{"meta":{"query_hash":"40b637905f92","filters":{"topic":"Railway Systems and Energy Efficiency"},"cohort_total":388,"direct_labels_cover":0,"predictions_cover":388,"exported":388,"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/40b637905f92","api":"https://metacan.xera.ac/api/v1/cohort?topic=Railway+Systems+and+Energy+Efficiency"},"results":[{"id":"W115303295","doi":"","title":"Mixed-Mode and Fallback Operation System Developments: Changing the Equation in the Operator’s Favor","year":2006,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Interlocking; Automatic train control; Function (biology); Track circuit; Automation; Control system; Block (permutation group theory); Computer science; Control (management); Engineering; Telecommunications; Reliability engineering; Electronic circuit; Electrical engineering","score_opus":0.010730694680968461,"score_gpt":0.18804757459474108,"score_spread":0.17731687991377262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W115303295","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":[0.94623065,0.00030274267,0.0328824,0.0001164326,0.00030727524,0.0002617595,0.0000011669171,0.00011816113,0.019779403],"genre_scores_gemma":[0.99923736,0.00000772989,0.00027680432,0.000042098764,0.00009806243,0.00006470448,0.000008943641,0.00001174522,0.00025257337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932295,0.000045186993,0.00020991248,0.00010430746,0.00013782112,0.00017982315],"domain_scores_gemma":[0.9998087,0.000029826491,0.0000136868475,0.00011947461,0.000016597,0.00001168501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044038746,0.00009822427,0.00008088144,0.00006450798,0.00014751164,0.0001367034,0.00011028343,0.00004044404,0.0000026758564],"category_scores_gemma":[0.000004507153,0.00005325155,0.000012531521,0.00020179088,0.000009874431,0.00013659551,0.000013984441,0.000056995024,0.000016976539],"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":[0.0000015740146,0.00001796949,0.0010381337,0.00010602725,0.000012879343,0.0000036271329,0.004594334,0.84417653,0.0067555346,0.13889004,0.0007731323,0.0036302109],"study_design_scores_gemma":[0.0003243784,0.00001215944,0.0061146943,0.00008839342,0.0000060964853,0.000021367518,0.010239417,0.97556597,0.0030550663,0.000020775611,0.004319027,0.00023265932],"about_ca_topic_score_codex":0.0005063014,"about_ca_topic_score_gemma":0.0009271264,"teacher_disagreement_score":0.13886927,"about_ca_system_score_codex":0.000068555775,"about_ca_system_score_gemma":0.0000102166205,"threshold_uncertainty_score":0.21715353},"labels":[],"label_agreement":null},{"id":"W1507370507","doi":"","title":"Passenger Rail Planner's Guide 2006","year":2006,"lang":"en","type":"article","venue":"Railway age","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Memphis; Miami; Atlanta; Metropolitan area; Phoenix; Salt lake; Light rail transit; Baton rouge; Transport engineering; Archaeology; Engineering; Public transport; History","score_opus":0.00455341154230359,"score_gpt":0.18224754258693499,"score_spread":0.1776941310446314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507370507","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":[0.28747404,0.0023633856,0.004706601,0.000110338406,0.0017984125,0.00018438516,0.000039228595,0.0014486664,0.701875],"genre_scores_gemma":[0.96814233,0.000023542441,0.0005767742,0.000048765793,0.0008479429,0.00002800404,0.00004677326,0.00006857783,0.03021727],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986559,0.000022132988,0.00037614533,0.00023537294,0.0002290107,0.00048144377],"domain_scores_gemma":[0.9994529,0.000041450006,0.000032803528,0.00036134053,0.000024897357,0.00008661571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017747637,0.00023961415,0.00024577611,0.0001082762,0.00008616831,0.000071556045,0.00021150449,0.00013765898,0.00022029989],"category_scores_gemma":[0.000011739375,0.00022109612,0.000103747705,0.00022629919,0.00003491936,0.000121205725,0.000022239714,0.00014476957,0.00049107056],"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":[0.0000048714546,0.000067623536,0.00047435492,0.00009175368,0.000046112214,0.0004427259,0.00016731881,0.32443354,0.025719205,0.0055144806,0.6389223,0.0041157855],"study_design_scores_gemma":[0.00035164884,0.000019116284,0.0037360652,0.00003301287,0.000010276892,0.000030824063,0.0000405202,0.009294615,0.0024999145,0.00016719518,0.9834129,0.0004038916],"about_ca_topic_score_codex":0.00055139436,"about_ca_topic_score_gemma":0.00035602754,"teacher_disagreement_score":0.68066835,"about_ca_system_score_codex":0.0000648071,"about_ca_system_score_gemma":0.000013043225,"threshold_uncertainty_score":0.9016038},"labels":[],"label_agreement":null},{"id":"W1547588018","doi":"","title":"Recoverable Robustness for Train Shunting Problems","year":2009,"lang":"en","type":"article","venue":"Algorithmic operations research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":20,"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); Unavailability; Computer science; Shunting; Mathematical optimization; Exploit; Algorithm; Reliability engineering; Mathematics; Engineering","score_opus":0.05416012265873652,"score_gpt":0.3219233463933957,"score_spread":0.26776322373465916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1547588018","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":[0.2368528,0.001388782,0.72364867,0.0016047753,0.0009852673,0.0024864418,0.000053207033,0.0006943198,0.032285735],"genre_scores_gemma":[0.9703338,0.00010361874,0.022335263,0.000021459238,0.0005646109,0.0004216932,0.000047210204,0.000041838408,0.0061305137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860084,0.00005344245,0.0002718598,0.00023220698,0.0002840228,0.0005576134],"domain_scores_gemma":[0.99932384,0.000081146616,0.000005447657,0.00025156856,0.00025365688,0.00008431584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011980366,0.00011617735,0.00014934922,0.00022707289,0.00050446455,0.00023898191,0.00022854371,0.00009123484,0.00005092339],"category_scores_gemma":[0.00009322904,0.00011097563,0.00005092903,0.0005598311,0.000029197807,0.00028052175,0.000012193407,0.00024299226,0.000030443725],"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":[0.0000011357271,0.000029568515,0.0000019468662,0.000027655078,0.000008824553,0.0000010279482,0.00026776528,0.9459597,0.006695461,0.0017183105,0.0018906221,0.043398008],"study_design_scores_gemma":[0.00026065923,0.000104830746,0.000056402558,0.000038883438,0.0000020803236,0.0000069152384,0.00017459715,0.9850827,0.0011449776,0.00013570074,0.012846519,0.00014575907],"about_ca_topic_score_codex":0.000117958014,"about_ca_topic_score_gemma":0.00011638814,"teacher_disagreement_score":0.733481,"about_ca_system_score_codex":0.000110357585,"about_ca_system_score_gemma":0.00007018467,"threshold_uncertainty_score":0.4525455},"labels":[],"label_agreement":null},{"id":"W1557185721","doi":"10.1287/trsc.1100.0322","title":"Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Column generation; Heuristics; Crew; Computer science; Selection (genetic algorithm); Computation; Core (optical fiber); Column (typography); Mathematical optimization; Lagrangian; Operations research; Algorithm; Engineering; Mathematics; Artificial intelligence; Computer network","score_opus":0.007950100100018846,"score_gpt":0.22755699092012055,"score_spread":0.2196068908201017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557185721","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":[0.747133,0.000010683365,0.25162554,0.000019553117,0.00045893597,0.00016918422,0.0000057831303,0.00015357872,0.00042375195],"genre_scores_gemma":[0.98327905,0.0000037663767,0.016401146,0.000011591356,0.000075957985,0.00006625585,0.000025163255,0.000016759031,0.00012030615],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990748,0.0000034233572,0.00019864505,0.00023618528,0.0002609292,0.0002260056],"domain_scores_gemma":[0.9995835,0.000015575652,0.000037727314,0.00011778207,0.00017210642,0.00007332121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038169153,0.00009725372,0.000089729365,0.00011556545,0.0002734198,0.00007951777,0.00012470098,0.000049999944,0.000019888254],"category_scores_gemma":[0.000013834123,0.00008850804,0.00002472126,0.000617507,0.00009627375,0.0004405525,5.7499e-7,0.000097548625,0.0000043592363],"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":[0.000005319402,0.000007860189,0.00070748234,0.000021452208,0.0000026887762,2.7621243e-7,0.00037346233,0.31444684,0.68082887,0.0017347148,0.000017662884,0.0018533707],"study_design_scores_gemma":[0.0003141305,0.00006173071,0.021045215,0.000012599074,0.00001062202,0.000005501702,0.00006564199,0.92690545,0.050189752,0.000030895866,0.0011671967,0.00019126949],"about_ca_topic_score_codex":0.00005834638,"about_ca_topic_score_gemma":0.004135928,"teacher_disagreement_score":0.63063914,"about_ca_system_score_codex":0.000033961413,"about_ca_system_score_gemma":0.000085489686,"threshold_uncertainty_score":0.36092532},"labels":[],"label_agreement":null},{"id":"W1587383887","doi":"","title":"Stochastic modelling of train delays and delay propagation in stations","year":2006,"lang":"en","type":"article","venue":"Research Repository (Delft University of Technology)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":84,"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":"Fondation du cancer du sein du Québec","keywords":"Punctuality; Train; Weibull distribution; Dwell time; Log-normal distribution; Scheduling (production processes); Computer science; Standard deviation; Stochastic process; Queueing theory; Reliability (semiconductor); Engineering; Mathematical optimization; Transport engineering; Statistics; Mathematics; Computer network","score_opus":0.017699928920402723,"score_gpt":0.20993244266169525,"score_spread":0.19223251374129252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587383887","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":[0.88250756,0.00033089018,0.11529607,0.000024482311,0.000017455148,0.00012391228,0.0000029921835,0.000069895825,0.0016267472],"genre_scores_gemma":[0.9979443,0.000025240473,0.0018509823,7.0251645e-8,0.0000063992275,9.3243506e-7,0.0000018389168,0.0000071040417,0.00016316574],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993027,0.000030943025,0.00016513222,0.00013239383,0.00018309761,0.00018572433],"domain_scores_gemma":[0.99963,0.000039202132,0.000034967274,0.00015020366,0.00012138303,0.000024284187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025559877,0.00006043281,0.00013296997,0.00076808024,0.00009822307,0.000004839711,0.00014348398,0.00013046221,0.0000011035164],"category_scores_gemma":[0.000012268062,0.00007154921,0.000020435495,0.00058803393,0.00024545408,0.00009192323,0.000034034507,0.00019844138,5.8874247e-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":[0.00000840128,0.000036994094,0.00034820795,0.000081409984,0.000009438176,0.000022102251,0.0001762341,0.95398045,0.038653933,0.005736324,0.000027717215,0.00091879396],"study_design_scores_gemma":[0.0002805993,0.0000744107,0.0007152826,0.00010717026,0.0000056191598,0.000014366573,0.0016846539,0.98924637,0.0070439973,0.00066547457,0.00008089099,0.00008116949],"about_ca_topic_score_codex":0.0013769198,"about_ca_topic_score_gemma":0.00014089349,"teacher_disagreement_score":0.11543672,"about_ca_system_score_codex":0.000086814245,"about_ca_system_score_gemma":0.00004049202,"threshold_uncertainty_score":0.29176924},"labels":[],"label_agreement":null},{"id":"W1598867956","doi":"","title":"RAIL INNOVATIONS 2003","year":2003,"lang":"en","type":"article","venue":"Mass transit","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Monorail; Light rail transit; Transit (satellite); Transport engineering; Light rail; Rail transit; Engineering; Public transport; Joint (building); Telecommunications; Civil engineering","score_opus":0.007572745718745623,"score_gpt":0.17473420481113017,"score_spread":0.16716145909238456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1598867956","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":[0.11405076,0.0006776796,0.38954708,0.000101150334,0.0015438346,0.00013921419,0.000008504343,0.0005885992,0.49334317],"genre_scores_gemma":[0.996714,0.000011733952,0.0013986612,0.000034802695,0.000033122058,0.000013122921,0.0000036281958,0.000020388146,0.001770573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99951315,0.000009970942,0.00013932197,0.00008345184,0.000081704886,0.00017243011],"domain_scores_gemma":[0.999779,0.0000054427155,0.0000061543988,0.00013408186,0.000040173898,0.000035167148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008249385,0.00008307422,0.000087569424,0.000057842826,0.000044759978,0.000018373756,0.000054222186,0.00004899587,0.00020041298],"category_scores_gemma":[0.000010126632,0.000082018945,0.000021577773,0.00056835543,0.000011168733,0.000055170556,5.907066e-7,0.00006842694,0.00009247781],"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":[0.000002392682,0.00005359806,0.00091545726,0.00013375487,0.00008695585,0.000023011116,0.0010674896,0.7096764,0.079968244,0.17778386,0.023392051,0.0068967785],"study_design_scores_gemma":[0.0004064389,0.000018291134,0.0009727798,0.000018576175,0.0000106131265,0.00001064815,0.00009800399,0.014586134,0.011925082,0.0003177462,0.9713173,0.00031839695],"about_ca_topic_score_codex":0.0000087951,"about_ca_topic_score_gemma":0.000011203418,"teacher_disagreement_score":0.9479252,"about_ca_system_score_codex":0.00001779166,"about_ca_system_score_gemma":0.000012856536,"threshold_uncertainty_score":0.33446357},"labels":[],"label_agreement":null},{"id":"W1606659221","doi":"10.1002/atr.1183","title":"Solving train formation problem using simulated annealing algorithm in a simplex framework","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Simulated annealing; Simplex; Simplex algorithm; Computer science; Mathematical optimization; Algorithm; Mathematics; Linear