029072925,0.000297501],"about_ca_topic_score_codex":0.000009848641,"about_ca_topic_score_gemma":2.3074487e-7,"teacher_disagreement_score":0.38769636,"about_ca_system_score_codex":0.00003459595,"about_ca_system_score_gemma":0.00006735316,"threshold_uncertainty_score":0.9984677},"labels":[],"label_agreement":null},{"id":"W3097165355","doi":"10.1080/00207543.2020.1836420","title":"A novel approach for non-normal multi-response optimisation problems","year":2020,"lang":"en","type":"article","venue":"International Journal of Production Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":12,"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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Normality; Bayesian probability; Reliability (semiconductor); Computer science; Mathematical optimization; Normal distribution; Mathematics; Statistics; Artificial intelligence","score_opus":0.6027455265248953,"score_gpt":0.5750842144902224,"score_spread":0.027661312034672925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097165355","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":[0.04899951,0.000076461336,0.9310732,0.017550413,0.001272553,0.00069744006,0.000015623807,0.000013028207,0.0003017998],"genre_scores_gemma":[0.44215983,0.000010261854,0.5551149,0.00013900019,0.0012742452,0.000041563035,0.0000035809237,0.000017854134,0.0012387667],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99312437,0.0008313516,0.0010723816,0.0004847494,0.004205132,0.0002820059],"domain_scores_gemma":[0.9905311,0.0012323229,0.0005370848,0.00022479614,0.007230396,0.00024431423],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.024413692,0.00012826579,0.00025563806,0.00084781786,0.00015952032,0.00045639902,0.0015796542,0.00008092367,0.00014403813],"category_scores_gemma":[0.03158896,0.00009988074,0.00020986635,0.000853903,0.000194093,0.0010649587,0.00021564242,0.0005073374,0.000045051336],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006551253,0.0005737134,0.00027040753,0.000010796985,0.00010019361,0.0000067497804,0.0031187206,0.047591377,0.9139043,0.00016075974,0.009546527,0.018165166],"study_design_scores_gemma":[0.006719845,0.003218655,0.004897926,0.000082632556,0.000022724038,0.00071375456,0.008402471,0.44406602,0.4800439,0.0015573337,0.049796067,0.0004786813],"about_ca_topic_score_codex":0.000008657621,"about_ca_topic_score_gemma":3.7641303e-7,"teacher_disagreement_score":0.43386045,"about_ca_system_score_codex":0.00022420133,"about_ca_system_score_gemma":0.0003759798,"threshold_uncertainty_score":0.9765684},"labels":[],"label_agreement":null},{"id":"W3101287979","doi":"","title":"EXISTENCE AND CONSTRUCTION OF RANDOMIZATION DEFINING CONTRAST SUBSPACES FOR REGULAR FACTORIAL DESIGNS","year":2012,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Burnaby Hospital; Acadia University; Simon Fraser University","funders":"","keywords":"Mathematics; Fractional factorial design; Factorial; Factorial experiment; Restricted randomization; Randomization; Linear subspace; Statistics; Pure mathematics","score_opus":0.2150164648240925,"score_gpt":0.44667812055141404,"score_spread":0.23166165572732153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101287979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23159912,0.00037384298,0.76495117,0.000026366892,0.0007746918,0.00036526917,0.000008017242,0.000017975248,0.00188351],"genre_scores_gemma":[0.5314073,0.000006421571,0.46840304,0.00001000321,0.00006252872,0.000012765665,0.000001346911,0.000004706252,0.00009188718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983943,0.00032337246,0.0004502001,0.00021099493,0.00043391308,0.00018723795],"domain_scores_gemma":[0.9961051,0.0031686688,0.0002593559,0.0001618075,0.0002113623,0.000093750816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037686906,0.000098707394,0.0003088798,0.000111117886,0.000095292766,0.0000799525,0.00012654958,0.00007220431,0.00010549542],"category_scores_gemma":[0.0037151927,0.00007092847,0.00006399204,0.00020768336,0.00020581628,0.00061648036,0.00003119516,0.000031940883,0.000004768944],"study_design_candidate":"bench_or_experimental","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":[0.0029595715,0.00008348254,0.03862689,0.000016126347,0.000042783187,1.8129388e-7,0.0024943603,0.000029051778,0.30567402,0.600352,0.00045410483,0.049267463],"study_design_scores_gemma":[0.009520548,0.0003631823,0.0036859943,0.000025116076,0.00005625017,0.000027680671,0.0067201364,0.004695182,0.88933843,0.08390565,0.0013203281,0.00034147056],"about_ca_topic_score_codex":0.000013302128,"about_ca_topic_score_gemma":0.0000015053419,"teacher_disagreement_score":0.5836644,"about_ca_system_score_codex":0.00001652097,"about_ca_system_score_gemma":0.000024529754,"threshold_uncertainty_score":0.44477013},"labels":[],"label_agreement":null},{"id":"W3108522401","doi":"10.1137/21m1389845","title":"Multi-experiment Parameter Identifiability of ODEs and Model Theory","year":2022,"lang":"en","type":"article","venue":"SIAM Journal on Applied Algebra and Geometry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"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":"Fields Institute for Research in Mathematical Sciences; National Science Foundation","keywords":"Identifiability; Ode; Mathematics; Context (archaeology); Applied mathematics; Polynomial; Algorithm; Mathematical optimization; Computer science; Theoretical computer science; Statistics; Mathematical analysis","score_opus":0.07830093866502531,"score_gpt":0.3879804277715685,"score_spread":0.3096794891065432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108522401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95537823,0.0015528137,0.04135326,0.000085302796,0.0002244007,0.00025116224,0.000021826514,0.000014745553,0.0011182294],"genre_scores_gemma":[0.94017214,0.000048751066,0.058970693,0.00036774724,0.000027874134,0.000029091867,8.987378e-7,0.000017577679,0.00036520028],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9961897,0.0006628921,0.00084343215,0.0005668702,0.0014390568,0.0002980341],"domain_scores_gemma":[0.99691945,0.0018709612,0.00042863705,0.00044526238,0.000079645026,0.0002560331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009003787,0.00022609555,0.00049752026,0.0005666126,0.00049663795,0.00017810475,0.000514871,0.00006411835,0.0008878693],"category_scores_gemma":[0.00065251807,0.00016902457,0.00014154198,0.00061147526,0.00032446938,0.00018405031,0.0005413166,0.00052376033,0.000010843634],"study_design_candidate":"theoretical_or_conceptual","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":[0.0028790243,0.0024001303,0.0051623085,0.000042130847,0.00026731406,0.000043163705,0.006064524,0.0141018955,0.64365995,0.11494414,0.0013115539,0.20912385],"study_design_scores_gemma":[0.0034782367,0.0012799549,0.012140871,0.000021101596,0.000061895305,0.0002444278,0.016421579,0.03748612,0.15223226,0.77505255,0.00078771217,0.00079329417],"about_ca_topic_score_codex":0.0000015057117,"about_ca_topic_score_gemma":1.15922134e-7,"teacher_disagreement_score":0.6601084,"about_ca_system_score_codex":0.00006933277,"about_ca_system_score_gemma":0.000050179155,"threshold_uncertainty_score":0.9721546},"labels":[],"label_agreement":null},{"id":"W3114356518","doi":"10.12697/acutm.2011.15.05","title":"On approximating the distribution of indefinite quadratic expressions in singular normal vectors","year":2011,"lang":"en","type":"article","venue":"Acta et Commentationes Universitatis Tartuensis de Mathematica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Western University","funders":"","keywords":"Mathematics; Quadratic equation; Definite quadratic form; Isotropic quadratic form; Quadratic form (statistics); Distribution (mathematics); Quadratic function; Binary quadratic form; Moment (physics); Applied mathematics; Quadratic programming; Mathematical analysis; Combinatorics; Mathematical optimization; Geometry; Physics","score_opus":0.1513518693766131,"score_gpt":0.382392578907339,"score_spread":0.23104070953072592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114356518","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8879182,0.00001478903,0.10253833,0.0011487041,0.000077538265,0.00041711942,0.000033018554,0.000027904402,0.007824399],"genre_scores_gemma":[0.8735027,0.0000031301138,0.1260894,0.00032456973,0.0000034207908,0.000012597428,0.00001789258,0.000011748609,0.000034554505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967961,0.001256049,0.0007257566,0.00026495007,0.0007208173,0.00023634269],"domain_scores_gemma":[0.99208486,0.0067028548,0.00050779316,0.00049895153,0.00013036224,0.000075189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002559696,0.00017527302,0.00032516647,0.00026887015,0.00021177015,0.00006847419,0.0005577373,0.00006508198,0.00045788012],"category_scores_gemma":[0.0025289897,0.0001278437,0.00010595364,0.0007226116,0.00019607967,0.0004978585,0.00016104014,0.0001834829,0.000039577448],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051019364,0.0019898592,0.012850118,0.0001024141,0.00018191885,0.000055774315,0.27873945,0.0011340145,0.04401483,0.64925516,0.0061447187,0.0050215684],"study_design_scores_gemma":[0.0035303447,0.0011645124,0.103027105,0.00078129995,0.00021680792,0.00005121127,0.24172759,0.054791007,0.069919154,0.52341694,0.00031797105,0.0010560695],"about_ca_topic_score_codex":0.0000916114,"about_ca_topic_score_gemma":0.0000123476175,"teacher_disagreement_score":0.12583822,"about_ca_system_score_codex":0.00012664753,"about_ca_system_score_gemma":0.00005268614,"threshold_uncertainty_score":0.5213314},"labels":[],"label_agreement":null},{"id":"W3123495936","doi":"","title":"Empirical Likelihood for Regression Discontinuity Design","year":2014,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"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 Alberta; National Science Foundation","keywords":"Regression discontinuity design; Empirical likelihood; Nonparametric regression; Covariate; Inference; Mathematics; Regression; Econometrics; Causal inference; Statistics; Nonparametric statistics; Regression analysis; Parametric statistics; Local regression; Computer science; Polynomial regression; Confidence interval; Artificial intelligence","score_opus":0.27433309297858915,"score_gpt":0.5212791377135418,"score_spread":0.24694604473495269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123495936","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5611771,0.0017332019,0.18615977,0.006109763,0.009064008,0.019096157,0.00054638955,0.0004589403,0.21565469],"genre_scores_gemma":[0.5561722,0.0012354992,0.43052638,0.00035852107,0.001012424,0.0019015358,0.000053707892,0.00022006202,0.008519726],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9876959,0.0047138184,0.0019485613,0.0026977134,0.0014759994,0.0014680249],"domain_scores_gemma":[0.97862554,0.016858581,0.00064582354,0.002837067,0.00048487473,0.0005481395],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.040102318,0.0005813513,0.0014504965,0.0010913659,0.00033506713,0.0009599838,0.0033846607,0.0009682376,0.0002430301],"category_scores_gemma":[0.02082936,0.0004632505,0.00060948054,0.00035431312,0.00063427794,0.00025505075,0.0034808137,0.0020645312,0.000084153144],"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":[0.0010119564,0.0003738709,0.004804548,0.000064166976,0.000059631708,0.000019186462,0.00087594514,0.008561233,0.0019808176,0.00021212887,0.0047188723,0.97731763],"study_design_scores_gemma":[0.0039871656,0.0017838813,0.011391301,0.00094792416,0.00003775094,0.00002945261,0.0031128766,0.43522862,0.019338435,0.37669066,0.1447304,0.0027215334],"about_ca_topic_score_codex":0.000023535235,"about_ca_topic_score_gemma":0.000039279843,"teacher_disagreement_score":0.9745961,"about_ca_system_score_codex":0.0011121343,"about_ca_system_score_gemma":0.0010401298,"threshold_uncertainty_score":0.9997819},"labels":[],"label_agreement":null},{"id":"W3127481972","doi":"10.5539/ijsp.v10n2p36","title":"A- Optimal Slope Design for Second Degree Kronecker Model Mixture Experiment With Four Ingredients With Application in Selected Fruits Blending","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Kronecker delta; Kronecker product; Mathematics; Centroid; Simplex; Degree (music); Matrix (chemical analysis); Class (philosophy); Function (biology); Product (mathematics); Applied mathematics; Pure mathematics; Combinatorics; Computer science; Geometry","score_opus":0.1251633926486471,"score_gpt":0.39461566673063586,"score_spread":0.2694522740819888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127481972","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":[0.28988498,0.00008833356,0.7093506,0.00012561491,0.0000836564,0.00027965405,0.000104786,0.0000024448598,0.00007996579],"genre_scores_gemma":[0.42494556,0.0000056771078,0.57489556,0.00002919786,0.00002625416,0.000022827273,0.0000066682164,0.000008028028,0.000060217673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973923,0.00022821197,0.00075873977,0.0003793264,0.0010617758,0.00017966313],"domain_scores_gemma":[0.9952967,0.001105879,0.00052688504,0.00016030027,0.002793161,0.000117040494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002165858,0.00016001082,0.00030859612,0.00016009931,0.000063443666,0.0002247692,0.00034454974,0.000059334332,0.000079072866],"category_scores_gemma":[0.0011697774,0.00011122841,0.000033729943,0.00027372292,0.000094405135,0.0003318295,0.000072335664,0.00018538245,8.631349e-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":[0.020394234,0.0028857035,0.06777439,0.0001346282,0.001201622,0.0006435,0.01206981,0.5010216,0.1577825,0.020516861,0.0031434589,0.21243165],"study_design_scores_gemma":[0.0075058714,0.0023494111,0.024941675,0.0002695303,0.00007985265,0.0008044111,0.0010757268,0.7555403,0.10355812,0.102400094,0.00079756795,0.00067740126],"about_ca_topic_score_codex":0.0000043600858,"about_ca_topic_score_gemma":0.00005295079,"teacher_disagreement_score":0.25451872,"about_ca_system_score_codex":0.00019905507,"about_ca_system_score_gemma":0.00041725265,"threshold_uncertainty_score":0.4535763},"labels":[],"label_agreement":null},{"id":"W3128335591","doi":"10.1080/24754269.2020.1867795","title":"Selecting baseline designs using a minimum aberration criterion when some two-factor interactions are important","year":2021,"lang":"en","type":"article","venue":"Statistical Theory and Related Fields","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Baseline (sea); Optimal design; Class (philosophy); Computer science; Mathematical optimization; Factor (programming language); Mathematics; Algorithm; Statistics; Artificial intelligence","score_opus":0.1384077182270826,"score_gpt":0.45168840872861005,"score_spread":0.31328069050152746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128335591","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090358615,0.00051036786,0.9061264,0.0003829701,0.0009219217,0.00014314885,0.00008765267,0.000048878446,0.0014200599],"genre_scores_gemma":[0.8832149,0.000027020904,0.11486235,0.00034581008,0.00009222385,0.0000053495546,0.000020067067,0.000017135291,0.001415164],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9958516,0.0022311565,0.000776798,0.000506349,0.0003821156,0.000252],"domain_scores_gemma":[0.99214464,0.0070039104,0.00022110692,0.0002507122,0.00021140938,0.00016824293],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002778304,0.00017012164,0.00030272495,0.00011836363,0.0003282935,0.00025574001,0.00011939774,0.00018153008,0.0034445454],"category_scores_gemma":[0.009324285,0.00013428488,0.0000705653,0.00031532545,0.00012918113,0.00041297323,0.00009346539,0.0005046701,0.000028300487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011237344,0.00048061134,0.00093373697,0.000041284162,0.0002835511,0.0011285784,0.009652692,0.000529686,0.3934605,0.49454364,0.0018597712,0.09596222],"study_design_scores_gemma":[0.0006165568,0.00017180917,0.00035922063,0.000098129975,0.00007000066,0.00034126703,0.0050839945,0.12678523,0.022840114,0.84296864,0.0003229047,0.00034212176],"about_ca_topic_score_codex":0.000009596838,"about_ca_topic_score_gemma":0.000006193799,"teacher_disagreement_score":0.7928563,"about_ca_system_score_codex":0.000039835042,"about_ca_system_score_gemma":0.00007904327,"threshold_uncertainty_score":0.9990206},"labels":[],"label_agreement":null},{"id":"W3140762921","doi":"10.1002/cjs.11596","title":"Optimal design under complete class with ancillary functions","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation of Sri Lanka","keywords":"Class (philosophy); Extension (predicate logic); Nonlinear system; Computer science; Mathematical optimization; Logit; Poisson distribution; Mathematics; Algorithm; Machine learning; Artificial intelligence; Statistics","score_opus":0.23451173477753795,"score_gpt":0.37321062531664906,"score_spread":0.1386988905391111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140762921","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":[0.0045785545,0.0004887133,0.9917217,0.00060040294,0.00044056022,0.00005454542,0.0003265736,0.0000034136744,0.0017855667],"genre_scores_gemma":[0.13181059,0.000007957958,0.86597145,0.0007290687,0.000097952274,0.0000010043804,0.0000046336118,0.000018897052,0.0013584468],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975505,0.0005872035,0.00056805654,0.00019236341,0.00072893966,0.0003729581],"domain_scores_gemma":[0.995447,0.0015964656,0.00029685508,0.0002735873,0.0013943083,0.0009917922],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019438262,0.00013182512,0.00030560154,0.00027764286,0.00023690706,0.00035214602,0.0003664366,0.00006096155,0.0015527208],"category_scores_gemma":[0.0019195696,0.000104841325,0.000060322564,0.0006114093,0.00024865367,0.00020291243,0.00001689091,0.00028233687,0.0000696098],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029559192,0.00010082146,0.0061385315,0.000012698453,0.0003342942,0.014584011,0.0020310879,0.37695011,0.011402056,0.043623757,0.5146573,0.0298697],"study_design_scores_gemma":[0.0064295954,0.0062603382,0.07869515,0.0003510561,0.0006153618,0.031471573,0.04522627,0.1196015,0.010972795,0.12683475,0.57105905,0.0024825856],"about_ca_topic_score_codex":0.0002739532,"about_ca_topic_score_gemma":0.0023533462,"teacher_disagreement_score":0.25734863,"about_ca_system_score_codex":0.00034085,"about_ca_system_score_gemma":0.0062376363,"threshold_uncertainty_score":0.9993961},"labels":[],"label_agreement":null},{"id":"W3148502279","doi":"","title":"A grouping genetic algorithm for joint stratification and sample allocation designs","year":2019,"lang":"en","type":"article","venue":"Survey methodology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Cartesian product; Selection (genetic algorithm); Genetic algorithm; Algorithm; Population stratification; Population; Sample size determination; Multivariate statistics; Mathematics; Computer science; Statistics; Artificial intelligence; Combinatorics","score_opus":0.6926526353938932,"score_gpt":0.5259590208262171,"score_spread":0.16669361456767606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148502279","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":[0.072807506,0.00023090377,0.925258,0.00008504971,0.00061008136,0.0008307349,0.00005404353,0.000029594894,0.00009411509],"genre_scores_gemma":[0.049631543,0.000011061066,0.949787,0.00014369421,0.00004889657,0.00008476043,0.00003594809,0.000021124859,0.00023597875],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9857646,0.011922876,0.00076240726,0.0008227359,0.00040641718,0.0003209584],"domain_scores_gemma":[0.9548246,0.04381722,0.00031287718,0.00055868557,0.00038439926,0.000102187165],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04159457,0.00017204815,0.00048707597,0.00025083884,0.00011284356,0.00011341471,0.00031264,0.00016257846,0.00021102533],"category_scores_gemma":[0.026597185,0.0001482143,0.000078486475,0.00045140123,0.00011923323,0.00019855647,0.00008854775,0.00010577614,0.000067748224],"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":[0.00009803681,0.000038363934,0.0069626886,0.000008204789,0.000024852414,3.8599188e-7,0.0004910755,0.00019509392,0.25860268,0.0024806357,0.00013500822,0.730963],"study_design_scores_gemma":[0.0009891202,0.0008692655,0.62079054,0.000008959852,0.00002266672,0.000026714828,0.00095227326,0.16246624,0.064211845,0.14853145,0.0006713975,0.0004595217],"about_ca_topic_score_codex":0.00057721656,"about_ca_topic_score_gemma":0.000058736758,"teacher_disagreement_score":0.73050344,"about_ca_system_score_codex":0.000046385863,"about_ca_system_score_gemma":0.000079261714,"threshold_uncertainty_score":0.98688006},"labels":[],"label_agreement":null},{"id":"W3151692440","doi":"10.6000/1927-5951.2013.03.02.7","title":"Comparison of Outlier Detection Methods in Crossover Design Bioequivalence Studies","year":2013,"lang":"en","type":"article","venue":"Journal of Pharmacy and Nutrition Sciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Outlier; Bioequivalence; Crossover; Identification (biology); Principal component analysis; Statistics; Anomaly detection; Test (biology); Computer science; Logarithm; Data mining; Mathematics; Artificial intelligence; Medicine","score_opus":0.6076415696690599,"score_gpt":0.6589908163347381,"score_spread":0.051349246665678194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151692440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7866863,0.015110547,0.19633876,0.00057666574,0.0007490143,0.00032802176,0.0000014783797,0.0000058230653,0.00020338873],"genre_scores_gemma":[0.709185,0.0005367492,0.29014835,0.00005470612,0.00004366405,0.000007620996,1.4738363e-8,0.000002463997,0.000021438416],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9955446,0.001547201,0.0013656598,0.00025387396,0.0010927822,0.00019588563],"domain_scores_gemma":[0.9945143,0.0035776093,0.0010049295,0.000098276956,0.0007004961,0.000104425984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013742544,0.00012220848,0.0005292695,0.000740553,0.0001733928,0.00022001374,0.0004993372,0.00003487364,0.00017114263],"category_scores_gemma":[0.0026049686,0.000080213045,0.00010202616,0.0013329213,0.0008816525,0.0020530084,0.00008498374,0.00018422972,0.000007888084],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003103532,0.00034992665,0.015734522,0.000026365875,0.000017175042,0.0000027909655,0.0020486459,0.00030130835,0.83378905,0.00007294622,0.00067765615,0.14666928],"study_design_scores_gemma":[0.0015795331,0.0008771564,0.0037002312,0.00009186229,0.000017449152,0.00004140577,0.0103787435,0.016219882,0.94049734,0.025497906,0.0009680495,0.00013041188],"about_ca_topic_score_codex":0.000020821813,"about_ca_topic_score_gemma":0.0000011365972,"teacher_disagreement_score":0.14653887,"about_ca_system_score_codex":0.000040591825,"about_ca_system_score_gemma":0.00005415981,"threshold_uncertainty_score":0.47629187},"labels":[],"label_agreement":null},{"id":"W3153502698","doi":"10.1002/aic.17394","title":"Model‐based design of experiments for polyether production from bio‐based 1,3‐propanediol","year":2021,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Design of experiments; Monte Carlo method; Process engineering; Production (economics); Catalysis; Biological system; Environmental science; Computer science; Nuclear engineering; Simulation; Mathematics; Chemistry; Statistics; Engineering; Organic chemistry","score_opus":0.38997501257917133,"score_gpt":0.48286713365762685,"score_spread":0.09289212107845551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153502698","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":[0.067958176,0.00091081805,0.92889047,0.0007851454,0.00087846204,0.00034963613,0.000027316526,0.000019832374,0.00018016553],"genre_scores_gemma":[0.35658145,0.000004993663,0.6422799,0.00034788717,0.00020927768,0.000026517986,0.0000047366643,0.000025464691,0.00051978393],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9957869,0.0010352315,0.00093094225,0.00053551735,0.001394265,0.0003171666],"domain_scores_gemma":[0.9965395,0.0011802946,0.00058859016,0.0006088008,0.00089315965,0.00018967192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041912273,0.00021163428,0.00044042376,0.00023558715,0.000196163,0.00019360743,0.0005596011,0.00012961052,0.00070947286],"category_scores_gemma":[0.0033034235,0.00016131964,0.00027060666,0.0005332129,0.0001096988,0.0003924102,0.00005337697,0.00021918255,0.000027018257],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005028799,0.00027064944,0.00030554854,0.0000027966607,0.000036595055,0.000009340726,0.0002996177,0.053477447,0.93414843,0.000023723931,0.0033530158,0.007569975],"study_design_scores_gemma":[0.0008975447,0.00019945926,0.00010147047,0.000032512085,0.000026510124,0.00001298442,0.0004238845,0.24343026,0.7488691,0.005590299,0.0002730826,0.00014290604],"about_ca_topic_score_codex":0.000008434075,"about_ca_topic_score_gemma":6.496139e-7,"teacher_disagreement_score":0.28862327,"about_ca_system_score_codex":0.00010741106,"about_ca_system_score_gemma":0.0006571945,"threshold_uncertainty_score":0.77682304},"labels":[],"label_agreement":null},{"id":"W3162447044","doi":"10.1101/2021.05.15.21257266","title":"Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; Princess Margaret Cancer Centre; Public Health Ontario; University of Toronto; University Health Network","funders":"National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; Terveyden ja hyvinvoinnin laitos; Oulun Yliopisto; Government of Ontario; Broad Institute; Compute Canada; University of Toronto; Ontario Institute for Cancer Research","keywords":"Genome-wide association study; Computer science; Context (archaeology); Inference; Sampling (signal processing); Genetic association; Sample size determination; Statistical power; Data mining; Computational biology; Statistics; Mathematics; Biology; Genetics; Artificial intelligence; Single-nucleotide polymorphism; Filter (signal processing)","score_opus":0.22947150803477032,"score_gpt":0.47840054487732114,"score_spread":0.24892903684255083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162447044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49752447,0.0014653736,0.4999149,0.00022831866,0.00021883681,0.00054542115,0.000042106472,0.000029939005,0.00003062098],"genre_scores_gemma":[0.55433875,0.00009595665,0.44493264,0.000092947776,0.000062481646,0.00025876335,0.000067649424,0.00001985009,0.00013095455],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943137,0.0019699498,0.0011409052,0.0012100484,0.0009699763,0.0003954156],"domain_scores_gemma":[0.98086154,0.016590258,0.00083061116,0.00045207026,0.0011739308,0.0000916057],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01240119,0.00035690534,0.0011897776,0.0012874998,0.00019594189,0.0009800609,0.00043913623,0.00024159183,0.00008236353],"category_scores_gemma":[0.026687816,0.00032192198,0.00033145762,0.0022286368,0.00006532722,0.0004531822,0.00044446922,0.00036898127,0.0000035277872],"study_design_candidate":"observational","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":[0.00054119865,0.00070467236,0.48987612,0.00019369263,0.0056844126,0.000039077986,0.025121026,0.31464556,0.14979088,0.0003972286,0.00027380872,0.012732315],"study_design_scores_gemma":[0.0058203717,0.001316202,0.33432877,0.00026993104,0.00239895,0.0000065048434,0.08213714,0.4116987,0.023718895,0.13486561,0.0007029467,0.0027359945],"about_ca_topic_score_codex":0.00022679742,"about_ca_topic_score_gemma":0.00056025886,"teacher_disagreement_score":0.15554735,"about_ca_system_score_codex":0.0005337467,"about_ca_system_score_gemma":0.00035373122,"threshold_uncertainty_score":0.9999233},"labels":[],"label_agreement":null},{"id":"W3162849139","doi":"10.1287/inte.2021.1080","title":"Theory-Driven Practical Approach to Integrate R&amp;D and Production Planning for Portfolio Management in Agribusiness","year":2021,"lang":"en","type":"article","venue":"INFORMS Journal on Applied Analytics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of British Columbia","funders":"","keywords":"Production (economics); Flexibility (engineering); Modern portfolio theory; Portfolio; Computer science; Function (biology); Operations research; Population; Agribusiness; Yield (engineering); Economics; Microeconomics; Mathematics; Agriculture; Geography; Statistics; Financial economics","score_opus":0.20873042411796147,"score_gpt":0.4650840388606759,"score_spread":0.25635361474271445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162849139","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":[0.056721725,0.000048406866,0.8808235,0.0008733376,0.00043430857,0.0006937617,0.0000050506474,0.000022917178,0.060377028],"genre_scores_gemma":[0.26678157,0.000066322194,0.7289169,0.0011075009,0.00023665198,0.00005975639,0.000011817709,0.000029048702,0.0027904406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971713,0.000094638344,0.0009283792,0.00045822718,0.0009866346,0.0003608598],"domain_scores_gemma":[0.99817586,0.0005172274,0.00036108197,0.00036519032,0.00034159442,0.00023905053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052879653,0.00021968214,0.00041096634,0.00068287045,0.000174056,0.0006981848,0.00029411385,0.00009452615,0.00003533115],"category_scores_gemma":[0.0018212433,0.00014922244,0.00009235606,0.0013480443,0.00006681858,0.00041013843,0.00016877083,0.000425862,0.00003637503],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030090471,0.0009974219,0.0009785332,0.00006656264,0.00033992823,0.00020519014,0.0032016793,0.16179234,0.0057823914,0.5119593,0.013892134,0.29777548],"study_design_scores_gemma":[0.0070391884,0.00087636965,0.013640241,0.0005267834,0.0003864904,0.0031177518,0.072800115,0.052104443,0.023180699,0.5596546,0.26411226,0.0025610467],"about_ca_topic_score_codex":5.3303376e-7,"about_ca_topic_score_gemma":9.1157483e-7,"teacher_disagreement_score":0.29521444,"about_ca_system_score_codex":0.00015970008,"about_ca_system_score_gemma":0.00010499316,"threshold_uncertainty_score":0.6732613},"labels":[],"label_agreement":null},{"id":"W3163288341","doi":"10.31234/osf.io/3qzhp","title":"Tutorial: Artificial Neural Networks to Analyze Single-Case Experimental Designs","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Université de Montréal; Institut universitaire en santé mentale de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"","keywords":"Artificial neural network; Computer science; Artificial intelligence; Replication (statistics); Machine learning; Monte Carlo method; Mathematics","score_opus":0.5074276768181737,"score_gpt":0.48136149394804745,"score_spread":0.026066182870126242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163288341","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091001846,0.000538238,0.87428105,0.0016026989,0.017227868,0.0022067903,0.000060894192,0.00060609804,0.012474537],"genre_scores_gemma":[0.7815399,9.0850983e-7,0.21282788,0.0015330579,0.0030672215,0.00015184695,0.00001936265,0.00010131356,0.00075851096],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98918486,0.002090426,0.0023014646,0.0029232153,0.002576865,0.0009231975],"domain_scores_gemma":[0.9937394,0.0020328176,0.00061544805,0.0019906522,0.0003677041,0.0012539513],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003737327,0.0009505982,0.0014997339,0.00059198425,0.0003248194,0.0026493163,0.0024227411,0.0006719271,0.005046471],"category_scores_gemma":[0.0030540426,0.00080265605,0.0008230433,0.0015701681,0.00022230722,0.00036166943,0.004477341,0.0011149842,0.0011258065],"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":[0.0022887061,0.0015642642,0.00016042443,0.000017264052,0.0002918835,0.009116472,0.0048478497,0.28390086,0.5076017,0.0050462643,0.118213154,0.066951185],"study_design_scores_gemma":[0.0005534901,0.0015725312,0.000030353938,0.00003839868,0.000104979685,0.0005331266,0.005005852,0.72138935,0.25255063,0.010732708,0.005243149,0.00224545],"about_ca_topic_score_codex":0.00027350287,"about_ca_topic_score_gemma":0.000032697117,"teacher_disagreement_score":0.69053805,"about_ca_system_score_codex":0.0004159721,"about_ca_system_score_gemma":0.00016749892,"threshold_uncertainty_score":0.9996519},"labels":[],"label_agreement":null},{"id":"W316531273","doi":"10.1007/s00362-015-0688-9","title":"D-optimal designs based on the second-order least squares estimator","year":2015,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Optimal design; Estimator; Applied mathematics; Mathematical optimization; Least-squares function approximation; Univariate; Polynomial; Polynomial regression; Regression analysis; Statistics; Multivariate