programming; Combinatorics","score_opus":0.012791597679928438,"score_gpt":0.25002289881911105,"score_spread":0.23723130113918262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606659221","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":[0.65237695,0.00051625975,0.34668657,0.0000050136828,0.00030048197,0.00006667449,0.000002480798,0.000025559953,0.00002004789],"genre_scores_gemma":[0.90450823,0.00005117116,0.09524109,0.0000064022497,0.00016059566,7.5447883e-7,0.000008604785,0.000022112745,0.0000010457928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987591,0.000017951037,0.00071071635,0.000053418495,0.0001943287,0.0002645163],"domain_scores_gemma":[0.9995471,0.000042863565,0.00020379333,0.00005414393,0.00007633368,0.00007572645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032984503,0.00011666944,0.00020684766,0.0002105973,0.000039948838,0.000015321582,0.00005766568,0.00008949146,0.0000068103477],"category_scores_gemma":[0.000013004771,0.000113410366,0.00006868909,0.00032883976,0.0000071248446,0.0013628824,5.766792e-7,0.00021629993,5.5478375e-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":[0.000009424981,0.000019380588,0.0004316392,0.000072621966,0.000010106551,0.000005888127,0.008032187,0.9600995,0.0085005015,0.000056422887,0.0000012952357,0.022760995],"study_design_scores_gemma":[0.0011641702,0.000077481425,0.02312592,0.0009974192,0.000041012856,0.000040986582,0.0027221984,0.96593237,0.0040194853,0.0006944857,0.00084731536,0.00033715848],"about_ca_topic_score_codex":0.000011377369,"about_ca_topic_score_gemma":0.000010883358,"teacher_disagreement_score":0.2521313,"about_ca_system_score_codex":0.00011466799,"about_ca_system_score_gemma":0.000013641471,"threshold_uncertainty_score":0.46247405},"labels":[],"label_agreement":null},{"id":"W1658881915","doi":"","title":"DRIVERLESS METROS POISED TO EXPAND","year":2000,"lang":"en","type":"article","venue":"Railway gazette international","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"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":"Train; Automatic train control; Transport engineering; Doors; Service (business); Engineering; Plan (archaeology); Automation; Telecommunications; Dedicated line; Line (geometry); Aeronautics; Electrical engineering; Control system; Business","score_opus":0.004137141167203575,"score_gpt":0.19649260735829469,"score_spread":0.19235546619109112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1658881915","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":[0.83068943,0.00007858391,0.0038028301,0.00045118656,0.0015856439,0.00011116403,0.000029169109,0.00033389527,0.16291808],"genre_scores_gemma":[0.98666865,0.000026509357,0.00069937226,0.00031426095,0.0005493671,0.000039079685,0.000021418053,0.000036923415,0.011644408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99889636,0.000012897127,0.00025956356,0.00021910915,0.0003430393,0.00026901855],"domain_scores_gemma":[0.9995696,0.000026892221,0.000016122243,0.00020329063,0.000048319125,0.00013578452],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00010210206,0.00016702036,0.00015234161,0.00016461624,0.000049746843,0.0000820422,0.00036341883,0.000063676925,0.0045850878],"category_scores_gemma":[0.000013323192,0.00016583795,0.00008134682,0.00018903709,0.000017907956,0.00015257475,0.000019132063,0.00010144544,0.0014190476],"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":[0.00005672126,0.0001284703,0.0019544305,0.00003219694,0.00029239518,0.00006893323,0.0017913927,0.66830313,0.03955108,0.0033716746,0.1020945,0.18235508],"study_design_scores_gemma":[0.0006903729,0.00003628515,0.009678192,0.000054038002,0.0000083266395,0.00002794532,0.000055140867,0.041647207,0.0049488065,0.000082846636,0.9423387,0.00043213906],"about_ca_topic_score_codex":0.000045249264,"about_ca_topic_score_gemma":0.00002442996,"teacher_disagreement_score":0.8402442,"about_ca_system_score_codex":0.00011317756,"about_ca_system_score_gemma":0.000010826065,"threshold_uncertainty_score":0.9993585},"labels":[],"label_agreement":null},{"id":"W1781051676","doi":"10.1002/atr.1317","title":"A multi‐objective subway timetable optimization approach with minimum passenger time and energy consumption","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":90,"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":"State Key Laboratory of Rail Traffic Control and Safety; China Scholarship Council; National Natural Science Foundation of China","keywords":"Dwell time; Energy consumption; Schedule; Train; Beijing; Genetic algorithm; Computer science; Fuzzy logic; Mathematical optimization; Operations research; Simulation; Real-time computing; Engineering","score_opus":0.009070703200376087,"score_gpt":0.19656740008543208,"score_spread":0.187496696885056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1781051676","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":[0.45583045,0.0009534684,0.54257816,0.000007624321,0.0001656487,0.000057584453,0.0000055318433,0.000044728295,0.0003567732],"genre_scores_gemma":[0.8829666,0.00016116322,0.11661199,0.000005325455,0.00005560881,0.000005926067,0.000031842104,0.00002593533,0.00013563638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992605,0.00001961272,0.00030909872,0.00009788608,0.00019409566,0.00011880453],"domain_scores_gemma":[0.9994668,0.000017516075,0.00015097609,0.0000596596,0.00020304626,0.00010202429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001422709,0.0001205772,0.0002069604,0.00012337875,0.000028013616,0.000018805245,0.000039668525,0.000056817767,0.000006726297],"category_scores_gemma":[0.0000060653338,0.00009750032,0.00003052053,0.0001411538,0.000023188977,0.000525892,6.091037e-7,0.00007954118,8.222724e-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":[0.000085956475,0.000037225567,0.00022395185,0.000025399271,0.000042435357,0.0000065363192,0.0009861448,0.99206054,0.005481611,0.000022819442,0.00001926394,0.0010081067],"study_design_scores_gemma":[0.003647893,0.00039728498,0.011975396,0.00012140295,0.00012671643,0.00009345193,0.0008013221,0.9794107,0.0024668223,0.000011589348,0.00061694934,0.00033045848],"about_ca_topic_score_codex":0.000009044787,"about_ca_topic_score_gemma":0.000015213713,"teacher_disagreement_score":0.42713612,"about_ca_system_score_codex":0.000046285702,"about_ca_system_score_gemma":0.000027086888,"threshold_uncertainty_score":0.39759478},"labels":[],"label_agreement":null},{"id":"W1845289528","doi":"10.1109/isic.1994.367834","title":"Reducing travel energy costs for a subway train via fuzzy logic controls","year":2002,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fuzzy logic; Energy (signal processing); PID controller; Computer science; Energy cost; Control engineering; Engineering; Artificial intelligence; Simulation; Mathematics; Statistics; Architectural engineering","score_opus":0.016030029405950122,"score_gpt":0.19789624884887966,"score_spread":0.18186621944292952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1845289528","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":[0.012047748,0.0018355754,0.69274795,0.00023506675,0.0009146588,0.00030013238,0.000015996473,0.0005795265,0.29132333],"genre_scores_gemma":[0.9903105,0.000045412504,0.001984733,0.00012443418,0.00025609945,0.00011482772,0.0000060718685,0.000044086726,0.0071138525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989162,0.000016519487,0.00030739757,0.00021705312,0.000116323594,0.0004264935],"domain_scores_gemma":[0.99955475,0.00007435936,0.00002666045,0.00020392697,0.000030684405,0.00010958691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015525935,0.0001906048,0.00027085442,0.00008405487,0.000074145675,0.00003898484,0.00013593392,0.00011388017,0.00020737533],"category_scores_gemma":[0.000018682855,0.00016290085,0.00011978723,0.00013945498,0.000019976615,0.000089885114,0.0000059867075,0.000059587663,0.000027258857],"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":[0.00002373224,0.00020014649,0.000015272217,0.00016476144,0.00019036523,0.000024661707,0.0011667401,0.21991445,0.24338834,0.14601307,0.03957533,0.34932312],"study_design_scores_gemma":[0.0010453181,0.00011695596,0.00006514834,0.000029308412,0.000014346883,0.000030234709,0.00013153949,0.96395004,0.0053155557,0.0004692423,0.028447684,0.00038461736],"about_ca_topic_score_codex":0.00023692648,"about_ca_topic_score_gemma":0.000084391744,"teacher_disagreement_score":0.9782627,"about_ca_system_score_codex":0.00008562557,"about_ca_system_score_gemma":0.0000039523184,"threshold_uncertainty_score":0.66429037},"labels":[],"label_agreement":null},{"id":"W1914666677","doi":"10.1002/atr.1261","title":"Design and analysis of demand‐adapted railway timetables","year":2014,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":119,"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":"Scheduling (production processes); Process (computing); Quality (philosophy); Integer programming; Service quality; Mathematical model; Public transport; Order (exchange)","score_opus":0.006452185978115887,"score_gpt":0.20059487120367134,"score_spread":0.19414268522555544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1914666677","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":[0.50586635,0.00047077058,0.49349028,0.0000044129656,0.00007796346,0.000022833492,0.0000013610081,0.000009343102,0.000056703568],"genre_scores_gemma":[0.97878367,0.0001943249,0.020972839,0.0000029972712,0.000019831601,8.86424e-7,0.000005352044,0.0000090231015,0.000011070771],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992616,0.000022695485,0.00043811742,0.000054207732,0.00014408771,0.000079272184],"domain_scores_gemma":[0.99955684,0.00006877631,0.00017405207,0.000060305818,0.000095355776,0.00004465314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029508726,0.0000752376,0.0003058491,0.00026808152,0.000017759916,0.0000056688486,0.000047251473,0.000034724362,0.000009962391],"category_scores_gemma":[0.000010553964,0.00006331562,0.00008144046,0.00036913174,0.00001343657,0.00018898201,3.229208e-7,0.00005229128,2.0227327e-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":[0.000022226028,0.000008977481,0.0005206974,0.00002798082,0.00024146114,0.0000012615707,0.0004719961,0.95993036,0.032668028,0.00018317191,0.000005278748,0.005918549],"study_design_scores_gemma":[0.0017767078,0.00040584017,0.42529234,0.00016502186,0.002149598,0.000008241955,0.00033705097,0.5354144,0.03171876,0.00037460917,0.0020305968,0.00032683005],"about_ca_topic_score_codex":0.0000031871336,"about_ca_topic_score_gemma":0.0000118252565,"teacher_disagreement_score":0.47291735,"about_ca_system_score_codex":0.000008523697,"about_ca_system_score_gemma":0.0000062966033,"threshold_uncertainty_score":0.2581936},"labels":[],"label_agreement":null},{"id":"W191686056","doi":"","title":"INTERCITY PASSENGER RAIL","year":2000,"lang":"en","type":"article","venue":"Transportation in the New Millennium","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":3,"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":"Quarter (Canadian coin); Investment (military); Engineering; Transport engineering; Technology transfer; State (computer science); World class; Business; International trade; Political science; Geography; Computer science; Manufacturing engineering","score_opus":0.0058058021958967265,"score_gpt":0.18702381803674692,"score_spread":0.1812180158408502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W191686056","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":[0.9732662,0.0008142356,0.0012394688,0.00028923055,0.0003433696,0.00012278916,0.0000059299646,0.0001604753,0.023758309],"genre_scores_gemma":[0.9978612,0.00050316023,0.00009439268,0.00008928647,0.00011451884,0.000017463206,0.000016767217,0.000016634396,0.0012865645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99932003,0.000019839843,0.00024515172,0.000108178516,0.00013261863,0.00017417864],"domain_scores_gemma":[0.99975073,0.000026182039,0.000011021225,0.00016669871,0.000007677096,0.00003767973],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012341981,0.000107389875,0.00010728895,0.000046987843,0.000029091265,0.000015874763,0.00016476361,0.00005870336,0.00082162605],"category_scores_gemma":[0.0000010953559,0.00008061376,0.00005099247,0.0002099967,0.0000145413815,0.00008757793,3.1885125e-7,0.00012163865,0.0001190431],"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":[0.000062001454,0.00011083027,0.0020654667,0.00013981025,0.00004394461,0.00006098701,0.040294047,0.8044858,0.0026745703,0.0020767858,0.07964864,0.06833714],"study_design_scores_gemma":[0.0018642858,0.00008565066,0.27049962,0.00014014216,0.000037366866,0.000016872653,0.0023342767,0.014778288,0.0018251131,0.00043274276,0.70724946,0.0007362055],"about_ca_topic_score_codex":0.00040806763,"about_ca_topic_score_gemma":0.0009990042,"teacher_disagreement_score":0.7897075,"about_ca_system_score_codex":0.000016252478,"about_ca_system_score_gemma":0.000008942651,"threshold_uncertainty_score":0.8996229},"labels":[],"label_agreement":null},{"id":"W1968850517","doi":"10.1115/jrc2010-36155","title":"Optimizing Wheel/Rail Performance on High Speed, Mixed Traffic Corridors","year":2010,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Transport engineering; Automotive engineering; Key (lock); Computer science; Field (mathematics); Grinding; Engineering; Mechanical engineering; Computer security","score_opus":0.006249058547754998,"score_gpt":0.17341438419755775,"score_spread":0.16716532564980274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968850517","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":[0.92725605,0.0000267986,0.00022269497,0.000030498064,0.00346617,0.00007137671,0.0000015585473,0.000589733,0.06833512],"genre_scores_gemma":[0.99421275,0.000020089115,0.0014779988,0.00003339698,0.00028848354,0.000006396879,0.0000046320806,0.000045078938,0.003911182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903756,0.000006694155,0.00022502473,0.00019649576,0.0001824899,0.00035175457],"domain_scores_gemma":[0.99950373,0.00002743582,0.000021759048,0.0003125965,0.000023263097,0.000111193476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013709147,0.00019613691,0.00018106945,0.00009792497,0.00009202782,0.00005327246,0.00020010036,0.00011711888,0.00043280542],"category_scores_gemma":[0.000009585098,0.00016549863,0.00005779691,0.00015111924,0.000028787794,0.00010212378,0.000013995826,0.00029258287,0.00042946672],"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":[0.000003216614,0.000020238076,0.000043511292,0.000027014556,0.00000942693,0.0000034095774,0.0001382888,0.97585875,0.0077624354,0.000584557,0.0030585125,0.012490658],"study_design_scores_gemma":[0.00043259835,0.00008717749,0.0019502838,0.000038075494,0.0000071015193,0.000019156902,0.00010666599,0.9571264,0.011903826,0.0000024454546,0.027918732,0.0004075478],"about_ca_topic_score_codex":0.000048559432,"about_ca_topic_score_gemma":0.000091407266,"teacher_disagreement_score":0.06695669,"about_ca_system_score_codex":0.000025388243,"about_ca_system_score_gemma":0.000010729803,"threshold_uncertainty_score":0.67488384},"labels":[],"label_agreement":null},{"id":"W1969293351","doi":"10.1109/glocom.2011.6133933","title":"Resource Allocation for On-Demand Data Delivery to High-Speed Trains via Trackside Infostations","year":2011,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Computer science; Quality of service; Schedule; Scheduling (production processes); A priori and a posteriori; Resource allocation; Real-time computing; Resource (disambiguation); Mathematical optimization; Distributed computing; Computer network","score_opus":0.05436451896384988,"score_gpt":0.23410918224750246,"score_spread":0.17974466328365257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969293351","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":[0.27683094,0.000045282468,0.6750676,0.00021772448,0.00038516487,0.0005505291,0.000118756296,0.00047379965,0.046310175],"genre_scores_gemma":[0.98908675,0.0000049421574,0.009406408,0.00017190611,0.00009985908,0.000028691375,0.00015719207,0.000029421897,0.0010148273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926513,0.000009671045,0.00022821646,0.00019764673,0.00010155223,0.00019779432],"domain_scores_gemma":[0.9993074,0.00006202825,0.00001821992,0.000481546,0.0000376421,0.00009315031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017444392,0.000112795875,0.000112496156,0.000102553604,0.00007082451,0.000022442478,0.00026752127,0.00005435698,0.00005742982],"category_scores_gemma":[0.00003226365,0.00010365952,0.000024614123,0.00014088074,0.000010223433,0.00016646215,0.000022009275,0.000044142565,0.00007024324],"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":[0.00007468508,0.00018558359,0.000039849157,0.00012147558,0.00013609187,0.000004058469,0.008026723,0.7806915,0.01722758,0.030255416,0.09916514,0.064071886],"study_design_scores_gemma":[0.0012837459,0.00044407515,0.00933315,0.0000851852,0.000062691826,0.000008674429,0.0013463321,0.76147103,0.013677063,0.00047261454,0.21093154,0.00088391313],"about_ca_topic_score_codex":0.00027184555,"about_ca_topic_score_gemma":0.00037193132,"teacher_disagreement_score":0.71225584,"about_ca_system_score_codex":0.0000279687,"about_ca_system_score_gemma":0.0000119635615,"threshold_uncertainty_score":0.42271125},"labels":[],"label_agreement":null},{"id":"W1970449742","doi":"10.1243/09544097jrrt314","title":"The exergetic and environmental impact assessment of underground electric train braking","year":2010,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ontario Tech University","funders":"","keywords":"Traction motor; Automotive engineering; Traction (geology); Regenerative brake; Electric vehicle; Engineering; Train; Electric motor; Traction control system; Single-phase electric power; Voltage; Electrical engineering; Mechanical engineering; Power (physics); Brake; Physics","score_opus":0.004079456000110363,"score_gpt":0.1944674974322196,"score_spread":0.19038804143210925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970449742","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":[0.9945323,0.001080658,0.0035388726,0.0001224963,0.00042088746,0.00008693575,0.0000037830714,0.000007709537,0.00020637058],"genre_scores_gemma":[0.998504,0.0010781854,0.00033758138,0.000002778857,0.000060233666,0.0000014190568,2.193494e-7,0.0000098383325,0.0000057532648],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99892604,0.000006963243,0.0005324342,0.000076680626,0.0003019417,0.00015592972],"domain_scores_gemma":[0.9995345,0.00005770228,0.0002198063,0.000056179008,0.000050764666,0.00008106035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068092626,0.00013445951,0.00028970907,0.00008885386,0.0000890323,0.00001740625,0.00018351375,0.000089907415,0.000004792711],"category_scores_gemma":[0.00003448487,0.00007737512,0.00015546278,0.00015259879,0.00013635337,0.00016125021,0.000013054417,0.00032393244,2.794448e-8],"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":[0.00004089695,0.000066658555,0.00017726157,0.00020403966,0.00019689338,6.3420697e-7,0.00034174797,0.024995621,0.9502136,0.016512642,0.00004495948,0.007205032],"study_design_scores_gemma":[0.006341472,0.0025988289,0.024789091,0.0014805252,0.00084730244,0.0016292491,0.0026532358,0.46352217,0.48741165,0.0025776932,0.005150486,0.0009983093],"about_ca_topic_score_codex":0.0000050239514,"about_ca_topic_score_gemma":0.000001935924,"teacher_disagreement_score":0.46280196,"about_ca_system_score_codex":0.000026317657,"about_ca_system_score_gemma":0.000034951423,"threshold_uncertainty_score":0.31552657},"labels":[],"label_agreement":null},{"id":"W1975328420","doi":"10.1115/jrc2012-74123","title":"Moving Away From Diesel and Towards All-Electric Locomotives in North America: Planning and Logistics of Ultra-Capacitor/Battery Technology","year":2012,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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, Okanagan Campus; University of British Columbia","funders":"","keywords":"Electrification; Incentive; Work (physics); Battery (electricity); Zero emission; Automotive industry; Sustainable transport; Business; Transport engineering; Electricity; Power (physics); Engineering; Sustainability; Electrical engineering; Economics","score_opus":0.013959627958013181,"score_gpt":0.21380254423980788,"score_spread":0.1998429162817947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975328420","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":[0.96470064,0.0039295005,0.02929395,0.00001822067,0.00014260322,0.000058347505,0.0000067658184,0.00010091474,0.0017490562],"genre_scores_gemma":[0.99882346,0.00021083777,0.00084489,0.000012808971,0.000064723616,0.000007739184,0.0000036460663,0.000016631617,0.000015232335],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992243,0.000014008809,0.00023525376,0.00013678987,0.00007812897,0.00031149207],"domain_scores_gemma":[0.99968976,0.00008120185,0.000039168383,0.000119039076,0.000013458795,0.000057384717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006638877,0.0001439213,0.00026561122,0.00023756198,0.00002447765,0.000011818231,0.00007689481,0.000096224925,0.000006056568],"category_scores_gemma":[0.00004411308,0.000126003,0.000014185127,0.00031776665,0.00008531612,0.00010829335,0.000024166653,0.00014483364,0.0000011940474],"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":[0.000006644572,0.00006646277,0.90174735,0.00014495803,0.000098281045,0.00000942793,0.0054489602,0.03770658,0.029002855,0.00031066564,0.00010306999,0.025354734],"study_design_scores_gemma":[0.0007976286,0.00022117035,0.830321,0.00018866554,0.0000510412,0.000034866214,0.00727562,0.13740829,0.020740744,0.00014838763,0.0018970489,0.0009155395],"about_ca_topic_score_codex":0.00040615938,"about_ca_topic_score_gemma":0.00004901216,"teacher_disagreement_score":0.099701695,"about_ca_system_score_codex":0.000025651487,"about_ca_system_score_gemma":0.000005451813,"threshold_uncertainty_score":0.5138253},"labels":[],"label_agreement":null},{"id":"W1976994312","doi":"10.1115/jrc2013-2465","title":"An Enhanced Optimization Model for Scheduling Freight Trains","year":2013,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada); Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Train; Computer science; Scalability; Schedule; Scheduling (production processes); Convergence (economics); Mathematical optimization; Column generation; Track (disk drive); Freight trains; Mathematics","score_opus":0.011915658609930782,"score_gpt":0.2100370759583542,"score_spread":0.19812141734842342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976994312","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":[0.07232467,0.000023909532,0.9164939,0.000012433374,0.00012281118,0.00016061489,0.0000014991181,0.00031005606,0.010550144],"genre_scores_gemma":[0.80201644,0.0000051350567,0.19718921,0.000019169835,0.00006391907,0.00009035562,0.000009977491,0.00002201269,0.00058380404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953085,0.0000029513785,0.00013694123,0.000107459135,0.000052879903,0.00016893925],"domain_scores_gemma":[0.99974966,0.000008066665,0.000010305903,0.00012933365,0.00004244363,0.000060209466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004445202,0.00008253594,0.000083977124,0.00003884929,0.000046009343,0.000044821638,0.000078451245,0.000056555855,0.00011214442],"category_scores_gemma":[0.0000049090245,0.00007174116,0.00002974605,0.000059415288,0.000005413835,0.00026774264,0.0000024937376,0.000027492108,0.000014127494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.288137e-7,0.000008102475,7.281789e-7,0.000012964391,0.0000039729925,1.7428304e-8,0.00022550409,0.977221,0.019503798,0.0016130056,0.000089342546,0.0013212366],"study_design_scores_gemma":[0.0001297146,0.0000144253845,0.0000044135672,0.000005630968,0.0000021349401,2.2872496e-7,0.00007772781,0.9952442,0.0042641815,0.00011737215,0.000028284076,0.00011167263],"about_ca_topic_score_codex":0.00002376392,"about_ca_topic_score_gemma":0.000011309789,"teacher_disagreement_score":0.72969174,"about_ca_system_score_codex":0.00001730773,"about_ca_system_score_gemma":0.0000067106366,"threshold_uncertainty_score":0.29255196},"labels":[],"label_agreement":null},{"id":"W1984105207","doi":"10.1115/jrc2014-3779","title":"Locomotive Assignment Under Consist Busting and Maintenance Constraints","year":2014,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada); Concordia University","funders":"","keywords":"Column generation; Train; Heuristics; Scalability; Decomposition; Computer science; String (physics); Optimization problem; Assignment problem; Dynamic programming; Mathematical optimization; Engineering; Algorithm; Mathematics","score_opus":0.005514610561638583,"score_gpt":0.1710907089233346,"score_spread":0.16557609836169604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984105207","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":[0.12548123,0.000080922306,0.5494226,0.000103421255,0.00032666555,0.00008828807,0.0000018726698,0.00026518045,0.3242298],"genre_scores_gemma":[0.99816716,0.000007254205,0.0010073902,0.00007976874,0.000041543113,0.000005296334,7.0450386e-7,0.000012370533,0.000678517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994601,0.000014229282,0.00013511356,0.00012213245,0.00007447713,0.0001939735],"domain_scores_gemma":[0.99973917,0.00006384571,0.000015399688,0.000095390475,0.00001865799,0.000067513654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015580287,0.000097592696,0.00011752891,0.00002492206,0.000049201786,0.000029750956,0.00004298877,0.000038033955,0.000075949174],"category_scores_gemma":[0.0000213381,0.00008016175,0.000016431735,0.000046828438,0.000101725134,0.000037690956,0.000014604154,0.000057620804,0.000022032003],"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":[0.000004573276,0.00005376162,0.0035995438,0.00022824162,0.00013999129,0.00001446397,0.00077457255,0.50206167,0.014408075,0.37396327,0.008285809,0.09646604],"study_design_scores_gemma":[0.0017402187,0.00013048654,0.018559968,0.00034475434,0.000029632243,0.0001507405,0.0032686123,0.93867517,0.005903243,0.0019836894,0.028148515,0.0010649986],"about_ca_topic_score_codex":0.000027645876,"about_ca_topic_score_gemma":0.000016478756,"teacher_disagreement_score":0.8726859,"about_ca_system_score_codex":0.00002928377,"about_ca_system_score_gemma":0.000005057584,"threshold_uncertainty_score":0.3268901},"labels":[],"label_agreement":null},{"id":"W1984790502","doi":"10.3141/1838-02","title":"Commuter Rail Reelectrification: Is It Still Justified?","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":9,"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":"Propulsion; Overhead (engineering); Engineering; State (computer science); Third rail; Rail transportation; Electrical engineering; Alternating current; Traction motor; Train; Light rail; Telecommunications; Electrically powered spacecraft propulsion; Transport engineering; Voltage; Computer science; Public transport; Geography; Mechanical engineering","score_opus":0.09294371638948859,"score_gpt":0.34870808343387627,"score_spread":0.25576436704438765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984790502","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":[0.98286974,0.00093995425,0.00641474,0.0032576825,0.0013561343,0.0007885111,0.000059987695,0.00007477618,0.004238477],"genre_scores_gemma":[0.9940918,0.0016700663,0.0011703187,0.000078649144,0.0001809245,0.000065864326,0.000011877973,0.000076755925,0.002653721],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932372,0.00090144103,0.0014888673,0.0003370031,0.0030582654,0.000977208],"domain_scores_gemma":[0.995545,0.00063111266,0.00022789091,0.0006910939,0.0025165942,0.00038828753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046957433,0.0002782337,0.00044251198,0.0008576928,0.00050587964,0.0001612537,0.0010619066,0.00023647744,0.0008546231],"category_scores_gemma":[0.00014525538,0.00021597627,0.00036680975,0.0024310285,0.00037086144,0.0005372829,0.000002532361,0.0021962083,0.000077055796],"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":[0.0014386801,0.0010881498,0.121963196,0.002298823,0.00120223,0.00043131065,0.040230315,0.12220141,0.06539762,0.0466208,0.57019657,0.026930904],"study_design_scores_gemma":[0.0023277425,0.000596766,0.21182412,0.0005808999,0.00008355447,0.0000042243637,0.004452113,0.001957573,0.01683136,0.0024197933,0.7583443,0.00057753746],"about_ca_topic_score_codex":0.0015634827,"about_ca_topic_score_gemma":0.0079202205,"teacher_disagreement_score":0.18814775,"about_ca_system_score_codex":0.00025462767,"about_ca_system_score_gemma":0.00038839554,"threshold_uncertainty_score":0.95415545},"labels":[],"label_agreement":null},{"id":"W1985602650","doi":"10.1109/vtcfall.2012.6399365","title":"Cross-Layer