statistics","score_opus":0.23009796809667618,"score_gpt":0.4446194924701144,"score_spread":0.21452152437343822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W316531273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0077147866,0.000050889077,0.7530712,0.002828727,0.00066100346,0.000565324,0.00030876408,0.00012169533,0.2346776],"genre_scores_gemma":[0.5569759,1.8028216e-7,0.43784198,0.0023896703,0.00006221699,0.00004719405,0.00000767445,0.00003107695,0.0026441081],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99486625,0.0013292847,0.00055783003,0.0006731974,0.0020715622,0.0005018742],"domain_scores_gemma":[0.9869541,0.011362747,0.00012301138,0.0007527008,0.00026939358,0.00053802854],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00397359,0.00028473954,0.00035293668,0.00012323447,0.00025376567,0.00037747782,0.0008363651,0.000099230856,0.015492361],"category_scores_gemma":[0.017698238,0.00016409517,0.000094136216,0.0006028974,0.00064572814,0.00014250845,0.000102008955,0.000309018,0.002592872],"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":[0.0023075475,0.000837023,0.0020585016,0.000020512105,0.0000867247,0.0003369657,0.0015994291,0.036614764,0.019465094,0.47914034,0.4053476,0.052185494],"study_design_scores_gemma":[0.0038417743,0.004182808,0.009737058,0.00006301948,0.000073052666,0.000041945197,0.012533122,0.72787035,0.009974626,0.07403611,0.15585843,0.0017877053],"about_ca_topic_score_codex":0.000018959501,"about_ca_topic_score_gemma":0.000010141977,"teacher_disagreement_score":0.69125557,"about_ca_system_score_codex":0.000107676045,"about_ca_system_score_gemma":0.00033447533,"threshold_uncertainty_score":0.9981837},"labels":[],"label_agreement":null},{"id":"W3168146613","doi":"10.1002/cjs.70004","title":"Optimal relevant subset designs in nonlinear models","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Nonlinear system; Computer science; Mathematical optimization; Mathematics; Physics","score_opus":0.17923569708139178,"score_gpt":0.4174536109789767,"score_spread":0.23821791389758493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3168146613","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":[0.03022595,0.00057654787,0.96275574,0.0004927585,0.00081109046,0.00012495671,0.0003928331,0.0000025105778,0.0046176426],"genre_scores_gemma":[0.24923217,0.00001710113,0.7495086,0.00030932686,0.000039817944,0.000001100839,0.0000027572955,0.0000110037645,0.0008781083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967785,0.00049495045,0.0014189045,0.00022752082,0.0006949838,0.00038514214],"domain_scores_gemma":[0.9962768,0.0017367598,0.00038815424,0.0003287577,0.0007102844,0.0005592202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004768758,0.0001499759,0.0004669823,0.0011938993,0.00009906321,0.00027444848,0.00087948784,0.00009096137,0.0004998869],"category_scores_gemma":[0.005499277,0.00012490018,0.00008473314,0.000950434,0.00018556466,0.00031688783,0.00002845492,0.00036953308,0.000039549384],"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":[0.0004516736,0.00019170048,0.021053579,0.00003162298,0.00014646302,0.007070959,0.005135245,0.32140374,0.0033329246,0.22553703,0.27423173,0.14141335],"study_design_scores_gemma":[0.0024108568,0.0007756033,0.015952101,0.0002725245,0.00006745627,0.00029081543,0.0044076904,0.5300669,0.0032720366,0.40640488,0.03540163,0.0006774822],"about_ca_topic_score_codex":0.0016140164,"about_ca_topic_score_gemma":0.0077663693,"teacher_disagreement_score":0.2388301,"about_ca_system_score_codex":0.00034039415,"about_ca_system_score_gemma":0.0031418325,"threshold_uncertainty_score":0.6583545},"labels":[],"label_agreement":null},{"id":"W3185479023","doi":"10.1002/sim.9135","title":"Optimal design of cluster randomized trials allowing unequal allocation of clusters and unequal cluster size between arms","year":2021,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"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":"Medical Research Council; Medical Research Council Canada","keywords":"Cluster (spacecraft); Variance (accounting); Range (aeronautics); Sample size determination; Statistics; Cluster size; Statistical power; Computer science; Cluster randomised controlled trial; Mathematics; Randomized controlled trial; Medicine; Physics; Engineering","score_opus":0.22106253953040395,"score_gpt":0.4884150912243174,"score_spread":0.2673525516939135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185479023","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":[0.039210126,0.00057652366,0.95733166,0.0009852832,0.0005004678,0.0010376105,0.000100382196,0.000009714085,0.00024824025],"genre_scores_gemma":[0.4206701,0.00008418675,0.57873636,0.00019276026,0.0000998234,0.00002211764,0.000025301733,0.000018892646,0.00015047519],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9816263,0.011239006,0.0041480656,0.00065386045,0.0019872047,0.0003455794],"domain_scores_gemma":[0.87781143,0.11973431,0.0011934576,0.0004900986,0.00061232154,0.00015837656],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05531043,0.00029121464,0.0030768882,0.0003744228,0.00004789658,0.00004081473,0.00040385616,0.0001723701,0.00040434068],"category_scores_gemma":[0.1583154,0.00020576896,0.0001352701,0.00077494857,0.00097196415,0.0001808963,0.00022282662,0.00026019374,0.000004518288],"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":[0.18573023,0.0010612546,0.005711845,0.0009010863,0.002102235,0.00038706104,0.079064146,0.24713546,0.21769576,0.028692367,0.01788786,0.21363069],"study_design_scores_gemma":[0.36121807,0.0020899472,0.0027903288,0.0011492411,0.0012117794,0.000020472497,0.020609662,0.46953467,0.046792272,0.09347388,0.00012670808,0.0009829566],"about_ca_topic_score_codex":0.000100039964,"about_ca_topic_score_gemma":0.000008223641,"teacher_disagreement_score":0.38145998,"about_ca_system_score_codex":0.00006407774,"about_ca_system_score_gemma":0.00020772731,"threshold_uncertainty_score":0.9727567},"labels":[],"label_agreement":null},{"id":"W3190223951","doi":"10.1016/j.jim.2021.113122","title":"Interval estimation for concentration in the ELISA setting","year":2021,"lang":"en","type":"article","venue":"Journal of Immunological Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Interval estimation; Interval (graph theory); Point estimation; Statistics; Estimation; Confidence interval; Mathematics; Maximum likelihood; Estimation theory; Prediction interval; Engineering; Combinatorics","score_opus":0.277197518063798,"score_gpt":0.5750951929862936,"score_spread":0.29789767492249564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190223951","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":[0.1295035,0.0014161693,0.86492056,0.0025579273,0.00080640195,0.00016245808,0.0000016257403,0.000004916565,0.0006264695],"genre_scores_gemma":[0.16376626,0.000025516598,0.8356677,0.00040198685,0.00007528721,0.000008568395,7.18017e-7,0.0000040176556,0.000049888415],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9912332,0.0064610993,0.0012866098,0.00020071212,0.00062189734,0.00019644121],"domain_scores_gemma":[0.9833592,0.01504831,0.00073206885,0.0002549251,0.0005703271,0.00003513827],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.02996144,0.00010753797,0.00038695487,0.00009093413,0.00009335844,0.00028395248,0.0006848331,0.00010973773,0.00017380295],"category_scores_gemma":[0.054471325,0.000050359075,0.00025920128,0.00057340186,0.00010089062,0.00042729572,0.00008269407,0.00036852705,0.000004374743],"study_design_candidate":"bench_or_experimental","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":[0.00018075683,0.00012150586,0.00030753028,0.0000023730515,0.000017495946,0.000027126252,0.000967518,0.00038920928,0.32245314,0.0031860215,0.0001732301,0.6721741],"study_design_scores_gemma":[0.0021650074,0.0013359265,0.032059625,0.00011658355,0.000048224952,0.0005728593,0.017837767,0.033171825,0.6016093,0.3009484,0.009856872,0.00027763672],"about_ca_topic_score_codex":0.0000010418447,"about_ca_topic_score_gemma":2.788549e-7,"teacher_disagreement_score":0.67189646,"about_ca_system_score_codex":0.00008185747,"about_ca_system_score_gemma":0.00009413784,"threshold_uncertainty_score":0.9988588},"labels":[],"label_agreement":null},{"id":"W3194781842","doi":"10.5539/ijsp.v10n5p85","title":"Control of Weekly Time Trend in Time-Stratified Case-Crossover Design","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Logistic regression; Crossover; Statistics; Crossover study; Time series; Computer science; Econometrics; Mathematics; Medicine; Machine learning","score_opus":0.08930711638578459,"score_gpt":0.41020047898353,"score_spread":0.3208933625977454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194781842","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3385554,0.00024136229,0.6587158,0.00054514024,0.00036346135,0.00018049641,0.0008217064,0.0000031909228,0.000573475],"genre_scores_gemma":[0.6899225,0.0000062939016,0.30974057,0.000051600593,0.000035460034,0.0000012386678,0.0000027876752,0.000004575884,0.00023495748],"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965934,0.0007348566,0.0012076667,0.00021542699,0.0011343876,0.00011427524],"domain_scores_gemma":[0.9937625,0.004175085,0.0005959527,0.00015577042,0.0012158856,0.00009481821],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004810124,0.00010576785,0.00038279072,0.00014565639,0.00002715255,0.00020036064,0.0003531055,0.00005675646,0.0011199673],"category_scores_gemma":[0.0048483363,0.00008316924,0.00007463007,0.00017874481,0.00018655208,0.00022806242,0.000068902315,0.00015899434,0.000010481489],"study_design_candidate":"theoretical_or_conceptual","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":[0.011557391,0.005030366,0.06788541,0.00007896299,0.0012350015,0.023449415,0.005099828,0.01813218,0.491346,0.054812126,0.020019716,0.3013536],"study_design_scores_gemma":[0.00844098,0.0013140321,0.033214513,0.00014343708,0.00008410679,0.0072360463,0.00040810613,0.08342896,0.041498534,0.8223283,0.0014204754,0.000482462],"about_ca_topic_score_codex":0.000018533387,"about_ca_topic_score_gemma":0.000018860928,"teacher_disagreement_score":0.7675162,"about_ca_system_score_codex":0.00007257042,"about_ca_system_score_gemma":0.00021144799,"threshold_uncertainty_score":0.9997932},"labels":[],"label_agreement":null},{"id":"W3198850522","doi":"10.1080/00224065.2021.1963199","title":"Bayesian analysis and follow-up experiments for supersaturated multistratum designs","year":2021,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Manitoba","funders":"","keywords":"Bayesian probability; Computer science; Ambiguity; Design of experiments; Supersaturation; Econometrics; Statistics; Mathematics; Artificial intelligence","score_opus":0.28396530967039196,"score_gpt":0.5227176254558292,"score_spread":0.23875231578543726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198850522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48971933,0.0014845943,0.5053991,0.0025697602,0.00049638515,0.0001450736,0.000020219511,0.000030389268,0.0001351465],"genre_scores_gemma":[0.6896806,0.000023830107,0.30960363,0.00016605454,0.000028087909,0.0000063856337,0.0000020634366,0.000009926201,0.00047946148],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99600625,0.0008377289,0.0015658647,0.00046774247,0.00080340623,0.0003190147],"domain_scores_gemma":[0.99563974,0.0015326837,0.0008414688,0.00056230754,0.001254387,0.00016941778],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0052303174,0.00019499345,0.000983783,0.0011539834,0.000149647,0.00019312011,0.00063864974,0.000328466,0.00018207698],"category_scores_gemma":[0.008820836,0.00015386907,0.0004471176,0.0029337057,0.00027606715,0.0003278568,0.00014583464,0.0003101124,0.0000040836617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041184205,0.00023948264,0.015840618,0.000007935582,0.00096165005,0.00011638778,0.0007239049,0.000019699033,0.9440155,0.005684706,0.0009133156,0.031064982],"study_design_scores_gemma":[0.00255472,0.0006196145,0.006905556,0.000017454511,0.00034247234,0.00025331118,0.013459702,0.0015400343,0.9332518,0.038839057,0.0018971001,0.0003192065],"about_ca_topic_score_codex":0.000013978739,"about_ca_topic_score_gemma":0.000026388741,"teacher_disagreement_score":0.19996122,"about_ca_system_score_codex":0.00008791812,"about_ca_system_score_gemma":0.00018208395,"threshold_uncertainty_score":0.9995283},"labels":[],"label_agreement":null},{"id":"W3212178720","doi":"10.1007/s42519-021-00215-x","title":"Optimal Incomplete-Block Designs with Low Replication: A Unified Approach Using Graphs","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Division of Mathematical Sciences; Isaac Newton Institute for Mathematical Sciences","keywords":"Mathematics; Graph; Mathematical optimization; Block design; Block (permutation group theory); Combinatorics","score_opus":0.22138377466980186,"score_gpt":0.4770341172452323,"score_spread":0.25565034257543046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212178720","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":[0.01862091,0.0005026986,0.9718994,0.00031576457,0.00008990833,0.000100406425,0.00001603569,0.0000065057584,0.008448359],"genre_scores_gemma":[0.2665691,0.000030235471,0.7328392,0.0003735157,0.00005449888,0.0000014602988,0.000001150068,0.000011240223,0.00011956017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98986316,0.007587443,0.00082972745,0.00041577924,0.0010955732,0.00020830554],"domain_scores_gemma":[0.9586941,0.038696885,0.0008012097,0.0004545013,0.0010808131,0.0002724646],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.023752214,0.00016094615,0.00042963697,0.00013958376,0.00022852905,0.00041023263,0.00030987748,0.000072795854,0.00029299027],"category_scores_gemma":[0.045013648,0.000107900916,0.00006522909,0.00069908716,0.00037156572,0.0009955482,0.00009883333,0.00043486198,0.000010068819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013413706,0.000689917,0.00009093249,0.000024002191,0.00023136847,0.0010745286,0.0009916826,0.0032340484,0.031452682,0.9326537,0.0004891148,0.015654288],"study_design_scores_gemma":[0.004401995,0.0043092025,0.0019510728,0.00020723508,0.0009854634,0.07177502,0.03958413,0.027336188,0.017269565,0.8173459,0.013758418,0.001075806],"about_ca_topic_score_codex":0.0000023153832,"about_ca_topic_score_gemma":8.0914674e-8,"teacher_disagreement_score":0.2479482,"about_ca_system_score_codex":0.000036912003,"about_ca_system_score_gemma":0.00028520665,"threshold_uncertainty_score":0.96303064},"labels":[],"label_agreement":null},{"id":"W3216140741","doi":"10.1016/j.jspi.2021.11.004","title":"A systematic construction of compromise designs under baseline parameterization","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"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","keywords":"Compromise; Mathematics; Baseline (sea); Class (philosophy); Factor (programming language); Mathematical optimization; Algorithm; Computer science; Artificial intelligence; Programming language","score_opus":0.21962148667157078,"score_gpt":0.47046168891290385,"score_spread":0.2508402022413331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216140741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08652424,0.00043163844,0.9126396,0.000044077704,0.00014848854,0.000052475225,0.000017315378,0.0000030265842,0.0001391058],"genre_scores_gemma":[0.5976051,0.000011472781,0.40232113,0.000031191244,0.0000122723895,5.702527e-7,0.0000013704902,0.0000025265604,0.000014329208],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997135,0.0008545364,0.0011194791,0.00014162602,0.0006528561,0.00009650503],"domain_scores_gemma":[0.9908634,0.0075882203,0.0006908969,0.00012102712,0.0006182306,0.000118220516],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0025965655,0.00008594256,0.0004956099,0.0001262592,0.000049069185,0.0001259605,0.00012135413,0.000047269114,0.00013167739],"category_scores_gemma":[0.014920124,0.00005991186,0.000040684183,0.0002528883,0.00017169803,0.00022259483,0.00003571359,0.00013766461,0.0000030530275],"study_design_candidate":"bench_or_experimental","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":[0.0014758673,0.0009123837,0.032760434,0.00526106,0.0005266244,0.0008933367,0.004567068,0.03199783,0.59620506,0.2835116,0.0013992225,0.040489484],"study_design_scores_gemma":[0.002930667,0.0026211266,0.032168157,0.011687855,0.00041501914,0.0029944193,0.015623382,0.51789635,0.06695849,0.34598565,0.000043542736,0.0006753446],"about_ca_topic_score_codex":0.0000020529574,"about_ca_topic_score_gemma":9.582942e-8,"teacher_disagreement_score":0.52924657,"about_ca_system_score_codex":0.000017744715,"about_ca_system_score_gemma":0.00011388044,"threshold_uncertainty_score":0.9933776},"labels":[],"label_agreement":null},{"id":"W3217612029","doi":"10.1002/cjs.11719","title":"Let's practice what we preach: Planning and interpreting simulation studies with design and analysis of experiments","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University; Acadia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Fractional factorial design; Factorial experiment; Computer science; Taguchi methods; Main effect; Design of experiments; Variance (accounting); Factorial analysis; Variety (cybernetics); Population; Management science; Industrial engineering; Mathematics; Statistics; Machine learning; Engineering; Artificial intelligence","score_opus":0.2990031300577587,"score_gpt":0.4902431970378035,"score_spread":0.1912400669800448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217612029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07720426,0.017938443,0.9041408,0.00023280487,0.00020439409,0.00014715384,0.00006605688,0.000001956339,0.00006417879],"genre_scores_gemma":[0.63509333,0.00006148465,0.36472124,0.00008845341,0.0000065520153,0.0000017688442,9.493067e-7,0.0000064303194,0.000019772293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975403,0.000877375,0.00059772696,0.00017480583,0.0006605743,0.00014924459],"domain_scores_gemma":[0.9912415,0.007047172,0.0008417166,0.00012138768,0.0005047246,0.00024346233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035877198,0.00010378571,0.00039633497,0.00079287274,0.0002520759,0.00026271943,0.00018263499,0.000018056458,0.000067834895],"category_scores_gemma":[0.004183091,0.0000836818,0.000027134774,0.00066337484,0.00018761055,0.0007355289,0.00005496738,0.00016273564,1.5399834e-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":[0.0005497463,0.000033354376,0.016932169,0.000012618319,0.0016361565,0.00050510385,0.09310037,0.8260044,0.0007368031,0.00065121596,0.0007750325,0.05906302],"study_design_scores_gemma":[0.0009855083,0.0027328364,0.005633576,0.00024452602,0.0015329784,0.00028837007,0.47128353,0.509687,0.00090390915,0.0032512634,0.0030758022,0.00038071556],"about_ca_topic_score_codex":0.00016148259,"about_ca_topic_score_gemma":0.00006287695,"teacher_disagreement_score":0.5578891,"about_ca_system_score_codex":0.00014609248,"about_ca_system_score_gemma":0.00026915947,"threshold_uncertainty_score":0.5007853},"labels":[],"label_agreement":null},{"id":"W34343354","doi":"10.1002/cncr.33796","title":"Proportionally balanced designs: Some further results and general constructions","year":2001,"lang":"en","type":"article","venue":"Utilitas Mathematica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"MorphoSys","keywords":"Mathematics; Property (philosophy); Arithmetic; Gray (unit); Core (optical fiber); Unit (ring theory); Test (biology); Mathematics education; Computer science; Epistemology","score_opus":0.1728849121065833,"score_gpt":0.4317869108608392,"score_spread":0.2589019987542559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W34343354","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7637416,0.0013625884,0.12116243,0.0061050365,0.0008883308,0.001937129,0.0002591953,0.00040270638,0.104140975],"genre_scores_gemma":[0.40884447,0.00002964715,0.5817741,0.00032105873,0.00011936269,0.00006765678,0.000005427528,0.000025101968,0.008813196],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99681866,0.00032969055,0.00095120847,0.0005869663,0.0009898513,0.0003235935],"domain_scores_gemma":[0.99757105,0.0011285591,0.0002485574,0.0006703274,0.00018922769,0.00019226193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029750243,0.00020347816,0.0003729219,0.00018298463,0.00018058126,0.00014833942,0.0003769206,0.000090149966,0.0013052046],"category_scores_gemma":[0.0033074606,0.00014644382,0.00010032958,0.00043081702,0.0004458199,0.00045767947,0.00012647756,0.00011949053,0.0005006975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011708126,0.0012187317,0.003415954,0.00006698035,0.00024436976,0.00016527032,0.0093484465,0.00010711968,0.11131537,0.72174746,0.05508851,0.09611101],"study_design_scores_gemma":[0.0012389065,0.00021974361,0.0056009977,0.00004704376,0.000028927812,0.00045742028,0.0024115543,0.011939836,0.003959237,0.9638196,0.00987901,0.00039770006],"about_ca_topic_score_codex":0.0000058402525,"about_ca_topic_score_gemma":0.000001292813,"teacher_disagreement_score":0.46061167,"about_ca_system_score_codex":0.00003892378,"about_ca_system_score_gemma":0.00007149652,"threshold_uncertainty_score":0.99960774},"labels":[],"label_agreement":null},{"id":"W38677103","doi":"10.1007/978-3-7908-1952-6_5","title":"Optimal Three-Treatment Response-Adaptive Designs for Phase III Clinical Trials with Binary Responses","year":2007,"lang":"en","type":"book-chapter","venue":"Contributions to statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"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 Manitoba","funders":"","keywords":"Optimal design; Adaptive design; Computation; Binary number; Computer science; Mathematical optimization; Clinical trial; Phase (matter); Algorithm; Mathematics; Medicine; Machine learning; Arithmetic","score_opus":0.6096584639607404,"score_gpt":0.6179857379436275,"score_spread":0.008327273982887129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W38677103","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":[0.00020721467,0.0006839758,0.90887696,0.00030801032,0.00082104246,0.0061102863,0.078826405,0.00011217141,0.004053931],"genre_scores_gemma":[0.0010311767,0.000061970764,0.7682557,0.00026427663,0.000590874,0.00048462104,0.0004796357,0.00013996077,0.2286918],"study_design_codex":"randomized_trial","study_design_gemma":"not_applicable","domain_scores_codex":[0.98606265,0.0039774207,0.0049400474,0.0019865748,0.0020041557,0.001029167],"domain_scores_gemma":[0.86361295,0.12857874,0.0018737145,0.0015235671,0.0034702327,0.00094080024],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.050847944,0.0009801462,0.0034539527,0.0010281531,0.0008332503,0.00055944675,0.0010098375,0.00086450955,0.0012619498],"category_scores_gemma":[0.07613914,0.0007617238,0.00082292093,0.00037544436,0.0009852755,0.00018213964,0.00024182511,0.00057329744,0.00053874863],"study_design_candidate":"not_applicable","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":[0.3568108,0.0013350107,0.000017604678,0.00000784619,0.0015795567,0.001094768,0.0002878044,0.0005843455,0.0012985326,0.32729465,0.12644613,0.18324295],"study_design_scores_gemma":[0.028463427,0.068019696,0.00015242248,0.00028842795,0.0020664337,0.0000856498,0.00030952957,0.0072666234,0.001409965,0.07419636,0.81554997,0.0021915107],"about_ca_topic_score_codex":0.000042389733,"about_ca_topic_score_gemma":0.00015370094,"teacher_disagreement_score":0.68910384,"about_ca_system_score_codex":0.0013394849,"about_ca_system_score_gemma":0.002877395,"threshold_uncertainty_score":0.999651},"labels":[],"label_agreement":null},{"id":"W4200345827","doi":"10.1002/cjs.11674","title":"Minimax A‐, c‐, and I‐optimal regression designs for models with heteroscedastic errors","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Heteroscedasticity; Optimal design; Mathematical optimization; Variance (accounting); Mathematics; Class (philosophy); Computer science; Focus (optics); Construct (python library); Econometrics; Statistics; Artificial intelligence","score_opus":0.2645952701138097,"score_gpt":0.41250841492005336,"score_spread":0.14791314480624368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200345827","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":[0.0432407,0.00073891686,0.9548083,0.00019713875,0.00030774917,0.00010993735,0.0003453515,0.000002307804,0.00024961555],"genre_scores_gemma":[0.32294044,0.000010333627,0.6765203,0.00012810297,0.000040170187,0.000002073062,0.000003024262,0.000017368831,0.00033819227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979988,0.00022120048,0.0006221194,0.00025696214,0.0005660806,0.00033486745],"domain_scores_gemma":[0.99579865,0.0015786231,0.00038933248,0.00021488646,0.001114835,0.00090364955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012205433,0.00016116316,0.00038566385,0.00027861926,0.00020820483,0.0003144952,0.00028764445,0.000067555215,0.00013596188],"category_scores_gemma":[0.0026431154,0.00011413468,0.000057129397,0.00028054966,0.00023890821,0.00032677985,0.000020134907,0.00016499417,0.0000031088591],"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":[0.0036191535,0.0004029559,0.023012238,0.00029380014,0.00079742324,0.019170143,0.025239779,0.13906842,0.07161569,0.14896755,0.2109478,0.35686505],"study_design_scores_gemma":[0.011214943,0.011471435,0.012627682,0.0014487235,0.0007032442,0.011552479,0.027872432,0.48010272,0.0474242,0.3732156,0.019668931,0.0026976445],"about_ca_topic_score_codex":0.00011650081,"about_ca_topic_score_gemma":0.0014352492,"teacher_disagreement_score":0.3541674,"about_ca_system_score_codex":0.000105495805,"about_ca_system_score_gemma":0.0017481468,"threshold_uncertainty_score":0.4654277},"labels":[],"label_agreement":null},{"id":"W4200472720","doi":"10.5705/ss.202021.0024","title":"On Construction of Nonregular Two-Level Factorial Designs With Maximum Generalized Resolutions","year":2021,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Fractional factorial design; Factorial; Statistics; Factorial experiment; Mathematical analysis","score_opus":0.29358897282061996,"score_gpt":0.47063372037859424,"score_spread":0.17704474755797428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200472720","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":[0.013758815,0.000057300636,0.97411436,0.00020027236,0.0009634006,0.00022647476,0.00075247075,0.000034157176,0.009892729],"genre_scores_gemma":[0.16968545,0.000005445741,0.8294514,0.00007920048,0.000090203015,0.000014165511,0.000027537666,0.000020237172,0.00062636996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99536365,0.0013681068,0.0008313899,0.0006710775,0.0014541358,0.0003116531],"domain_scores_gemma":[0.9930265,0.0049087266,0.00034171419,0.00085459894,0.0006836471,0.0001847999],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014772419,0.0002083558,0.000514736,0.00015829441,0.00018361087,0.00013190367,0.00036626103,0.000089248824,0.002474655],"category_scores_gemma":[0.0066408715,0.0001604463,0.000108264634,0.0007799111,0.00061870017,0.00014794261,0.00009977031,0.00017524698,0.00010935638],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016506292,0.00041180575,0.00025743217,0.0000083789655,0.00012784792,0.00012430626,0.00039153977,0.0006913219,0.13587175,0.8107922,0.008328152,0.041344646],"study_design_scores_gemma":[0.0065758443,0.0020518885,0.0058771404,0.000096996344,0.0001554579,0.00016940958,0.00086278876,0.0045668827,0.15716025,0.81380326,0.007883416,0.0007966839],"about_ca_topic_score_codex":0.00004845067,"about_ca_topic_score_gemma":0.000024015915,"teacher_disagreement_score":0.15592663,"about_ca_system_score_codex":0.000076312055,"about_ca_system_score_gemma":0.00049736624,"threshold_uncertainty_score":0.9984372},"labels":[],"label_agreement":null},{"id":"W4205685297","doi":"10.1002/0470013192.bsa239","title":"Functional Data Analysis","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistics in Behavioral Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1304,"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":"Functional data analysis; Flexibility (engineering); Computer science; Functional principal component analysis; Functional analysis; Data mining; Algorithm; Mathematics; Statistics; Biology; Machine learning","score_opus":0.20961765085941922,"score_gpt":0.4953812503248293,"score_spread":0.2857635994654101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205685297","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028483465,0.0005144595,0.21982686,0.000028176994,0.0016265756,0.00041865566,0.006092207,0.00005612027,0.77115214],"genre_scores_gemma":[0.0012337827,0.000211436,0.65931344,0.000017895263,0.00015866976,0.000011214432,0.00019837108,0.00008026812,0.33877495],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9920384,0.00022849994,0.0011663741,0.0016178816,0.0044586468,0.00049016846],"domain_scores_gemma":[0.99543035,0.0007037633,0.00083793973,0.0025604225,0.00023799793,0.00022955306],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005776207,0.00032167538,0.00082182843,0.0030724574,0.00008088689,0.00015572959,0.004721376,0.00019049407,0.027821453],"category_scores_gemma":[0.0021135567,0.00027322362,0.00008480747,0.007038528,0.0017423207,0.00045454345,0.0013016138,0.0003236093,0.00031023158],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025477299,0.00068937,0.035082582,0.000008272552,0.000040939936,0.000056257824,0.00026105664,0.0003431485,0.00038298772,0.006779161,0.7002522,0.2560786],"study_design_scores_gemma":[0.0008021454,0.0002922266,0.19846907,0.00009312644,0.0008669948,0.000008229084,0.00061450567,0.009627615,0.00016714384,0.005033217,0.78237474,0.0016509846],"about_ca_topic_score_codex":0.0013823141,"about_ca_topic_score_gemma":0.0018234279,"teacher_disagreement_score":0.43948656,"about_ca_system_score_codex":0.00015824765,"about_ca_system_score_gemma":0.00061531947,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W4205853537","doi":"10.5539/ijsp.v11n1p1","title":"D-Optimal Split-plot Designs With Random Whole Plot Factor and Fixed Sub-plot Factor","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Restricted randomization; Plot (graphics); Estimator; Statistics; Split plot; Variance (accounting)","score_opus":0.1227747019828963,"score_gpt":0.4012446365417039,"score_spread":0.2784699345588076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205853537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6275344,0.0003288771,0.3703583,0.00036135703,0.00044798956,0.00015259931,0.0007186732,0.000008198793,0.00008961112],"genre_scores_gemma":[0.5916132,0.00007122904,0.40796167,0.00007820811,0.00011889177,0.0000037590642,0.000007331296,0.000015150491,0.0001305921],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99602896,0.0005764323,0.001026419,0.00044331225,0.0017037997,0.00022109279],"domain_scores_gemma":[0.9925218,0.0032012567,0.00059497845,0.00022991015,0.0031638523,0.00028818264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017911664,0.00022863416,0.0005186776,0.00014889448,0.00011177177,0.0006489122,0.00043923449,0.00007563954,0.00042167143],"category_scores_gemma":[0.0057028146,0.00015808511,0.00008722314,0.0001766695,0.0003406913,0.00047328766,0.00017656606,0.00029752753,0.000009321046],"study_design_candidate":"observational","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":[0.020625692,0.0030949505,0.22643055,0.00017274673,0.002126365,0.002156014,0.0069203926,0.0029117984,0.25145015,0.03351414,0.008933631,0.44166356],"study_design_scores_gemma":[0.021461777,0.0075692297,0.6086613,0.00042142978,0.00024856412,0.002010325,0.0021393904,0.013467907,0.12667266,0.19245534,0.023097299,0.0017947766],"about_ca_topic_score_codex":0.000018279874,"about_ca_topic_score_gemma":0.000023665249,"teacher_disagreement_score":0.43986878,"about_ca_system_score_codex":0.00010914183,"about_ca_system_score_gemma":0.00032410436,"threshold_uncertainty_score":0.6827213},"labels":[],"label_agreement":null},{"id":"W4212780544","doi":"10.1007/978-981-16-9170-6_10","title":"Data with Designed Structure","year":2022,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Row; Extension (predicate logic); Data structure; Computer science; Row and column spaces; Context (archaeology); Column (typography); Space (punctuation); Data mining; Algorithm; Theoretical computer science; Database; Programming