Handoff Design in Communication-Based Train Control (CBTC) Systems Using WLANs","year":2012,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"","keywords":"Handover; Computer science; Physical layer; Control system; Computer network; Communications system; Real-time computing; Engineering; Wireless; Telecommunications","score_opus":0.040881770354515436,"score_gpt":0.2608283897606973,"score_spread":0.21994661940618185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985602650","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":[0.33534533,0.0030148176,0.6506404,0.000019217492,0.00058786664,0.0004152486,0.000007641967,0.00031534382,0.009654137],"genre_scores_gemma":[0.9975996,0.0000053498625,0.001983577,0.00003057822,0.000083471496,0.000054614542,0.0000037736506,0.000036033834,0.00020297326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988373,0.00013613685,0.00037235572,0.000102101185,0.00013741657,0.00041471273],"domain_scores_gemma":[0.99924594,0.0002174442,0.00003583826,0.00037065113,0.000031915504,0.000098206794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008515834,0.00015845224,0.00024454558,0.000116397045,0.00008210342,0.00008843112,0.00017778072,0.00011176779,0.0000568897],"category_scores_gemma":[0.000025021578,0.00014020763,0.000048502647,0.00019207549,0.00003282514,0.00020015777,0.000007068408,0.00011647966,0.000029436833],"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":[0.00000597215,0.000033608096,0.008054167,0.000039888928,0.000012810325,8.338313e-7,0.0002650078,0.98193836,0.008789903,0.0006792081,0.000083380866,0.000096857664],"study_design_scores_gemma":[0.0009965111,0.000009189467,0.0034035116,0.00006615519,0.0000068160602,0.00000489143,0.00013234964,0.99364537,0.0007766581,0.0000036555502,0.00075978786,0.00019509337],"about_ca_topic_score_codex":0.0012457196,"about_ca_topic_score_gemma":0.00006524214,"teacher_disagreement_score":0.66225433,"about_ca_system_score_codex":0.00012212334,"about_ca_system_score_gemma":0.000025080953,"threshold_uncertainty_score":0.57175016},"labels":[],"label_agreement":null},{"id":"W1990288287","doi":"10.1115/jrc2010-36014","title":"High Speed Locomotive Development: A European Experience","year":2010,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Bombardier (Canada)","funders":"","keywords":"Propulsion; Axle; Unit (ring theory); Engineering; Automotive engineering; Power (physics); Aeronautics; Diesel locomotive; Transport engineering; Computer science; Mechanical engineering; Aerospace engineering; Psychology","score_opus":0.0062561538766350185,"score_gpt":0.18089181937314744,"score_spread":0.1746356654965124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990288287","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":[0.8396236,0.000014021499,0.0031871796,0.0000037861182,0.0008737073,0.00003617698,3.0286824e-7,0.000444481,0.15581676],"genre_scores_gemma":[0.99596906,0.0000024202627,0.0023238736,0.0000143350335,0.00009884783,0.0000031860945,0.0000016977614,0.000024611472,0.001561999],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994695,0.0000063800603,0.00014715444,0.00010406304,0.000087781795,0.0001851294],"domain_scores_gemma":[0.99974453,0.0000070005894,0.000007901806,0.00015246068,0.000015962725,0.000072150506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076392505,0.00010336268,0.000080555685,0.000038516526,0.000046932517,0.000031505253,0.00015305378,0.000027971391,0.00026800734],"category_scores_gemma":[0.0000052799674,0.00008737118,0.000016632754,0.00010298647,0.000023348106,0.000074406016,0.000019068759,0.000100100035,0.00037853885],"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":[0.0000058796077,0.00014718859,0.0025339876,0.00014325953,0.00012017656,0.00015292468,0.040109422,0.1520905,0.63105553,0.0144825615,0.010191967,0.14896657],"study_design_scores_gemma":[0.0007373331,0.000037204674,0.046033036,0.00005150981,0.000005246724,0.000057309164,0.0012725935,0.052098315,0.36399156,0.00003170596,0.5344006,0.001283581],"about_ca_topic_score_codex":0.000017362881,"about_ca_topic_score_gemma":0.00004600217,"teacher_disagreement_score":0.5242086,"about_ca_system_score_codex":0.000010881665,"about_ca_system_score_gemma":0.0000056613962,"threshold_uncertainty_score":0.48654774},"labels":[],"label_agreement":null},{"id":"W2003703327","doi":"10.1115/jrc2009-63019","title":"Reducing Energy Costs With Electric, Diesel and Dual-Powered Locomotives","year":2009,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Bombardier (Canada)","funders":"","keywords":"Automotive engineering; Energy consumption; Catenary; Engineering; Efficient energy use; Regenerative brake; Powertrain; Diesel fuel; Electric vehicle; Fuel efficiency; Battery electric vehicle; Electrical engineering; Power (physics); Torque","score_opus":0.003368862267697009,"score_gpt":0.17223038818804662,"score_spread":0.16886152592034961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003703327","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":[0.73104876,0.0030183748,0.021964705,0.00012942971,0.00015686614,0.00009002743,9.218663e-7,0.000639128,0.24295175],"genre_scores_gemma":[0.9980454,0.00020508541,0.0003961925,0.000037541962,0.0000622451,0.000004534736,0.0000014750018,0.000016431999,0.0012311188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933726,0.000012477226,0.00013046092,0.00016258024,0.00010232757,0.00025488032],"domain_scores_gemma":[0.9997242,0.00002377873,0.000015323772,0.00013635852,0.000022002248,0.000078323166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006727983,0.00014030816,0.00015201935,0.00008004262,0.000058585076,0.000041659518,0.000047661062,0.0000457286,0.000017103623],"category_scores_gemma":[0.000006645748,0.0001046464,0.000016644088,0.00020463008,0.0000142715335,0.00010157262,0.000006024782,0.000056530193,0.0000029666944],"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":[0.00007845504,0.00023954574,0.0009706534,0.0000992893,0.00025795924,0.00015945852,0.0024791304,0.14155003,0.24382986,0.08365326,0.011051209,0.51563114],"study_design_scores_gemma":[0.0042114076,0.0031627703,0.06602784,0.0006961468,0.000095913674,0.0008424685,0.001659429,0.60949355,0.233128,0.00092937564,0.07593519,0.0038179385],"about_ca_topic_score_codex":0.00016157932,"about_ca_topic_score_gemma":0.00004192605,"teacher_disagreement_score":0.5118132,"about_ca_system_score_codex":0.000038403177,"about_ca_system_score_gemma":0.0000081511525,"threshold_uncertainty_score":0.42673564},"labels":[],"label_agreement":null},{"id":"W2005405613","doi":"10.1115/jrc2014-3864","title":"Deadlock Avoidance and Detection in Railway Simulation Systems","year":2014,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deadlock prevention algorithms; Train; Scheduling (production processes); Reservation; Distributed computing; Deadlock; Algorithm; Time complexity; Job shop scheduling; Real-time computing; Mathematical optimization; Computer network; Mathematics; Routing (electronic design automation)","score_opus":0.005017902604425129,"score_gpt":0.1836620999466539,"score_spread":0.17864419734222875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005405613","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":[0.77737325,0.00022434784,0.21044251,0.0000031493753,0.00036928648,0.00006692952,1.6623467e-7,0.00016683649,0.011353511],"genre_scores_gemma":[0.99958104,0.0000075595644,0.000053013682,0.000004234714,0.00007300367,0.000009842834,3.9166605e-7,0.0000112236585,0.00025969726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955845,0.000020540527,0.00014679777,0.00009577067,0.000061650484,0.00011682068],"domain_scores_gemma":[0.9998166,0.000045220237,0.000011756227,0.00008656884,0.000010866585,0.000029008215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016568002,0.00006923332,0.00009195606,0.00006648488,0.000025115196,0.000028266424,0.000028148836,0.000053186508,0.0000030285723],"category_scores_gemma":[0.000017644898,0.000062915366,0.000009554272,0.00010709132,0.0000063743823,0.000085459294,0.0000047519843,0.000047481997,0.00001384307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.4141243e-7,0.0000020703433,0.00045997996,0.000037845763,0.0000013764106,1.725663e-7,0.000054589338,0.9871906,0.0027118698,0.0005196942,0.0000073137417,0.009013646],"study_design_scores_gemma":[0.00013643538,0.000013182653,0.0050059427,0.000021343569,8.988991e-7,0.000002201843,0.00003093655,0.989476,0.00069809204,0.00001914962,0.004512935,0.00008284382],"about_ca_topic_score_codex":0.00020499251,"about_ca_topic_score_gemma":0.00022611111,"teacher_disagreement_score":0.22220778,"about_ca_system_score_codex":0.000028459519,"about_ca_system_score_gemma":0.0000012571178,"threshold_uncertainty_score":0.25656143},"labels":[],"label_agreement":null},{"id":"W2014096215","doi":"10.5267/j.msl.2011.12.012","title":"A hybrid method to solve railroad passenger scheduling problem","year":2012,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Computer science; Scheduling (production processes); Operations research; Mathematical optimization; Mathematics","score_opus":0.009380607581404207,"score_gpt":0.2338835022972147,"score_spread":0.22450289471581047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014096215","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":[0.22059155,0.00005618194,0.745917,0.0014803804,0.0010256144,0.00034400544,8.067278e-7,0.00036737294,0.03021708],"genre_scores_gemma":[0.7650658,0.0000028746526,0.23285055,0.0016414247,0.00017948852,0.00007816229,7.393986e-7,0.000024045843,0.00015692583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9980156,0.000013989644,0.00022637896,0.0003213528,0.00050572737,0.0009169731],"domain_scores_gemma":[0.99934965,0.000015812684,0.000026871603,0.00035653816,0.0000103179655,0.00024082138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014652121,0.00018231788,0.00014715352,0.00038122278,0.00019926277,0.00015653892,0.00049937645,0.000016713311,0.000027096192],"category_scores_gemma":[0.000007710282,0.00016822794,0.0000515669,0.000778511,0.00006320882,0.0005649993,0.0001443155,0.00009538355,0.00028521533],"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":[0.0000023539667,0.00003930838,0.0007174925,0.00014354775,0.0000428193,0.000017530132,0.0010533441,0.803823,0.13543265,0.013359815,0.009674617,0.03569356],"study_design_scores_gemma":[0.0011086473,0.00009204593,0.025847329,0.00042104622,0.0001254355,0.00006312215,0.0016032514,0.31647158,0.06288039,0.00028004037,0.5880748,0.0030323258],"about_ca_topic_score_codex":0.000021956148,"about_ca_topic_score_gemma":8.621368e-7,"teacher_disagreement_score":0.5784002,"about_ca_system_score_codex":0.0001409112,"about_ca_system_score_gemma":0.0000026885177,"threshold_uncertainty_score":0.68601364},"labels":[],"label_agreement":null},{"id":"W2016910252","doi":"10.1002/atr.5670370206","title":"Train scheduling simulation that minimises operational conflicts due to service constraints","year":2003,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":7,"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":"Train; Scheduling (production processes); Track (disk drive); Computer science; Real-time computing; Dialog box; Schedule; Dedicated line; Process (computing); Simulation; Operations research; Engineering; Telecommunications; Operating system","score_opus":0.017843646138855123,"score_gpt":0.24613668550950601,"score_spread":0.2282930393706509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016910252","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":[0.8262261,0.00014570275,0.17247714,0.00006432509,0.0005460314,0.00010226822,0.000008720024,0.000029276445,0.00040044016],"genre_scores_gemma":[0.9815712,0.000013867445,0.018177329,0.000111963105,0.0000754591,0.0000039290317,0.0000125169645,0.000019873367,0.00001387421],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990055,0.000019584826,0.00047022378,0.000093111616,0.00026439124,0.00014720793],"domain_scores_gemma":[0.99939966,0.000067811234,0.000111613495,0.00006415982,0.00024067271,0.000116070776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019560229,0.00012505073,0.00020135044,0.00012543402,0.00005307325,0.000026233736,0.0000669279,0.00005860089,0.000055062614],"category_scores_gemma":[0.00003671925,0.00012173426,0.000060486247,0.00021249379,0.000012267104,0.00042249396,3.289494e-7,0.0001063621,0.0000047632416],"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":[0.000018199722,0.000016658074,0.00011399866,0.000037229,0.0000211498,0.00002146737,0.0021652225,0.97622335,0.017809553,0.0006295338,0.000004246418,0.0029393677],"study_design_scores_gemma":[0.016644983,0.0012103884,0.3408576,0.0026811503,0.00041112606,0.000837587,0.022139605,0.34036335,0.18734205,0.0014098727,0.08280458,0.0032977005],"about_ca_topic_score_codex":0.000002245969,"about_ca_topic_score_gemma":0.000031982898,"teacher_disagreement_score":0.63586,"about_ca_system_score_codex":0.000041909574,"about_ca_system_score_gemma":0.000043247204,"threshold_uncertainty_score":0.49641788},"labels":[],"label_agreement":null},{"id":"W2025036941","doi":"10.3141/2289-06","title":"Dual-Mode and New Diesel Locomotive Developments","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Bombardier (Canada)","funders":"","keywords":"Automotive engineering; Engineering; Diesel fuel; Truck; Traction motor; Catenary; Auxiliary power unit; Diesel engine; Powertrain; Electrical engineering; Voltage; Torque","score_opus":0.08239107041597524,"score_gpt":0.35110395483443363,"score_spread":0.2687128844184584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025036941","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":[0.99373585,0.0010785664,0.0024758428,0.00054817233,0.00089463027,0.00044232406,0.000024875217,0.000043965625,0.00075579126],"genre_scores_gemma":[0.9956285,0.0013872171,0.0015263787,0.000012337152,0.00033311936,0.000026618907,0.00000624152,0.000056079556,0.0010235421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99524,0.0003738635,0.0009612727,0.00020510958,0.0022326428,0.0009871642],"domain_scores_gemma":[0.99753225,0.0004140595,0.00014536845,0.0002619286,0.0009891955,0.0006571861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026335502,0.00022565723,0.0003540409,0.0006733142,0.00039979917,0.0000925805,0.0004535881,0.00015502788,0.00014517557],"category_scores_gemma":[0.00007691849,0.00016808746,0.00016202318,0.0012727671,0.00028047472,0.0007932064,0.000005885923,0.0013397232,0.000024833249],"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":[0.00073488464,0.0004291197,0.80626553,0.00091373123,0.00071303017,0.000115717514,0.036232494,0.030669114,0.01673168,0.0091023175,0.036542002,0.061550386],"study_design_scores_gemma":[0.0009191184,0.00015971815,0.9615032,0.00027232175,0.000025077808,0.0000015669557,0.0017862861,0.0003556271,0.0021607513,0.00059250015,0.032011542,0.00021226775],"about_ca_topic_score_codex":0.004317984,"about_ca_topic_score_gemma":0.010431074,"teacher_disagreement_score":0.1552377,"about_ca_system_score_codex":0.00019836752,"about_ca_system_score_gemma":0.00027504464,"threshold_uncertainty_score":0.6854408},"labels":[],"label_agreement":null},{"id":"W2026095449","doi":"10.1109/tits.2012.2188288","title":"Handoff Performance Improvements in MIMO-Enabled Communication-Based Train Control Systems","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Handover; Computer network; Computer science; MIMO; Real-time computing; Partially observable Markov decision process; Latency (audio); Wireless; Channel (broadcasting); Markov process; Markov chain; Markov model; Telecommunications","score_opus":0.01425284474062742,"score_gpt":0.21421801725468717,"score_spread":0.19996517251405976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026095449","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":[0.35150233,0.0011752031,0.6423402,0.000011317244,0.0029706894,0.0009821163,0.0001134521,0.00031445903,0.0005902689],"genre_scores_gemma":[0.9981586,0.0001752784,0.000053515552,0.000024277337,0.00007337285,0.0010400804,0.000049658578,0.00007497276,0.00035024685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740964,0.00013550786,0.0011649095,0.0002562223,0.00044177298,0.000591965],"domain_scores_gemma":[0.99892646,0.00014995836,0.00012835115,0.00049177365,0.00010498202,0.00019846547],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007007656,0.00036581224,0.0004707077,0.00044081738,0.00018208657,0.00008247393,0.000247368,0.00020686159,0.000049412854],"category_scores_gemma":[0.0000017156206,0.00036291452,0.00015013674,0.00051374506,0.000044511038,0.00042302874,1.2416403e-7,0.0003320362,0.00009605897],"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":[0.000048747028,0.00022316093,0.0011835813,0.0004224676,0.00007544908,0.0000012477706,0.0011729193,0.99380565,0.0016152414,0.00014162615,0.000045252575,0.0012646422],"study_design_scores_gemma":[0.0023493362,0.00014271172,0.0027678956,0.0007921772,0.00008189507,0.0000049377104,0.0017276759,0.97237253,0.014517072,0.0000011040363,0.004557252,0.0006854102],"about_ca_topic_score_codex":0.00095152645,"about_ca_topic_score_gemma":0.00023445721,"teacher_disagreement_score":0.6466563,"about_ca_system_score_codex":0.00027814633,"about_ca_system_score_gemma":0.00004089425,"threshold_uncertainty_score":0.9998823},"labels":[],"label_agreement":null},{"id":"W2030624679","doi":"10.1061/40766(174)50","title":"Open-Standards Radio Based Train Control Proven for APMs","year":2005,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Bandwidth (computing); Monorail; Computer science; Control system; Train; Engineering; Telecommunications; Computer network; Electrical engineering","score_opus":0.008389567047540321,"score_gpt":0.2331073094962702,"score_spread":0.22471774244872988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030624679","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":[0.0044802674,0.00027192,0.89277124,0.00072149263,0.00038292486,0.00096690026,0.00012699394,0.0004217787,0.099856496],"genre_scores_gemma":[0.98858535,0.0000017021191,0.0077117574,0.0002152671,0.00024522148,0.00019484528,0.000006818072,0.000035499295,0.0030035505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992669,0.000009831063,0.00019892724,0.00013961429,0.00013329845,0.0002513823],"domain_scores_gemma":[0.99965096,0.000041897943,0.000015257308,0.00017647796,0.000042199496,0.00007322563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003697157,0.000116702875,0.00019049286,0.00003854285,0.00005287036,0.00008844572,0.00024639114,0.000057070367,0.00018867778],"category_scores_gemma":[0.00002432277,0.00009564107,0.00006364507,0.00006777475,0.000011477917,0.00013100434,0.000007200287,0.000042519532,0.000011067086],"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":[0.000030587995,0.000040932762,0.000012047978,0.00006107868,0.00003489833,0.0000010507297,0.00010416635,0.8688178,0.003687666,0.0051730615,0.06678238,0.05525436],"study_design_scores_gemma":[0.0013585122,0.00003783341,0.00001561549,0.000008790019,0.0000041992175,9.624176e-7,0.000011323863,0.5191135,0.0013162472,0.000009312216,0.4780237,0.00009999487],"about_ca_topic_score_codex":0.0000340028,"about_ca_topic_score_gemma":0.00011625179,"teacher_disagreement_score":0.98410505,"about_ca_system_score_codex":0.00010166821,"about_ca_system_score_gemma":0.000053029093,"threshold_uncertainty_score":0.39001298},"labels":[],"label_agreement":null},{"id":"W2036221507","doi":"10.1287/opre.1080.0642","title":"Optimal Real-Time Traffic Control in Metro Stations","year":2009,"lang":"en","type":"article","venue":"Operations Research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Train; Computer science; Scheduling (production processes); Schedule; Routing (electronic design automation); Real-time computing; Branch and bound; Control (management); Process (computing); Real-time Control System; Column generation; Operations research; Mathematical optimization; Computer network; Engineering; Mathematics","score_opus":0.022008900855764395,"score_gpt":0.3124288843492416,"score_spread":0.2904199834934772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036221507","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":[0.9751032,0.00012907565,0.0027858866,0.00041288423,0.000051623163,0.00027057424,0.00001471716,0.00013156164,0.021100476],"genre_scores_gemma":[0.9964749,0.00005556877,0.001579955,0.000009203823,0.000065589324,0.00006885255,0.000019522266,0.000013869277,0.0017125222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988336,0.00011045979,0.00022961834,0.00015179567,0.0002977937,0.0003767253],"domain_scores_gemma":[0.99950194,0.00007725324,0.0000029654225,0.0002142276,0.00012042557,0.00008317463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006707108,0.00008209718,0.00012846738,0.00041356016,0.00018866378,0.00013161292,0.00015117569,0.00005836428,0.00020101489],"category_scores_gemma":[0.00006198264,0.00007891757,0.000027756256,0.0008039528,0.00003019703,0.00017965566,0.000005808408,0.00021910142,0.00027611398],"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":[0.000002793674,0.000045898236,0.000013984608,0.0000027684607,0.00000479673,0.000005888758,0.00029590106,0.9790224,0.015993921,0.0012473689,0.0013561301,0.0020081818],"study_design_scores_gemma":[0.00035356314,0.00008269414,0.0016764384,0.000011005939,0.0000013352333,0.0000012141096,0.00023391422,0.99611205,0.00021390553,0.0000060119132,0.0012166572,0.00009122984],"about_ca_topic_score_codex":0.00019896372,"about_ca_topic_score_gemma":0.0006283695,"teacher_disagreement_score":0.021371717,"about_ca_system_score_codex":0.00013041955,"about_ca_system_score_gemma":0.00006916471,"threshold_uncertainty_score":0.35489786},"labels":[],"label_agreement":null},{"id":"W2039435134","doi":"10.1007/s11067-008-9091-6","title":"Design of a Railway Scheduling Model for Dense Services","year":2008,"lang":"en","type":"article","venue":"Networks and Spatial Economics","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"D-Wave Systems (Canada)","funders":"","keywords":"Mathematical optimization; Scheduling (production processes); Discretization; Train; Computer science; Job shop scheduling; Schedule; Network topology; Heuristic; Mathematics","score_opus":0.016435974812857666,"score_gpt":0.1830659189713709,"score_spread":0.16662994415851323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039435134","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":[0.36603814,0.0004822162,0.63311166,0.0000030769595,0.00013956185,0.00008252759,0.0000026331545,0.000025735812,0.000114434886],"genre_scores_gemma":[0.99069667,0.0012281924,0.007812665,0.000018843457,0.00016802507,0.000016957409,0.000004372997,0.000023333025,0.00003096177],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947476,0.0000048090064,0.00022716097,0.000113029106,0.00001783414,0.00016239162],"domain_scores_gemma":[0.9997532,0.000040931332,0.000042846917,0.000096026466,0.000018706723,0.000048293314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009881993,0.00010028674,0.0001964367,0.000023230195,0.000068087276,0.000012656405,0.00006468922,0.00008816997,0.0000016266699],"category_scores_gemma":[0.0000014838816,0.0001024572,0.00003661272,0.00002229066,0.000022034626,0.00006242214,0.0000115557195,0.00004326943,4.341187e-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":[0.000015044667,0.000004860067,0.000120931065,0.000036496232,0.000015971327,2.5748713e-7,0.00025993783,0.9958182,0.000042604497,0.00020728039,0.00001679613,0.0034616082],"study_design_scores_gemma":[0.00026980444,0.000027264387,0.00005157737,0.000015812386,0.000006665627,0.0000046075697,0.000014228876,0.9992222,0.00005187698,0.00008513889,0.00013937119,0.00011140712],"about_ca_topic_score_codex":0.00011558897,"about_ca_topic_score_gemma":0.00015759905,"teacher_disagreement_score":0.62529904,"about_ca_system_score_codex":0.000013468512,"about_ca_system_score_gemma":0.0000142825465,"threshold_uncertainty_score":0.41780832},"labels":[],"label_agreement":null},{"id":"W2048051147","doi":"10.1061/41193(424)11","title":"PHX Sky Train: APM Adaptation for Phoenix","year":2011,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Phoenix; Scope (computer science); Schedule; Transport engineering; Plan (archaeology); International airport; Modernization theory; Computer science; Engineering; Metropolitan area","score_opus":0.04626204644528586,"score_gpt":0.19064849357765398,"score_spread":0.14438644713236812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048051147","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":[0.085665695,0.00013803731,0.5792612,0.000013829163,0.0008219116,0.00018778534,0.000004226018,0.0005286875,0.3333786],"genre_scores_gemma":[0.9915412,0.0000050893977,0.006493415,0.000019260038,0.000085957334,0.000042613134,0.0000034934665,0.000019199138,0.0017897405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996053,0.0000029111898,0.000117630705,0.00007945078,0.000049752143,0.00014493358],"domain_scores_gemma":[0.99983317,0.000011148762,0.000009296302,0.00009194959,0.000017930513,0.00003649813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006733671,0.00006958081,0.0000733132,0.000033432414,0.00002698527,0.00000824997,0.000059768343,0.000039748866,0.00015172856],"category_scores_gemma":[0.0000054778634,0.000059970902,0.000041381056,0.000056467463,0.0000065840964,0.000070506365,0.0000026171108,0.000023212126,0.000036154164],"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":[0.00005813979,0.00021195234,0.00017844267,0.0004975919,0.00020112474,0.0000090342055,0.04171047,0.30929893,0.031071458,0.17658436,0.038211066,0.40196744],"study_design_scores_gemma":[0.0006671083,0.00013340916,0.0010203264,0.000019126908,0.00001530469,0.0000074520367,0.001313765,0.8571231,0.019792778,0.0007878045,0.11872979,0.00039006418],"about_ca_topic_score_codex":0.0000992557,"about_ca_topic_score_gemma":0.0000674048,"teacher_disagreement_score":0.90587556,"about_ca_system_score_codex":0.000012826145,"about_ca_system_score_gemma":0.00000493404,"threshold_uncertainty_score":0.24455425},"labels":[],"label_agreement":null},{"id":"W2052976910","doi":"10.1109/oceans.2014.7003293","title":"Underwater power cable approximation models for offshore applications","year":2014,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Dalhousie University","funders":"","keywords":"Underwater; Power (physics); Electric power transmission; Port (circuit theory); Transmission line; Transmission (telecommunications); Electronic engineering; Computer science; Physics; Engineering; Electrical engineering; Telecommunications; Geology","score_opus":0.010395062076868564,"score_gpt":0.19615525132565187,"score_spread":0.1857601892487833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052976910","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":[0.0013679928,0.000025131858,0.861794,0.0000371936,0.000069463735,0.00019659262,0.000001941374,0.00022149121,0.1362862],"genre_scores_gemma":[0.9865729,0.0000019482902,0.010514109,0.000027270316,0.000057869616,0.0003152587,0.000016181506,0.00002040309,0.0024740575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957746,0.000003374682,0.0001205106,0.000097371,0.00005922829,0.00014204261],"domain_scores_gemma":[0.9997533,0.000014221188,0.000009237862,0.00016091006,0.000028995586,0.000033315922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087683205,0.00006950759,0.00007723634,0.0000337983,0.00004890972,0.000025030788,0.00006743135,0.000046087716,0.000033790107],"category_scores_gemma":[0.0000011652087,0.000057374244,0.00003109219,0.000058090878,0.000008814644,0.00009969974,0.0000053178755,0.000022636217,0.000031332103],"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":[4.452478e-7,0.000007425255,0.0000025938077,0.000032984393,0.000005193525,6.3841203e-9,0.00008425805,0.87925726,0.00067077216,0.11606593,0.002085644,0.0017874845],"study_design_scores_gemma":[0.000088296074,0.000009233792,0.0000062061417,0.0000030150036,0.0000027074936,5.872491e-7,0.00004460762,0.9249615,0.0012674055,0.0070092482,0.06652266,0.00008454287],"about_ca_topic_score_codex":0.000016575112,"about_ca_topic_score_gemma":0.0000142214085,"teacher_disagreement_score":0.98520494,"about_ca_system_score_codex":0.000020088262,"about_ca_system_score_gemma":0.0000032189212,"threshold_uncertainty_score":0.23396538},"labels":[],"label_agreement":null},{"id":"W2055524141","doi":"10.1002/atr.109","title":"Increase