language; Geography","score_opus":0.37916313067099466,"score_gpt":0.4645155050292597,"score_spread":0.08535237435826504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212780544","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008853686,0.0046420204,0.024020845,0.00011058064,0.0028301526,0.0023409594,0.014865985,0.000340981,0.9499631],"genre_scores_gemma":[0.0023809876,0.000106232765,0.28843397,0.00023115097,0.00021422462,0.000023335939,0.0012643184,0.00027667423,0.7070691],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99050915,0.00023085983,0.0010531527,0.0019623712,0.005788394,0.00045607833],"domain_scores_gemma":[0.99151754,0.0018012096,0.00092660746,0.0050821723,0.00037563994,0.00029682228],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0030877446,0.0006644716,0.000947918,0.002104019,0.0003452654,0.0005078331,0.0059617762,0.00045890932,0.039066274],"category_scores_gemma":[0.0013177635,0.0004947227,0.00016508855,0.0015048706,0.00025227532,0.00068027433,0.0026412215,0.0011097953,0.00032697932],"study_design_candidate":"not_applicable","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":[0.0006156725,0.00032390803,0.002030814,0.000026971402,0.000223394,0.0019547287,0.0003259213,0.00014392113,0.003128205,0.1067911,0.14248718,0.7419482],"study_design_scores_gemma":[0.00048279218,0.00071019755,0.00041618658,0.000017290924,0.0002800662,0.0001621122,0.00009299905,0.000054477954,0.0003169752,0.0064125997,0.99008316,0.0009711461],"about_ca_topic_score_codex":0.000024398953,"about_ca_topic_score_gemma":0.000017674258,"teacher_disagreement_score":0.847596,"about_ca_system_score_codex":0.00033489722,"about_ca_system_score_gemma":0.0004456493,"threshold_uncertainty_score":0.99975044},"labels":[],"label_agreement":null},{"id":"W4225429113","doi":"10.1007/s00362-022-01317-9","title":"Constructing K-optimal designs for regression models","year":2022,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Multicollinearity; Polynomial regression; Design matrix; Applied mathematics; Optimal design; Estimator; Regression analysis; Mathematical optimization; Convexity; Regression; Linear regression; Statistics","score_opus":0.25308950876934433,"score_gpt":0.47460593235966464,"score_spread":0.2215164235903203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225429113","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":[0.0032585121,0.00006717723,0.9527953,0.00024996453,0.00056850706,0.00045263706,0.0005808469,0.00006169352,0.041965336],"genre_scores_gemma":[0.3952518,5.9397115e-7,0.6032907,0.00022662546,0.00003267707,0.00012212737,0.00002024745,0.000019134015,0.0010360708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961166,0.0008102812,0.0005826597,0.00065976096,0.0014048341,0.00042590188],"domain_scores_gemma":[0.99144894,0.007710246,0.0001686793,0.00035765395,0.00010291437,0.00021156258],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003290407,0.00017539156,0.00033400502,0.00013043189,0.0006705102,0.0001371409,0.0005775985,0.000043684075,0.004629318],"category_scores_gemma":[0.0037748616,0.0001383413,0.00011597158,0.000376127,0.00027192073,0.00017763935,0.00031003388,0.00023369693,0.000034059187],"study_design_candidate":"theoretical_or_conceptual","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":[0.00062632037,0.0000933035,0.00018201108,0.0000065533345,0.000023428207,0.000038488484,0.00068426604,0.011767072,0.024471374,0.7272093,0.014070379,0.22082746],"study_design_scores_gemma":[0.0016742607,0.0012416269,0.00011378147,0.000011623659,0.000039952596,0.00009363715,0.03021297,0.48611704,0.003831389,0.46027094,0.015719477,0.0006732894],"about_ca_topic_score_codex":0.000012367558,"about_ca_topic_score_gemma":0.0000012905334,"teacher_disagreement_score":0.47434998,"about_ca_system_score_codex":0.00018684306,"about_ca_system_score_gemma":0.0001192719,"threshold_uncertainty_score":0.9962806},"labels":[],"label_agreement":null},{"id":"W4226017130","doi":"10.2139/ssrn.4070650","title":"Multi-Stage Online Robust Parameter Design Based on Bayesian Gp Model","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Stage (stratigraphy); Bayesian probability; Computer science; Statistics; Artificial intelligence; Mathematics; Data mining; Geology","score_opus":0.23745104516131404,"score_gpt":0.42951019720395656,"score_spread":0.1920591520426425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226017130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010865127,0.00045216284,0.9867682,0.0007951282,0.0003131543,0.00034439313,0.000036902442,0.000053386866,0.00037156756],"genre_scores_gemma":[0.5121525,0.0000429694,0.47776243,0.0012815003,0.00008372265,0.000034806075,0.0000059105323,0.000060455495,0.00857574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9905378,0.0026614177,0.00090951275,0.0007564097,0.0025636873,0.0025711611],"domain_scores_gemma":[0.99676514,0.0015841568,0.00048162355,0.0007548354,0.0001503212,0.00026394826],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.018420767,0.0003486232,0.00045697714,0.0006496724,0.0009047954,0.0002932422,0.0017903113,0.00008801995,0.0011772272],"category_scores_gemma":[0.001525907,0.00028177557,0.0003671397,0.0009437289,0.0000934413,0.00034600176,0.00021314334,0.0034894468,0.0000661027],"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":[0.0005042057,0.0006190269,0.00012310385,6.391697e-7,0.000034789362,0.00002551233,0.00012700283,0.9751567,0.0050974595,0.0031830943,0.00039276452,0.014735678],"study_design_scores_gemma":[0.0013917075,0.0014860681,0.000056756682,0.0000044774233,0.000016408741,0.00012331466,0.0021684463,0.94388974,0.00062522857,0.049456146,0.00046291205,0.000318791],"about_ca_topic_score_codex":0.000020715444,"about_ca_topic_score_gemma":0.000039704053,"teacher_disagreement_score":0.5090057,"about_ca_system_score_codex":0.0020899253,"about_ca_system_score_gemma":0.0031048148,"threshold_uncertainty_score":0.99996346},"labels":[],"label_agreement":null},{"id":"W4230811037","doi":"10.24200/jams.vol5iss2pp79-84","title":"Neural Network Assisted Experimental Designs for Food Research","year":2000,"lang":"en","type":"article","venue":"Journal of Agricultural and Marine Sciences [JAMS]","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Fractional factorial design; Factorial; Factorial experiment; Artificial neural network; Box–Behnken design; Design of experiments; Mathematics; Experimental data; Statistics; Computer science; Applied mathematics; Artificial intelligence; Response surface methodology","score_opus":0.3936017160934224,"score_gpt":0.4944129017924242,"score_spread":0.10081118569900177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230811037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98855764,0.0010974902,0.00020260643,0.0014731166,0.00060424197,0.0003603024,0.0000062418417,0.00001376375,0.0076846215],"genre_scores_gemma":[0.91265965,0.00005277382,0.082616,0.00015744938,0.0007716332,0.00001472299,0.0000013726711,0.000007151454,0.0037192488],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9950151,0.0007364584,0.00095803844,0.00047382797,0.0021719588,0.0006446118],"domain_scores_gemma":[0.99655974,0.0018757305,0.00036862394,0.00016229022,0.00067958515,0.00035404327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008857691,0.00021708751,0.00046732248,0.00021709337,0.00093967875,0.0008499005,0.0011378521,0.00008397406,0.0008157881],"category_scores_gemma":[0.00070085825,0.00010532159,0.00024847547,0.0017542968,0.00068209635,0.0012431276,0.00026321638,0.0003099345,0.000016791035],"study_design_candidate":"observational","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":[0.0016414095,0.00090785127,0.0071599805,0.000017916875,0.00014427536,0.000040448846,0.0022475438,0.0099013,0.22559658,0.006107393,0.14043939,0.6057959],"study_design_scores_gemma":[0.0063837897,0.043743517,0.71148384,0.00019760663,0.00010025326,0.0049153278,0.024014864,0.0062867575,0.09264064,0.046451416,0.06201727,0.0017647524],"about_ca_topic_score_codex":0.000022474214,"about_ca_topic_score_gemma":0.000016282931,"teacher_disagreement_score":0.7043238,"about_ca_system_score_codex":0.00007192698,"about_ca_system_score_gemma":0.00008768766,"threshold_uncertainty_score":0.8932308},"labels":[],"label_agreement":null},{"id":"W4234356797","doi":"10.1002/9781118445112.stat04084","title":"<scp>L</scp> atin Hypercube Designs","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Simon Fraser University","funders":"","keywords":"Latin hypercube sampling; Orthogonality; Hypercube; Computer science; Stratification (seeds); Class (philosophy); Space (punctuation); Computer experiment; Strengths and weaknesses; Univariate; Theoretical computer science; Mathematics; Parallel computing; Simulation; Geometry; Statistics; Operating system; Artificial intelligence; Multivariate statistics; Monte Carlo method","score_opus":0.22721201081274833,"score_gpt":0.44994346414642594,"score_spread":0.2227314533336776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234356797","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":[0.000063286134,0.0016622001,0.6433435,0.000039321214,0.0012486744,0.0008229179,0.023156563,0.00048790517,0.32917562],"genre_scores_gemma":[0.00008520813,0.0006979609,0.5315152,0.0003598343,0.00043609244,0.000044456407,0.0016122301,0.0006452791,0.46460375],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9881349,0.0019438287,0.0021680337,0.00233636,0.0040607145,0.0013561507],"domain_scores_gemma":[0.9845881,0.009281994,0.0018761347,0.0026618377,0.0007382439,0.00085372047],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.003059783,0.0013408478,0.0021165481,0.0012850467,0.00023361042,0.0006453849,0.0030996858,0.00096776313,0.010645563],"category_scores_gemma":[0.016587352,0.0010948455,0.00022196902,0.0012297982,0.000784503,0.00018817374,0.00065614015,0.0012407664,0.007833518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016863974,0.00030930346,0.000105104125,0.000052026564,0.00009006799,0.00007593549,0.00019345495,0.00003529211,0.00079339644,0.0136667015,0.93675584,0.047906015],"study_design_scores_gemma":[0.00071403524,0.000582126,0.00021351027,0.00032551636,0.000102266175,0.00002374325,0.0005813154,0.003329883,0.00016640495,0.028162444,0.9652244,0.00057434343],"about_ca_topic_score_codex":0.00036379686,"about_ca_topic_score_gemma":0.00045541968,"teacher_disagreement_score":0.13542815,"about_ca_system_score_codex":0.00022138127,"about_ca_system_score_gemma":0.00060670305,"threshold_uncertainty_score":0.99993426},"labels":[],"label_agreement":null},{"id":"W4236908534","doi":"10.1007/978-1-4613-0049-6_3","title":"Optimal Regression Designs in Asymmetric Domains","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Quadratic equation; Inverse; Regression; Linear regression; Polynomial regression; Optimality criterion; Minification; Reduction (mathematics); Regression analysis; Applied mathematics; Mathematical optimization; Statistics; Geometry","score_opus":0.16339756209150985,"score_gpt":0.42620436567210607,"score_spread":0.2628068035805962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236908534","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":[0.000039863186,0.0023485224,0.86453754,0.00007560802,0.00069883635,0.00057868526,0.00042232295,0.000041272666,0.13125739],"genre_scores_gemma":[0.010301974,0.00037161607,0.9541565,0.00031943273,0.00017102911,0.000021273061,0.00006102858,0.00014560593,0.03445151],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99322397,0.00054629846,0.0017414677,0.0013626706,0.0024461066,0.00067946664],"domain_scores_gemma":[0.9838301,0.01385302,0.0007356996,0.0011258798,0.0002433022,0.00021202459],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025833843,0.0007928645,0.0013166063,0.0023865725,0.00010492396,0.00026761869,0.0012142095,0.0010239861,0.0032144752],"category_scores_gemma":[0.011634121,0.00062760577,0.00017815571,0.000998304,0.0003395558,0.00014951536,0.0003330065,0.0015718971,0.00065249857],"study_design_candidate":"theoretical_or_conceptual","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":[0.00034190636,0.00023516724,0.0009540931,0.000054303022,0.000051834315,0.0028041308,0.0014874442,0.017811278,0.00051914423,0.09557174,0.009554106,0.8706148],"study_design_scores_gemma":[0.001813872,0.0008236857,0.00083784247,0.0006620535,0.00006619123,0.000098798046,0.000028180819,0.033450276,0.0018942782,0.907635,0.0506987,0.0019911672],"about_ca_topic_score_codex":0.00004564078,"about_ca_topic_score_gemma":0.00009918849,"teacher_disagreement_score":0.8686237,"about_ca_system_score_codex":0.0005756356,"about_ca_system_score_gemma":0.00014105374,"threshold_uncertainty_score":0.9996175},"labels":[],"label_agreement":null},{"id":"W4237127189","doi":"10.1002/0471667196.ess3198","title":"Precedence Testing","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Statistical Sciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Citation; Library science; Encyclopedia; Information retrieval; Computer science; Mathematics","score_opus":0.14496001568713834,"score_gpt":0.4646128452832503,"score_spread":0.31965282959611196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237127189","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016170776,0.00083367317,0.059160087,0.00006841818,0.0006920457,0.000278171,0.0002405147,0.000097595315,0.9386133],"genre_scores_gemma":[0.00034653788,0.000104294595,0.6082994,0.00004380312,0.00032508062,0.000013130658,0.0000021810517,0.00007360092,0.390792],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9936056,0.0004977674,0.0010080524,0.001093562,0.0032617203,0.00053326314],"domain_scores_gemma":[0.98973024,0.008580837,0.000755381,0.0005294572,0.00013394124,0.00027012464],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004125641,0.00034193514,0.00072891975,0.00051645533,0.00013529169,0.00017073442,0.002139073,0.00021602455,0.033973347],"category_scores_gemma":[0.021998046,0.00023826052,0.00008234327,0.0017643451,0.002188561,0.00020381517,0.0003511096,0.00024340015,0.0011442374],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006531653,0.00008816868,0.0014299914,0.000016818369,0.000009434258,0.000011601792,0.00010438666,0.000033173266,0.0001128453,0.023608444,0.5002092,0.47436938],"study_design_scores_gemma":[0.00015645966,0.00043857144,0.00211408,0.00018514882,0.000024306293,0.000008855828,0.00024575484,0.0015366735,0.00011444574,0.021056723,0.97355866,0.0005602984],"about_ca_topic_score_codex":0.00039908962,"about_ca_topic_score_gemma":0.00006157424,"teacher_disagreement_score":0.54913926,"about_ca_system_score_codex":0.00003438332,"about_ca_system_score_gemma":0.00032866024,"threshold_uncertainty_score":0.9996335},"labels":[],"label_agreement":null},{"id":"W4238551536","doi":"10.1002/9781119214656.index","title":"Index","year":2018,"lang":"en","type":"paratext","venue":"Wiley series in probability and statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Statistics Canada","funders":"","keywords":"Index (typography); Library science; Statistics; Series (stratigraphy); Probability and statistics; Mathematics; Computer science; Geology","score_opus":0.10898075716528334,"score_gpt":0.42219224488609297,"score_spread":0.31321148772080964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238551536","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":[0.004404573,0.0077573387,0.6869256,0.0004669016,0.0133022815,0.0029811724,0.010045812,0.000083797095,0.27403256],"genre_scores_gemma":[0.0023082735,0.0013287992,0.85501456,0.0002468416,0.00034102594,0.00011608384,0.00017267482,0.000055112654,0.14041665],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99455774,0.0012296637,0.001366813,0.0012018596,0.0011819794,0.00046195264],"domain_scores_gemma":[0.99549603,0.0023844119,0.00041259013,0.0010797487,0.0004418335,0.00018535745],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004368847,0.00043931245,0.0009517836,0.00026721964,0.00017496974,0.00048615265,0.00086940255,0.00051856256,0.009430917],"category_scores_gemma":[0.007200092,0.00036441986,0.00006479269,0.00062035787,0.0019295487,0.00037587015,0.00060179865,0.0005708883,0.0020592941],"study_design_candidate":"not_applicable","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":[0.0005757448,0.00021315826,0.002925031,0.00024294182,0.00003128646,0.00002867552,0.0018644457,0.00011320948,0.00004577925,0.028395731,0.89092463,0.07463936],"study_design_scores_gemma":[0.00031975517,0.0005479222,0.0013653309,0.00016512036,0.000010789702,0.000020969144,0.0003719226,0.0012535463,0.00014010197,0.5717076,0.42352834,0.00056859356],"about_ca_topic_score_codex":0.0001095524,"about_ca_topic_score_gemma":0.00027815753,"teacher_disagreement_score":0.5433119,"about_ca_system_score_codex":0.00015340035,"about_ca_system_score_gemma":0.00031683408,"threshold_uncertainty_score":0.9998808},"labels":[],"label_agreement":null},{"id":"W4243037178","doi":"10.1007/978-94-007-0753-5_103553","title":"Response Rate","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University; University of Northern British Columbia","funders":"","keywords":"Computer science","score_opus":0.22419330054434053,"score_gpt":0.4581927232231468,"score_spread":0.23399942267880627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243037178","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00002462846,0.00015649654,0.052599963,0.000586449,0.00067534985,0.00020419601,0.000010345314,0.00009183456,0.94565076],"genre_scores_gemma":[0.00042578418,0.000008187226,0.058058694,0.001787999,0.00011777491,0.000004997892,0.0000019722636,0.00005782918,0.93953675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99503404,0.0013774608,0.0008781138,0.0009198931,0.0015370854,0.00025338438],"domain_scores_gemma":[0.9865526,0.011116906,0.00037133388,0.0014891563,0.0002587395,0.00021125049],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.02567624,0.00037064686,0.00065891794,0.00046600375,0.00008971086,0.00031059844,0.0012259718,0.00038744515,0.038167264],"category_scores_gemma":[0.0057627126,0.0002553548,0.0003303245,0.0000724274,0.00021493035,0.000097262186,0.000343273,0.0002953673,0.034512457],"study_design_candidate":"not_applicable","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":[0.0018258923,0.0000074392824,0.0000017802831,0.0000015399048,0.000027305758,0.00004942769,0.00006709433,0.00000922524,0.009060207,0.67958707,0.25834402,0.051018972],"study_design_scores_gemma":[0.00013431316,0.0001553608,0.000027684619,0.000014607893,0.0000068208133,0.000008893296,0.000011600659,0.00013427228,0.002282035,0.15842102,0.8385112,0.00029217708],"about_ca_topic_score_codex":0.0000033073204,"about_ca_topic_score_gemma":0.0000014516093,"teacher_disagreement_score":0.5801672,"about_ca_system_score_codex":0.00007268011,"about_ca_system_score_gemma":0.000107451866,"threshold_uncertainty_score":0.99998987},"labels":[],"label_agreement":null},{"id":"W4245844465","doi":"10.22541/au.161792041.14901274/v1","title":"Model-based Design of Experiments for Polyether Production from Bio-based 1,3-Propanediol","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Monte Carlo method; Design of experiments; Batch reactor; Catalysis; Process engineering; Production (economics); Biological system; Bayesian probability; Nuclear engineering; Materials science; Chemistry; Computer science; Simulation; Environmental science; Mathematics; Engineering; Statistics; Organic chemistry","score_opus":0.4147161210013667,"score_gpt":0.4825162292812129,"score_spread":0.06780010827984623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245844465","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":[0.037000764,0.0008370107,0.9564423,0.00033434728,0.0019559604,0.0027399203,0.00017453238,0.00011390175,0.00040129267],"genre_scores_gemma":[0.31486103,0.0000029880869,0.68309677,0.00024170731,0.00013618679,0.000637875,0.00010696183,0.00006663803,0.00084985694],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99211365,0.0014435828,0.0016333829,0.0022566968,0.0021078612,0.00044484274],"domain_scores_gemma":[0.993178,0.0019678576,0.0010034118,0.0024975818,0.0011544611,0.0001987075],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0046272315,0.0006169783,0.0012295279,0.0005282771,0.000121576755,0.0003518951,0.0015869599,0.0005849946,0.0011485862],"category_scores_gemma":[0.0033553608,0.00049237575,0.0006299069,0.0005150209,0.00026052195,0.00023161725,0.0004902661,0.0003415332,0.000027339309],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062453456,0.00041823625,0.00008938023,0.000029142431,0.000060543687,0.0000018754928,0.00033163186,0.42787403,0.56454253,0.000032274904,0.0011600925,0.004835708],"study_design_scores_gemma":[0.00046029626,0.000119139586,0.000017788669,0.000079420555,0.000033395958,1.9368565e-7,0.00038100887,0.45614633,0.5388231,0.0036187512,0.000026940168,0.0002936067],"about_ca_topic_score_codex":0.00020740896,"about_ca_topic_score_gemma":0.0000035405274,"teacher_disagreement_score":0.27786028,"about_ca_system_score_codex":0.0002108344,"about_ca_system_score_gemma":0.0013021461,"threshold_uncertainty_score":0.9997645},"labels":[],"label_agreement":null},{"id":"W4246135378","doi":"10.1002/9781118445112.stat06634","title":"Design Effects","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Cancer Care Ontario","funders":"","keywords":"Relevance (law); Context (archaeology); Sample size determination; Cluster sampling; Sampling design; Sampling (signal processing); Sample (material); Computer science; Cluster (spacecraft); Management science; Statistics; Mathematics; Geography; Sociology; Engineering; Political science; Physics; Programming language","score_opus":0.1878789604205307,"score_gpt":0.4605545715467135,"score_spread":0.27267561112618277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246135378","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":[0.000002592655,0.0016396571,0.87917024,0.00003869015,0.0013860727,0.0009937875,0.0058400496,0.0003523477,0.11057659],"genre_scores_gemma":[0.000052158815,0.0005075131,0.63823324,0.000313196,0.0003340217,0.00005287656,0.000745477,0.0006033405,0.35915816],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9896767,0.002689518,0.0015006076,0.0018673795,0.0032853787,0.0009804151],"domain_scores_gemma":[0.98765063,0.0076267673,0.001437975,0.0022317646,0.00045279978,0.0006000894],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002889032,0.0010623594,0.0017868258,0.001076563,0.00015004801,0.0004523278,0.0023918727,0.0007396235,0.012212145],"category_scores_gemma":[0.0069042793,0.0008327859,0.00013746985,0.0008842817,0.00052778906,0.0001215109,0.0004611281,0.0008940961,0.0066848085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007095102,0.000236983,0.00001809422,0.00007840403,0.00007921993,0.000095941876,0.000067023604,0.000058148842,0.000589673,0.010430918,0.870494,0.11778062],"study_design_scores_gemma":[0.0011373415,0.0010825522,0.00009395788,0.0006287085,0.00012970084,0.000017765218,0.00008168129,0.008037884,0.00044773798,0.063207746,0.92365026,0.0014846465],"about_ca_topic_score_codex":0.00018445951,"about_ca_topic_score_gemma":0.00012357655,"teacher_disagreement_score":0.24858157,"about_ca_system_score_codex":0.00016451228,"about_ca_system_score_gemma":0.0004194685,"threshold_uncertainty_score":0.9994123},"labels":[],"label_agreement":null},{"id":"W4248569092","doi":"10.1002/asmb.739","title":"Robust designs for misspecified exponential regression models","year":2009,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homoscedasticity; Heteroscedasticity; Mathematics; Nonlinear regression; Exponential function; Statistics; Minimax; Context (archaeology); Mean squared error; Applied mathematics; Optimal design; Variance function; Function (biology); Mathematical optimization; Regression analysis","score_opus":0.3525976592181178,"score_gpt":0.4021451797838638,"score_spread":0.04954752056574602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248569092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041748073,0.00027097572,0.9508727,0.0003990487,0.00024004823,0.0008664709,0.000014205258,0.000048771653,0.0055397158],"genre_scores_gemma":[0.88532084,0.000011165795,0.11373059,0.0002611924,0.00017853093,0.00014470064,0.000008975233,0.000029157358,0.00031485752],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965427,0.00009643486,0.00088874897,0.0010235055,0.00089614425,0.0005524537],"domain_scores_gemma":[0.9979501,0.00071058207,0.00026347995,0.00059807324,0.00025711258,0.0002206927],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018129238,0.00038500165,0.0006358609,0.00040394967,0.00024543775,0.00027986313,0.0006150834,0.0006550666,0.00007947755],"category_scores_gemma":[0.00029784165,0.00030311517,0.00007200389,0.00092951686,0.00015760268,0.00066624745,0.00013803093,0.0004817145,0.000005783676],"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":[0.00077099627,0.00020610212,0.0000027528142,0.0000072058297,0.0000065446575,0.00000567817,0.00046172753,0.80988204,0.012087856,0.12002773,0.0010876408,0.055453736],"study_design_scores_gemma":[0.0017439144,0.000072262126,0.00023077716,0.00008779007,0.000012793329,0.000009798482,0.000626996,0.6047215,0.00088433974,0.39120764,0.000033412132,0.0003688187],"about_ca_topic_score_codex":0.000019886551,"about_ca_topic_score_gemma":0.0000014357379,"teacher_disagreement_score":0.84357274,"about_ca_system_score_codex":0.00006924063,"about_ca_system_score_gemma":0.000106298496,"threshold_uncertainty_score":0.9999421},"labels":[],"label_agreement":null},{"id":"W4252037630","doi":"10.1007/978-94-007-0753-5_967","title":"Experimental Design","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Geology","score_opus":0.3281369811954554,"score_gpt":0.4584861129229134,"score_spread":0.130349131727458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252037630","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000001372402,0.0006218428,0.3573783,0.00004703442,0.0005787983,0.00030808037,0.0000038634753,0.000094775205,0.64096594],"genre_scores_gemma":[0.0014129301,0.000005113721,0.24031207,0.0006087058,0.00022950712,0.000015823085,0.0000030270187,0.0000817712,0.7573311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9944956,0.00032215478,0.0010764522,0.0012170149,0.0025326395,0.0003561626],"domain_scores_gemma":[0.9952574,0.0023343835,0.0004452553,0.0014738651,0.00019311288,0.00029602507],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0042032944,0.00057835336,0.0008817655,0.00043873585,0.00013388925,0.00039571,0.0015918737,0.00049077696,0.056984674],"category_scores_gemma":[0.0005059368,0.00042321792,0.00043448934,0.000070403104,0.00028557572,0.00015321508,0.00043600413,0.0003340111,0.023662822],"study_design_candidate":"not_applicable","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":[0.00008238003,0.000027451739,8.0594776e-7,0.0000015514638,0.000039718678,0.000030222714,0.00011982794,0.00004283306,0.014617204,0.7512278,0.19674282,0.037067417],"study_design_scores_gemma":[0.00030103006,0.00047630983,0.0000014642479,0.00002595542,0.000015424721,0.000031882013,0.000075117096,0.00081308436,0.09390401,0.14299941,0.7606185,0.00073780003],"about_ca_topic_score_codex":0.000004869824,"about_ca_topic_score_gemma":3.0648405e-7,"teacher_disagreement_score":0.6082284,"about_ca_system_score_codex":0.00014429448,"about_ca_system_score_gemma":0.00009177786,"threshold_uncertainty_score":0.99982196},"labels":[],"label_agreement":null},{"id":"W4252816699","doi":"10.1002/9781118445112.stat06533.pub2","title":"Within‐Case Designs: Distribution‐Free Methods","year":2017,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Bootstrapping (finance); Nonparametric statistics; Univariate; Multivariate statistics; Statistics; Parametric statistics; Missing data; Econometrics; Statistical hypothesis testing; Rank (graph theory); Computer science; Mathematics","score_opus":0.37290841146743414,"score_gpt":0.5510677845579196,"score_spread":0.17815937309048546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252816699","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":[0.0000040471077,0.0024300497,0.79371667,0.00007306884,0.0018195402,0.00081684015,0.117752016,0.00032016705,0.08306762],"genre_scores_gemma":[0.000029796885,0.0006662573,0.65060884,0.00010506304,0.00034621448,0.000059479917,0.003543378,0.0004661127,0.34417483],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9881878,0.0031835695,0.00208121,0.0023863246,0.0030138658,0.0011472297],"domain_scores_gemma":[0.98419577,0.004461395,0.0034510365,0.006056016,0.0009731943,0.00086256885],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.00654342,0.0013332092,0.0021434105,0.00073613034,0.00065353146,0.0011738227,0.0049643,0.0009917503,0.0089130765],"category_scores_gemma":[0.028079923,0.0010708465,0.00024431184,0.0006989709,0.0013803564,0.00035464106,0.0014601381,0.0014013343,0.00092992943],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008487715,0.0002897642,0.000018740828,0.000049444192,0.0001446424,0.0018857137,0.00010237827,0.000015421785,0.0002161549,0.024605243,0.8165266,0.15606105],"study_design_scores_gemma":[0.001130972,0.0004914044,0.000028876804,0.0005314288,0.00021699171,0.00060601626,0.00038341756,0.00421875,0.00032098158,0.11056425,0.879968,0.0015389106],"about_ca_topic_score_codex":0.0012023109,"about_ca_topic_score_gemma":0.0014546146,"teacher_disagreement_score":0.26110724,"about_ca_system_score_codex":0.0003410943,"about_ca_system_score_gemma":0.0009047605,"threshold_uncertainty_score":0.9999419},"labels":[],"label_agreement":null},{"id":"W4252970442","doi":"10.1002/9781118445112.stat07335","title":"Nested Experimental Designs","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Nested set model; Differential (mechanical device); Subclass; Pollution; Environmental science; Computer science; Engineering; Data mining; Biology; Ecology","score_opus":0.29179061056081645,"score_gpt":0.49028302741641494,"score_spread":0.19849241685559849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252970442","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":[0.000063772844,0.0030179245,0.73148465,0.00006123026,0.0018812347,0.0011459853,0.019185955,0.00068969076,0.24246955],"genre_scores_gemma":[0.0005296143,0.00025468154,0.5765642,0.0003337144,0.00043065546,0.00006115656,0.0018305113,0.0007217743,0.4192737],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9899466,0.0015828611,0.001866789,0.0020551549,0.0034914569,0.0010571629],"domain_scores_gemma":[0.9927049,0.0022953115,0.0015506144,0.0022904878,0.00044474794,0.00071390305],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0018246954,0.0011842721,0.0017900177,0.0011283384,0.00019407365,0.0005369839,0.0025083544,0.00075927697,0.033888094],"category_scores_gemma":[0.002663769,0.00097201654,0.00018615267,0.000947039,0.00070870586,0.00017228286,0.00057712453,0.0008562767,0.0052297697],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009454465,0.00057117455,0.000056893932,0.000029706798,0.00008309008,0.000091444985,0.00016433909,0.000020622225,0.0033431503,0.015601589,0.9462165,0.0337269],"study_design_scores_gemma":[0.0011764358,0.00096466026,0.00011934379,0.0003457853,0.00007791352,0.000030037465,0.0006465739,0.0034831981,0.0011984789,0.011224319,0.97914666,0.0015865748],"about_ca_topic_score_codex":0.00035401486,"about_ca_topic_score_gemma":0.0002491981,"teacher_disagreement_score":0.17680417,"about_ca_system_score_codex":0.00024788256,"about_ca_system_score_gemma":0.00043863157,"threshold_uncertainty_score":0.999273},"labels":[],"label_agreement":null},{"id":"W4253629597","doi":"10.1002/1521-4036(200102)43:1<63::aid-bimj63>3.3.co;2-s","title":"Dose Response Analysis Using Robust Covariance Estimation","year":2001,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Women's Health Research Institute","funders":"","keywords":"Jackknife resampling; Mathematics; Monte Carlo method; Covariance; Statistics; Robustness (evolution); Analysis of covariance; Delta method; Variance (accounting); Statistic; Nonlinear regression; Regression analysis; Applied mathematics; Estimator","score_opus":0.3927318620297389,"score_gpt":0.5171799684373057,"score_spread":0.12444810640756682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253629597","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":[0.28090614,0.0003748212,0.7177586,0.00027866664,0.00032036944,0.00005927584,0.0000035995467,0.000019201552,0.0002793648],"genre_scores_gemma":[0.47472188,0.000015655954,0.52467704,0.00011084255,0.000100331476,8.677751e-7,5.691199e-7,0.000008319123,0.00036449984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9936886,0.0019622494,0.0010552909,0.0004402545,0.0024552543,0.0003983561],"domain_scores_gemma":[0.9942832,0.0037619362,0.00054618047,0.0004650347,0.0004906228,0.0004530054],"candidate_categories":["metaresearch","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.014590482,0.00017590675,0.0004729502,0.007516448,0.00038813183,0.0010460251,0.0008296143,0.00013190153,0.0016857139],"category_scores_gemma":[0.02064319,0.00012820322,0.00040265548,0.04041414,0.00012679408,0.00074521685,0.00012953843,0.00029163877,0.00024447672],"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":[0.0055442443,0.0007524099,0.029775213,0.0000016675738,0.00064359576,0.0010893089,0.00038931466,0.3874338,0.15492453,0.0003211399,0.0028299035,0.41629487],"study_design_scores_gemma":[0.0009115331,0.00032223048,0.10815333,0.00000900826,0.00022081098,0.0010406532,0.0002416526,0.88039356,0.0019250363,0.0026303336,0.003825463,0.00032638336],"about_ca_topic_score_codex":0.000014483557,"about_ca_topic_score_gemma":3.2829192e-7,"teacher_disagreement_score":0.49295977,"about_ca_system_score_codex":0.00037099645,"about_ca_system_score_gemma":0.00014061063,"threshold_uncertainty_score":0.999991},"labels":[],"label_agreement":null},{"id":"W4253857795","doi":"10.1007/978-94-007-0753-5_982","title":"Factorial