of metro line capacity by optimisation of track circuit length and location: In a distance to go system","year":2010,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":9,"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":"Train; Line (geometry); Track (disk drive); Interval (graph theory); Service (business); Set (abstract data type); Engineering; Signalling; Track circuit; Transport engineering; Computer science; Reliability engineering; Electronic circuit; Electrical engineering; Mechanical engineering; Mathematics","score_opus":0.0062740787855134475,"score_gpt":0.19904066250433572,"score_spread":0.19276658371882227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055524141","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":[0.9448229,0.0002626501,0.054525632,0.000010278722,0.00019527756,0.000088462424,0.000021172104,0.00000962395,0.00006397241],"genre_scores_gemma":[0.9960372,0.00003326192,0.0038712858,0.0000015730587,0.00003098425,0.0000037217283,0.0000074764916,0.000010442067,0.0000040914156],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99901,0.000011868495,0.00065187097,0.0000713757,0.00017607602,0.00007875445],"domain_scores_gemma":[0.99935746,0.000033341712,0.0002432829,0.00007955679,0.00022190188,0.00006443742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025070566,0.00008178988,0.00024383022,0.00013219069,0.0000111521895,0.000004082692,0.00005853441,0.000050555744,0.0000020365085],"category_scores_gemma":[0.000032004227,0.00007878721,0.000030204223,0.00031652095,0.000018402712,0.00025518943,4.1456929e-7,0.00012339427,1.5792303e-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":[0.000047912934,0.000040481264,0.0020423569,0.00043512118,0.000012652577,0.0000020203022,0.0013398468,0.7202678,0.27197438,0.0006613356,0.000005152906,0.0031709515],"study_design_scores_gemma":[0.005639174,0.0011542412,0.58530915,0.002402741,0.00017554208,0.000046385998,0.005101301,0.02914255,0.36888283,0.00022224184,0.0012053197,0.00071852526],"about_ca_topic_score_codex":0.000053674954,"about_ca_topic_score_gemma":0.00050773635,"teacher_disagreement_score":0.6911252,"about_ca_system_score_codex":0.00003962733,"about_ca_system_score_gemma":0.000019222121,"threshold_uncertainty_score":0.32128492},"labels":[],"label_agreement":null},{"id":"W2072623824","doi":"10.1109/glocom.2014.7037496","title":"A novel communication-based train control (CBTC) system with coordinated multi-point transmission and reception","year":2014,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Handover; Computer science; Control system; Transmission (telecommunications); Wireless; Train; Real-time computing; Control (management); Computer network; Engineering; Telecommunications; Electrical engineering","score_opus":0.006196925938446049,"score_gpt":0.17399803894334762,"score_spread":0.16780111300490158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072623824","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":[0.030379089,0.00011803699,0.96553767,0.00013274877,0.000039084218,0.0002092827,0.000003539787,0.00046611737,0.003114411],"genre_scores_gemma":[0.99099,0.000004788335,0.008773996,0.000028877328,0.000012884071,0.000042023697,0.000010989638,0.000028115848,0.00010834799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993669,0.000053182597,0.0002183902,0.000121756115,0.00007970875,0.0001600598],"domain_scores_gemma":[0.9995167,0.00007552464,0.000031810825,0.00024177697,0.000045504814,0.00008864263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032071094,0.00014336298,0.00019832261,0.00006440563,0.00009438389,0.00003572272,0.00009263084,0.00008078381,0.00001263112],"category_scores_gemma":[0.000007034718,0.00010470769,0.000026657994,0.00011797596,0.000033412165,0.00006942802,0.0000028994818,0.000086221364,0.0000053036233],"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":[0.0000653618,0.00012402156,0.0002508423,0.00051754504,0.000060242823,0.0000011259295,0.0010573206,0.8504494,0.12216688,0.0042644627,0.00019352086,0.020849295],"study_design_scores_gemma":[0.0026525503,0.00007075701,0.0008518709,0.00023110452,0.000013066426,0.000012396205,0.00037060218,0.99389774,0.0008916048,7.123438e-7,0.0008388581,0.00016876873],"about_ca_topic_score_codex":0.0002873365,"about_ca_topic_score_gemma":0.00011674589,"teacher_disagreement_score":0.96061087,"about_ca_system_score_codex":0.000044773606,"about_ca_system_score_gemma":0.000009148787,"threshold_uncertainty_score":0.42698556},"labels":[],"label_agreement":null},{"id":"W2082715560","doi":"10.1109/jsac.2012.120512","title":"Efficient On-Demand Data Service Delivery to High-Speed Trains in Cellular/Infostation Integrated Networks","year":2012,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":67,"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; Computer network; Bottleneck; Scheduling (production processes); Quality of service; Distributed computing; Provisioning; Schedule; Resource allocation; Mathematical optimization","score_opus":0.038591125500475824,"score_gpt":0.2617042065967713,"score_spread":0.22311308109629546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082715560","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":[0.9845372,0.000720454,0.011895405,0.00050673925,0.00073485,0.0002637178,0.00003566488,0.00011277985,0.0011932229],"genre_scores_gemma":[0.99792755,0.00030736686,0.0011538591,0.00020278942,0.00015960315,0.000015560745,0.00017427602,0.000040342195,0.00001866963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817085,0.00029964308,0.00063839817,0.00016621036,0.0002516786,0.0004731963],"domain_scores_gemma":[0.9977988,0.00035378739,0.0001020387,0.0013317111,0.00020196666,0.00021170636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008360352,0.00021856616,0.00025733848,0.00054713857,0.00018532597,0.000084661595,0.0012590161,0.00013363782,0.000016015392],"category_scores_gemma":[0.00010459403,0.0002062112,0.000028544599,0.0020792708,0.00002059038,0.00017592734,0.000080787686,0.00096692005,0.00004453104],"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":[0.000024785144,0.00031118497,0.00043188233,0.0000065195045,0.000023262068,0.000002617984,0.0011098748,0.99221635,0.002893176,0.00022234478,0.00094146904,0.0018165247],"study_design_scores_gemma":[0.00058213575,0.000059174512,0.01265146,0.00036055845,0.000012775126,0.00001828576,0.000309242,0.98206073,0.0003052296,0.00000817356,0.0033663292,0.0002659135],"about_ca_topic_score_codex":0.00030926373,"about_ca_topic_score_gemma":0.001750773,"teacher_disagreement_score":0.013390366,"about_ca_system_score_codex":0.00040684914,"about_ca_system_score_gemma":0.00007224319,"threshold_uncertainty_score":0.8409049},"labels":[],"label_agreement":null},{"id":"W2087189120","doi":"10.1007/s11276-015-0932-1","title":"Cooperative and cognitive wireless networks for train control systems","year":2015,"lang":"en","type":"article","venue":"Wireless Networks","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"","keywords":"Computer science; Train; Handover; Wireless; Reinforcement learning; Computer network; Communications system; Cognitive network; Control (management); Wireless network; Cognitive radio; Telecommunications; Artificial intelligence","score_opus":0.0128795535240293,"score_gpt":0.2111480412233194,"score_spread":0.1982684876992901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087189120","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":[0.14842802,0.008648732,0.83752966,0.000023400482,0.00218104,0.0009601921,0.000045889905,0.0003660889,0.0018169752],"genre_scores_gemma":[0.9976557,0.0001363,0.000029438046,0.0000672654,0.001245484,0.00044677118,0.000052244362,0.00009779464,0.0002689833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982373,0.00010241773,0.0004461306,0.00037001385,0.00018187499,0.0006622432],"domain_scores_gemma":[0.99882567,0.00033535363,0.00007994009,0.0001842452,0.00023832323,0.00033648178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005248358,0.00036979766,0.0006141737,0.00006615278,0.00015522641,0.00017023012,0.00015551796,0.0003168216,0.0000027730066],"category_scores_gemma":[0.000023061388,0.00033780382,0.00007655579,0.00023705217,0.000116679745,0.00016361626,0.000021737225,0.0002710042,0.0000038381067],"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":[0.00007444111,0.00002209301,0.00018122618,0.000052906573,0.00014597339,0.00000971816,0.000430174,0.9812604,0.000025530788,0.002407035,0.0030100786,0.012380401],"study_design_scores_gemma":[0.0026335677,0.00014849282,0.000106356834,0.00024365389,0.00006265161,0.000025105779,0.0010109602,0.99340934,0.00001257266,0.000008729209,0.0019071946,0.0004313904],"about_ca_topic_score_codex":0.00007825392,"about_ca_topic_score_gemma":0.000072609175,"teacher_disagreement_score":0.84922767,"about_ca_system_score_codex":0.000068102345,"about_ca_system_score_gemma":0.00003591194,"threshold_uncertainty_score":0.9999074},"labels":[],"label_agreement":null},{"id":"W2089903253","doi":"10.1016/j.conengprac.2008.03.003","title":"High dynamics control under voltage/current and harmonic constraints: SM-PMSM application for AC railways","year":2008,"lang":"en","type":"article","venue":"Control Engineering Practice","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of Nottingham; Lakehead University; Areva","keywords":"Harmonic; Control theory (sociology); Voltage; Current (fluid); Dynamics (music); Control (management); Control engineering; Engineering; Computer science; Electrical engineering; Physics; Acoustics","score_opus":0.007439036876986378,"score_gpt":0.2068998761042377,"score_spread":0.19946083922725133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089903253","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":[0.007888507,0.0032074724,0.9858269,0.00031812,0.001085205,0.0007636267,0.00008637516,0.00056841935,0.00025536297],"genre_scores_gemma":[0.9971884,0.00035803558,0.0014762257,0.00009975089,0.00032550882,0.0003890677,0.000030110104,0.00008828326,0.000044644155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984941,0.000024205661,0.0004441748,0.00034790387,0.00020212917,0.00048750546],"domain_scores_gemma":[0.99842316,0.00080550375,0.00011300747,0.0003324351,0.0001492848,0.00017659356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041194816,0.00033503096,0.0004198626,0.00012455217,0.00015152118,0.000058713103,0.00017137216,0.00015199464,0.0000068637564],"category_scores_gemma":[0.0003636314,0.00035424918,0.00009299867,0.00016333933,0.00007310974,0.00041422073,0.000014406484,0.00031761857,0.00001638716],"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":[0.00004141051,0.00004805377,0.000028487573,0.00015537467,0.00017992377,0.0000058843707,0.00009220105,0.9603653,0.0034855094,0.0193834,0.00026107446,0.015953392],"study_design_scores_gemma":[0.0031249102,0.000055856282,0.0008149897,0.000036694946,0.00012656017,0.00012462077,0.000052106723,0.9620326,0.00007292265,0.000042123847,0.033119407,0.00039718646],"about_ca_topic_score_codex":0.000038171154,"about_ca_topic_score_gemma":0.00000649479,"teacher_disagreement_score":0.9892999,"about_ca_system_score_codex":0.00019481638,"about_ca_system_score_gemma":0.00004890589,"threshold_uncertainty_score":0.9998909},"labels":[],"label_agreement":null},{"id":"W2091885448","doi":"10.1016/j.trpro.2014.10.008","title":"Simultaneous Frequency and Capacity Setting in Uncapacitated Metro Lines in Presence of a Competing Mode","year":2014,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"HEC Montréal","funders":"","keywords":"Mode (computer interface); Transport engineering; Computer science; Engineering","score_opus":0.024126149604045622,"score_gpt":0.28379647681748094,"score_spread":0.2596703272134353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091885448","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":[0.9977064,0.00013831242,0.0012793196,0.00002445923,0.000032861943,0.0002268864,0.00001276049,0.00008116829,0.0004978409],"genre_scores_gemma":[0.99798197,0.000036766494,0.0018739132,0.000002135449,0.00002262444,0.000042835287,0.0000108829,0.000022431455,0.0000064385513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985834,0.00008512409,0.000442218,0.00019858427,0.00034356766,0.00034713827],"domain_scores_gemma":[0.99908024,0.0005460521,0.00003547726,0.000102800295,0.00016852138,0.0000669218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010783839,0.00010676506,0.00020495485,0.00036384646,0.000035900386,0.000014476178,0.00011391842,0.00007971004,0.0000036371378],"category_scores_gemma":[0.0005685675,0.000106551335,0.00001714674,0.00074915955,0.00008815996,0.00014036726,0.0000027435813,0.0003134186,0.0000012892626],"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":[0.000013859116,0.000044916113,0.16191879,0.0012110948,0.000007201859,0.000013758451,0.011017224,0.77309215,0.048508905,0.0021775619,0.0000059441177,0.0019886321],"study_design_scores_gemma":[0.00048691014,0.000060854196,0.11160792,0.00037543284,0.000002067627,6.412164e-7,0.0012604737,0.88133097,0.004016068,0.0006855799,0.000018700448,0.00015439816],"about_ca_topic_score_codex":0.0031885977,"about_ca_topic_score_gemma":0.016968645,"teacher_disagreement_score":0.10823884,"about_ca_system_score_codex":0.000036724196,"about_ca_system_score_gemma":0.000027382985,"threshold_uncertainty_score":0.94689023},"labels":[],"label_agreement":null},{"id":"W2094583655","doi":"10.1109/iecon.2012.6389245","title":"Factors