Design","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Factorial experiment; Mathematics; Statistics","score_opus":0.39426346481649804,"score_gpt":0.4613500265654087,"score_spread":0.06708656174891064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253857795","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.5552801e-7,0.00006872797,0.46619144,0.000028021583,0.0019108234,0.00019742927,0.0000049112086,0.000059661314,0.53153884],"genre_scores_gemma":[0.00019067216,0.0000075455623,0.21169844,0.00025196603,0.0009278415,0.000005170528,0.0000026557186,0.000056157343,0.7868596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950551,0.0003119856,0.0009217271,0.0009576355,0.0024701648,0.0002834251],"domain_scores_gemma":[0.99355614,0.0043030772,0.0004123797,0.0012260803,0.0002692502,0.00023309464],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0048056673,0.00044715282,0.0007883796,0.00034905766,0.00009379224,0.00038109865,0.0014029582,0.00056714274,0.041101113],"category_scores_gemma":[0.0015536587,0.00030572916,0.00036478697,0.000057681187,0.00017105443,0.00011839829,0.000278265,0.00034552423,0.019989282],"study_design_candidate":"theoretical_or_conceptual","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":[0.00007384496,0.000006337075,0.0000010741363,0.0000012235267,0.000027873126,0.000010794615,0.000048996386,0.000026763213,0.0010432916,0.70660776,0.21940652,0.07274554],"study_design_scores_gemma":[0.00013891487,0.00015312636,0.0000011653207,0.000008855002,0.000010472873,0.0000043489563,0.000004743542,0.000191227,0.0018173709,0.34427813,0.6530885,0.0003031527],"about_ca_topic_score_codex":0.000006841568,"about_ca_topic_score_gemma":0.0000010119409,"teacher_disagreement_score":0.433682,"about_ca_system_score_codex":0.00008871924,"about_ca_system_score_gemma":0.00011189375,"threshold_uncertainty_score":0.9999395},"labels":[],"label_agreement":null},{"id":"W4281559236","doi":"10.5772/intechopen.104694","title":"Practical and Optimal Crossover Designs for Clinical Trials","year":2022,"lang":"en","type":"book-chapter","venue":"IntechOpen eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Alberta","funders":"","keywords":"Crossover; Sample size determination; Computer science; Statistical power; Crossover study; Adaptive design; Optimal design; Mathematical optimization; Clinical trial; Statistics; Mathematics; Machine learning; Medicine","score_opus":0.8203676719140467,"score_gpt":0.6616420322207063,"score_spread":0.15872563969334041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281559236","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000029508386,0.00041037006,0.13169792,0.00054282905,0.0019618561,0.003631411,0.00035811547,0.00009812016,0.8612699],"genre_scores_gemma":[0.00032280746,0.00006138687,0.3307267,0.00094488903,0.00048400208,0.0003217925,0.000012556322,0.00013918003,0.66698664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9888263,0.002413497,0.0041834293,0.0019924603,0.0020555384,0.0005288292],"domain_scores_gemma":[0.9497732,0.04608081,0.001966684,0.0013111333,0.00043824053,0.00042992784],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.060820185,0.0006611028,0.002557486,0.00040331756,0.00040822013,0.0011158116,0.0014249948,0.000872317,0.011148765],"category_scores_gemma":[0.041661046,0.00051812845,0.0012648875,0.000041566764,0.00096515624,0.00032954497,0.0016367254,0.0015552149,0.00036959292],"study_design_candidate":"not_applicable","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":[0.0044818474,0.00008445381,0.000006909124,0.000013079704,0.00038934077,0.00015032048,0.00023235302,0.000002966225,0.0011191174,0.5721174,0.16009334,0.2613089],"study_design_scores_gemma":[0.0014340121,0.0011443567,0.000002298084,0.00003994432,0.00016501056,0.00010524266,0.00013869342,0.00019507714,0.0011873653,0.12472417,0.87029546,0.0005683935],"about_ca_topic_score_codex":0.000010858887,"about_ca_topic_score_gemma":0.0000034588816,"teacher_disagreement_score":0.7102021,"about_ca_system_score_codex":0.00016633265,"about_ca_system_score_gemma":0.0007669245,"threshold_uncertainty_score":0.99992114},"labels":[],"label_agreement":null},{"id":"W4281676701","doi":"10.1080/24725854.2022.2067915","title":"Modeling and optimization for multiple correlated responses with distribution variability","year":2022,"lang":"en","type":"article","venue":"IISE Transactions","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":13,"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 Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Copula (linguistics); Joint probability distribution; Smoothing; Nonparametric statistics; Multivariate statistics; Parametric statistics; Multivariate normal distribution; Mathematical optimization; Computer science; Marginal distribution; Semiparametric model; Econometrics; Mathematics; Statistics; Machine learning; Random variable","score_opus":0.10504898542697239,"score_gpt":0.3866190691078836,"score_spread":0.2815700836809112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281676701","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07310829,0.000038754588,0.92539155,0.00021296222,0.00015859706,0.00052093883,0.00046516885,0.00006665422,0.000037082646],"genre_scores_gemma":[0.8072445,0.0000035901298,0.19220948,0.00002242941,0.000006080169,0.00024712677,0.000039232484,0.000010597242,0.00021697533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801034,0.0006042738,0.00036083622,0.00040780767,0.00046197447,0.0001547886],"domain_scores_gemma":[0.9975832,0.0018293986,0.00007053411,0.00026160135,0.00017646367,0.00007881239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026714273,0.00010315973,0.00015682113,0.00009677992,0.00090359576,0.00009178103,0.00014832804,0.00003846779,0.00048823445],"category_scores_gemma":[0.0007109673,0.000087769586,0.00005927557,0.00057205686,0.000068572466,0.0002973027,0.000013324733,0.00014371658,0.0000013445293],"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":[0.0011134242,0.000106690815,0.00013776973,0.0000016939786,0.00001085151,6.279428e-7,0.00031629115,0.9936194,0.0014083897,0.00012649462,0.000017336037,0.0031410474],"study_design_scores_gemma":[0.0006599376,0.00022358875,0.00006595668,0.0000022198667,0.000030033498,0.00001799836,0.00089263765,0.99582297,0.0008522761,0.001022587,0.00029667272,0.00011312035],"about_ca_topic_score_codex":0.000047353176,"about_ca_topic_score_gemma":0.000007817626,"teacher_disagreement_score":0.73413616,"about_ca_system_score_codex":0.000116916795,"about_ca_system_score_gemma":0.00007528652,"threshold_uncertainty_score":0.69498193},"labels":[],"label_agreement":null},{"id":"W4284697950","doi":"10.31234/osf.io/akcnd","title":"The exact distribution of the Cohen’s d p in repeated-measure designs","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Ottawa","funders":"","keywords":"Measure (data warehouse); Distribution (mathematics); Mathematics; Sampling (signal processing); Statistics; Mathematical analysis; Computer science; Data mining","score_opus":0.2088293543379032,"score_gpt":0.44905196595205704,"score_spread":0.24022261161415384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284697950","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5865386,0.006310179,0.1940818,0.008114718,0.013530936,0.008786383,0.0010154435,0.00031495013,0.18130696],"genre_scores_gemma":[0.9906498,0.000039283255,0.0035003414,0.00008017411,0.000034264383,0.00018607872,0.000018871331,0.00001954727,0.00547163],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9897132,0.0049040564,0.0013311988,0.00082131126,0.0028954234,0.00033479233],"domain_scores_gemma":[0.9925479,0.0038508228,0.0007793387,0.0024762661,0.00027957693,0.00006605808],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.018286075,0.0002747043,0.00048589444,0.000102707134,0.0003771375,0.00029151383,0.0036607878,0.00021494433,0.0012015436],"category_scores_gemma":[0.007992064,0.00012980326,0.00041131576,0.0010337662,0.00034138485,0.00009159795,0.0033487328,0.0010833694,0.000024954443],"study_design_candidate":"observational","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":[0.00272151,0.0020547204,0.19998991,0.00011409656,0.00053973874,0.000094322684,0.0102200145,0.11079615,0.08908223,0.10673345,0.2130053,0.26464853],"study_design_scores_gemma":[0.0017931169,0.0005379394,0.44933382,0.00033573617,0.00012343217,0.000041583204,0.020396303,0.0257696,0.14604156,0.2723062,0.08142523,0.0018954729],"about_ca_topic_score_codex":0.0005247641,"about_ca_topic_score_gemma":0.0000895533,"teacher_disagreement_score":0.40411118,"about_ca_system_score_codex":0.0004729029,"about_ca_system_score_gemma":0.00041693382,"threshold_uncertainty_score":0.9997115},"labels":[],"label_agreement":null},{"id":"W4286608159","doi":"10.1080/10618600.2022.2104858","title":"Using CVX to Construct Optimal Designs for Biomedical Studies with Multiple Objectives","year":2022,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":17,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Optimal design; Computer science; Construct (python library); MATLAB; Mathematical optimization; Code (set theory); Software; Machine learning; Mathematics; Programming language","score_opus":0.2339448673563404,"score_gpt":0.4839443142539315,"score_spread":0.24999944689759107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286608159","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":[0.15124644,0.00021965186,0.84753734,0.00032306512,0.00020707116,0.00016344026,0.0002949728,0.0000035398318,0.0000044657063],"genre_scores_gemma":[0.40280288,0.0000022719787,0.59698087,0.00015312564,0.000041528383,0.000005137897,0.0000023570929,0.0000056648237,0.0000061274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99705416,0.00038159863,0.00069695595,0.00021912405,0.0014787166,0.00016945915],"domain_scores_gemma":[0.98910874,0.009327639,0.00038284063,0.000058235066,0.0008965426,0.00022602975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020562445,0.00012447803,0.0003991605,0.00038901035,0.00043801335,0.00009186889,0.0002238464,0.000023334771,0.000039611827],"category_scores_gemma":[0.00255231,0.00008388057,0.00007225209,0.0006237481,0.00043047007,0.000115179195,0.00012945847,0.00018976505,4.2385986e-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":[0.0058604334,0.0007425734,0.013390822,0.000037847643,0.000804018,0.00028198204,0.004218376,0.79105514,0.0038462097,0.10909461,0.007655417,0.06301258],"study_design_scores_gemma":[0.0032336607,0.0075837686,0.012814011,0.00003477433,0.00010799266,0.0014775596,0.008808469,0.35834357,0.00015948385,0.60403097,0.0030375647,0.00036814407],"about_ca_topic_score_codex":0.0000024104697,"about_ca_topic_score_gemma":6.379551e-7,"teacher_disagreement_score":0.49493638,"about_ca_system_score_codex":0.000057423087,"about_ca_system_score_gemma":0.0001873653,"threshold_uncertainty_score":0.34205505},"labels":[],"label_agreement":null},{"id":"W4292056390","doi":"10.1016/j.cie.2022.108551","title":"Multi-stage online robust parameter design based on Bayesian GP model","year":2022,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Hyperparameter; Robust optimization; Mathematical optimization; Robustness (evolution); Cluster analysis; Data mining; Machine learning; Mathematics","score_opus":0.43584653665836154,"score_gpt":0.4000761011655973,"score_spread":0.03577043549276426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292056390","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":[0.007552274,0.000026484995,0.9891616,0.00019474601,0.0021370496,0.00057545706,0.00007720502,0.00022589421,0.000049252503],"genre_scores_gemma":[0.22539566,3.4280978e-7,0.7734002,0.00047876226,0.00022012104,0.00006193291,0.000017308048,0.000062294224,0.00036336595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955514,0.0006715888,0.0008046983,0.000878837,0.001529379,0.00056408485],"domain_scores_gemma":[0.99555653,0.0030151613,0.0002035295,0.0008683886,0.00006386987,0.00029250022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0032305447,0.00039547405,0.0005310221,0.0007012313,0.00026878787,0.0002808943,0.0014268892,0.00015866614,0.00031226524],"category_scores_gemma":[0.0015353069,0.00038329326,0.00024708983,0.0011769794,0.000045407585,0.00023598784,0.00046886422,0.00097385247,0.000021229247],"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":[0.0001441851,0.00020239466,0.00002417523,0.0000014588886,0.000015988715,0.000040926203,0.00010592982,0.9822892,0.00426531,0.00007136063,0.0016244136,0.011214646],"study_design_scores_gemma":[0.0018238939,0.00038072636,0.000035965797,0.000019362255,0.000009628866,0.000004842839,0.000090562884,0.9937239,0.0016638732,0.00002811016,0.0018098673,0.00040921985],"about_ca_topic_score_codex":0.000014027693,"about_ca_topic_score_gemma":2.5765058e-7,"teacher_disagreement_score":0.2178434,"about_ca_system_score_codex":0.00038358563,"about_ca_system_score_gemma":0.00017764313,"threshold_uncertainty_score":0.9998619},"labels":[],"label_agreement":null},{"id":"W4298218231","doi":"10.1287/moor.2014.0682","title":"<i>K</i>-Optimal Design via Semidefinite Programming and Entropy Optimization","year":2014,"lang":"en","type":"article","venue":"Mathematics of Operations Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Semidefinite programming; Mathematics; Mathematical optimization; Semidefinite embedding; Convex optimization; Minification; Entropy maximization; Dual (grammatical number); Entropy (arrow of time); Linear programming; Positive-definite matrix; Regular polygon; Quadratically constrained quadratic program; Principle of maximum entropy; Quadratic programming","score_opus":0.29742725395388764,"score_gpt":0.5002663857463289,"score_spread":0.20283913179244123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298218231","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":[0.021996269,0.00009263974,0.9752879,0.00022480529,0.000038600178,0.00068876415,0.0000029878756,0.000027227432,0.0016408109],"genre_scores_gemma":[0.19816452,0.000015717744,0.8010958,0.0000137875195,0.000027070684,0.000080914295,0.0000031062564,0.000019501482,0.0005796255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950031,0.0014748904,0.00082442665,0.00041435042,0.0019245679,0.00035865977],"domain_scores_gemma":[0.99376994,0.004157502,0.00008774264,0.0006618727,0.0011681208,0.0001547998],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.016972905,0.00014391553,0.00033719387,0.00056001544,0.0004188168,0.00063700305,0.000581011,0.00009103633,0.00028807422],"category_scores_gemma":[0.008683549,0.00011209964,0.00005639914,0.0011261207,0.00040483766,0.00043634043,0.00029080702,0.00022514242,0.00009494457],"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":[0.00003758073,0.0004208925,0.00008822813,0.00004308749,0.000024001682,0.0000017962436,0.0039505665,0.8483805,0.08532044,0.04106403,0.00044404232,0.020224798],"study_design_scores_gemma":[0.0002484159,0.0003215109,0.000013187246,0.000027217928,0.0000058963533,0.000011911509,0.0009759708,0.962717,0.029625928,0.005652652,0.0002877614,0.00011255855],"about_ca_topic_score_codex":0.000020572437,"about_ca_topic_score_gemma":0.0000020576408,"teacher_disagreement_score":0.17616825,"about_ca_system_score_codex":0.00003859515,"about_ca_system_score_gemma":0.00007967908,"threshold_uncertainty_score":0.99966675},"labels":[],"label_agreement":null},{"id":"W4301813752","doi":"","title":"Batch sequential designs for computer experiments","year":2024,"lang":"en","type":"article","venue":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Computer science","score_opus":0.09920601683024739,"score_gpt":0.3816435283657692,"score_spread":0.2824375115355218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4301813752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057349984,0.00053560396,0.9128444,0.00021261956,0.0019986343,0.00057227706,0.00027835954,0.00013327524,0.026074842],"genre_scores_gemma":[0.8295615,0.000010079573,0.16938902,0.000079870166,0.000042874075,0.0000689732,0.0003136996,0.000009681821,0.0005243207],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9966886,0.00009744127,0.0012384697,0.00044131224,0.0012471402,0.00028701688],"domain_scores_gemma":[0.99763703,0.0010043604,0.00030582654,0.0004534829,0.00043843815,0.00016087494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026724702,0.00020683343,0.00038281956,0.0005286778,0.000199782,0.0005840249,0.0005643874,0.00012941532,0.00023154548],"category_scores_gemma":[0.00031770178,0.00016108544,0.00021309083,0.0008604773,0.0006413551,0.0015865605,0.00027849988,0.00007803563,0.00004543283],"study_design_candidate":"bench_or_experimental","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":[0.000828983,0.00091282616,0.00044447603,0.00029463708,0.00026174742,0.000008341585,0.00014171147,0.0017576468,0.16491312,0.5923566,0.1312033,0.106876574],"study_design_scores_gemma":[0.0019379659,0.0020746903,0.006064181,0.00031321,0.00014023608,0.0000768349,0.00009986693,0.011672862,0.48583457,0.015974818,0.47484133,0.00096945255],"about_ca_topic_score_codex":0.000010359988,"about_ca_topic_score_gemma":0.000001587552,"teacher_disagreement_score":0.7722115,"about_ca_system_score_codex":0.00005395398,"about_ca_system_score_gemma":0.00014344297,"threshold_uncertainty_score":0.65688735},"labels":[],"label_agreement":null},{"id":"W4307356996","doi":"10.1214/22-aos2215","title":"A study of orthogonal array-based designs under a broad class of space-filling criteria","year":2022,"lang":"en","type":"article","venue":"The Annals of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":18,"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":"Orthogonal array; Orthogonality; Mathematics; Partition (number theory); Class (philosophy); Space (punctuation); Orthogonal basis; Computer experiment; Orthogonal transformation; Design of experiments; Algorithm; Combinatorics; Geometry; Computer science; Statistics; Taguchi methods; Artificial intelligence","score_opus":0.5178728409424104,"score_gpt":0.5247503710326535,"score_spread":0.006877530090243167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307356996","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5409648,0.00014652046,0.45650253,0.000296863,0.00016924474,0.00042862023,0.0009187717,0.000008953834,0.0005637045],"genre_scores_gemma":[0.8687698,0.0000042760394,0.13088226,0.0001831466,0.000013201781,0.000014540191,0.0000050235544,0.000016682137,0.00011104642],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99431866,0.0019704883,0.0010800713,0.0003061315,0.0020859372,0.0002387004],"domain_scores_gemma":[0.9925161,0.0051767086,0.00088891655,0.00071036414,0.00063693005,0.00007098236],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0061141634,0.00016080945,0.0005468748,0.0002445795,0.00017415467,0.000034337878,0.0009811302,0.000027998562,0.0010002138],"category_scores_gemma":[0.0016463079,0.00011666796,0.000108142194,0.000860403,0.00031802343,0.000071934475,0.00023388675,0.00017704767,0.0000048814063],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0043226057,0.0069104065,0.0049673757,0.00010402778,0.00050575857,0.00006142569,0.032420248,0.17282994,0.65853024,0.08171975,0.024386108,0.013242136],"study_design_scores_gemma":[0.0043884465,0.01671072,0.030135386,0.00009289961,0.00026545385,0.00002222935,0.18154734,0.062407356,0.41621175,0.28588858,0.0013583966,0.0009714519],"about_ca_topic_score_codex":0.00009796396,"about_ca_topic_score_gemma":0.00001098355,"teacher_disagreement_score":0.327805,"about_ca_system_score_codex":0.000017517823,"about_ca_system_score_gemma":0.00020229685,"threshold_uncertainty_score":0.99991304},"labels":[],"label_agreement":null},{"id":"W4309199057","doi":"10.1002/cjs.11744","title":"General minimum lower‐order confounding three‐level split‐plot designs when the whole plot factors are important","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Mathematics; Fractional factorial design; Statistics; Split plot; Ranking (information retrieval); Plot (graphics); Factorial experiment; Computer science; Artificial intelligence","score_opus":0.28392338757740637,"score_gpt":0.39454191338269723,"score_spread":0.11061852580529086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309199057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32530642,0.0013516417,0.65376955,0.0021799658,0.007020697,0.0006040556,0.008769685,0.000018682747,0.0009793446],"genre_scores_gemma":[0.6485843,0.000012083521,0.3458664,0.0014747138,0.00041011436,0.0000135500395,0.000028787932,0.00009997683,0.0035100344],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99500674,0.00072869455,0.0014529194,0.00034588767,0.0018017262,0.000664042],"domain_scores_gemma":[0.9944137,0.0020507395,0.001290687,0.0005173736,0.000730363,0.0009971361],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0047397856,0.0002867872,0.0005439405,0.0004978927,0.0011106649,0.0006275335,0.0017072448,0.000065437234,0.0038207439],"category_scores_gemma":[0.0039682467,0.0002027399,0.00016156195,0.0006650754,0.0003643922,0.00027455215,0.00012202778,0.0007021673,0.000027731823],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003952581,0.00023212896,0.16646302,0.000021193631,0.00054814416,0.0068056826,0.017146954,0.010174811,0.008771641,0.06585109,0.70346195,0.020128127],"study_design_scores_gemma":[0.0030395158,0.0036431905,0.13890003,0.00010570953,0.00035885093,0.0017312604,0.059545733,0.029075889,0.0011535049,0.26444557,0.49586704,0.0021337192],"about_ca_topic_score_codex":0.0030080774,"about_ca_topic_score_gemma":0.012380503,"teacher_disagreement_score":0.32327792,"about_ca_system_score_codex":0.0006644614,"about_ca_system_score_gemma":0.0027311796,"threshold_uncertainty_score":0.9970899},"labels":[],"label_agreement":null},{"id":"W4315797072","doi":"10.17615/63ad-p767","title":"Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach","year":2023,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Fraction (chemistry); Statistics; Selection (genetic algorithm); Variable (mathematics); Mathematics; Computer science; Chemistry; Artificial intelligence; Chromatography; Mathematical analysis","score_opus":0.28369269118736984,"score_gpt":0.4086968479530062,"score_spread":0.12500415676563637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315797072","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":[0.06675521,0.00016389214,0.91886675,0.0005956678,0.0001929394,0.0002838395,0.00022509394,0.00024043847,0.012676139],"genre_scores_gemma":[0.037858006,0.00000565385,0.9587825,0.000062525236,0.000054749114,0.000016864738,0.00030780927,0.000015269923,0.0028966437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977725,0.00031810885,0.0003729706,0.00055492827,0.0008191017,0.00016237897],"domain_scores_gemma":[0.997285,0.0015370448,0.00019654189,0.0007581385,0.0001550721,0.00006822247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019307225,0.00012027553,0.00039182036,0.00061229704,0.0002131232,0.00042327866,0.00056276366,0.000072628376,0.00025268027],"category_scores_gemma":[0.0009159062,0.00008623718,0.00003763811,0.0098481355,0.00018343577,0.002490686,0.0003473019,0.00010650143,0.000009980426],"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":[0.00025091093,0.0001853133,0.41317606,0.000022231729,0.001097303,0.0000024668943,0.001493012,0.01393722,0.005826111,0.54559445,0.016137555,0.0022773654],"study_design_scores_gemma":[0.00039239667,0.00016003619,0.16270202,0.000009754964,0.00054357725,0.000004855406,0.0058111693,0.7347248,0.001673124,0.08455065,0.009135379,0.0002921919],"about_ca_topic_score_codex":0.000118882475,"about_ca_topic_score_gemma":0.000014488981,"teacher_disagreement_score":0.72078764,"about_ca_system_score_codex":0.000010205313,"about_ca_system_score_gemma":0.00013051942,"threshold_uncertainty_score":0.47317028},"labels":[],"label_agreement":null},{"id":"W4317869010","doi":"10.1002/sim.9666","title":"Two‐phase designs with current status data","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Current (fluid); Phase (matter); Computer science; Statistics; Econometrics; Mathematics; Physics; Thermodynamics","score_opus":0.5036974145021728,"score_gpt":0.6117296971940839,"score_spread":0.10803228269191112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317869010","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":[0.005069629,0.0009102339,0.98700774,0.00040628968,0.0012119298,0.00045301457,0.0012201361,0.0000931062,0.0036279187],"genre_scores_gemma":[0.093087524,0.00057821075,0.90346503,0.00029733544,0.00036775318,0.00004422246,0.0010198551,0.000060213984,0.0010798682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99504834,0.0006010839,0.00081599836,0.0007968413,0.0021830767,0.0005546756],"domain_scores_gemma":[0.9924915,0.0053190254,0.00020418165,0.0015018594,0.00020087294,0.00028260038],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0074989665,0.000208487,0.0004837239,0.0006016292,0.00008807986,0.00006657415,0.0012558027,0.000032118707,0.0010874065],"category_scores_gemma":[0.011915169,0.00013227419,0.000011122579,0.0024489474,0.0005104751,0.00022109796,0.00041703027,0.00030263083,0.0004586357],"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":[0.0004192258,0.00025976208,0.005574651,0.000023748962,0.00002336635,0.00069193257,0.0024397892,0.00041654354,0.0028958274,0.029259542,0.29515538,0.66284025],"study_design_scores_gemma":[0.025738787,0.0049873055,0.01961633,0.0006117888,0.00013979955,0.00005591789,0.015037183,0.41575345,0.00095890096,0.29893425,0.21683505,0.0013312433],"about_ca_topic_score_codex":0.0001540818,"about_ca_topic_score_gemma":0.000115721195,"teacher_disagreement_score":0.661509,"about_ca_system_score_codex":0.00007645118,"about_ca_system_score_gemma":0.00019664111,"threshold_uncertainty_score":0.9998257},"labels":[],"label_agreement":null},{"id":"W4318457182","doi":"10.3389/fpsyg.2022.1045436","title":"Analysis of proportions using arcsine transform with any experimental design","year":2023,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":22,"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 Ottawa; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Social psychology; Applied psychology; Statistics; Mathematics","score_opus":0.2240579974239774,"score_gpt":0.5027118910303702,"score_spread":0.2786538936063928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318457182","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":[0.108981356,0.0003063498,0.88707376,0.00016520121,0.00057408225,0.00039628794,0.000047771387,0.000046171135,0.0024090498],"genre_scores_gemma":[0.3751035,0.0000146756165,0.62446564,0.00010016514,0.0000145252,0.00005458917,0.000018768638,0.000019388688,0.00020875892],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970171,0.00052757625,0.0007800388,0.00063684705,0.0006603259,0.0003781469],"domain_scores_gemma":[0.9987347,0.0002712222,0.00022934184,0.00058647495,0.00008600293,0.00009225244],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023982269,0.00017043792,0.0006688272,0.0027986497,0.000067832705,0.000028283695,0.00054342166,0.00011754514,0.000951682],"category_scores_gemma":[0.00018290727,0.00013100894,0.00016311057,0.008943734,0.0004247761,0.00021424817,0.000036110716,0.00013129528,0.000019439538],"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":[0.0049294643,0.002477737,0.3082478,0.000014584387,0.0028564308,0.00045617012,0.013368648,0.0546243,0.29828352,0.00053074985,0.11199983,0.20221077],"study_design_scores_gemma":[0.006309264,0.0037137654,0.1573779,0.00007789153,0.000925471,0.00009644557,0.03185511,0.64891165,0.109376684,0.032763954,0.007023297,0.0015685259],"about_ca_topic_score_codex":0.000021185859,"about_ca_topic_score_gemma":0.00000522294,"teacher_disagreement_score":0.5942874,"about_ca_system_score_codex":0.0000691619,"about_ca_system_score_gemma":0.000060037597,"threshold_uncertainty_score":0.99996156},"labels":[],"label_agreement":null},{"id":"W4323718577","doi":"10.48550/arxiv.2303.04746","title":"Necessary and sufficient conditions for multiple objective optimal regression designs","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Optimal design; Mathematical optimization; Construct (python library); Computer science; Linear programming; Optimality criterion; Function (biology); Mathematics; Algorithm; Machine learning","score_opus":0.4495448281470336,"score_gpt":0.3700793063775778,"score_spread":0.07946552176945582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323718577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52153414,0.00009316858,0.4751267,0.00007717701,0.00084453897,0.0010764477,0.00042578406,0.00018369082,0.0006383052],"genre_scores_gemma":[0.9712011,0.00006816396,0.022725305,0.000050529707,0.00007834321,0.000018293558,0.00007502676,0.000051314997,0.0057318984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99598247,0.0007085412,0.000491359,0.0019844575,0.00035369417,0.00047947557],"domain_scores_gemma":[0.992469,0.005160458,0.0005174621,0.0010417227,0.0005009354,0.00031042183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0022056403,0.0004419497,0.00064506795,0.00074133824,0.0005495749,0.00028173323,0.0011704204,0.00043075677,0.000107425076],"category_scores_gemma":[0.0023037011,0.00041008485,0.00036643218,0.00096220867,0.00046601216,0.00037413085,0.0018562762,0.0004969658,0.00014039195],"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":[0.001433982,0.000623885,0.015961858,0.00010988008,0.00030967855,0.00045561843,0.0029600675,0.91760015,0.012034392,0.033377588,0.014103592,0.0010293248],"study_design_scores_gemma":[0.0016490794,0.000400559,0.010048672,0.00019988757,0.00016066407,0.000008790402,0.00841536,0.87977767,0.0058618416,0.0921547,0.00043576537,0.0008870403],"about_ca_topic_score_codex":0.000114428905,"about_ca_topic_score_gemma":0.000027201128,"teacher_disagreement_score":0.4524014,"about_ca_system_score_codex":0.0002571251,"about_ca_system_score_gemma":0.00023281107,"threshold_uncertainty_score":0.9998351},"labels":[],"label_agreement":null},{"id":"W4361215490","doi":"10.1002/cjs.11761","title":"A class of space‐filling designs with low‐dimensional stratification and column orthogonality","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Orthogonality; Fractional factorial design; Stratification (seeds); Orthogonal array; Mathematics; Class (philosophy); Column (typography); Space (punctuation); Factorial experiment; Algorithm; Computer science; Geometry; Statistics; Artificial intelligence; Taguchi methods","score_opus":0.16567345518021082,"score_gpt":0.38838616936196885,"score_spread":0.22271271418175803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361215490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66497886,0.000189616,0.33259892,0.0005256069,0.00031998963,0.00019906336,0.00068704167,0.000006448716,0.00049447326],"genre_scores_gemma":[0.7466176,0.0000064632713,0.25309363,0.000048372884,0.000026617545,6.644886e-7,0.000006030946,0.000009980036,0.00019063386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9977991,0.00028753627,0.0007014874,0.00017779485,0.0008123922,0.00022170565],"domain_scores_gemma":[0.9961411,0.0016271629,0.00056374964,0.00018228072,0.0009359359,0.0005498054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029987937,0.000105173785,0.00029807692,0.00040837738,0.00013649187,0.00013303375,0.00023507877,0.000053366522,0.00018345613],"category_scores_gemma":[0.0019444588,0.000083091516,0.00003364483,0.0007279654,0.00034555927,0.00017649157,0.000013023926,0.00015896544,0.000012236982],"study_design_candidate":"observational","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":[0.0012085102,0.00023034053,0.19195974,0.0002269063,0.00056579796,0.0039107,0.012270688,0.05787993,0.13358796,0.3509728,0.137283,0.10990361],"study_design_scores_gemma":[0.0035403543,0.003941967,0.6135589,0.0006519241,0.0002571181,0.0011308792,0.014320042,0.10781589,0.024619434,0.22198029,0.0068908995,0.0012923259],"about_ca_topic_score_codex":0.0006821493,"about_ca_topic_score_gemma":0.0044034705,"teacher_disagreement_score":0.42159912,"about_ca_system_score_codex":0.000077533834,"about_ca_system_score_gemma":0.0016456263,"threshold_uncertainty_score":0.3388374},"labels":[],"label_agreement":null},{"id":"W4362591564","doi":"10.1007/s12561-023-09369-7","title":"Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies","year":2023,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Statistics; Covariate; Sampling (signal processing); Computer science; Robustness (evolution); Sample size determination; Trait; Econometrics; Mathematics; Data mining; Biology; Genetics","score_opus":0.6738625109802414,"score_gpt":0.6789437062369973,"score_spread":0.005081195256755877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362591564","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":[0.2590231,0.00042224623,0.73931974,0.00014237984,0.0004578,0.00048945734,0.000106182,0.000017826096,0.000021278125],"genre_scores_gemma":[0.32236898,0.00003774114,0.6774257,0.000021518315,0.0000105337385,0.00006613656,0.0000027347444,0.0000059153604,0.000060762384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918636,0.0037788143,0.00088250934,0.0005458506,0.002639513,0.00028972072],"domain_scores_gemma":[0.96576583,0.032339633,0.00051515555,0.00020473768,0.0011203943,0.000054258628],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08639104,0.0001258685,0.00031211463,0.00065456965,0.00020591554,0.00015506896,0.0002937795,0.000057268935,0.00001922712],"category_scores_gemma":[0.108783215,0.00010332357,0.000030437819,0.001485112,0.00026551407,0.00023312662,0.000102623344,0.00006044493,0.0000064913734],"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":[0.0002726194,0.00011482835,0.0017866155,0.000022140386,0.000044856206,0.0000016250445,0.005650919,0.08796117,0.20960079,0.00790491,0.00040176063,0.68623775],"study_design_scores_gemma":[0.00083452766,0.0002856819,0.014713103,0.000016214173,0.000044444656,9.788041e-7,0.0057147034,0.5859669,0.014332578,0.3779759,0.00001131411,0.00010364382],"about_ca_topic_score_codex":0.000018997762,"about_ca_topic_score_gemma":0.000041099785,"teacher_disagreement_score":0.6861341,"about_ca_system_score_codex":0.00032710045,"about_ca_system_score_gemma":0.0002632065,"threshold_uncertainty_score":0.9407527},"labels":[],"label_agreement":null},{"id":"W4367624121","doi":"10.28924/2291-8639-21-2023-40","title":"Random and Fixed Effects Selection for Weighted Ridge","year":2023,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Random effects model; Selection (genetic algorithm); Model selection; Fixed effects model; Applied mathematics; Statistics; Maximum likelihood; Mathematical optimization; Algorithm; Computer science; Artificial intelligence; Panel data","score_opus":0.05133078806435695,"score_gpt":0.45658732526441,"score_spread":0.40525653720005306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367624121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19254123,0.00018951026,0.8055779,0.0011816176,0.00010811492,0.00019350206,0.000023765735,0.00001240481,0.00017196717],"genre_scores_gemma":[0.95362747,0.0001895686,0.045222823,0.00013484822,0.0002657783,0.000067963745,0.0000148036925,0.0000066314365,0.0004701115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985922,0.000103863,0.0004901067,0.00017973638,0.0005552282,0.00007884666],"domain_scores_gemma":[0.99675083,0.0020210037,0.00036240922,0.00008457387,0.00069994805,0.000081242666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018731909,0.00007024761,0.00025337512,0.0009110849,0.00010844931,0.00021393251,0.00026510522,0.00003412568,0.000032286967],"category_scores_gemma":[0.00047042008,0.000051090024,0.0001961205,0.0012818158,0.00004789599,0.00018457854,0.00004678664,0.000059723025,0.000008831641],"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":[0.0010086928,0.00036776968,0.07632271,0.000016703192,0.0060956883,0.000009685464,0.00084797293,0.0027938695,0.21326615,0.036980353,0.012467007,0.64982337],"study_design_scores_gemma":[0.00809659,0.0005550214,0.1910825,0.000041616062,0.0022536463,0.00010445014,0.001001937,0.39572558,0.06730803,0.24780387,0.08547489,0.0005518646],"about_ca_topic_score_codex":0.000008503697,"about_ca_topic_score_gemma":0.000005317829,"teacher_disagreement_score":0.7610862,"about_ca_system_score_codex":0.00002221445,"about_ca_system_score_gemma":0.000020845577,"threshold_uncertainty_score":0.20833908},"labels":[],"label_agreement":null},{"id":"W4383742371","doi":"10.1016/j.jspi.2023.06.005","title":"Optimal designs for comparing curves in regression models with asymmetric errors","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Mathematics; Optimal design; Equivalence (formal languages); Particle swarm optimization; Mathematical optimization; Applied mathematics; Optimality criterion; Regression; Statistics; Discrete mathematics","score_opus":0.39553916756623175,"score_gpt":0.5122542929729154,"score_spread":0.1167151254066836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383742371","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11514615,0.00061263365,0.8832588,0.00010478109,0.00008041765,0.00010518121,0.000013165599,0.000010654306,0.00066820456],"genre_scores_gemma":[0.6760094,0.000048943428,0.32383698,0.00003640671,0.000015338494,0.000003386902,0.0000017467075,0.000006190468,0.000041619787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977881,0.0002250527,0.0007295199,0.00022349252,0.000779246,0.00025461317],"domain_scores_gemma":[0.9913929,0.0077413837,0.0003445426,0.000105095634,0.0002420333,0.00017405595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003833766,0.00012900181,0.00047496278,0.0006175806,0.00008885996,0.00013932673,0.00027804097,0.000048518497,0.000010611523],"category_scores_gemma":[0.0056158705,0.000077859855,0.0000330011,0.0009231809,0.00012206758,0.00050023326,0.00006642302,0.00025920427,0.0000036864928],"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":[0.0046416847,0.00036732925,0.18238541,0.00030121952,0.00010010625,0.0010279282,0.004913595,0.62207377,0.003128597,0.03695815,0.031730454,0.11237178],"study_design_scores_gemma":[0.0012485593,0.0015855356,0.060726732,0.0015663989,0.000022747423,0.00008774919,0.0018224348,0.8951863,0.000500617,0.036913976,0.00010050418,0.00023846547],"about_ca_topic_score_codex":0.000008391044,"about_ca_topic_score_gemma":5.941075e-7,"teacher_disagreement_score":0.56086326,"about_ca_system_score_codex":0.000027970562,"about_ca_system_score_gemma":0.00010385643,"threshold_uncertainty_score":0.67231274},"labels":[],"label_agreement":null},{"id":"W4385324378","doi":"10.5539/ijsp.v12n4p40","title":"An Approximate Confidence Interval for the Variance of Random Effects of One-Way Analysis of Variance in the Completely Randomized Design","year":2023,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variance (accounting); Confidence interval; Mathematics; Statistics; Monte Carlo method; Coverage probability; Variance-based sensitivity analysis; One-way analysis of variance; Interval (graph theory); Analysis of variance; CDF-based nonparametric confidence interval; Robust confidence intervals; Combinatorics","score_opus":0.1477719726789253,"score_gpt":0.442113446215026,"score_spread":0.29434147353610074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385324378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05234926,0.00017762896,0.94601274,0.00022689,0.0002453624,0.00073110446,0.00023669681,0.0000016292765,0.00001867502],"genre_scores_gemma":[0.75999755,0.00007850515,0.23984548,0.00002924491,0.000016087311,0.000022754612,0.0000028641903,0.0000033097683,0.0000041984667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943742,0.0024003882,0.001682678,0.00019558614,0.0012369428,0.00011015944],"domain_scores_gemma":[0.9415551,0.055033717,0.001505061,0.00026854975,0.0016021177,0.000035470668],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.027578142,0.000106474254,0.0009230526,0.0002989835,0.000037408423,0.00007976264,0.0011367851,0.000036086025,0.000028914272],"category_scores_gemma":[0.01818537,0.000056781646,0.00023357291,0.000688143,0.0005866824,0.00017209588,0.000065966655,0.00012179514,2.1379155e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.18950962,0.0018063922,0.005405499,0.00040497218,0.00557904,0.00003856592,0.019483635,0.116668604,0.06975296,0.5276392,0.0002959689,0.06341556],"study_design_scores_gemma":[0.018625244,0.0004676096,0.029470002,0.00009507522,0.00040044342,0.0000061639894,0.0003094458,0.48097593,0.005005868,0.46455982,0.000009554847,0.00007485006],"about_ca_topic_score_codex":0.00009182277,"about_ca_topic_score_gemma":0.000014013809,"teacher_disagreement_score":0.7076483,"about_ca_system_score_codex":0.000025253128,"about_ca_system_score_gemma":0.000089929636,"threshold_uncertainty_score":0.9900849},"labels":[],"label_agreement":null},{"id":"W4385800516","doi":"10.1007/s42519-023-00340-9","title":"Robust Optimal Design When Missing Data Happen at Random","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"MacEwan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Missing data; Statistics; Mathematical optimization","score_opus":0.4908837358598463,"score_gpt":0.5233044670330016,"score_spread":0.032420731173155304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385800516","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":[0.00060856296,0.0007462105,0.99074423,0.0019251279,0.00036828304,0.0001368941,0.00006701353,0.000015681191,0.0053879786],"genre_scores_gemma":[0.012391808,0.0001878648,0.98489326,0.00056423596,0.00018135614,0.0000012574507,0.000006152843,0.00001997589,0.0017540789],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9861217,0.010744635,0.0010388893,0.000405454,0.001389091,0.00030021544],"domain_scores_gemma":[0.83693296,0.16132447,0.00068557385,0.0004602413,0.00028970197,0.00030705272],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.083325826,0.0001640458,0.00048694602,0.00020142303,0.0003230995,0.00056987716,0.000969418,0.000081982624,0.0021249142],"category_scores_gemma":[0.17949447,0.000111008805,0.00005515276,0.00037910143,0.00033590477,0.0021648821,0.0006257373,0.00039010594,0.00042847323],"study_design_candidate":"theoretical_or_conceptual","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":[0.11473118,0.00040664952,0.000074711344,0.000031861095,0.00050258025,0.004328692,0.004226556,0.0077745263,0.019731792,0.16103339,0.28255618,0.40460187],"study_design_scores_gemma":[0.005010348,0.0017354472,0.00055426,0.00011027043,0.0005049699,0.00605807,0.009566826,0.07267097,0.0016933144,0.75118345,0.15035495,0.00055710674],"about_ca_topic_score_codex":0.0000026548555,"about_ca_topic_score_gemma":8.979519e-8,"teacher_disagreement_score":0.59015006,"about_ca_system_score_codex":0.000039891012,"about_ca_system_score_gemma":0.00011510194,"threshold_uncertainty_score":0.9987873},"labels":[],"label_agreement":null},{"id":"W4385884614","doi":"10.1007/978-981-99-2240-6_8","title":"Propositions for Quantification Theory","year":2023,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Epistemology; Management science; Computer science; Philosophy; Engineering","score_opus":0.5321277403936201,"score_gpt":0.5207459977069897,"score_spread":0.011381742686630392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385884614","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007471289,0.00083263166,0.5107277,0.00017736664,0.0040994394,0.0041765426,0.001558225,0.0005244141,0.47782895],"genre_scores_gemma":[0.0005547752,0.000073511525,0.07238748,0.000047239835,0.00016168458,0.0003465368,0.00020397759,0.00014534226,0.92607945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957212,0.00013031437,0.001061343,0.00094493007,0.0018320702,0.00031013932],"domain_scores_gemma":[0.99219286,0.0049506365,0.000638229,0.0011788426,0.00088284444,0.0001565537],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0063313916,0.00035396958,0.000537392,0.0023143373,0.00028495872,0.0003804742,0.001048392,0.00053445104,0.0007514895],"category_scores_gemma":[0.0043007312,0.00030136108,0.00049734587,0.0009587934,0.00019420886,0.000206051,0.00018021488,0.0003114462,0.003314532],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035429155,0.000052830408,0.000019656614,0.000007345781,0.000014952317,0.000006664428,0.000044980716,0.000005494946,0.0012350716,0.79953295,0.013146234,0.18589838],"study_design_scores_gemma":[0.00034167583,0.0003850088,0.00051736506,0.000055325563,0.00028185712,0.000011917479,0.00013759169,0.00016052477,0.0031161811,0.7596642,0.23459502,0.0007333824],"about_ca_topic_score_codex":0.0000026767054,"about_ca_topic_score_gemma":0.0000016917119,"teacher_disagreement_score":0.4482505,"about_ca_system_score_codex":0.00018118543,"about_ca_system_score_gemma":0.0001608067,"threshold_uncertainty_score":0.99994385},"labels":[],"label_agreement":null},{"id":"W4386421016","doi":"10.1007/s00362-023-01469-2","title":"Bayesian and maximin A-optimal designs for spline regression models with unknown knots","year":2023,"lang":"en","type":"article","venue":"Statistical Papers","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Optimal design; Mathematical optimization; Mathematics; Bayesian probability; Optimality criterion; Prior probability; Quadratic equation; Algorithm; Statistics","score_opus":0.18987846769652303,"score_gpt":0.4530553777483672,"score_spread":0.2631769100518442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386421016","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":[0.0059092194,0.000059911843,0.9852228,0.00048659396,0.00012851029,0.00054517295,0.00019227412,0.00011541818,0.0073400787],"genre_scores_gemma":[0.28995407,0.0000126052,0.70728993,0.00013762609,0.000040557086,0.00007183778,0.000030048246,0.000035855683,0.0024274655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969799,0.00028631842,0.0004716936,0.0007938388,0.0009586279,0.0005096474],"domain_scores_gemma":[0.99402386,0.0050053173,0.00010456467,0.00037026822,0.00013004607,0.00036591582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019264343,0.00025272457,0.0004033843,0.00019511626,0.0002496633,0.00019155382,0.00031293338,0.000103388644,0.00036804785],"category_scores_gemma":[0.0018630035,0.00015865477,0.00005559608,0.0006223091,0.0003998437,0.00023169431,0.00012694135,0.00013824586,0.000079618454],"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":[0.003400972,0.00020250282,0.00034650776,0.00007058114,0.00008742167,0.00029370104,0.0016189436,0.012056429,0.051662713,0.256333,0.026641013,0.64728624],"study_design_scores_gemma":[0.0018105579,0.001493085,0.0015715288,0.00007089534,0.00005081166,0.000029967714,0.0016458188,0.840247,0.0023941547,0.14472173,0.005383636,0.00058084755],"about_ca_topic_score_codex":0.0000109879875,"about_ca_topic_score_gemma":0.000008711402,"teacher_disagreement_score":0.82819057,"about_ca_system_score_codex":0.00003962933,"about_ca_system_score_gemma":0.0000794345,"threshold_uncertainty_score":0.6469754},"labels":[],"label_agreement":null},{"id":"W4387641884","doi":"10.1111/stan.12328","title":"Asymptotic comparison of negative multinomial and multivariate normal experiments","year":2023,"lang":"en","type":"article","venue":"Statistica Neerlandica","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Multinomial distribution; Mathematics; Multivariate statistics; Multivariate normal distribution; Statistics; Logarithm; Hellinger distance; Covariance; Mathematical analysis","score_opus":0.19219449486733747,"score_gpt":0.504022000913631,"score_spread":0.31182750604629356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387641884","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82625526,0.00016853891,0.16087164,0.00020838744,0.001014887,0.0008500142,0.0006161381,0.00019569279,0.009819412],"genre_scores_gemma":[0.859707,0.000003889159,0.13965935,0.0000315026,0.000034964352,0.00002353893,0.000014619243,0.00001853132,0.00050659163],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9966069,0.0005052645,0.00087970396,0.00051860017,0.0011052331,0.0003842898],"domain_scores_gemma":[0.99394345,0.0050083217,0.0003381173,0.00035096027,0.00013023164,0.000228945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001443146,0.00020883484,0.0005700621,0.00035137404,0.000163637,0.00011306497,0.0003970056,0.00008504623,0.000654608],"category_scores_gemma":[0.0043835514,0.00016664196,0.00006273715,0.0007477246,0.00039874387,0.00018465733,0.00029971052,0.00015314734,0.00040650243],"study_design_candidate":"bench_or_experimental","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":[0.0028342435,0.0011295478,0.15395199,0.000093709146,0.00041311557,0.0001826997,0.069397256,0.0013698678,0.47100988,0.0340052,0.052223366,0.21338913],"study_design_scores_gemma":[0.006436708,0.0016986438,0.43637687,0.000078613375,0.00007827945,0.000010809393,0.014109512,0.35299134,0.15468553,0.031036882,0.0015575462,0.0009392738],"about_ca_topic_score_codex":0.00015088818,"about_ca_topic_score_gemma":0.000004781801,"teacher_disagreement_score":0.35162148,"about_ca_system_score_codex":0.000031385156,"about_ca_system_score_gemma":0.0000541108,"threshold_uncertainty_score":0.71674985},"labels":[],"label_agreement":null},{"id":"W4388429898","doi":"10.2991/978-94-6463-258-3_79","title":"RS3 Feasibility Modelling of a Semi-Circular Notched Sample Spalling Experiment","year":2023,"lang":"en","type":"book-chapter","venue":"Atlantis highlights in engineering/Atlantis Highlights in Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Dalhousie University","funders":"","keywords":"Spall; Sample (material); Materials science; Structural engineering; Composite material; Forensic engineering; Engineering; Physics; Thermodynamics","score_opus":0.11414915261832694,"score_gpt":0.33099698328898713,"score_spread":0.2168478306706602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388429898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065179616,0.011526445,0.8556909,0.00083229085,0.0263664,0.008445375,0.0009237837,0.0037029588,0.027332209],"genre_scores_gemma":[0.767792,0.0089568235,0.17934257,0.000048637918,0.0016131531,0.00047451814,0.000544284,0.0019880799,0.039239977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9844918,0.00020021413,0.005528408,0.003570455,0.004059154,0.0021499535],"domain_scores_gemma":[0.98946816,0.0050387,0.0012069241,0.0032384687,0.00042389898,0.00062387227],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0045116968,0.002466382,0.0043873396,0.0050350036,0.00014708868,0.00037253372,0.0032053092,0.0018429409,0.0003675641],"category_scores_gemma":[0.001216797,0.002335095,0.0011302237,0.0020056767,0.00021979296,0.0008077463,0.0009880873,0.0018290894,0.0002832648],"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":[0.00009967688,0.00017483125,0.00034687473,0.0006223662,0.00024605633,0.0010166804,0.0010410078,0.8197745,0.013091589,0.16321836,0.00023967776,0.00012834687],"study_design_scores_gemma":[0.0032662502,0.00037247472,0.0012027252,0.005498453,0.0001909493,0.00014030558,0.00009547469,0.8372391,0.049506526,0.006048104,0.09099002,0.005449623],"about_ca_topic_score_codex":0.0007861303,"about_ca_topic_score_gemma":0.00012199846,"teacher_disagreement_score":0.70261234,"about_ca_system_score_codex":0.0012949315,"about_ca_system_score_gemma":0.00023166728,"threshold_uncertainty_score":0.9994529},"labels":[],"label_agreement":null},{"id":"W4389063115","doi":"10.51387/23-nejsds13edi","title":"Editorial. Design and Analysis of Experiments for Data Science","year":2023,"lang":"en","type":"article","venue":"The New England Journal of Statistics in Data Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Queen's University","funders":"","keywords":"Data science; Statistical analysis; New england; Library science; Computer science; Statistics; Political science; Mathematics; Law","score_opus":0.39248641983575977,"score_gpt":0.5391577650210972,"score_spread":0.14667134518533748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389063115","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":[0.008192393,0.00021507767,0.97877055,0.00019196214,0.011134857,0.00024981349,0.0011892356,0.0000049223263,0.00005120795],"genre_scores_gemma":[0.07311686,0.0002670997,0.92464614,0.00002722377,0.0018563069,0.0000016034555,0.000026470785,0.000007660106,0.000050662733],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99421805,0.00032660202,0.0010190525,0.0006292117,0.0034468928,0.00036020615],"domain_scores_gemma":[0.98633885,0.009876181,0.0007531497,0.0020450298,0.0007603096,0.00022648678],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.072105676,0.000116698924,0.0004362185,0.0015082777,0.00031226463,0.0004901935,0.0101941535,0.000029066938,0.000021063597],"category_scores_gemma":[0.044099625,0.000069074835,0.000022542066,0.008318181,0.0019342473,0.002735078,0.0025588037,0.00015612294,0.0000027204228],"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":[0.0012147019,0.00022906114,0.003284176,0.000015888249,0.00025395965,0.000033319153,0.008037365,0.014253333,0.27055597,0.01348602,0.44034892,0.24828728],"study_design_scores_gemma":[0.0025442743,0.0004621377,0.006650323,0.000038793773,0.0002874329,0.000019403686,0.0016940738,0.927683,0.010348177,0.03336464,0.016675228,0.00023250963],"about_ca_topic_score_codex":0.000047604655,"about_ca_topic_score_gemma":0.000009379649,"teacher_disagreement_score":0.9134297,"about_ca_system_score_codex":0.000045984954,"about_ca_system_score_gemma":0.0011847633,"threshold_uncertainty_score":0.9951612},"labels":[],"label_agreement":null},{"id":"W4391052357","doi":"10.1093/biomet/asae001","title":"A note on minimax robustness of designs against correlated or heteroscedastic responses","year":2024,"lang":"en","type":"article","venue":"Biometrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Minimax; Heteroscedasticity; Robustness (evolution); Econometrics; Statistics; Applied mathematics; Mathematical optimization","score_opus":0.3303164790836578,"score_gpt":0.4982238210605573,"score_spread":0.1679073419768995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391052357","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7095792,0.0019455067,0.28008294,0.00021898762,0.0042405445,0.0006415122,0.00018339165,0.0002770989,0.0028307505],"genre_scores_gemma":[0.9245287,0.00002708254,0.068593785,0.000111483896,0.00008872561,0.00002211433,0.0000048026304,0.000045410103,0.006577862],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99522907,0.0008888446,0.0009812824,0.0008260373,0.0016847895,0.00038996487],"domain_scores_gemma":[0.9849685,0.013672857,0.00019970041,0.00074369594,0.00021732986,0.00019790433],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0042483225,0.00028730585,0.00053631415,0.004004262,0.00009295295,0.00035497802,0.0008668502,0.00020634811,0.00067584944],"category_scores_gemma":[0.015243695,0.00018622488,0.00024317559,0.0114362305,0.0002882026,0.00026994463,0.00017376145,0.00020785397,0.0007158853],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042189253,0.00043757525,0.00018049472,0.00005851371,0.00008923907,0.00044465944,0.0006163997,0.0019128395,0.7400633,0.00031038976,0.009591861,0.24207579],"study_design_scores_gemma":[0.0018698614,0.0058087115,0.0041250144,0.00083463546,0.00009098805,0.00013478598,0.0008350886,0.20191003,0.75597,0.00049170165,0.026781065,0.0011480915],"about_ca_topic_score_codex":0.0000076258816,"about_ca_topic_score_gemma":9.878688e-7,"teacher_disagreement_score":0.2409277,"about_ca_system_score_codex":0.00014768193,"about_ca_system_score_gemma":0.00025560634,"threshold_uncertainty_score":0.99305135},"labels":[],"label_agreement":null},{"id":"W4391714737","doi":"10.1007/978-3-031-17299-1_208","title":"Binary Response","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Psychology","score_opus":0.37127820127617145,"score_gpt":0.48908975759177586,"score_spread":0.11781155631560442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391714737","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000042268515,0.00018428195,0.0012429166,0.0005991983,0.0012001168,0.00024865777,0.000033967983,0.00030690987,0.9961417],"genre_scores_gemma":[0.00009485467,0.00002701851,0.031839743,0.00041614487,0.000116072886,0.000008752895,0.000004350598,0.000100511636,0.96739256],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99525243,0.00037463175,0.0008444816,0.0009597002,0.0022955914,0.00027313983],"domain_scores_gemma":[0.99050903,0.007438474,0.0002656769,0.0013902904,0.00020185132,0.00019466304],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.009133064,0.0003562024,0.00057985634,0.00086638075,0.000103109385,0.0002365596,0.0012731954,0.00042347677,0.022947688],"category_scores_gemma":[0.003039533,0.00025757347,0.00036702678,0.00019981516,0.00020905673,0.00015532247,0.00065808685,0.00034625272,0.081545606],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013081627,0.000017205064,0.000003598512,0.0000029770276,0.00004827335,0.0005829588,0.00014863534,0.000019283607,0.009024884,0.46663412,0.48485392,0.037355993],"study_design_scores_gemma":[0.00012457672,0.00025745475,0.00008858547,0.000029615569,0.000009143812,0.000013532453,0.00007777772,0.00015160616,0.0006981076,0.28400666,0.71417034,0.00037261154],"about_ca_topic_score_codex":0.000005001292,"about_ca_topic_score_gemma":0.0000016038928,"teacher_disagreement_score":0.22931643,"about_ca_system_score_codex":0.00008674768,"about_ca_system_score_gemma":0.00015424285,"threshold_uncertainty_score":0.99998766},"labels":[],"label_agreement":null},{"id":"W4391727415","doi":"10.1007/978-3-031-17299-1_982","title":"Factorial Design","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Factorial experiment; Mathematics; Computer science; Statistics","score_opus":0.5905598012494433,"score_gpt":0.49456100081554016,"score_spread":0.09599880043390313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391727415","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.8880891e-7,0.000054212283,0.22609465,0.0000527763,0.004572404,0.00032404807,0.000020394902,0.00023617524,0.76864517],"genre_scores_gemma":[0.000028616067,0.00002136238,0.111595705,0.0001192694,0.00076760526,0.000009204901,0.000004804563,0.000098016324,0.8873554],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99480826,0.00020749016,0.00090614596,0.0009859055,0.0027845155,0.000307704],"domain_scores_gemma":[0.99272037,0.005436357,0.00032053253,0.0010716214,0.00024302115,0.00020808072],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0044329194,0.00041881265,0.0006939561,0.0005024036,0.00009908293,0.00038901155,0.0013476176,0.0005445663,0.023785552],"category_scores_gemma":[0.0019378968,0.000295678,0.0003719129,0.00013633716,0.00014711087,0.00017787793,0.00039622598,0.0003647753,0.05532108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007556646,0.000007962256,0.0000010649592,0.0000014689201,0.000046707388,0.000064365304,0.00009050299,0.00004345808,0.0010560029,0.6294196,0.3312619,0.037931357],"study_design_scores_gemma":[0.00013199095,0.00012534815,0.000002852528,0.000012590639,0.000011464997,0.00000335579,0.00001898612,0.00015973825,0.001414212,0.60399,0.39379293,0.0003365163],"about_ca_topic_score_codex":0.000010063342,"about_ca_topic_score_gemma":0.00000206197,"teacher_disagreement_score":0.11871026,"about_ca_system_score_codex":0.00009170865,"about_ca_system_score_gemma":0.00015119511,"threshold_uncertainty_score":0.9999495},"labels":[],"label_agreement":null},{"id":"W4391727538","doi":"10.1007/978-3-031-17299-1_967","title":"Experimental Design","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science","score_opus":0.49280400042793965,"score_gpt":0.48754331427103403,"score_spread":0.005260686156905614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391727538","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000016828583,0.00048670877,0.071936436,0.000086361375,0.001369893,0.0005029103,0.000015497724,0.00036645166,0.9252341],"genre_scores_gemma":[0.00022862508,0.000014353315,0.13484861,0.00029466514,0.00019422971,0.000028054217,0.0000054213524,0.00014357033,0.8642425],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942495,0.00021577113,0.0010568775,0.0012484248,0.002842647,0.00038673216],"domain_scores_gemma":[0.99497783,0.002929727,0.00035084662,0.0012994297,0.00017611771,0.0002660595],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0039101453,0.00054274296,0.00077793223,0.00062701694,0.00014114263,0.0004025298,0.0015346349,0.00047469343,0.033015937],"category_scores_gemma":[0.0006288814,0.00040900224,0.00044107673,0.0001605911,0.00024830195,0.00023188851,0.00061209337,0.00035088143,0.065209664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085195825,0.000034862576,8.212267e-7,0.0000018492863,0.00006578211,0.00017031611,0.000219222,0.00006731636,0.015071016,0.6639995,0.30104023,0.019243889],"study_design_scores_gemma":[0.00042815748,0.00058341067,0.0000053188332,0.000054247288,0.000024987727,0.00003671575,0.00042505647,0.001004244,0.112253234,0.50831497,0.3756515,0.0012181682],"about_ca_topic_score_codex":0.0000071052714,"about_ca_topic_score_gemma":6.136539e-7,"teacher_disagreement_score":0.15568453,"about_ca_system_score_codex":0.00014738982,"about_ca_system_score_gemma":0.00012245671,"threshold_uncertainty_score":0.9998362},"labels":[],"label_agreement":null},{"id":"W4391729267","doi":"10.1007/978-3-031-17299-1_2866","title":"Statistical Experimental Design","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Computer science","score_opus":0.4801238961375549,"score_gpt":0.5028730631552649,"score_spread":0.022749167017710015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391729267","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.456763e-7,0.00018831542,0.31158325,0.000050596565,0.00085297413,0.00037841915,0.000060500417,0.00022451012,0.6866606],"genre_scores_gemma":[0.00019506675,0.000010830891,0.2905614,0.00020574563,0.00013704532,0.000023429406,0.000013275861,0.00011368134,0.7087395],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9937405,0.00029789316,0.0011522633,0.0012974446,0.00307911,0.00043277894],"domain_scores_gemma":[0.99189013,0.0062254258,0.00028357655,0.001080098,0.00016996873,0.00035078428],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0036998603,0.00054400734,0.00083528104,0.00049646467,0.00013898166,0.00041849655,0.0012335649,0.00044305658,0.0519963],"category_scores_gemma":[0.0011796807,0.000411929,0.0002682454,0.00012645597,0.00038137654,0.00018280032,0.0005557155,0.00040139418,0.055606224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006917638,0.000025455522,8.674935e-7,0.000001400982,0.000035103363,0.00017962602,0.00008355265,0.000023047738,0.0019995496,0.78990394,0.19581126,0.011867018],"study_design_scores_gemma":[0.0004526718,0.0007592838,0.00001676326,0.000041568364,0.000031937077,0.000041254254,0.00034499564,0.0018245099,0.018553834,0.77511364,0.20167531,0.0011442171],"about_ca_topic_score_codex":0.000009495975,"about_ca_topic_score_gemma":9.943789e-7,"teacher_disagreement_score":0.02207894,"about_ca_system_score_codex":0.00015792662,"about_ca_system_score_gemma":0.00015543206,"threshold_uncertainty_score":0.9998332},"labels":[],"label_agreement":null},{"id":"W4392435127","doi":"10.1016/j.jmva.2024.105299","title":"Variable selection in multivariate regression models with measurement error in covariates","year":2024,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Covariate; Spurious relationship; Mathematics; Multivariate statistics; Observational error; Feature selection; Statistics; Errors-in-variables models; Regression analysis; Regression; Variable (mathematics); Penalty method; Variables; Econometrics; Computer science; Mathematical optimization; Artificial intelligence","score_opus":0.16308878658946238,"score_gpt":0.4417963584206746,"score_spread":0.2787075718312122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392435127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062079206,0.0012565785,0.93451613,0.00039752797,0.00035172427,0.00029441726,0.000006614321,0.000033934542,0.0010638428],"genre_scores_gemma":[0.6734105,0.000016116699,0.32624733,0.000031557774,0.00006007116,0.000010147455,0.0000010542157,0.000023842746,0.00019936544],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99127156,0.0019754556,0.0023093715,0.00075843325,0.0031989997,0.00048617425],"domain_scores_gemma":[0.99608254,0.0013627453,0.00089781196,0.00035994124,0.0010889613,0.00020798034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02060952,0.0003797312,0.0012871351,0.0048237583,0.000093839524,0.0005458309,0.00072115427,0.00020916176,0.00035824743],"category_scores_gemma":[0.001963568,0.00023037287,0.00044177865,0.01127899,0.000059449496,0.0019080135,0.000111297224,0.0007631598,0.000021327605],"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":[0.0012752126,0.00057770027,0.006861668,0.0000193295,0.0013351422,0.0002878746,0.0027756495,0.8533995,0.12247669,0.005967206,0.00011473839,0.004909286],"study_design_scores_gemma":[0.0016960592,0.00030669666,0.018287046,0.0004732005,0.0005984097,0.00004298531,0.00078814983,0.94982314,0.0041100252,0.02330079,0.00025286054,0.00032062933],"about_ca_topic_score_codex":0.0019597525,"about_ca_topic_score_gemma":0.0004872004,"teacher_disagreement_score":0.6113313,"about_ca_system_score_codex":0.00088271446,"about_ca_system_score_gemma":0.00048815788,"threshold_uncertainty_score":0.9394333},"labels":[],"label_agreement":null},{"id":"W4392565704","doi":"10.1093/biomtc/ujad038","title":"Two-phase