for an LRV voltage booster simulation","year":2012,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Booster (rocketry); Computer science; Voltage; Track (disk drive); Sensitivity (control systems); Simulation; Automotive engineering; Electronic engineering; Engineering; Electrical engineering; Aerospace engineering","score_opus":0.04981998616277245,"score_gpt":0.2661478227792667,"score_spread":0.21632783661649424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094583655","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":[0.7665352,0.00004993128,0.21910845,0.00000134874,0.00056377164,0.00008278552,0.0000026083574,0.00021702301,0.013438885],"genre_scores_gemma":[0.9984536,3.379128e-7,0.0001532446,0.000011634698,0.00020693374,0.0000075854364,0.000010909555,0.000018675944,0.0011370605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996248,0.0000027550523,0.00009155386,0.00005090278,0.00004809703,0.0001818999],"domain_scores_gemma":[0.9997827,0.000034449677,0.000007241999,0.00009868152,0.000010750716,0.000066201086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006372804,0.00007013019,0.0000643506,0.000028294005,0.000029266444,0.00001554634,0.00003792219,0.00004326311,0.000060738672],"category_scores_gemma":[0.000007287878,0.000052572585,0.000029222612,0.000033179906,0.0000032824103,0.00027462305,0.0000033921972,0.000018737288,0.000014951533],"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":[0.0000014975475,0.00002470546,0.0071119103,0.000035295747,0.00001062834,2.783689e-8,0.0011349719,0.98012817,0.0048145438,0.0018104117,0.0009027133,0.004025145],"study_design_scores_gemma":[0.00014098045,0.000038597664,0.013625923,0.000004066747,0.0000056137033,1.9960439e-7,0.00025134935,0.8970733,0.0037811834,0.000021589281,0.084865965,0.00019123357],"about_ca_topic_score_codex":0.00001710298,"about_ca_topic_score_gemma":0.000010564662,"teacher_disagreement_score":0.23191842,"about_ca_system_score_codex":0.000015438449,"about_ca_system_score_gemma":0.0000012361027,"threshold_uncertainty_score":0.21438476},"labels":[],"label_agreement":null},{"id":"W2097494045","doi":"10.1109/tie.2014.2386794","title":"Power Quality Issues in Railway Electrification: A Comprehensive Perspective","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":338,"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":"Electrification; Perspective (graphical); Quality (philosophy); Engineering; Traction substation; Traction power network; Power (physics); Power quality; Compensation (psychology); Electric power system; Computer science; Transport engineering; Electrical engineering; Electricity; Voltage","score_opus":0.02556696157100111,"score_gpt":0.2690556633314732,"score_spread":0.24348870176047208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097494045","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":[0.67433465,0.0021098703,0.29518452,0.0013880867,0.0036750103,0.001066699,0.000029438774,0.0011510066,0.021060707],"genre_scores_gemma":[0.9990014,0.00019041855,0.00004334814,0.000043162894,0.000181157,0.000070837064,0.000002582784,0.000042681742,0.00042441397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99822855,0.00018482625,0.00045640225,0.00033463092,0.00026842224,0.0005271535],"domain_scores_gemma":[0.9992265,0.00016117824,0.000054649307,0.0003604556,0.00010721566,0.00009000523],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033568614,0.0002584548,0.0003590696,0.00025732818,0.00012480085,0.00004860054,0.00019665218,0.0003158254,0.00009741583],"category_scores_gemma":[0.000020297364,0.00027198382,0.000121108875,0.0007174491,0.000045578763,0.00013280103,3.727431e-7,0.0008908971,0.000060891936],"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":[0.00020126918,0.0003368891,0.00001555033,0.000019372717,0.00015086053,0.000002394832,0.0014090334,0.9317591,0.022306625,0.02629751,0.0013155865,0.016185774],"study_design_scores_gemma":[0.01382637,0.0037270805,0.00074440707,0.00031433787,0.00013274213,0.00006243941,0.004404495,0.12870876,0.42280266,0.0041736374,0.41721267,0.0038903996],"about_ca_topic_score_codex":0.00033717143,"about_ca_topic_score_gemma":0.0002949506,"teacher_disagreement_score":0.8030504,"about_ca_system_score_codex":0.00066045945,"about_ca_system_score_gemma":0.000106114916,"threshold_uncertainty_score":0.99997324},"labels":[],"label_agreement":null},{"id":"W2098679097","doi":"10.4236/jemaa.2010.21003","title":"A Probabilistic Analysis on the Harmonic Cancellation Characteristics of the Scott Transformer","year":2010,"lang":"en","type":"article","venue":"Journal of Electromagnetic Analysis and Application","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Alberta","funders":"","keywords":"Transformer; Electrical engineering; Probabilistic logic; Harmonic analysis; Electromagnetic coil; Total harmonic distortion; Single phase; Harmonic; Computer science; Control theory (sociology); Voltage; Electronic engineering; Physics; Acoustics; Engineering","score_opus":0.0028203357708462785,"score_gpt":0.18240376577016468,"score_spread":0.1795834299993184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098679097","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":[0.9866918,0.000119061646,0.012491466,0.0002555302,0.00003486379,0.000096172516,0.0000025208285,0.000004682427,0.0003039398],"genre_scores_gemma":[0.9996958,0.00011043612,0.000069706286,0.000012660417,0.00005580336,0.000008935419,0.0000020213702,0.0000058690102,0.000038811406],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99916035,0.000025999158,0.0004283077,0.00008073783,0.00020296984,0.00010160972],"domain_scores_gemma":[0.99933153,0.00006206401,0.00025882968,0.00021087458,0.000104307976,0.00003239313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034489157,0.00008654801,0.00026062966,0.00017707252,0.000070489135,0.000024461233,0.00014944079,0.000045558718,0.000030232864],"category_scores_gemma":[0.000019219127,0.00004654113,0.00023252552,0.0013849887,0.000048483587,0.000027343924,0.000002673688,0.00019026375,4.524477e-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":[0.00002709696,0.00011722723,0.011442574,0.000060086437,0.0026613988,3.2705256e-7,0.00045312828,0.06667548,0.88385165,0.006920964,0.0000824661,0.027707612],"study_design_scores_gemma":[0.00016918212,0.00018947244,0.55255026,0.000014089361,0.0057334173,0.0000070034484,0.00003571014,0.42614603,0.013895035,0.00039274275,0.00072342786,0.00014366226],"about_ca_topic_score_codex":0.000040899162,"about_ca_topic_score_gemma":0.00021293316,"teacher_disagreement_score":0.8699566,"about_ca_system_score_codex":0.000018995455,"about_ca_system_score_gemma":0.000019873018,"threshold_uncertainty_score":0.1897892},"labels":[],"label_agreement":null},{"id":"W2099967494","doi":"10.1109/pes.2008.4596926","title":"An investigation on the effectiveness of Scott transformer on harmonic reduction","year":2008,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of Alberta","funders":"","keywords":"Harmonics; Transformer; Distribution transformer; Delta-wye transformer; Electrical engineering; Energy efficient transformer; Harmonic analysis; Current transformer; Quadrature booster; Power system harmonics; Single-phase electric power; Single phase; Transformer effect; Engineering; Electronic engineering; Voltage; Power factor","score_opus":0.020502895114616206,"score_gpt":0.2046666638283843,"score_spread":0.1841637687137681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099967494","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":[0.9862632,0.000019237754,0.0009802993,0.000021784774,0.00023674854,0.00012488173,6.3505007e-7,0.00009233793,0.012260843],"genre_scores_gemma":[0.99981827,0.000017366332,0.000021924268,0.000008154929,0.000040207637,0.000018792225,0.0000026261282,0.000012249283,0.0000603937],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995377,0.000069825874,0.0001081316,0.00008557368,0.0001131283,0.00008564195],"domain_scores_gemma":[0.99974686,0.00004975814,0.000011015491,0.000149105,0.000016306041,0.00002698365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025736383,0.00007362044,0.00007918402,0.000047955808,0.000058131012,0.0000044805543,0.00005965738,0.00003990611,0.000017764492],"category_scores_gemma":[0.0000051162824,0.000046651487,0.000030458199,0.00013576588,0.000038730584,0.00007644342,5.6860654e-7,0.000063877604,0.00001561064],"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":[0.000037639726,0.00004577017,0.00024667336,0.00007511653,0.000018877454,6.3738094e-7,0.0007462217,0.46582234,0.51374465,0.017641613,0.00019408223,0.0014263772],"study_design_scores_gemma":[0.00016297858,0.00024878042,0.015593472,0.00008272363,0.0000036383055,0.000007980209,0.00008343592,0.017127978,0.96634954,0.00015654335,0.000088608234,0.000094296585],"about_ca_topic_score_codex":0.00007840946,"about_ca_topic_score_gemma":0.0000035427465,"teacher_disagreement_score":0.4526049,"about_ca_system_score_codex":0.000030146257,"about_ca_system_score_gemma":0.000009306607,"threshold_uncertainty_score":0.19023924},"labels":[],"label_agreement":null},{"id":"W2108788979","doi":"10.1109/eicccc.2006.277191","title":"K9® Auxiliary Power Unit - Locomotive Idle Reduction System A New Technology towards Reducing GHG Emissions from the Railroad Sector","year":2006,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"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":"Auxiliary power unit; Idle; Automotive engineering; Timer; Shutdown; NOx; Electrical engineering; Engineering; Power (physics); Fuel efficiency; Computer science; Voltage; Operating system; Nuclear engineering; Combustion; Physics; Microcontroller","score_opus":0.008341331691447098,"score_gpt":0.19225564551119215,"score_spread":0.18391431381974505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108788979","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":[0.89181274,0.0016606608,0.020707965,0.0008126535,0.002638833,0.00036911078,0.000021782067,0.0022396285,0.07973663],"genre_scores_gemma":[0.9921739,0.000009183932,0.0010949839,0.000008993032,0.0006284326,0.000028161461,0.000017622322,0.00005106879,0.005987668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865943,0.000039573642,0.00039737258,0.0003221571,0.00020232229,0.0003791564],"domain_scores_gemma":[0.9992145,0.00003379163,0.000059027872,0.0005430471,0.000056897457,0.00009276484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015122896,0.00025204226,0.00026618195,0.0001501276,0.0002120336,0.000058980764,0.00031054075,0.00025360257,0.00028448662],"category_scores_gemma":[0.000024040764,0.00017566481,0.00009220794,0.0006886512,0.000055150562,0.00012427873,0.000055819706,0.00028196245,0.00009652224],"study_design_candidate":"simulation_or_modeling","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":[0.000036355315,0.00019464418,0.001046388,0.00016327963,0.00039730855,0.00007953551,0.004672298,0.55457056,0.16252106,0.025800124,0.22073747,0.029781003],"study_design_scores_gemma":[0.004531899,0.0005234337,0.02301155,0.0035993257,0.00039340102,0.0013841799,0.068026714,0.21838324,0.21569978,0.0033887129,0.45663512,0.004422658],"about_ca_topic_score_codex":0.013346659,"about_ca_topic_score_gemma":0.00013769057,"teacher_disagreement_score":0.3361873,"about_ca_system_score_codex":0.00017810172,"about_ca_system_score_gemma":0.00008734924,"threshold_uncertainty_score":0.99322355},"labels":[],"label_agreement":null},{"id":"W2110804857","doi":"10.1287/trsc.1100.0349","title":"A New Resource-Constrained Multicommodity Flow Model for Conflict-Free Train Routing and Scheduling","year":2010,"lang":"en","type":"article","venue":"Transportation Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"D-Wave Systems (Canada)","funders":"","keywords":"Integer programming; Pairwise comparison; Scheduling (production processes); Computer science; Linear programming relaxation; Mathematical optimization; Linear programming; Train; Schedule; Graph; Routing (electronic design automation); Mathematics; Theoretical computer science; Artificial intelligence; Geography; Computer network","score_opus":0.019277741198247468,"score_gpt":0.24031102186300446,"score_spread":0.221033280664757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110804857","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":[0.48953912,0.000014035957,0.5095919,0.000052635183,0.00012858585,0.00012180947,0.00002533239,0.0001231252,0.00040349274],"genre_scores_gemma":[0.86740035,0.0000014917925,0.13242859,0.000020051171,0.00005276432,0.000012823262,0.0000074986415,0.000013406289,0.00006304396],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894124,0.000003250264,0.00027146997,0.00025804024,0.00022584446,0.0003001243],"domain_scores_gemma":[0.999428,0.00005802832,0.000036698242,0.00023397908,0.000057291538,0.00018601198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005985675,0.00012427587,0.00013827125,0.000097769975,0.00022082387,0.000080571124,0.00028003595,0.00006537439,0.000006828253],"category_scores_gemma":[0.00008830952,0.00012274842,0.000039146074,0.00024602085,0.00016856342,0.00029117044,0.0000034600616,0.00013819878,8.665206e-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":[0.0000048837005,0.0000053692524,0.00019123199,0.00002646977,0.0000028698594,5.023104e-7,0.0052758763,0.8955615,0.08377123,0.0055603017,0.00002554496,0.009574191],"study_design_scores_gemma":[0.0005818638,0.000009404783,0.0029298537,0.000016583044,0.000007424067,0.0000019332388,0.00019641247,0.9934136,0.0021674552,0.00021927535,0.00030217064,0.00015398246],"about_ca_topic_score_codex":0.000057706897,"about_ca_topic_score_gemma":0.0003472697,"teacher_disagreement_score":0.3778612,"about_ca_system_score_codex":0.000012259632,"about_ca_system_score_gemma":0.000095947384,"threshold_uncertainty_score":0.50055355},"labels":[],"label_agreement":null},{"id":"W2113254970","doi":"10.1109/epec.2010.5697222","title":"Stationary applications of energy storage technologies