designs with failure time processes subject to nonsusceptibility","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"University of Waterloo","funders":"","keywords":"Covariate; Accelerated failure time model; Bivariate analysis; Proportional hazards model; Computer science; Logistic regression; Statistics; Fraction (chemistry); Phase (matter); Multivariate statistics; Econometrics; Mathematics; Machine learning","score_opus":0.1900245702038443,"score_gpt":0.48801193031734763,"score_spread":0.29798736011350335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392565704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3747536,0.0027963256,0.6094777,0.00088025036,0.00069166545,0.0014840678,0.00023334526,0.0006777167,0.009005362],"genre_scores_gemma":[0.648712,0.00000475344,0.34804565,0.00015441627,0.0001121564,0.00004240145,0.0000065681625,0.000040081064,0.002881964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99532276,0.00035762417,0.000586462,0.0011619296,0.0020949072,0.00047632187],"domain_scores_gemma":[0.9936448,0.0043709055,0.00009294442,0.0008429327,0.00063849514,0.0004099746],"candidate_categories":["metaresearch","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0050921915,0.0002994994,0.00041168183,0.003809037,0.00014411613,0.0011989346,0.0009974712,0.00010561733,0.0011822729],"category_scores_gemma":[0.010549194,0.00019433587,0.00010598033,0.04135369,0.00016822036,0.00055762345,0.00021246153,0.00017329643,0.0041066837],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007537545,0.0012304966,0.0027628178,0.00017258569,0.000120587385,0.0003778357,0.001786819,0.00019120605,0.5676083,0.0006456804,0.051317245,0.3730327],"study_design_scores_gemma":[0.0037905308,0.011430477,0.0020520738,0.00031372329,0.00017970422,0.00028551323,0.0025632887,0.011713754,0.6886192,0.005254316,0.2710436,0.002753801],"about_ca_topic_score_codex":0.000023137605,"about_ca_topic_score_gemma":0.000011155293,"teacher_disagreement_score":0.3702789,"about_ca_system_score_codex":0.00018353651,"about_ca_system_score_gemma":0.0004718147,"threshold_uncertainty_score":0.99983793},"labels":[],"label_agreement":null},{"id":"W4393065619","doi":"10.1016/j.ejca.2024.113697","title":"A Multicountry Discrete Choice Experiment (DCE) to Understand Patients’ Preferences for HR+/HER2− Early Breast Cancer (EBC) Treatments","year":2024,"lang":"en","type":"article","venue":"European Journal of Cancer","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Canadian Breast Cancer Network; Jewish General Hospital","funders":"","keywords":"Breast cancer; Medicine; Oncology; Internal medicine; Cancer","score_opus":0.13649237935061648,"score_gpt":0.4587046145328622,"score_spread":0.32221223518224573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393065619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95528585,0.011303052,0.021968555,0.0014727645,0.0046467357,0.00090227165,0.0005650393,0.0000468655,0.0038088842],"genre_scores_gemma":[0.9830293,0.00020031739,0.011833934,0.0003642682,0.0008266508,0.000034605862,0.0000014835432,0.000080883125,0.0036285555],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99553657,0.000708289,0.0010908619,0.00057358213,0.0016588598,0.0004318425],"domain_scores_gemma":[0.9974565,0.00069092144,0.00043526458,0.00033221097,0.0006517561,0.0004333369],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019594764,0.0003342152,0.0004826263,0.00033280387,0.00016005561,0.0008499092,0.00087839016,0.00003700295,0.0010136196],"category_scores_gemma":[0.000308846,0.00021531327,0.00030442726,0.00053421775,0.000107422886,0.00079529354,0.00013773987,0.00020977361,0.000093791765],"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":[0.0035843686,0.00070022803,0.048709918,0.000083150604,0.0015626181,0.00018763094,0.0338907,0.0031930814,0.036093142,0.00024892643,0.05872641,0.8130198],"study_design_scores_gemma":[0.010454325,0.0075956685,0.70735955,0.0028912406,0.00075427274,0.00006398432,0.009339661,0.001898758,0.030989086,0.0013432356,0.22529902,0.0020112186],"about_ca_topic_score_codex":0.00017993017,"about_ca_topic_score_gemma":0.00003420854,"teacher_disagreement_score":0.81100863,"about_ca_system_score_codex":0.0006051898,"about_ca_system_score_gemma":0.00024310852,"threshold_uncertainty_score":0.99989957},"labels":[],"label_agreement":null},{"id":"W4399322744","doi":"10.1007/s11222-024-10436-2","title":"Jittering and clustering: strategies for the construction of robust designs","year":2024,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence; Data mining; Mathematics","score_opus":0.22412019715494208,"score_gpt":0.4466583959822138,"score_spread":0.22253819882727172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399322744","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":[0.014107868,0.0008895702,0.9842318,0.000052965595,0.0003396019,0.000114637216,0.00003260185,0.000013479384,0.00021747197],"genre_scores_gemma":[0.47955406,0.0000144681035,0.5203715,0.000009457374,0.000028710232,0.0000013083884,4.17275e-7,0.0000040459654,0.000016066426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992415,0.000057856905,0.00025131178,0.00018727453,0.00016632686,0.00009575825],"domain_scores_gemma":[0.9963043,0.0034632487,0.00006274997,0.000082445105,0.000062240564,0.000025048295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011384254,0.00006594617,0.00011670312,0.00005114919,0.00015166836,0.00051330676,0.00008782663,0.000018798846,0.000008760069],"category_scores_gemma":[0.00019985937,0.000042995987,0.000016177397,0.00010893035,0.00016962229,0.00007844993,0.00009558321,0.000048953043,4.4753483e-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":[0.000018495142,0.0000039313804,0.000118536846,0.0000817302,0.000024168372,0.0000029112086,0.0018264846,0.006920354,0.007395909,0.26618335,0.0002924754,0.7171317],"study_design_scores_gemma":[0.000067423796,0.000075965814,0.00038393136,0.000039576673,0.0000114781315,0.000022762111,0.004951209,0.92267656,0.0003758776,0.071054496,0.00028546757,0.00005524487],"about_ca_topic_score_codex":0.000014363889,"about_ca_topic_score_gemma":0.0000025530908,"teacher_disagreement_score":0.9157562,"about_ca_system_score_codex":0.0000057253505,"about_ca_system_score_gemma":0.000027958085,"threshold_uncertainty_score":0.4949829},"labels":[],"label_agreement":null},{"id":"W4399330943","doi":"10.52933/jdssv.v4i3.83","title":"An Efficient Way to Find Optimal Crossover Designs Using CVX for Precision Medicine","year":2024,"lang":"en","type":"article","venue":"Journal of Data Science Statistics and Visualisation","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Ontario Medical Association","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Human Genome Research Institute","keywords":"Crossover; Crossover study; Optimal design; Mathematical optimization; Regular polygon; Construct (python library); Computer science; Convex optimization; Algorithm; Mathematics; Medicine; Artificial intelligence; Machine learning","score_opus":0.5004361897846052,"score_gpt":0.6208016945122043,"score_spread":0.1203655047275991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399330943","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":[0.20920868,0.00014956265,0.7890254,0.00011997206,0.00081287295,0.00020358349,0.0004584463,0.00000498123,0.000016450598],"genre_scores_gemma":[0.3944041,0.000009566946,0.60529816,0.000058591522,0.0001869779,9.013856e-7,0.000012375813,0.000008048167,0.000021295042],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958621,0.00019753315,0.00094859244,0.00058366376,0.002152477,0.00025562764],"domain_scores_gemma":[0.99549025,0.0024139027,0.00035217626,0.00047890475,0.00092158257,0.00034319115],"candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.021281537,0.00013475458,0.0002798651,0.000726102,0.00037254344,0.0013849548,0.0011856285,0.000040090334,0.00009130341],"category_scores_gemma":[0.008488682,0.000087830565,0.00002509314,0.0011415152,0.00046833052,0.0025421437,0.00023525173,0.000107199216,0.0000048102065],"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":[0.00037241488,0.00012180344,0.00007558949,0.00002126648,0.000013596872,0.000018324276,0.005091198,0.0230985,0.7017424,0.022345722,0.0057431883,0.24135597],"study_design_scores_gemma":[0.00033629627,0.001475871,0.0018151272,0.00010527362,0.000034503235,0.000058581856,0.0011718324,0.9832632,0.0050872536,0.005054726,0.0014677076,0.00012965832],"about_ca_topic_score_codex":0.000017654023,"about_ca_topic_score_gemma":0.0000013568423,"teacher_disagreement_score":0.96016467,"about_ca_system_score_codex":0.00011909458,"about_ca_system_score_gemma":0.00033650623,"threshold_uncertainty_score":0.99986327},"labels":[],"label_agreement":null},{"id":"W4402146830","doi":"10.28924/2291-8639-22-2024-150","title":"Finding Robust Response Surface Designs With Blocking Using a Model-Weighted A-Optimality Criterion","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Rajamangala University of Technology Srivijaya","keywords":"Mathematics; Blocking (statistics); Response surface methodology; Mathematical optimization; Statistics","score_opus":0.22823889434991934,"score_gpt":0.4875128794081905,"score_spread":0.25927398505827115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402146830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4017832,0.00030569185,0.5973087,0.0003967049,0.000036881564,0.000055242053,0.000016678283,0.0000090875,0.00008778399],"genre_scores_gemma":[0.6925116,0.000026648342,0.3072091,0.00004017032,0.0000686203,0.000003114988,0.0000020302496,0.000008243299,0.00013043983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971794,0.00032057182,0.0007962573,0.0003401575,0.0012327229,0.00013088835],"domain_scores_gemma":[0.9972066,0.0011508894,0.00040554392,0.00021771106,0.0008963048,0.00012289967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042769974,0.00013641448,0.00031875653,0.0009974326,0.00015058645,0.00094017625,0.0005516529,0.000051133626,0.000115834],"category_scores_gemma":[0.00017254917,0.00009506803,0.0002472068,0.0017456915,0.000101430225,0.00058135024,0.00009122653,0.00018307375,0.0000049100645],"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":[0.00066799164,0.00013254664,0.004257824,0.000003986974,0.0016525104,0.000064778484,0.0007982864,0.7987238,0.18009818,0.0038713438,0.00009090553,0.009637809],"study_design_scores_gemma":[0.00016934802,0.000060066493,0.0012319048,0.000049072583,0.00044881026,0.00014413141,0.00038083544,0.98737794,0.0052403035,0.0041323663,0.0006315865,0.00013363046],"about_ca_topic_score_codex":0.000020843036,"about_ca_topic_score_gemma":0.000004243257,"teacher_disagreement_score":0.29072842,"about_ca_system_score_codex":0.00014946888,"about_ca_system_score_gemma":0.0001588737,"threshold_uncertainty_score":0.9066141},"labels":[],"label_agreement":null},{"id":"W4402412381","doi":"10.1214/24-ejs2284","title":"Selecting strong orthogonal arrays by linear allowable level permutations","year":2024,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematics; Orthogonal array; Combinatorics; Statistics; Applied mathematics; Taguchi methods","score_opus":0.1282196979000005,"score_gpt":0.448128365770885,"score_spread":0.31990866787088446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402412381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010208279,0.004012475,0.9836188,0.000239118,0.0005415016,0.0000889057,0.00021675098,0.000024255003,0.0010498916],"genre_scores_gemma":[0.6597227,0.00016415778,0.33563784,0.00007966099,0.00029396077,0.0000029669936,0.000013777085,0.000042916876,0.0040420066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99579805,0.0003810158,0.0011609021,0.00031944292,0.001650447,0.0006901468],"domain_scores_gemma":[0.99571574,0.0028837747,0.00040680595,0.00018384469,0.0006247036,0.00018515547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050938986,0.00020457785,0.00038140416,0.00033608172,0.00020488093,0.0004218643,0.0005692285,0.000075119744,0.00072914665],"category_scores_gemma":[0.0026262244,0.00016267969,0.00015110611,0.00086971995,0.00009808623,0.00054780184,0.00004918895,0.0009478938,0.0001152803],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018637776,0.00027960129,0.0005515892,0.000034224468,0.00056272215,0.00027377057,0.002280318,0.011643514,0.12398355,0.6159896,0.14526486,0.098949835],"study_design_scores_gemma":[0.0013888105,0.003320918,0.00037328168,0.0001751388,0.00028171678,0.0019745256,0.004491547,0.23272145,0.028276604,0.62706655,0.0989786,0.00095084426],"about_ca_topic_score_codex":0.00001074008,"about_ca_topic_score_gemma":0.000016597447,"teacher_disagreement_score":0.64951444,"about_ca_system_score_codex":0.00041912927,"about_ca_system_score_gemma":0.0015658848,"threshold_uncertainty_score":0.79836446},"labels":[],"label_agreement":null},{"id":"W4402438503","doi":"10.1080/08982112.2024.2381005","title":"On the construction of saturated split-plot designs for quadratic response surface models","year":2024,"lang":"en","type":"article","venue":"Quality Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Response surface methodology; Plot (graphics); Mathematics; Quadratic equation; Surface (topology); Statistics; Econometrics; Geometry","score_opus":0.3980598917181631,"score_gpt":0.4867958580602684,"score_spread":0.08873596634210534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402438503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48391002,0.00018516932,0.5146654,0.00034169407,0.00039054564,0.00028081078,0.000025627256,0.000089705434,0.00011101968],"genre_scores_gemma":[0.8361373,0.000002056744,0.16354707,0.000031463842,0.000021382828,0.000021828988,0.0000012896215,0.000021535052,0.00021606115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99726117,0.0008103839,0.0006849432,0.00036291662,0.00067290047,0.00020766561],"domain_scores_gemma":[0.9802661,0.01896062,0.00009261902,0.00048115104,0.00013909586,0.000060398695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011388783,0.00016172358,0.00028709523,0.00014647018,0.000068476416,0.00019951603,0.0003467129,0.000079109595,0.000062223386],"category_scores_gemma":[0.005832378,0.00010618519,0.00015018255,0.00073988544,0.00007975664,0.00026769278,0.000037383044,0.00015892676,0.000028070554],"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":[0.00041804192,0.000015684589,0.000002875334,0.000036548685,0.000027456086,0.0000012234929,0.0008940478,0.23811512,0.4157016,0.34342182,0.00024158544,0.0011240126],"study_design_scores_gemma":[0.00012238503,0.00014851875,0.00010828797,0.000078779995,0.000009315789,0.000004499605,0.00078343635,0.7978234,0.15792431,0.042608973,0.0002354978,0.0001525496],"about_ca_topic_score_codex":0.000016458092,"about_ca_topic_score_gemma":4.8074884e-7,"teacher_disagreement_score":0.55970836,"about_ca_system_score_codex":0.00008747306,"about_ca_system_score_gemma":0.000077439865,"threshold_uncertainty_score":0.69823223},"labels":[],"label_agreement":null},{"id":"W4404960182","doi":"10.3390/s24237632","title":"Radar Sensor Data Fitting for Accurate Linear Sprint Modelling","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Victoria","funders":"","keywords":"Sprint; Radar; Computer science; Linear model; Remote sensing; Data mining; Telecommunications; Machine learning; Geography","score_opus":0.4893269840653866,"score_gpt":0.5202498308642206,"score_spread":0.030922846798834003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404960182","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":[0.13790543,0.0008628626,0.8538534,0.001072057,0.0017791506,0.0006107599,0.00026909346,0.00029860274,0.0033486418],"genre_scores_gemma":[0.2105894,0.000024822211,0.78427505,0.00012300452,0.0004772457,0.000008825375,0.00002141512,0.00005341195,0.0044268183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964771,0.00030452383,0.00074547896,0.0011680648,0.00087415916,0.00043066806],"domain_scores_gemma":[0.9941962,0.003950817,0.00011574109,0.0014297365,0.0001564285,0.00015108754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057598576,0.00022514415,0.0003368727,0.00023193109,0.00018120525,0.00058000797,0.0011789069,0.00010282849,0.00016115997],"category_scores_gemma":[0.0026878987,0.00017217959,0.00016193798,0.00062179175,0.00008459755,0.00049753475,0.00047734487,0.00022683453,0.0007483343],"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":[0.00033311074,0.00011469602,0.00005951516,0.0001270501,0.00019628285,0.00025885238,0.0029866337,0.62678754,0.09280836,0.0115880715,0.013322744,0.25141713],"study_design_scores_gemma":[0.00012898605,0.00003845819,0.0000045365387,0.000037032292,0.000015689411,0.000015804339,0.001006913,0.87990314,0.021507813,0.0034459163,0.0936961,0.00019961348],"about_ca_topic_score_codex":0.00004037544,"about_ca_topic_score_gemma":0.0000015229689,"teacher_disagreement_score":0.2531156,"about_ca_system_score_codex":0.00004769379,"about_ca_system_score_gemma":0.0001054317,"threshold_uncertainty_score":0.96185726},"labels":[],"label_agreement":null},{"id":"W4405951691","doi":"10.1002/cjs.11836","title":"A new class of asymptotic maximin distance Latin hypercube designs","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Minimax; Latin hypercube sampling; Class (philosophy); Hypercube; Combinatorics; Computer science; Mathematics; Mathematical optimization; Discrete mathematics; Mathematical economics; Statistics; Artificial intelligence; Monte Carlo method","score_opus":0.17522472695890867,"score_gpt":0.3926225213818179,"score_spread":0.21739779442290924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405951691","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":[0.003966748,0.0026225243,0.9881774,0.00038570134,0.00130663,0.00009039283,0.00041975544,0.0000054938787,0.0030253576],"genre_scores_gemma":[0.40547267,0.000020041758,0.5921762,0.000085013555,0.000116963216,4.886658e-7,0.0000015604685,0.000022229484,0.002104838],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99725986,0.00025804868,0.0010700655,0.0002162205,0.00087654556,0.00031928648],"domain_scores_gemma":[0.9959149,0.0021019808,0.00031727258,0.00027192547,0.00042827835,0.00096565817],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0019391804,0.00015609011,0.00041372207,0.000577243,0.00007096735,0.00033774806,0.00067057635,0.000074394266,0.0016140094],"category_scores_gemma":[0.0033750695,0.00012600378,0.00011538485,0.000786281,0.00019471557,0.0002594592,0.000018163608,0.00028009556,0.00009604892],"study_design_candidate":"theoretical_or_conceptual","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":[0.00008829815,0.000029279046,0.004105199,0.000082713406,0.00014234731,0.003164452,0.0045022173,0.0016023202,0.0067423075,0.2062791,0.37425587,0.39900592],"study_design_scores_gemma":[0.0015334825,0.0024562262,0.02262858,0.0011610707,0.00036057903,0.0014580021,0.0052802563,0.048871115,0.010754778,0.5803726,0.3238771,0.0012462795],"about_ca_topic_score_codex":0.001205859,"about_ca_topic_score_gemma":0.003219952,"teacher_disagreement_score":0.40150592,"about_ca_system_score_codex":0.00025269503,"about_ca_system_score_gemma":0.003554553,"threshold_uncertainty_score":0.99929863},"labels":[],"label_agreement":null},{"id":"W4406143287","doi":"10.1137/1.9781611978315.33","title":"Experimental Design Using Interlacing Polynomials","year":2025,"lang":"en","type":"book-chapter","venue":"Society for Industrial and Applied Mathematics eBooks","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Interlacing; Computer science; Mathematics; Artificial intelligence","score_opus":0.3898590081219434,"score_gpt":0.41936578054783946,"score_spread":0.029506772425896055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406143287","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004179944,0.0004507646,0.34196275,0.000034081517,0.0009901824,0.004390785,0.00017544239,0.00012540724,0.6514526],"genre_scores_gemma":[0.0011259168,0.000003700495,0.6722224,0.00020028219,0.00065645797,0.0001451622,0.0000086024365,0.00010961522,0.32552785],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99623394,0.00003633992,0.0014791372,0.0009262995,0.0008903675,0.00043392694],"domain_scores_gemma":[0.9952868,0.00291171,0.00089811184,0.00060249417,0.00011705398,0.00018383574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0036149574,0.00067534955,0.0013416072,0.00017010851,0.00046192555,0.00051988213,0.0006634279,0.0010946838,0.00014341647],"category_scores_gemma":[0.00024365634,0.0005678432,0.0009391534,0.00005094549,0.00041562004,0.00006674012,0.00054468855,0.000543833,0.000014826203],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003436844,0.0000936922,1.00011704e-7,0.0001294895,0.0006852379,0.0000020229754,0.005694187,0.000048565733,0.16078615,0.7397173,0.03558259,0.05691697],"study_design_scores_gemma":[0.0035772973,0.00037481711,1.2014447e-8,0.0007604494,0.0005367059,0.000018911762,0.007375689,0.0061507714,0.36036223,0.5799462,0.039343398,0.001553504],"about_ca_topic_score_codex":0.0000029282255,"about_ca_topic_score_gemma":1.291651e-7,"teacher_disagreement_score":0.33025965,"about_ca_system_score_codex":0.00023560497,"about_ca_system_score_gemma":0.0003144887,"threshold_uncertainty_score":0.9996773},"labels":[],"label_agreement":null},{"id":"W4406164450","doi":"10.61091/ars161-07","title":"A New Series of Affine Resolvable PBIB(4) Designs in Two Replicates","year":2024,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Indian Agricultural Research Institute; Indian Council of Agricultural Research","keywords":"Mathematics; Series (stratigraphy); Affine transformation; Combinatorics; Pure mathematics; Biology","score_opus":0.12324048761226183,"score_gpt":0.4414839682629218,"score_spread":0.31824348065066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406164450","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8760946,0.013362794,0.006187685,0.0028624453,0.007157894,0.0012584343,0.000021926717,0.0004919186,0.09256234],"genre_scores_gemma":[0.9164737,0.000029198944,0.07244518,0.000044812332,0.000016765056,0.000025305413,0.0000017328668,0.000031683718,0.010931619],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996874,0.00039696816,0.00083271455,0.00068376824,0.0008824817,0.00033011293],"domain_scores_gemma":[0.9970282,0.0017124435,0.00012329253,0.000873097,0.00011783612,0.00014513922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036310265,0.00019693247,0.00045308095,0.00048562384,0.000048945938,0.00026600165,0.00083168864,0.00007204421,0.0008285141],"category_scores_gemma":[0.0016677191,0.00016110044,0.00012455221,0.0025383641,0.0001158807,0.0006791099,0.00025527863,0.00020257749,0.00030231968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016492514,0.00011222395,0.0026037237,0.000016975735,0.00002277603,0.00006968828,0.0011840956,0.00009273309,0.15176542,0.79220885,0.033858337,0.017900236],"study_design_scores_gemma":[0.00049375166,0.00023492414,0.0007677526,0.00007235551,0.000007067956,0.000014870208,0.00029939722,0.0008956891,0.20502113,0.7812281,0.010794943,0.00017001537],"about_ca_topic_score_codex":0.00044003993,"about_ca_topic_score_gemma":0.00002700941,"teacher_disagreement_score":0.08163072,"about_ca_system_score_codex":0.000088606605,"about_ca_system_score_gemma":0.00022563935,"threshold_uncertainty_score":0.9071648},"labels":[],"label_agreement":null},{"id":"W4406808516","doi":"10.1002/sim.10321","title":"Bioequivalence Design With Sampling Distribution Segments","year":2025,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"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; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bioequivalence; Sampling (signal processing); Sample size determination; Computer science; Statistics; Sampling distribution; Sampling design; Mathematics; Econometrics; Medicine","score_opus":0.22427735462934364,"score_gpt":0.5229092611377446,"score_spread":0.298631906508401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406808516","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":[0.0012490004,0.00026406004,0.99502414,0.00041249712,0.00041354267,0.0002969844,0.000088956636,0.00002160862,0.0022291758],"genre_scores_gemma":[0.16397,0.00004317413,0.83448094,0.00031915682,0.000035909987,0.00002486655,0.000036842357,0.000008215878,0.0010809038],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9971043,0.0004538249,0.000630746,0.00045074848,0.0010869551,0.00027345773],"domain_scores_gemma":[0.99481195,0.004322701,0.00014758996,0.0004073792,0.00023020925,0.000080187885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047796383,0.0001524652,0.00034270016,0.00025197066,0.00009712664,0.000049858307,0.00046528774,0.000050652678,0.0003052957],"category_scores_gemma":[0.00797304,0.00009981777,0.000012136413,0.0013893443,0.00041861064,0.00010292058,0.000085632586,0.00017801517,0.00003352675],"study_design_candidate":"theoretical_or_conceptual","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":[0.0015634916,0.000414239,0.046213977,0.00009815891,0.00009595497,0.00029670636,0.002294649,0.006791167,0.041465644,0.35414192,0.10388607,0.44273803],"study_design_scores_gemma":[0.0064323805,0.0024677585,0.08995401,0.001480535,0.0001258791,0.000027372713,0.0059090364,0.06972103,0.016922373,0.79519147,0.010945429,0.0008227165],"about_ca_topic_score_codex":0.00006122252,"about_ca_topic_score_gemma":0.000009031988,"teacher_disagreement_score":0.4419153,"about_ca_system_score_codex":0.00016099418,"about_ca_system_score_gemma":0.00010856549,"threshold_uncertainty_score":0.95450497},"labels":[],"label_agreement":null},{"id":"W4407762147","doi":"10.1037/met0000730","title":"Evaluating statistical fit of confirmatory bifactor models: Updated recommendations and a review of current practice.","year":2025,"lang":"en","type":"review","venue":"Psychological Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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","funders":"","keywords":"Current (fluid); Confirmatory factor analysis; Psychology; Econometrics; Statistics; Statistical analysis; Psychometrics; Statistical hypothesis testing; Clinical psychology; Structural equation modeling; Mathematics; Engineering","score_opus":0.8475355186061376,"score_gpt":0.7654928065091957,"score_spread":0.08204271209694192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407762147","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.4049135e-7,0.6438505,0.3500727,0.00010220033,0.00058785063,0.0011586659,0.00033126227,0.000021643444,0.0038750097],"genre_scores_gemma":[1.632096e-7,0.51327443,0.4863393,0.00015671486,0.000014998669,0.0001225212,0.00003467985,0.00001247067,0.000044710327],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.95958906,0.032943662,0.004419345,0.0013515985,0.0013445287,0.00035181383],"domain_scores_gemma":[0.93544346,0.059070207,0.0030529625,0.001258019,0.0009421862,0.00023318034],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.040541023,0.000563541,0.004408319,0.0005282657,0.00008358014,0.00006962153,0.0012402743,0.00040324792,0.0026957938],"category_scores_gemma":[0.09656748,0.00036141693,0.00059168995,0.001963258,0.0004932892,0.00028330635,0.0005671101,0.00092901837,0.000023902307],"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":[0.000027832974,0.00034786947,2.3143984e-7,0.014479227,0.000078378616,0.0000015630008,0.000027505706,6.567786e-7,0.0000097780385,0.0062022135,0.0042720605,0.9745527],"study_design_scores_gemma":[0.00026399372,0.00040192893,0.0000027396102,0.062017858,0.0010787769,0.000038983977,0.00006364709,0.0007026532,0.000008993041,0.017860383,0.91716003,0.00040000927],"about_ca_topic_score_codex":0.0000033482108,"about_ca_topic_score_gemma":4.7570303e-8,"teacher_disagreement_score":0.9741527,"about_ca_system_score_codex":0.0000773626,"about_ca_system_score_gemma":0.00031378414,"threshold_uncertainty_score":0.9998838},"labels":[],"label_agreement":null},{"id":"W4407764960","doi":"10.1016/j.jmaa.2025.129897","title":"On noncentral Wishart mixtures of noncentral Wisharts and their use for testing random effects in factorial design models","year":2025,"lang":"en","type":"preprint","venue":"Journal of Mathematical Analysis and Applications","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Université du Québec à Trois-Rivières; Université de Sherbrooke; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Canada Research Chairs; McGill University; Pennsylvania State University; University of Pennsylvania","keywords":"Wishart distribution; Factorial experiment; Mathematics; Fractional factorial design; Statistics; Econometrics; Multivariate statistics","score_opus":0.17534155226186163,"score_gpt":0.41806343923283384,"score_spread":0.24272188697097222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407764960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04514089,0.0004672604,0.9529911,0.000076270626,0.0000656262,0.0010763321,0.00010384678,0.000004988969,0.000073687195],"genre_scores_gemma":[0.6073911,0.00006568477,0.39217955,0.00003686949,0.00013693095,0.00013539918,0.000004341854,0.000009747065,0.000040343217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966244,0.00047235456,0.0016225298,0.00044221248,0.0006167532,0.000221745],"domain_scores_gemma":[0.97382766,0.024019467,0.0011321658,0.00037887407,0.00046730248,0.0001745325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035005715,0.00027755825,0.001537569,0.0005866836,0.000086042026,0.00030506626,0.00048373293,0.0001915863,0.000012738523],"category_scores_gemma":[0.003907054,0.00017239666,0.0005147758,0.00080093276,0.00015102608,0.00020259475,0.00019771513,0.00036138925,3.947025e-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":[0.00542322,0.0065220376,0.005141858,0.0022836302,0.0074504744,0.000019050125,0.009784301,0.6023353,0.048129037,0.19444624,0.002131037,0.116333835],"study_design_scores_gemma":[0.0010561079,0.00012001994,0.000410072,0.00032396728,0.0005922154,0.0000033518386,0.000088186,0.32586133,0.003754958,0.6676212,0.000018098055,0.00015048555],"about_ca_topic_score_codex":0.000009002673,"about_ca_topic_score_gemma":0.0000016753522,"teacher_disagreement_score":0.56225026,"about_ca_system_score_codex":0.000049590246,"about_ca_system_score_gemma":0.00011588448,"threshold_uncertainty_score":0.7030131},"labels":[],"label_agreement":null},{"id":"W4407950092","doi":"10.1109/cdc56724.2024.10886446","title":"Generalizing Better Response Paths and Weakly Acyclic Games","year":2024,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Queen's University; University of Toronto","funders":"","keywords":"Computer science; Directed acyclic graph; Theoretical computer science; Algorithm","score_opus":0.14539225863276836,"score_gpt":0.46754895710693223,"score_spread":0.32215669847416384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407950092","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9542746,0.0033865778,0.022114873,0.0043987846,0.00062428764,0.00013575818,0.0000052014666,0.00017797099,0.0148819545],"genre_scores_gemma":[0.7576566,0.0000275077,0.21457349,0.002041433,0.00011878361,0.000015162572,4.4677853e-7,0.000023132992,0.025543489],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972555,0.0008053723,0.00038713758,0.0006058959,0.000708222,0.00023785511],"domain_scores_gemma":[0.99649096,0.0028995236,0.00003176144,0.00040628365,0.0000399215,0.00013158255],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005088407,0.00014476232,0.00019748078,0.00029963691,0.000086339984,0.0009858082,0.00031958407,0.0000673354,0.0015534228],"category_scores_gemma":[0.001209924,0.000093626266,0.00008609962,0.0005391996,0.0001170241,0.0004922723,0.0002215987,0.00011944085,0.00087389845],"study_design_candidate":"bench_or_experimental","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":[0.00013492098,0.000013819036,0.00063781487,0.00000345339,0.0000141858145,0.00012188809,0.001055228,0.0000056443755,0.74476886,0.0034991796,0.033766188,0.21597883],"study_design_scores_gemma":[0.00038256764,0.00037588543,0.020027518,0.0000707804,0.000023004775,0.00024623206,0.002368287,0.0267544,0.28700984,0.054536294,0.60749,0.00071517465],"about_ca_topic_score_codex":0.000020681902,"about_ca_topic_score_gemma":0.0000017606786,"teacher_disagreement_score":0.57372385,"about_ca_system_score_codex":0.000029417046,"about_ca_system_score_gemma":0.000037142738,"threshold_uncertainty_score":0.99990404},"labels":[],"label_agreement":null},{"id":"W4409023558","doi":"10.5539/ijsp.v14n1p45","title":"Algorithmic Construction of Bayesian Optimal Block Designs Using the Linear Mixed Effects Model","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Mathematics; Block (permutation group theory); Mathematical optimization; Algorithm; Computer science; Applied mathematics; Statistics; Combinatorics","score_opus":0.08904698293725741,"score_gpt":0.4315362966326311,"score_spread":0.3424893136953737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409023558","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":[0.17173997,0.0001081122,0.8270245,0.00017695883,0.0006607265,0.00013636837,0.000069254056,0.0000020423554,0.00008204139],"genre_scores_gemma":[0.4100223,0.000011618897,0.5898868,0.000028183593,0.000031919524,9.55975e-7,4.5756593e-7,0.0000025394952,0.000015266609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975621,0.00042788102,0.0008787079,0.00017278953,0.00086169556,0.00009680857],"domain_scores_gemma":[0.9954541,0.0022824367,0.00058583234,0.00015799342,0.001463821,0.000055866338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031702372,0.000102948165,0.0002651919,0.00017541183,0.000077838486,0.00010547868,0.00048560693,0.00005080315,0.00001742946],"category_scores_gemma":[0.0029001161,0.00006644249,0.0000814059,0.00018519722,0.00037260226,0.0001598831,0.00011659884,0.00017585418,3.5514913e-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":[0.0018207438,0.0006811321,0.016004669,0.0001136399,0.0007712146,0.0000589093,0.0018085598,0.31984243,0.07730213,0.24449883,0.0010385639,0.33605918],"study_design_scores_gemma":[0.00043083314,0.00011878398,0.0015522089,0.000048366324,0.00003661321,0.00007165318,0.00014580681,0.6988355,0.012717041,0.28594783,0.000037466147,0.000057928075],"about_ca_topic_score_codex":0.000016778475,"about_ca_topic_score_gemma":0.0000015007165,"teacher_disagreement_score":0.37899303,"about_ca_system_score_codex":0.000084722364,"about_ca_system_score_gemma":0.00022747448,"threshold_uncertainty_score":0.34719196},"labels":[],"label_agreement":null},{"id":"W4409109035","doi":"10.5539/ijsp.v14n1p50","title":"Algorithmic Construction of Bayesian Optimal Block Designs Using the Linear Mixed Effects Model","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Bayesian probability; Block (permutation group theory); Mathematical optimization; Mixed model; Linear model; Applied mathematics; Computer science; Algorithm; Statistics; Combinatorics","score_opus":0.08904698293725741,"score_gpt":0.4315362966326311,"score_spread":0.3424893136953737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409109035","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":[0.17173997,0.0001081122,0.8270245,0.00017695883,0.0006607265,0.00013636837,0.000069254056,0.0000020423554,0.00008204139],"genre_scores_gemma":[0.4100223,0.000011618897,0.5898868,0.000028183593,0.000031919524,9.55975e-7,4.5756593e-7,0.0000025394952,0.000015266609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975621,0.00042788102,0.0008787079,0.00017278953,0.00086169556,0.00009680857],"domain_scores_gemma":[0.9954541,0.0022824367,0.00058583234,0.00015799342,0.001463821,0.000055866338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031702372,0.000102948165,0.0002651919,0.00017541183,0.000077838486,0.00010547868,0.00048560693,0.00005080315,0.00001742946],"category_scores_gemma":[0.0029001161,0.00006644249,0.0000814059,0.00018519722,0.00037260226,0.0001598831,0.00011659884,0.00017585418,3.5514913e-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":[0.0018207438,0.0006811321,0.016004669,0.0001136399,0.0007712146,0.0000589093,0.0018085598,0.31984243,0.07730213,0.24449883,0.0010385639,0.33605918],"study_design_scores_gemma":[0.00043083314,0.00011878398,0.0015522089,0.000048366324,0.00003661321,0.00007165318,0.00014580681,0.6988355,0.012717041,0.28594783,0.000037466147,0.000057928075],"about_ca_topic_score_codex":0.000016778475,"about_ca_topic_score_gemma":0.0000015007165,"teacher_disagreement_score":0.37899303,"about_ca_system_score_codex":0.000084722364,"about_ca_system_score_gemma":0.00022747448,"threshold_uncertainty_score":0.34719196},"labels":[],"label_agreement":null},{"id":"W4410790021","doi":"10.1002/wics.70029","title":"Orthogonal Arrays: A Review","year":2025,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Orthogonal array; Mathematics; Statistics; Taguchi methods","score_opus":0.24005325499900423,"score_gpt":0.5504337736566163,"score_spread":0.31038051865761207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410790021","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.8252019e-9,0.61954266,0.37037715,0.00009423895,0.0010005124,0.0025345837,0.002109352,0.00005229729,0.004289204],"genre_scores_gemma":[9.559096e-9,0.66133934,0.33242655,0.00062158704,0.00019572696,0.0006426314,0.001661117,0.00005980347,0.0030532111],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98232234,0.0053310622,0.0069875335,0.0020643321,0.0026210684,0.0006736486],"domain_scores_gemma":[0.9793465,0.014358312,0.0033594968,0.0016656944,0.000862843,0.0004071287],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.008210615,0.0014091993,0.007735809,0.00089093845,0.00049261015,0.00043141987,0.0032019936,0.00035751218,0.0028229684],"category_scores_gemma":[0.007419619,0.0009924758,0.002181906,0.003123459,0.0003864056,0.00033224726,0.0029935995,0.0010902905,0.0057462775],"study_design_candidate":"not_applicable","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":[0.0000053397393,0.00008856019,4.0545217e-7,0.032709043,0.00008181383,0.00004327311,0.000025863877,0.000040430936,1.5096843e-8,0.0065276623,0.31136873,0.6491088],"study_design_scores_gemma":[0.00009463942,0.000111401365,8.451106e-7,0.18197039,0.00074924977,0.0001315801,0.000013229481,0.0004833086,1.3378681e-8,0.0511394,0.76462084,0.00068508735],"about_ca_topic_score_codex":0.0000012640081,"about_ca_topic_score_gemma":0.0000025505524,"teacher_disagreement_score":0.6484238,"about_ca_system_score_codex":0.00046661356,"about_ca_system_score_gemma":0.0013988067,"threshold_uncertainty_score":0.99986583},"labels":[],"label_agreement":null},{"id":"W4411442155","doi":"10.1007/978-3-662-69359-9_447","title":"Optimal Approximate Design","year":2025,"lang":"en","type":"book-chapter","venue":"International Encyclopedia of Statistical Science","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science","score_opus":0.0848798311340984,"score_gpt":0.42093849448073245,"score_spread":0.33605866334663403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411442155","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004565732,0.000064648484,0.4408707,0.000106367006,0.001174004,0.00022510633,0.00033283414,0.000021862577,0.5571999],"genre_scores_gemma":[0.00087020063,0.00013096067,0.57647943,0.0000877194,0.00010901006,0.000015337806,0.000013611598,0.000015588323,0.42227817],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99108356,0.0001074458,0.0015086539,0.0013458354,0.005523321,0.0004311718],"domain_scores_gemma":[0.9911069,0.0057337987,0.00067076273,0.00073545077,0.001446675,0.00030639878],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0052600596,0.00040664567,0.00073247036,0.0010054838,0.00015428642,0.00030689422,0.0040386603,0.00021057208,0.0066087586],"category_scores_gemma":[0.008469615,0.0003309077,0.00016859906,0.00039549402,0.0026792963,0.00051898934,0.0010284934,0.00041033697,0.00044440263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009837397,0.000051312945,0.000009347767,0.000007735976,0.000030957202,0.000035197354,0.000102555154,0.00053622003,0.0004827235,0.9166332,0.01478512,0.06722728],"study_design_scores_gemma":[0.0004269462,0.00033336095,0.00022097953,0.00020896245,0.00005869203,0.000027801661,0.000077539466,0.041011296,0.0017991748,0.63202655,0.32302538,0.000783342],"about_ca_topic_score_codex":0.000019807776,"about_ca_topic_score_gemma":9.540977e-7,"teacher_disagreement_score":0.30824026,"about_ca_system_score_codex":0.000286552,"about_ca_system_score_gemma":0.0010750025,"threshold_uncertainty_score":0.9999143},"labels":[],"label_agreement":null},{"id":"W4412792815","doi":"10.2139/ssrn.5374371","title":"Potential Outcome Modeling and Estimation in Did Designs with Staggered Treatments","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Outcome (game theory); Estimation; Econometrics; Computer science; Statistics; Mathematics; Engineering; Mathematical economics; Systems engineering","score_opus":0.11339549607505722,"score_gpt":0.4400589084767011,"score_spread":0.32666341240164387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412792815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29983875,0.002072977,0.69688404,0.00019442767,0.00023880867,0.0004072074,0.0000105886675,0.000021855889,0.00033136454],"genre_scores_gemma":[0.90888613,0.0007639497,0.088223144,0.00003681199,0.000048099548,0.0000320111,0.000007946241,0.000025893021,0.0019759962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99402094,0.00089769426,0.001235564,0.0008462021,0.0013265086,0.0016730787],"domain_scores_gemma":[0.9982913,0.00029300456,0.00056923844,0.0004983395,0.00020870945,0.00013936804],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008332488,0.00044132915,0.00076689065,0.000898801,0.00021936155,0.0006094598,0.0007988887,0.00027530303,0.000027951735],"category_scores_gemma":[0.00053390505,0.00031863712,0.00017232982,0.0004483405,0.00007224986,0.00040835704,0.00041562805,0.0029066768,0.000009195804],"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":[0.0010659036,0.00029873854,0.011606421,0.000021729966,0.0004571279,0.00006529218,0.00075625617,0.85819715,0.00078156503,0.007786413,0.000010274214,0.118953116],"study_design_scores_gemma":[0.0014777948,0.00039567397,0.00060858444,0.00011605775,0.00010310882,0.0001971501,0.0013626023,0.55583966,0.00014238204,0.43942213,0.0000029533696,0.0003319],"about_ca_topic_score_codex":0.00024308446,"about_ca_topic_score_gemma":0.00022632205,"teacher_disagreement_score":0.6090474,"about_ca_system_score_codex":0.0019968322,"about_ca_system_score_gemma":0.0030938238,"threshold_uncertainty_score":0.99992657},"labels":[],"label_agreement":null},{"id":"W4413457979","doi":"10.2139/ssrn.5394370","title":"Correntropy Meets Cross-Entropy: A Robust Loss Against Noisy Labels","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cross entropy; Artificial intelligence; Entropy (arrow of time); Computer science; Mathematics; Pattern recognition (psychology); Physics","score_opus":0.07808477875120536,"score_gpt":0.4289299879324521,"score_spread":0.35084520918124673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413457979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24329701,0.047357142,0.6629603,0.0055886805,0.018951291,0.0022587713,0.0003664262,0.0004202008,0.018800166],"genre_scores_gemma":[0.80652034,0.022666154,0.067795515,0.0016839256,0.0035219234,0.00017209126,0.00010030707,0.00027329221,0.09726646],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98325473,0.0023574606,0.0027335887,0.0020370109,0.0039050765,0.0057121273],"domain_scores_gemma":[0.9926041,0.0014219511,0.0020652055,0.0020050248,0.0013731663,0.0005305917],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.021166628,0.0010897203,0.0017103306,0.00119092,0.00084877835,0.002885311,0.0057351068,0.0008803641,0.00048978266],"category_scores_gemma":[0.0042778905,0.0008884118,0.0013091156,0.001257313,0.00050500344,0.0006408671,0.002697998,0.011268963,0.00053052825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.0030937467,0.0021477733,0.020399794,0.00015283296,0.003751377,0.0006703382,0.0018935974,0.16305324,0.013760995,0.3590755,0.01784439,0.4141564],"study_design_scores_gemma":[0.0039361203,0.0007565916,0.0008986218,0.00051219406,0.00025961507,0.0008614189,0.0019909858,0.0141059635,0.0051686573,0.9415808,0.027803931,0.0021251414],"about_ca_topic_score_codex":0.000088864035,"about_ca_topic_score_gemma":0.00013105125,"teacher_disagreement_score":0.5951648,"about_ca_system_score_codex":0.0048444048,"about_ca_system_score_gemma":0.01283314,"threshold_uncertainty_score":0.99964434},"labels":[],"label_agreement":null},{"id":"W4413463495","doi":"10.1080/00224065.2025.2534385","title":"Bayesian sequential I-optimal designs for split-plot experiments under model uncertainty","year":2025,"lang":"en","type":"article","venue":"Journal of Quality Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Bayesian probability; Econometrics; Statistics; Plot (graphics); Split plot; Bayesian inference; Mathematics; Computer science","score_opus":0.37045238201098957,"score_gpt":0.5594290052043334,"score_spread":0.1889766231933438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413463495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09212706,0.00047088155,0.89982367,0.005452235,0.00068114535,0.00036967942,0.000020704478,0.00006778307,0.000986827],"genre_scores_gemma":[0.5470279,0.000009691301,0.4506768,0.00082797697,0.00005372245,0.000027016213,9.763065e-7,0.00001780119,0.0013581406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9947477,0.0007189994,0.00231539,0.00056209107,0.0011167191,0.00053912215],"domain_scores_gemma":[0.99540436,0.0014309384,0.001182433,0.00081318483,0.0010153452,0.00015370887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008844015,0.00029712502,0.0009813441,0.0014392877,0.00023660755,0.00022533664,0.0018345779,0.00048379134,0.00013146164],"category_scores_gemma":[0.003894569,0.0002355257,0.0004863147,0.0012632231,0.0005123552,0.00058109994,0.00032370628,0.00050076627,0.0000115652165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016945048,0.0006757382,0.00033841605,0.000024827952,0.00031672273,0.00001914064,0.0004133975,0.055129126,0.36171323,0.5364152,0.0059593716,0.037300322],"study_design_scores_gemma":[0.0025069641,0.000782763,0.00013386521,0.00004732583,0.00006766388,0.000057439633,0.005196664,0.071844004,0.2147449,0.7016706,0.0025918314,0.0003560027],"about_ca_topic_score_codex":0.000013847033,"about_ca_topic_score_gemma":0.0000046088635,"teacher_disagreement_score":0.4549008,"about_ca_system_score_codex":0.00041085985,"about_ca_system_score_gemma":0.0006028338,"threshold_uncertainty_score":0.96044594},"labels":[],"label_agreement":null},{"id":"W4413859066","doi":"10.5539/ijsp.v14n3p23","title":"Sequential Tests Based on F-Distribution for Detecting Active Effects in Unreplicated Two-Level Factorial Designs","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Factorial experiment; Factorial; Statistics; Distribution (mathematics); Fractional factorial design; Mathematical analysis","score_opus":0.16235869691602423,"score_gpt":0.4788398377701344,"score_spread":0.31648114085411017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413859066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10394265,0.000010210949,0.89350766,0.00017603212,0.0013185984,0.00034813752,0.00060330064,0.000004208021,0.00008918112],"genre_scores_gemma":[0.7877422,8.2133477e-7,0.21208054,0.000059258276,0.000078920944,0.000012393683,0.000013050776,0.0000035885882,0.00000920016],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99773395,0.00044887292,0.00073676347,0.000269199,0.0006797287,0.0001314601],"domain_scores_gemma":[0.9888365,0.009261244,0.0004352136,0.00012618366,0.0012772841,0.000063575244],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0036026328,0.000116327974,0.00025213373,0.0002164329,0.000064942986,0.00017324324,0.00034034284,0.000058965546,0.000019310792],"category_scores_gemma":[0.020568633,0.00009265375,0.000071963856,0.00022113434,0.0000945369,0.00015237115,0.000051419716,0.00019230144,8.2714996e-7],"study_design_candidate":"theoretical_or_conceptual","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":[0.014716225,0.0012778259,0.029196719,0.00007813357,0.00023269806,0.00007115793,0.00047799337,0.015811685,0.081585035,0.095705844,0.0009610259,0.75988567],"study_design_scores_gemma":[0.004629705,0.0009430239,0.07951732,0.00015247031,0.000029328627,0.000009542763,0.000046371777,0.100883566,0.06462166,0.7486986,0.00029405396,0.00017433803],"about_ca_topic_score_codex":0.000041450126,"about_ca_topic_score_gemma":0.000025950474,"teacher_disagreement_score":0.7597113,"about_ca_system_score_codex":0.0003818005,"about_ca_system_score_gemma":0.00024591878,"threshold_uncertainty_score":0.9876815},"labels":[],"label_agreement":null},{"id":"W4414441757","doi":"10.1016/j.orp.2025.100355","title":"Development of a robust design optimization algorithm for hierarchical time series pharmaceutical problems","year":2025,"lang":"en","type":"article","venue":"Operations Research Perspectives","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Nexen (Canada)","funders":"National Research Foundation of Korea","keywords":"Quality by Design; Quality (philosophy); Optimization problem; Optimization algorithm; Hierarchical database model; Design of experiments; Optimal design; Development (topology)","score_opus":0.3568773472423729,"score_gpt":0.5492687362278529,"score_spread":0.19239138898547997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414441757","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":[0.00040021472,0.00045264032,0.9914367,0.0014070935,0.000063740554,0.0019212605,0.000025962227,0.000037940197,0.004254428],"genre_scores_gemma":[0.00260394,0.000038664817,0.9849211,0.00001791074,0.000042370095,0.00080377614,0.00001140579,0.000017104569,0.011543732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99492997,0.0017333484,0.00074227777,0.00072491425,0.0014215392,0.0004479365],"domain_scores_gemma":[0.99446833,0.0023234799,0.000039090017,0.00041292806,0.002612043,0.00014411645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010093908,0.0001695597,0.00033894487,0.0009578285,0.0010012576,0.00049709243,0.0007161612,0.00010922027,0.00083618134],"category_scores_gemma":[0.0057438216,0.00013766708,0.0000958889,0.0020972057,0.00074187067,0.0006721664,0.00028487312,0.0003053237,0.000051913874],"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":[0.000376378,0.0011795014,0.000008518559,0.00002470192,0.00016616126,0.0000022310762,0.018583119,0.6873879,0.11619415,0.036718026,0.0034117978,0.13594751],"study_design_scores_gemma":[0.0005910881,0.00022996814,0.000027911628,0.00003765475,0.000006893058,0.0000026091327,0.009182815,0.9155997,0.06894196,0.002115457,0.0031185388,0.00014538961],"about_ca_topic_score_codex":0.000006982174,"about_ca_topic_score_gemma":0.0000057546417,"teacher_disagreement_score":0.22821182,"about_ca_system_score_codex":0.00030125552,"about_ca_system_score_gemma":0.0012311703,"threshold_uncertainty_score":0.91555995},"labels":[],"label_agreement":null},{"id":"W4414534411","doi":"10.1002/pst.70031","title":"Adjustment for Inconsistency in Adaptive Phase 2/3 Designs With Dose Optimization","year":2025,"lang":"en","type":"article","venue":"Pharmaceutical Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"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 Victoria","funders":"Canada Research Chairs; Michael Smith Health Research BC","keywords":"Phase (matter); Selection (genetic algorithm); Maximum tolerated dose; Computerized adaptive testing; Adaptive design; Cutoff","score_opus":0.34858204789850666,"score_gpt":0.5624556527276947,"score_spread":0.21387360482918805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414534411","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":[0.0004937103,0.00034942967,0.99413574,0.0002729522,0.00029122006,0.0012180603,0.00030678345,0.0000323118,0.0028998153],"genre_scores_gemma":[0.11098755,0.00003226885,0.8874934,0.0007476104,0.000027964074,0.00022533459,0.000021307054,0.000016754591,0.00044783295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971134,0.0005033089,0.00073296524,0.00057270826,0.0006763983,0.00040118332],"domain_scores_gemma":[0.99387985,0.0051286686,0.00013748652,0.00028045886,0.0003826734,0.0001908455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018183453,0.00022089217,0.00038057493,0.00029928074,0.00011772394,0.0001210094,0.0003796767,0.00007204958,0.00041763383],"category_scores_gemma":[0.002350913,0.00016821623,0.000049372746,0.0009852106,0.00024689114,0.0001975319,0.00009928531,0.00018367762,0.000023935412],"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":[0.0097835725,0.0031144922,0.0009588351,0.000060064467,0.00016005497,0.000092093054,0.0007863194,0.10778921,0.0039673066,0.35239002,0.009740302,0.51115775],"study_design_scores_gemma":[0.0066197477,0.00078485307,0.00031606056,0.000037529022,0.00008671457,0.000003515235,0.00034799182,0.94136345,0.0056823273,0.041235905,0.0032635473,0.00025834516],"about_ca_topic_score_codex":0.000013734493,"about_ca_topic_score_gemma":0.000009904795,"teacher_disagreement_score":0.83357424,"about_ca_system_score_codex":0.00022801198,"about_ca_system_score_gemma":0.00023803746,"threshold_uncertainty_score":0.6859659},"labels":[],"label_agreement":null},{"id":"W4416046466","doi":"10.54117/ijps.v2i2.14","title":"Transforming Unreplicated Factorial Designs into Replicated Structures through Factor Projection","year":2025,"lang":"","type":"article","venue":"IPS Journal of Physical Sciences","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Trinity College","funders":"","keywords":"Factorial experiment; Plackett–Burman design; Factorial; Fractional factorial design; Main effect; Projection (relational algebra); Inference; Reliability (semiconductor)","score_opus":0.20772159382845312,"score_gpt":0.5064120970185956,"score_spread":0.29869050319014245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416046466","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7935418,0.00096690387,0.19486658,0.0010688528,0.004312749,0.0009845453,0.000016988439,0.00005384786,0.004187751],"genre_scores_gemma":[0.9700293,0.00006476745,0.028483283,0.00016258302,0.0010708101,0.000010642116,5.4973634e-7,0.000021847662,0.0001562004],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.989003,0.0018487315,0.0026978413,0.0014539672,0.0040416643,0.0009547775],"domain_scores_gemma":[0.99186,0.0037510283,0.0020177853,0.00069886295,0.0012815283,0.00039079765],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005060499,0.00070328184,0.0015219285,0.00088455406,0.0015337516,0.0015157136,0.0035189786,0.00033387027,0.0002465269],"category_scores_gemma":[0.004186083,0.00046018706,0.0009801026,0.0073179887,0.0021109092,0.0039345864,0.000282535,0.0011357848,0.000032825887],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007587537,0.0005673656,0.00037064642,0.00002886271,0.00016525012,0.000014901366,0.010479564,0.0014810932,0.8430848,0.014782501,0.0004681149,0.12779817],"study_design_scores_gemma":[0.0012080909,0.0034976178,0.0012715561,0.00025713583,0.00015448409,0.00003775345,0.003546418,0.0041524577,0.7447188,0.23524155,0.005353681,0.0005604429],"about_ca_topic_score_codex":0.00026073284,"about_ca_topic_score_gemma":0.0000070759847,"teacher_disagreement_score":0.22045904,"about_ca_system_score_codex":0.0005115324,"about_ca_system_score_gemma":0.0018754563,"threshold_uncertainty_score":0.999785},"labels":[],"label_agreement":null},{"id":"W4417152517","doi":"10.1002/cjce.70192","title":"The critical role of central point replication in response surface methodology: Implications for error estimation and statistical power in chemical engineering applications","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Replication (statistics); Surface (topology); Power (physics); Point (geometry); Estimation theory; Control theory (sociology); Estimation","score_opus":0.06433871603478379,"score_gpt":0.414921140400472,"score_spread":0.3505824243656882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417152517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48550957,0.00082914624,0.50558555,0.0074989228,0.000088094166,0.0004222334,0.000034410685,0.000007333957,0.00002475904],"genre_scores_gemma":[0.8136011,0.0000012628435,0.18630335,0.00003066512,0.00001235839,0.000037290032,0.0000012549056,0.000009072792,0.0000036506235],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998196,0.00019756005,0.00087204174,0.00022084909,0.00018973461,0.00032381734],"domain_scores_gemma":[0.9847134,0.014340167,0.00012768661,0.00039217054,0.00020969588,0.00021690558],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0059076548,0.00011637133,0.00028565983,0.00024004138,0.00006380234,0.00008070039,0.0005143036,0.00010453712,0.0000048505553],"category_scores_gemma":[0.028805785,0.000085056796,0.00006434349,0.00060208054,0.00019808249,0.0001193538,0.00003962509,0.0003332469,3.8689416e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020068773,0.000020393287,0.00050593534,0.000012726235,0.000013984816,9.832365e-7,0.00044337072,0.023642492,0.8844571,0.08485986,0.00006673164,0.005775724],"study_design_scores_gemma":[0.0008898616,0.000085963264,0.02738587,0.00014857494,0.00004504972,0.00010404396,0.00041960226,0.39982283,0.47076795,0.09879792,0.0012377669,0.0002945958],"about_ca_topic_score_codex":0.0001161763,"about_ca_topic_score_gemma":0.000029739233,"teacher_disagreement_score":0.41368917,"about_ca_system_score_codex":0.00031132073,"about_ca_system_score_gemma":0.00037606456,"threshold_uncertainty_score":0.979375},"labels":[],"label_agreement":null},{"id":"W5392957","doi":"10.1007/978-1-84800-113-8_18","title":"Present Worth Design of Engineering Systems with Degrading Components","year":2008,"lang":"en","type":"book-chapter","venue":"Springer series in reliability engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Warranty; Time to market; Quality (philosophy); Product (mathematics); Function (biology); Key (lock); Reliability engineering; Quality function deployment; Computer science; New product development; Risk analysis (engineering); Systems engineering; Engineering; Business; Marketing; Computer security","score_opus":0.09188461467966202,"score_gpt":0.30565571188393026,"score_spread":0.21377109720426823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5392957","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":[0.06798844,0.018902004,0.83531404,0.0001224822,0.008246556,0.009206729,0.00015542796,0.0013744658,0.05868985],"genre_scores_gemma":[0.42168793,0.00087457587,0.54209346,0.000009125357,0.00042766036,0.0002879409,0.000017288985,0.0006002033,0.034001783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938645,0.00012819633,0.0019960392,0.0012535938,0.0020938597,0.000663808],"domain_scores_gemma":[0.9952603,0.0018605042,0.00055759086,0.0017581607,0.0003202436,0.00024320863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003010508,0.0008655582,0.0017025445,0.0010092147,0.000065470165,0.0001429128,0.0012524839,0.00045137006,0.00006648154],"category_scores_gemma":[0.001089203,0.0007594541,0.00025159642,0.00043733732,0.00024675074,0.00056104903,0.00046235992,0.000906178,0.000024701176],"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":[0.00017265593,0.00004047128,0.0007099999,0.00027895588,0.000079460486,0.00010464362,0.00038136475,0.9924355,0.0028923908,0.0024294653,0.00006064428,0.0004144127],"study_design_scores_gemma":[0.0021906309,0.001473155,0.006995124,0.0071408404,0.00016396344,0.0005143705,0.00028592316,0.85527956,0.019421546,0.0009487535,0.100389384,0.0051967385],"about_ca_topic_score_codex":0.00006792793,"about_ca_topic_score_gemma":0.0000015810879,"teacher_disagreement_score":0.3536995,"about_ca_system_score_codex":0.0005354574,"about_ca_system_score_gemma":0.000117862095,"threshold_uncertainty_score":0.9994857},"labels":[],"label_agreement":null},{"id":"W57323835","doi":"10.1007/978-1-4613-0049-6_6","title":"Designs in the Presence of Trends","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; TRACE (psycholinguistics); Block (permutation group theory); Class (philosophy); Block size; Optimal design; Block design; Degree (music); Matrix (chemical analysis); Binary number; Term (time); Combinatorics; Mathematical optimization; Computer science; Arithmetic; Statistics; Artificial intelligence","score_opus":0.2325307607797628,"score_gpt":0.4434055247947282,"score_spread":0.2108747640149654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W57323835","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":[0.000012641872,0.0010960356,0.7907212,0.00014581525,0.0003011992,0.00028154798,0.00048132127,0.000009955238,0.2069503],"genre_scores_gemma":[0.05998426,0.00016068957,0.8888351,0.0005026163,0.00014868441,0.000023824135,0.000045493474,0.00008281025,0.050216522],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9956441,0.00053787575,0.0011259797,0.0005992475,0.0017879006,0.0003049326],"domain_scores_gemma":[0.982117,0.01623111,0.00049733004,0.00095538434,0.00014876568,0.000050395858],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023986027,0.00036507068,0.00070608326,0.00068583223,0.00004041114,0.00009940439,0.0014424348,0.00037661902,0.0035295743],"category_scores_gemma":[0.005345373,0.00023703807,0.00010662086,0.0004740882,0.0004137479,0.00006789788,0.00013307593,0.0007707997,0.00007080145],"study_design_candidate":"theoretical_or_conceptual","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":[0.00010654542,0.00012320805,0.0003937868,0.000026996515,0.000025976879,0.0003771031,0.005724764,0.005303473,0.00042094014,0.20492776,0.010993266,0.77157617],"study_design_scores_gemma":[0.00037458795,0.00035872214,0.00067996565,0.00011839549,0.000029559409,0.000025236317,0.000034159486,0.008118229,0.0007186662,0.9627958,0.026274148,0.000472556],"about_ca_topic_score_codex":0.00004818571,"about_ca_topic_score_gemma":0.00015825551,"teacher_disagreement_score":0.7711036,"about_ca_system_score_codex":0.00008212275,"about_ca_system_score_gemma":0.00005803183,"threshold_uncertainty_score":0.9973813},"labels":[],"label_agreement":null},{"id":"W6929954253","doi":"10.5281/zenodo.10236082","title":"Découverte du Riccia beyrichiana Hampe ex Lehm. (Ricciaceae – Marchantiophyta) au Québec","year":2017,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Natural (archaeology); Context (archaeology); Limiting","score_opus":0.12160227156376252,"score_gpt":0.365097331834727,"score_spread":0.24349506027096446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6929954253","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07408223,0.004133743,0.055653933,0.043260183,0.0064026075,0.0022199308,0.0009957943,0.001275035,0.81197655],"genre_scores_gemma":[0.8507384,0.00093543745,0.0136413155,0.00072162366,0.0020218552,2.4749508e-7,0.0002943777,0.0040629525,0.12758379],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9916702,0.002446125,0.0009851962,0.0015410385,0.002027579,0.0013298689],"domain_scores_gemma":[0.99357486,0.00035006058,0.0008286978,0.0027997477,0.0015847592,0.0008619055],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0063943253,0.00053738133,0.0006472968,0.00062508887,0.014961369,0.0068754237,0.0072414754,0.00028800953,0.051196754],"category_scores_gemma":[0.012280148,0.00055774587,0.0003258121,0.0010406876,0.0018740671,0.002102002,0.00677416,0.0007827484,0.07489215],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024410368,0.0005004682,0.00021830665,0.00007021229,0.00012194468,0.0001246299,0.0048450357,0.0000594994,0.0079383645,0.010859924,0.36379132,0.6112262],"study_design_scores_gemma":[0.0010184695,0.0006118407,0.012835389,0.00010920562,0.000051277766,0.00023303289,0.0014861756,0.0034594378,0.0013411734,0.0015442903,0.97669095,0.00061875064],"about_ca_topic_score_codex":0.011473121,"about_ca_topic_score_gemma":0.00029322767,"teacher_disagreement_score":0.77665615,"about_ca_system_score_codex":0.0013201999,"about_ca_system_score_gemma":0.00013879257,"threshold_uncertainty_score":0.9996874},"labels":[],"label_agreement":null},{"id":"W6931424524","doi":"10.5281/zenodo.4096547","title":"Assistir Possessor (2020) Dublado Filme Online Grátis zkc","year":2020,"lang":"pt","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Taxonomy (biology); Star trek","score_opus":0.18568459047544786,"score_gpt":0.38341416386582367,"score_spread":0.1977295733903758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931424524","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07637562,0.006016087,0.30945063,0.114619024,0.0048681013,0.0077314763,0.019336518,0.008821241,0.45278132],"genre_scores_gemma":[0.9201999,0.0006558969,0.03577729,0.006290875,0.0018749475,1.9828673e-7,0.004407797,0.007804638,0.022988461],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.990334,0.0030495627,0.0012719893,0.0018649341,0.0024481793,0.0010313529],"domain_scores_gemma":[0.994732,0.00037520195,0.0005552588,0.0013350861,0.0017288693,0.001273532],"candidate_categories":["metaresearch","metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0031779888,0.0005436131,0.0007033476,0.00039334732,0.0034811718,0.004544329,0.0049536685,0.00026573864,0.1518233],"category_scores_gemma":[0.013858099,0.0005464109,0.00029508385,0.0034925223,0.00060806336,0.0009955575,0.005297737,0.0009801941,0.07681362],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004846607,0.00064113014,0.000011320016,0.00009238616,0.00012714902,0.0002853604,0.0040956377,0.00020817788,0.032718413,0.0017067037,0.71270883,0.24692026],"study_design_scores_gemma":[0.0011780211,0.0014605743,0.0012637798,0.00006712629,0.00005433725,0.00022925036,0.0032289866,0.015252008,0.0021467076,0.0001867086,0.9742843,0.0006482176],"about_ca_topic_score_codex":0.000027814076,"about_ca_topic_score_gemma":3.004026e-7,"teacher_disagreement_score":0.84382427,"about_ca_system_score_codex":0.00031662663,"about_ca_system_score_gemma":0.00003094002,"threshold_uncertainty_score":0.99969876},"labels":[],"label_agreement":null},{"id":"W6931766968","doi":"10.5281/zenodo.7448708","title":"*oFfIcIaL*!LIVE!sTREAMs!* Jesse Smith vs. Matt Speciale Live Free Tv BROADCAST 12/16/2022","year":2022,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Promotion (chess); Broadcasting (networking)","score_opus":0.0981428991384819,"score_gpt":0.34410667914792625,"score_spread":0.24596378000944435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931766968","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00011281676,0.0011800246,0.0039620935,0.00040858914,0.0008729483,0.0012991318,0.0025679735,0.0014702907,0.98812616],"genre_scores_gemma":[0.00079377665,0.0006945297,0.007842377,0.00047941462,0.0017311473,8.140344e-7,0.0022892102,0.016325988,0.96984273],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9900231,0.003004921,0.00094005506,0.0018238816,0.003329142,0.00087890844],"domain_scores_gemma":[0.9950721,0.0003064907,0.00083239423,0.0027389356,0.0005529623,0.0004970962],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.003785561,0.0006144282,0.00080513954,0.0014489897,0.0025288975,0.0024910541,0.0072417953,0.0003037974,0.8157082],"category_scores_gemma":[0.0054388265,0.0006148884,0.0002934111,0.001823719,0.00059460464,0.0003450277,0.0088277655,0.0010424873,0.09478491],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017504874,0.0002939133,0.000004674735,0.00003281466,0.00008320314,0.00009700323,0.0010724849,0.00003030831,0.00043322644,0.0011943504,0.908923,0.08766002],"study_design_scores_gemma":[0.00076173333,0.0005882607,0.000051939754,0.000060286548,0.000035256064,0.00012737316,0.0030402406,0.00014938509,0.00015357301,0.0006027257,0.9938004,0.000628853],"about_ca_topic_score_codex":0.0003216028,"about_ca_topic_score_gemma":0.000009337044,"teacher_disagreement_score":0.7209233,"about_ca_system_score_codex":0.0008413793,"about_ca_system_score_gemma":0.000042786814,"threshold_uncertainty_score":0.9996303},"labels":[],"label_agreement":null},{"id":"W6931861974","doi":"10.5281/zenodo.7602522","title":"The Effect of Home Exercise Program on Motor Developmental Delay and Parental Satisfaction","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rehabilitation; Patient satisfaction; Quality of life (healthcare); Motor skill; Scale (ratio); Gross motor skill; Outpatient clinic","score_opus":0.07725563708995277,"score_gpt":0.37077366026944786,"score_spread":0.2935180231794951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931861974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9860594,0.000072327544,0.00018125505,0.00011747596,0.00017628196,0.0010367258,0.00007020487,0.00048513256,0.01180118],"genre_scores_gemma":[0.9975325,0.00007869041,0.0013589251,0.000009578212,0.000026111054,4.359326e-7,0.000073203424,0.00033668286,0.00058391463],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997199,0.0008528371,0.00034124264,0.00039271283,0.00093848066,0.0002757314],"domain_scores_gemma":[0.998781,0.00046309823,0.00012873772,0.00030638365,0.00018906668,0.0001316997],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0034901272,0.0001294874,0.00017384645,0.00026776738,0.0016989455,0.0008365012,0.00070489,0.000045889672,0.0011625852],"category_scores_gemma":[0.0018941686,0.00008785677,0.00005304145,0.0010213755,0.00027174465,0.00021699749,0.00087021233,0.0001466507,0.004349358],"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":[0.00037499474,0.000034769182,0.0002463187,0.000011075093,0.000015925558,0.000007630602,0.000415224,0.000016581203,0.012719401,0.00031885778,0.018286915,0.9675523],"study_design_scores_gemma":[0.0023652033,0.0069839563,0.22214392,0.000119875156,0.000031043048,0.00023793791,0.0029376105,0.0043485365,0.0451547,0.0010488159,0.7140586,0.00056983694],"about_ca_topic_score_codex":0.00001075643,"about_ca_topic_score_gemma":2.101963e-7,"teacher_disagreement_score":0.9669825,"about_ca_system_score_codex":0.00009374883,"about_ca_system_score_gemma":0.0000029012308,"threshold_uncertainty_score":0.9997505},"labels":[],"label_agreement":null},{"id":"W6990033152","doi":"","title":"Construction of Optimal Foldover Designs with the General Minimum Lower-Order Confounding","year":2016,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Nucleofection; Gestational period; Diafiltration; TSG101; Proteogenomics; Dysgeusia; Fusible alloy; Demotion; Liquation","score_opus":0.06134818838081948,"score_gpt":0.33500365785222436,"score_spread":0.2736554694714049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990033152","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9287945,0.00013815715,0.05283604,0.00039529335,0.000918178,0.00045241078,0.000063615735,0.000027886179,0.016373912],"genre_scores_gemma":[0.5923761,0.00010564257,0.37378648,0.000048394664,0.00023793051,0.0000026520559,0.00007094313,0.00008966842,0.033282198],"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","domain_scores_codex":[0.9970158,0.00044219077,0.00028069943,0.0005543184,0.001428934,0.00027805343],"domain_scores_gemma":[0.9965543,0.0008605052,0.0010747043,0.0006066158,0.0008140594,0.0000897864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011425479,0.00029781615,0.0006085553,0.0003822742,0.0003360198,0.00007199643,0.0010573327,0.00026594984,0.00026250278],"category_scores_gemma":[0.00019290973,0.00021145857,0.00023040103,0.0006089825,0.00065899483,0.00047060515,0.00011141483,0.00021085174,0.00005087013],"study_design_candidate":"bench_or_experimental","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":[0.02189695,0.00089527224,0.04449669,0.00045024304,0.002624101,0.00034182353,0.014451111,0.0018846574,0.7314864,0.04680348,0.06508832,0.06958097],"study_design_scores_gemma":[0.0072540753,0.0039066738,0.108821124,0.001142786,0.0013188095,0.00010927216,0.7182978,0.0034730446,0.118203916,0.0035312835,0.031185312,0.002755911],"about_ca_topic_score_codex":0.0004386622,"about_ca_topic_score_gemma":0.009621548,"teacher_disagreement_score":0.7038467,"about_ca_system_score_codex":0.00011526765,"about_ca_system_score_gemma":0.00024270311,"threshold_uncertainty_score":0.862303},"labels":[],"label_agreement":null},{"id":"W6999346864","doi":"","title":"Construction of optimal designs for nonlinear models","year":2019,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Optimal design; Nonlinear system; Cluster analysis; Bayesian probability; Fisher information; Discretization; Bayesian inference; Linear model; Optimality criterion","score_opus":0.1279659062739854,"score_gpt":0.35700656275986786,"score_spread":0.22904065648588245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999346864","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58230424,0.00021446541,0.4038828,0.000091213646,0.001287187,0.0011722584,0.00034474855,0.00003840313,0.010664709],"genre_scores_gemma":[0.065196626,0.000052870942,0.9276895,0.00000755341,0.000049377817,0.0000011855097,0.00021053162,0.000039676364,0.0067526884],"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","domain_scores_codex":[0.99755746,0.00022137423,0.00035918105,0.0005870779,0.0010457509,0.00022914582],"domain_scores_gemma":[0.99658394,0.0007761919,0.0010505304,0.0005758773,0.00092819094,0.00008530075],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011855486,0.00024683034,0.00077323086,0.00056379935,0.00014063917,0.000036054196,0.000980714,0.00039117868,0.000054066866],"category_scores_gemma":[0.00024683602,0.0002854692,0.00043162022,0.00044118194,0.00019656263,0.00053419924,0.00009162964,0.00018219482,0.0000531535],"study_design_candidate":"bench_or_experimental","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":[0.0315024,0.0024797313,0.0119107785,0.0033074485,0.0023863676,0.000082091785,0.025158491,0.11292645,0.47034773,0.10103396,0.04412805,0.1947365],"study_design_scores_gemma":[0.0040230583,0.0021063876,0.005840543,0.0004670055,0.0006076956,0.000014810737,0.5235771,0.34848785,0.09396294,0.014002244,0.0054921503,0.0014182077],"about_ca_topic_score_codex":0.00031687177,"about_ca_topic_score_gemma":0.0033613571,"teacher_disagreement_score":0.5238067,"about_ca_system_score_codex":0.00009833521,"about_ca_system_score_gemma":0.00024466493,"threshold_uncertainty_score":0.99995977},"labels":[],"label_agreement":null},{"id":"W7024608355","doi":"","title":"Selecting optimal follow-up split-plot designs","year":2019,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Optimal design; Selection (genetic algorithm); Design of experiments; Sequential analysis; Point (geometry)","score_opus":0.11285263140965615,"score_gpt":0.35204765760385676,"score_spread":0.2391950261942006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024608355","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93421566,0.0003361881,0.019040646,0.00013755744,0.0025768147,0.0007729346,0.000038406928,0.00013432377,0.04274746],"genre_scores_gemma":[0.6244969,0.00007408811,0.23556922,0.00005651897,0.00017570022,0.0000018496441,0.000201888,0.00015507643,0.13926874],"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","domain_scores_codex":[0.9951632,0.00066488254,0.00042139538,0.0011323697,0.0020827437,0.00053538824],"domain_scores_gemma":[0.99587554,0.0011737103,0.0010388693,0.0009619812,0.0007461457,0.00020372884],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0023489543,0.00046490337,0.0010066171,0.00083806564,0.00044142615,0.0001823135,0.0020352646,0.0005531329,0.00030193746],"category_scores_gemma":[0.00081382634,0.00054359453,0.00056185375,0.0011906888,0.00012832847,0.0007016865,0.00026099232,0.00057883834,0.001473176],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.013001541,0.001413066,0.0674151,0.0010463143,0.0020489113,0.00073145656,0.037755623,0.0057089496,0.5787901,0.011003653,0.15052922,0.13055603],"study_design_scores_gemma":[0.005051662,0.00198046,0.12713215,0.000689112,0.00073676015,0.00003829355,0.75030285,0.015795715,0.070342876,0.0017846336,0.023022711,0.0031228089],"about_ca_topic_score_codex":0.0012741884,"about_ca_topic_score_gemma":0.020242114,"teacher_disagreement_score":0.7125472,"about_ca_system_score_codex":0.00029326038,"about_ca_system_score_gemma":0.00034860524,"threshold_uncertainty_score":0.99970156},"labels":[],"label_agreement":null},{"id":"W7030727082","doi":"","title":"NRC - Model Based Field Calibration for the NRC 3D Auto-Synchronized Laser Range Sensor","year":2004,"lang":"en","type":"report","venue":"NPARC","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Calibration; Laser; Field (mathematics); Range (aeronautics); Metrology","score_opus":0.2053255701102702,"score_gpt":0.44874335377994684,"score_spread":0.24341778366967665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030727082","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":[0.000067293906,0.00038657145,0.8787393,0.0031557314,0.0019941546,0.0020262667,0.00028091233,0.00013621533,0.11321355],"genre_scores_gemma":[0.022342164,0.0001526902,0.8987447,0.0026336573,0.0012696515,0.001081452,0.00008200385,0.00019645951,0.073497206],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9897918,0.0005230269,0.0012922648,0.0011382018,0.006687096,0.00056757877],"domain_scores_gemma":[0.988343,0.0067316117,0.0007415227,0.0016464774,0.0023472682,0.0001901092],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.013844173,0.0005366584,0.0009592541,0.00030755732,0.00040385552,0.0005560466,0.0013224644,0.00070810004,0.004523278],"category_scores_gemma":[0.015727127,0.00033871044,0.000619617,0.00057680707,0.00017459232,0.00033627872,0.00017977974,0.00054765696,0.00010969724],"study_design_candidate":"not_applicable","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":[0.00081851694,0.00027996552,0.000027736698,0.00015071538,0.00017080616,0.00004616377,0.00041095846,0.045935832,0.024560107,0.00061790325,0.77224296,0.15473832],"study_design_scores_gemma":[0.001335031,0.00020152381,0.000006931874,0.000077357574,0.0001225065,0.000013807687,0.000072256975,0.83048654,0.01639933,0.0104486765,0.14036441,0.0004716108],"about_ca_topic_score_codex":0.00013733131,"about_ca_topic_score_gemma":0.00004239143,"teacher_disagreement_score":0.7845507,"about_ca_system_score_codex":0.0010638338,"about_ca_system_score_gemma":0.005548926,"threshold_uncertainty_score":0.9999065},"labels":[],"label_agreement":null},{"id":"W7033432302","doi":"","title":"Rangeland Health Assessments of NCC Properties in Saskatchewan","year":2023,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Rangeland; Exclosure; Forest health; Population; Vegetation (pathology)","score_opus":0.11670410099194412,"score_gpt":0.4275477039146987,"score_spread":0.3108436029227546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033432302","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021464492,0.00003776142,0.0021825202,0.0001627743,0.00091632595,0.0020790175,0.00007906189,0.0003462005,0.9920499],"genre_scores_gemma":[0.008595389,0.00019078514,0.016147561,0.00009602084,0.000080771024,0.00028210325,0.000008362953,0.0006129688,0.973986],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9877803,0.0036522492,0.0020343286,0.003471637,0.0023286755,0.0007328425],"domain_scores_gemma":[0.9911646,0.0013952447,0.0015040562,0.0054680933,0.00015055989,0.00031740882],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.024816515,0.0005991401,0.0016758309,0.0012585107,0.00008093804,0.00019080745,0.0030217431,0.0005463259,0.41106266],"category_scores_gemma":[0.0066122543,0.00052947307,0.000351405,0.0011370123,0.0003789474,0.0002557839,0.0022769999,0.0007060965,0.5114041],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037205016,0.0011990718,0.038053926,0.00040823306,0.00035177724,0.00006691986,0.005864303,0.00047474456,0.005200876,0.00024142348,0.72499955,0.22276714],"study_design_scores_gemma":[0.004317852,0.000029891342,0.03412003,0.0037844935,0.00008988192,0.000041231197,0.011253895,0.0015295458,0.01843477,0.010716665,0.91318405,0.002497717],"about_ca_topic_score_codex":0.0067900266,"about_ca_topic_score_gemma":0.004912559,"teacher_disagreement_score":0.22026943,"about_ca_system_score_codex":0.00046941012,"about_ca_system_score_gemma":0.00083509786,"threshold_uncertainty_score":0.99982387},"labels":[],"label_agreement":null},{"id":"W7033729863","doi":"","title":"Search for a heavy Standard Model Higgs boson in the channel H -> ZZ -> l(+)l(-) q(q)over-bar using the ATLAS detector","year":2012,"lang":"en","type":"article","venue":"Lancaster EPrints (Lancaster University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Agencia Nacional de Promoción Científica y Tecnológica; Fundação para a Ciência e a Tecnologia; H. Lundbeck A/S; Services Fédéraux des Affaires Scientifiques, Techniques et Culturelles; Bundesministerium für Wissenschaft und Forschung; Centre National pour la Recherche Scientifique et Technique; Centre National de la Recherche Scientifique; Japan Society for the Promotion of Science; Israel Science Foundation; Max-Planck-Gesellschaft; Lundbeckfonden; Bundesministerium für Bildung und Forschung; TRIUMF; Alexander von Humboldt-Stiftung; Comisión Nacional de Investigación Científica y Tecnológica; Danmarks Grundforskningsfond; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Ministry of Education, Culture, Sports, Science and Technology; Ministerstwo Edukacji i Nauki; CERN; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); Deutsche Forschungsgemeinschaft; National Natural Science Foundation of China; European Commission; General Secretariat for Research and Technology","keywords":"Higgs boson; Large Hadron Collider; Standard Model (mathematical formulation); Atlas (anatomy); ATLAS experiment; Luminosity; Boson; Atlas detector","score_opus":0.2219801864271945,"score_gpt":0.4006784341307305,"score_spread":0.178698247703536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033729863","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80415535,0.00008946756,0.18989891,0.00038308336,0.00032898254,0.0008855497,0.00007062475,0.000023587681,0.0041644676],"genre_scores_gemma":[0.9786803,0.000005920612,0.018654987,0.00044274874,0.00023408169,0.000008854627,0.0000027617077,0.000037260335,0.0019330762],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955222,0.0010814106,0.00046882118,0.00068774924,0.0012588312,0.0009809759],"domain_scores_gemma":[0.99701726,0.0012159809,0.00019777098,0.0011715101,0.0001860987,0.00021135541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044052033,0.00034564026,0.0004806067,0.00047684906,0.0003895039,0.00031541567,0.0019124773,0.00016436804,0.0002897269],"category_scores_gemma":[0.00014813084,0.00021779994,0.00026075906,0.0011734413,0.000228351,0.0010058626,0.0006568615,0.00042565478,0.00021747727],"study_design_candidate":"observational","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":[0.010761884,0.0012491122,0.8263407,0.00014380217,0.00030810625,0.000233944,0.06982707,0.0020055035,0.044370912,0.014871146,0.0033670317,0.026520792],"study_design_scores_gemma":[0.012015139,0.0011850593,0.14896199,0.00030181406,0.00025689817,0.00027852718,0.06001839,0.4895683,0.17257398,0.0042233127,0.10791334,0.0027032513],"about_ca_topic_score_codex":0.00006403069,"about_ca_topic_score_gemma":0.000020929914,"teacher_disagreement_score":0.6773787,"about_ca_system_score_codex":0.00030494167,"about_ca_system_score_gemma":0.00010064399,"threshold_uncertainty_score":0.8881624},"labels":[],"label_agreement":null},{"id":"W7098029336","doi":"","title":"Sequential Design for Computer Experiments with a Flexible Bayesian Additive Model.” Canadian","year":2012,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Design of experiments; Sequential analysis; Sequential estimation; Bayesian experimental design","score_opus":0.29490751558984385,"score_gpt":0.455892094913676,"score_spread":0.16098457932383214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098029336","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":[0.0012968575,0.00005694327,0.97451067,0.00011392318,0.00036333277,0.00094610103,0.000058261507,0.00006779076,0.022586131],"genre_scores_gemma":[0.35164425,4.1549785e-7,0.64379436,0.0007719944,0.00015737035,0.00016047015,0.000007675439,0.000027403068,0.0034360792],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996934,0.00034384168,0.00042284082,0.00055072294,0.0008542979,0.00089431],"domain_scores_gemma":[0.99757105,0.00066869904,0.00011703581,0.0004920801,0.0002456658,0.0009054508],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0022857955,0.00026968186,0.0003385193,0.00035519767,0.00028746444,0.00029052547,0.00059610914,0.000111348236,0.0013790381],"category_scores_gemma":[0.00012617922,0.00018662485,0.00011380893,0.00042873112,0.00012817752,0.0007816287,0.000079992555,0.00009100939,0.00020402139],"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":[0.0039365175,0.0016968618,0.005550235,0.000018708626,0.0007308887,0.000053950367,0.02789201,0.1550131,0.062009323,0.121962436,0.41727522,0.20386074],"study_design_scores_gemma":[0.0012824032,0.00068832614,0.00020480355,0.000014404028,0.000023838511,0.000029016624,0.0011478519,0.7406908,0.24109997,0.0038346092,0.0103430385,0.00064091326],"about_ca_topic_score_codex":0.0037740134,"about_ca_topic_score_gemma":0.0011360375,"teacher_disagreement_score":0.58567774,"about_ca_system_score_codex":0.0002653996,"about_ca_system_score_gemma":0.00042480257,"threshold_uncertainty_score":0.99953383},"labels":[],"label_agreement":null},{"id":"W7100261528","doi":"","title":"N: Medical statistics","year":2004,"lang":"en","type":"article","venue":"","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Minimax; Permission; Simple (philosophy); Genetic algorithm; Particle swarm optimization","score_opus":0.18699701784725473,"score_gpt":0.5272646146969973,"score_spread":0.3402675968497425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100261528","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":[0.0039190226,0.00003275985,0.8933719,0.0010489551,0.00028718842,0.000046524565,0.0000043872747,0.00003889338,0.10125042],"genre_scores_gemma":[0.15368363,0.0000030525937,0.8405742,0.0014501282,0.000047960846,0.0000026846578,7.163645e-7,0.0000055526984,0.00423206],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969902,0.00011708638,0.00033190227,0.00023466822,0.002175425,0.0001507068],"domain_scores_gemma":[0.99849486,0.0008584983,0.000040356623,0.00029512736,0.00009091307,0.00022025256],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002596373,0.00006327311,0.00012557986,0.00006974852,0.000048729362,0.00009645813,0.0005449431,0.00005450277,0.016924327],"category_scores_gemma":[0.0060454207,0.000040096864,0.000032063148,0.0003131344,0.00011483912,0.000117597825,0.00010938361,0.000080529375,0.0042073824],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016231466,0.000112087306,0.0004843061,5.893552e-7,0.000005390172,0.00010637767,0.00023175751,0.0001363188,0.0023510663,0.8161718,0.030184206,0.15019986],"study_design_scores_gemma":[0.00091198616,0.00019837264,0.0026292624,0.0000059931476,0.0000026290438,0.00006306168,0.00057801453,0.0019600021,0.02992196,0.92767996,0.035832897,0.00021585601],"about_ca_topic_score_codex":0.000038693463,"about_ca_topic_score_gemma":0.000011861083,"teacher_disagreement_score":0.149984,"about_ca_system_score_codex":0.000031635805,"about_ca_system_score_gemma":0.00013701052,"threshold_uncertainty_score":0.99656796},"labels":[],"label_agreement":null},{"id":"W7108440019","doi":"10.1080/03610926.2025.2596123","title":"Orthogonal-array composite minimaxloss designs for third-order models","year":2025,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Regina","funders":"","keywords":"Composite number; Component (thermodynamics); Work (physics); Welding","score_opus":0.22006699062714818,"score_gpt":0.5516383631028189,"score_spread":0.3315713724756707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7108440019","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":[0.0008018871,0.0039354693,0.9741088,0.00025131897,0.0002125261,0.00065238477,0.000111312336,0.000036964757,0.019889297],"genre_scores_gemma":[0.029386386,0.0002528037,0.9667667,0.00065799913,0.000009504301,0.00024586555,0.000025360678,0.000017925198,0.0026374368],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98528636,0.012536093,0.0010348062,0.00053259806,0.00031598617,0.000294166],"domain_scores_gemma":[0.9523321,0.045466904,0.00027214675,0.0013181966,0.0005116719,0.0000990012],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.032829184,0.0002311007,0.0005346439,0.0004054009,0.00042946712,0.00024807113,0.0011962939,0.00014669668,0.00006102096],"category_scores_gemma":[0.009747871,0.00020214538,0.00007252461,0.00084979925,0.00057744543,0.00032327953,0.00034238357,0.0002820126,0.0000060773873],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005028705,0.000090729656,0.0002261259,0.000019987498,0.000025566183,4.5624446e-7,0.0014716012,0.00038790115,0.0138905775,0.8495827,0.0007024757,0.13309903],"study_design_scores_gemma":[0.00074118504,0.00005973702,0.00086798624,0.000063501626,0.000032319418,0.000002719933,0.0013438475,0.027003486,0.00746853,0.957186,0.005024007,0.0002067248],"about_ca_topic_score_codex":0.000011197009,"about_ca_topic_score_gemma":0.000009046195,"teacher_disagreement_score":0.13289231,"about_ca_system_score_codex":0.000059594226,"about_ca_system_score_gemma":0.00013198407,"threshold_uncertainty_score":0.99859345},"labels":[],"label_agreement":null},{"id":"W7117899418","doi":"10.5979/cha.2023.136_9","title":"Local Case of Making a Yield and Quality Predictive Model for Development of Plucking Plan System","year":2023,"lang":"en","type":"article","venue":"Chagyo Kenkyu Hokoku (Tea Research Journal)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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":"Coast Mountain College","funders":"","keywords":"Plucking; Yield (engineering); Plan (archaeology); Quality (philosophy); Preventive maintenance","score_opus":0.7253552610965381,"score_gpt":0.5982834395439376,"score_spread":0.12707182155260055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117899418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.524163,0.00048862107,0.4736722,0.000075990465,0.00013938706,0.00059841725,0.00006671304,0.000030480674,0.00076518726],"genre_scores_gemma":[0.94067097,0.000025183332,0.05875068,0.000008473386,0.00006567009,0.00006830838,0.0000017432767,0.000030399684,0.0003785464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99249256,0.0012886154,0.0018458178,0.0006038929,0.002984494,0.00078461255],"domain_scores_gemma":[0.98927313,0.0075034336,0.00065792096,0.0005168034,0.0016828142,0.00036592595],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04783282,0.00022085788,0.0007493208,0.0013951531,0.0007622254,0.00022863904,0.00085591775,0.00017236268,0.000026975122],"category_scores_gemma":[0.00797853,0.00016935905,0.00017733988,0.0014857022,0.00053137104,0.00041096745,0.00069804094,0.0006383437,0.000011526496],"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":[0.008963004,0.0008714048,0.0065471856,0.0032550404,0.0010802081,0.0033792218,0.21856232,0.04726401,0.3212394,0.03200254,0.006032438,0.35080323],"study_design_scores_gemma":[0.0012230405,0.0005109861,0.00087802904,0.0009658887,0.000016501557,0.0010778933,0.117118634,0.8160658,0.056674376,0.005005236,0.00017717603,0.00028644974],"about_ca_topic_score_codex":0.000046395424,"about_ca_topic_score_gemma":0.000040986226,"teacher_disagreement_score":0.76880175,"about_ca_system_score_codex":0.00039123444,"about_ca_system_score_gemma":0.0007142898,"threshold_uncertainty_score":0.9804565},"labels":[],"label_agreement":null},{"id":"W7164841587","doi":"10.22271/maths.2025.v10.i1c.2392","title":"Resolving design of experiments for factorial layouts with applications to fraser valley dairy farm productivity","year":2025,"lang":"","type":"article","venue":"International Journal of Statistics and Applied Mathematics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fractional factorial design; Factorial experiment; Productivity; Matching (statistics); Factorial; Matrix (chemical analysis); Linear programming; Moment (physics); Function (biology); Weibull distribution","score_opus":0.10320062955310616,"score_gpt":0.43076714942661276,"score_spread":0.3275665198735066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7164841587","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":[0.002310293,0.0002540472,0.9935729,0.00019141233,0.00096745003,0.0015929557,0.0004636086,0.0000055787377,0.0006417421],"genre_scores_gemma":[0.13668019,0.0000720934,0.8625973,0.00006610954,0.0002608612,0.0001006004,0.000004328994,0.000026678943,0.00019184678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99571645,0.000087634704,0.0018412126,0.00043425994,0.00166548,0.00025496582],"domain_scores_gemma":[0.9917807,0.00380184,0.001633508,0.00034795635,0.0022483696,0.00018760122],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002663657,0.0003202013,0.000784839,0.00053867156,0.00015381942,0.0002769706,0.0010214162,0.00011000261,0.00004701058],"category_scores_gemma":[0.0013720702,0.00025381282,0.00009068104,0.0003732002,0.00023537206,0.00015491359,0.0002727704,0.00021956435,0.000003103915],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006408428,0.003715942,0.00013127788,0.00048917264,0.0019934617,0.000018034547,0.010624014,0.0132627785,0.21531913,0.59089506,0.0067976136,0.15034509],"study_design_scores_gemma":[0.0097360015,0.0027788817,0.00022059488,0.0015871963,0.00076033094,0.00005267689,0.012700747,0.025103921,0.3763619,0.55203223,0.017512335,0.001153162],"about_ca_topic_score_codex":0.0000040812533,"about_ca_topic_score_gemma":0.0000017757054,"teacher_disagreement_score":0.16104278,"about_ca_system_score_codex":0.00015102082,"about_ca_system_score_gemma":0.0004648242,"threshold_uncertainty_score":0.9999914},"labels":[],"label_agreement":null},{"id":"W810039741","doi":"10.4314/jagst.v4i1.31673","title":"A Bayesian Test for Equality of Scale Parameters of Several Exponential Distributions","year":2005,"lang":"en","type":"article","venue":"Journal of Agriculture Science and Technology","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","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 Manitoba","funders":"","keywords":"Chi-square test; Mathematics; Statistics; Pearson's chi-squared test; Likelihood-ratio test; Statistic; Test statistic; Bayesian probability; Likelihood principle; Applied mathematics; Monte Carlo method; Exponential distribution; Statistical hypothesis testing; Maximum likelihood; Likelihood function","score_opus":0.04623470849258032,"score_gpt":0.3927533099285435,"score_spread":0.3465186014359632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W810039741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8807543,0.00025446355,0.114653766,0.0037419167,0.00017186615,0.0001874325,0.00005050711,0.000009313307,0.0001764681],"genre_scores_gemma":[0.7879791,0.000010490877,0.21192355,0.00001866062,0.000028196317,0.0000024042438,2.8820824e-7,0.0000014327372,0.000035862504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99771506,0.00005705718,0.0008490988,0.00022696368,0.0009312768,0.00022054506],"domain_scores_gemma":[0.9967388,0.00050278945,0.0008634161,0.00020240192,0.0015910937,0.00010148125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003935709,0.00009646484,0.00038722513,0.00037788774,0.00014505662,0.00004848805,0.00083809084,0.00012407554,0.000013241583],"category_scores_gemma":[0.004634472,0.0000537699,0.000113905546,0.0021987604,0.001462101,0.00053150405,0.00012985086,0.00015668535,7.7388444e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037849743,0.00023085039,0.0027532256,0.0000042947563,0.000009311876,9.156675e-7,0.00022108603,0.00005417406,0.967295,0.0024753292,0.0011457831,0.025772171],"study_design_scores_gemma":[0.0005851365,0.0011453404,0.007475935,0.000031520707,0.0000279039,0.0002184478,0.0023856165,0.0008156925,0.9727017,0.012688728,0.0018047966,0.00011917595],"about_ca_topic_score_codex":0.0000043204104,"about_ca_topic_score_gemma":0.000006175129,"teacher_disagreement_score":0.097269796,"about_ca_system_score_codex":0.00005577762,"about_ca_system_score_gemma":0.00015305243,"threshold_uncertainty_score":0.5548231},"labels":[],"label_agreement":null},{"id":"W85037577","doi":"","title":"Use of complementary property of block designs in PBIB designs.","year":2007,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Property (philosophy); Block (permutation group theory); Arithmetic; Combinatorics; Epistemology","score_opus":0.30908903331026544,"score_gpt":0.43611893173867033,"score_spread":0.1270298984284049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W85037577","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9938062,0.00014740785,0.0023729939,0.00006120533,0.0007913643,0.0006644787,0.000012863479,0.000023619443,0.0021199097],"genre_scores_gemma":[0.9145449,0.0000060903535,0.08504179,0.000086600434,0.0000040050086,0.0000082540255,0.000002417031,0.000021082433,0.00028485557],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9952059,0.0007911404,0.0015978408,0.00050449045,0.0014577309,0.00044294135],"domain_scores_gemma":[0.995011,0.0031718567,0.0004946794,0.000796849,0.00037156523,0.00015409541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007786536,0.0002239756,0.0006369288,0.0005924341,0.000057031248,0.000050193714,0.00091818173,0.00010936823,0.00028092056],"category_scores_gemma":[0.0016416768,0.00015714871,0.000143883,0.0016740597,0.00028387137,0.0004946439,0.0002756184,0.00018328699,0.000033117485],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009266447,0.0017973621,0.18132332,0.000029951872,0.00006743036,0.0000768814,0.002168966,0.0002524359,0.7090345,0.078660965,0.00791817,0.017743388],"study_design_scores_gemma":[0.0020203376,0.001015725,0.053975724,0.0000778443,0.000019436258,0.000010897665,0.0011656337,0.00094222545,0.7993808,0.13881578,0.0021957909,0.00037981168],"about_ca_topic_score_codex":0.0006547481,"about_ca_topic_score_gemma":0.00004341733,"teacher_disagreement_score":0.12734759,"about_ca_system_score_codex":0.000113266244,"about_ca_system_score_gemma":0.000107645945,"threshold_uncertainty_score":0.64083385},"labels":[],"label_agreement":null}]}