for transit systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of Toronto","funders":"","keywords":"Flywheel; Energy storage; Transit (satellite); Flywheel energy storage; Computer science; Systems engineering; Emerging technologies; Engineering; Transport engineering; Automotive engineering; Public transport","score_opus":0.004727178565579466,"score_gpt":0.18880071084832117,"score_spread":0.1840735322827417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113254970","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":[0.01553632,0.00040813058,0.9720503,0.00002696937,0.0003079398,0.00017819437,0.000032259497,0.00064463064,0.010815254],"genre_scores_gemma":[0.9962284,0.00001717381,0.0026868163,0.0000015043321,0.000034602424,0.00039423682,0.000012674067,0.000013918492,0.00061065797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959755,0.0000019034418,0.00016455931,0.00007832722,0.00006036257,0.000097270036],"domain_scores_gemma":[0.99970657,0.000036567035,0.000018981771,0.00017761638,0.000045386845,0.000014878413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053429118,0.00006311446,0.000099819714,0.00007913583,0.000030042505,0.000008092082,0.0001112314,0.00007956636,0.0000069712823],"category_scores_gemma":[0.0000040139257,0.00005502031,0.000030921805,0.00012306744,0.000025554022,0.000043891258,0.0000038881158,0.00003992305,0.0000016040525],"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":[0.0000019858624,0.000032036245,0.00002777341,0.00024814755,0.000030092615,2.4692267e-7,0.00007335387,0.31090486,0.106192015,0.5589107,0.0018689736,0.021709818],"study_design_scores_gemma":[0.00029292435,0.000049640566,0.00010777734,0.00001674939,0.000014058857,0.000008683292,0.0011128993,0.43926173,0.029670082,0.0010568952,0.5281273,0.00028125124],"about_ca_topic_score_codex":0.00006533578,"about_ca_topic_score_gemma":0.000056191333,"teacher_disagreement_score":0.9806921,"about_ca_system_score_codex":0.000006646043,"about_ca_system_score_gemma":0.000009077775,"threshold_uncertainty_score":0.2243663},"labels":[],"label_agreement":null},{"id":"W2121134727","doi":"10.1109/rrcon.1989.77287","title":"Implementation of freight-car planned maintenance on CP Rail","year":2003,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Transport engineering; Reduction (mathematics); Computer science; Rail freight transport; Business; Automotive engineering; Engineering; Mathematics","score_opus":0.008391788283792433,"score_gpt":0.21480071030215037,"score_spread":0.20640892201835792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121134727","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":[0.73659915,0.000073174044,0.009443045,0.00002167273,0.0004746922,0.0000986948,0.0000062875524,0.00013163981,0.25315166],"genre_scores_gemma":[0.9986186,0.000011925567,0.00048637757,0.000021472362,0.000019008241,0.0000074442787,0.0000027175904,0.000011723879,0.00082074414],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995047,0.000010476852,0.00016879164,0.000078861514,0.00009158486,0.00014560428],"domain_scores_gemma":[0.9998027,0.000013752549,0.000019315698,0.000121041216,0.000015309319,0.000027865075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008442811,0.00007298911,0.000093770075,0.000042488435,0.000017658727,0.0000057626894,0.000048694663,0.000027784643,0.00031625945],"category_scores_gemma":[0.000005118762,0.00005887911,0.000029341018,0.000086567416,0.0000067752453,0.000036421534,0.000002256107,0.00003039431,0.000033494303],"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":[0.000020762445,0.00009360745,0.006484763,0.00021719877,0.00012860069,0.000012974452,0.0013203006,0.26076904,0.072106786,0.58592355,0.060115036,0.01280735],"study_design_scores_gemma":[0.0027052457,0.0006262539,0.017775862,0.00012974719,0.000020793239,0.000019033503,0.0045096744,0.013108428,0.7687891,0.001021037,0.19047695,0.00081788533],"about_ca_topic_score_codex":0.00010296622,"about_ca_topic_score_gemma":0.00009385061,"teacher_disagreement_score":0.6966823,"about_ca_system_score_codex":0.000022659813,"about_ca_system_score_gemma":0.0000067608003,"threshold_uncertainty_score":0.34628192},"labels":[],"label_agreement":null},{"id":"W2127038494","doi":"10.1109/rams.2011.5754483","title":"R&amp;amp;M&amp;amp;A&amp;amp;S of communication based train control systems applied to Urban Rail Transportation &amp;#x2014; A way to improve city sustainability","year":2011,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Thales (Canada)","funders":"","keywords":"Ampere; Sustainability; Computer science; Engineering; Electrical engineering; Voltage; Biology; Ecology","score_opus":0.022739074736701986,"score_gpt":0.23205152685014335,"score_spread":0.20931245211344138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127038494","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":[0.48911798,0.00024223259,0.49976373,0.00018913715,0.00051852874,0.0027046418,0.00027363343,0.0006932733,0.0064968425],"genre_scores_gemma":[0.9714235,0.000014194348,0.020158144,0.00017344275,0.0001858116,0.0012510045,0.0007150109,0.00018885708,0.0058900057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99388224,0.00039603302,0.002345956,0.0011013048,0.0009456049,0.0013288651],"domain_scores_gemma":[0.993957,0.00045657955,0.00045770349,0.0034352106,0.000920013,0.000773524],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027736656,0.0009891564,0.0015643546,0.000550398,0.0003406002,0.000161791,0.0011972183,0.00062347384,0.00066642324],"category_scores_gemma":[0.0004309888,0.00095476484,0.00046946752,0.0011569363,0.00019692877,0.00037160353,0.000053602776,0.00057441025,0.0006163173],"study_design_candidate":"not_applicable","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":[0.0018586866,0.0021809072,0.005459407,0.004732254,0.000807855,0.0000022950253,0.050803836,0.49819374,0.37109098,0.014169357,0.043712754,0.0069879275],"study_design_scores_gemma":[0.006768686,0.00018888271,0.016462298,0.00046145928,0.00043171312,0.000011272952,0.002382429,0.0044351597,0.0032507172,0.0005503181,0.961343,0.0037140711],"about_ca_topic_score_codex":0.013274006,"about_ca_topic_score_gemma":0.041414496,"teacher_disagreement_score":0.91763026,"about_ca_system_score_codex":0.0007923476,"about_ca_system_score_gemma":0.00022163667,"threshold_uncertainty_score":0.9992903},"labels":[],"label_agreement":null},{"id":"W2130917899","doi":"10.1049/ic:20080312","title":"Developments in track circuit condition monitoring","year":2008,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Bombardier (Canada)","funders":"","keywords":"Track circuit; Track (disk drive); Condition monitoring; Computer science; Electronic circuit; Electrical engineering; Electronic engineering; Reliability engineering; Engineering","score_opus":0.02440421566463978,"score_gpt":0.20825124230309522,"score_spread":0.18384702663845542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130917899","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":[0.89757216,0.00007377125,0.0007737391,0.0000012786043,0.00039260503,0.00003040484,2.6044427e-7,0.00015303292,0.101002775],"genre_scores_gemma":[0.9989225,0.000037273505,0.0002114811,0.0000030005965,0.000052151412,0.000010850254,0.0000016372611,0.000010321339,0.0007507334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9995664,0.0000037169534,0.00013651859,0.00007147222,0.00008021291,0.00014171332],"domain_scores_gemma":[0.9998925,0.000006799793,0.0000059713184,0.000058319467,0.0000073852652,0.00002907774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000040166917,0.000060604427,0.00007121162,0.00006624621,0.00002930813,0.0000057562193,0.000046060126,0.000036710546,0.000026155043],"category_scores_gemma":[0.0000029768025,0.00006024987,0.000012542165,0.00012362581,0.000006564525,0.000089537665,0.0000034295265,0.0000467729,0.00008021213],"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":[0.0000036179083,0.00013256173,0.40384328,0.00016079283,0.000055225806,0.00028257092,0.0069333715,0.49102816,0.058383178,0.005121293,0.0022501876,0.031805772],"study_design_scores_gemma":[0.0005976074,0.0000148019935,0.9456385,0.00008244657,0.000001593241,0.00006902991,0.00026037218,0.0059140264,0.03665881,0.000052156385,0.010302327,0.00040833902],"about_ca_topic_score_codex":0.000045639914,"about_ca_topic_score_gemma":0.000009272981,"teacher_disagreement_score":0.5417952,"about_ca_system_score_codex":0.000057161655,"about_ca_system_score_gemma":0.0000079076035,"threshold_uncertainty_score":0.24569184},"labels":[],"label_agreement":null},{"id":"W2135524714","doi":"10.1287/opre.49.4.531.11226","title":"Simultaneous Assignment of Locomotives and Cars to Passenger Trains","year":2001,"lang":"en","type":"article","venue":"Operations Research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":136,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematical optimization; Integer programming; Train; Computer science; Context (archaeology); Flow network; Branch and cut; Simplex algorithm; Node (physics); Integer (computer science); Decomposition; Multi-commodity flow problem; Linear programming relaxation; Linear programming; Branch and bound; Tree (set theory); Mathematics; Engineering","score_opus":0.03675200780820406,"score_gpt":0.3223997203708126,"score_spread":0.28564771256260857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135524714","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":[0.9816789,0.00020138512,0.0056020734,0.0002171262,0.000050313873,0.00021284117,0.0000069602224,0.000030463954,0.011999905],"genre_scores_gemma":[0.99709654,0.00010452546,0.0005627855,0.0000045030156,0.000040897976,0.00004205294,0.0000021553308,0.000012317976,0.0021342316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925506,0.000049875613,0.00013407986,0.00011331071,0.00023954145,0.0002081107],"domain_scores_gemma":[0.99955374,0.00009446298,0.0000020386976,0.0001490857,0.00010648945,0.000094169394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028832143,0.000058379963,0.00008988585,0.00014937001,0.00011534669,0.000046381294,0.000081391525,0.000034528537,0.00007092604],"category_scores_gemma":[0.000081259,0.000051619656,0.0000133980875,0.00032755733,0.00004334175,0.000050134895,0.000023404022,0.00009514306,0.000023120165],"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":[0.0000015967265,0.000028174234,0.00009936199,0.000010972236,0.00000999334,0.0000099658755,0.001444109,0.9338789,0.054084234,0.0006938491,0.00027185533,0.009467011],"study_design_scores_gemma":[0.0003053401,0.00029465003,0.0021101062,0.0000645204,0.0000037496663,0.000023700022,0.0026418413,0.9260732,0.013083765,0.000016074147,0.055171944,0.00021108681],"about_ca_topic_score_codex":0.00020793772,"about_ca_topic_score_gemma":0.00043633423,"teacher_disagreement_score":0.054900087,"about_ca_system_score_codex":0.000044489723,"about_ca_system_score_gemma":0.000021392922,"threshold_uncertainty_score":0.21049885},"labels":[],"label_agreement":null},{"id":"W2139422567","doi":"","title":"Train of Thought","year":2006,"lang":"en","type":"article","venue":"ITS International","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Public transport; The Internet; Telecommunications; Work (physics); Service (business); Wireless; Transport engineering; Focus (optics); Business; Computer science; Engineering; Marketing; World Wide Web; Geography","score_opus":0.004972276919013645,"score_gpt":0.19010470395777496,"score_spread":0.18513242703876132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139422567","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":[0.45737693,0.00028639697,0.008769357,0.00007375009,0.0016692698,0.000029155606,0.000022615264,0.00011008639,0.53166246],"genre_scores_gemma":[0.9957117,0.0000035582004,0.0002988704,0.0000047026056,0.00021402775,0.0000024912244,0.0000070809715,0.000006444799,0.0037511433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968064,0.0000023620285,0.00011564142,0.000040906954,0.000106642474,0.000053837168],"domain_scores_gemma":[0.99990565,0.000008350622,0.000012645184,0.000040189818,0.00002488127,0.000008282543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003737192,0.000036513873,0.000043465112,0.00004110737,0.000006103838,0.000006023765,0.00008923504,0.00002005223,0.00015822484],"category_scores_gemma":[0.0000042703277,0.000034225337,0.000025053821,0.000039647784,0.000006672408,0.000040664556,0.0000047851854,0.000022577275,0.000022309874],"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":[0.0000033953456,0.0000590818,0.0010787111,0.000031004372,0.00004635432,0.000006532592,0.00013171064,0.6254807,0.054805454,0.29280496,0.0158208,0.009731263],"study_design_scores_gemma":[0.00054174644,0.00002118369,0.01706575,0.000060082297,0.0000054837137,0.000023834078,0.00004186103,0.4643535,0.086849585,0.0010481852,0.42972353,0.00026523738],"about_ca_topic_score_codex":0.000018218028,"about_ca_topic_score_gemma":0.000009344634,"teacher_disagreement_score":0.5383348,"about_ca_system_score_codex":0.000014618285,"about_ca_system_score_gemma":0.0000031347965,"threshold_uncertainty_score":0.1732451},"labels":[],"label_agreement":null},{"id":"W2141298352","doi":"10.1115/jrc2014-3838","title":"An Extended Model of Light Railway System Used in Analysis of Normal Operation and Fault Conditions","year":2014,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Northern Alberta Institute of Technology","funders":"","keywords":"Catenary; Traction power network; Electrical impedance; Finite element method; Engineering; Traction (geology); Voltage; Electrical engineering; Power (physics); Structural engineering; Mechanical engineering; Physics","score_opus":0.006958201977440246,"score_gpt":0.21222812110606146,"score_spread":0.20526991912862122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141298352","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":[0.9202868,0.000016720789,0.07414977,0.0000053052,0.000028987874,0.000051918505,0.000011716154,0.000048041045,0.005400739],"genre_scores_gemma":[0.99947584,0.0000021359303,0.00041240707,0.0000021269357,0.000008838328,0.000008758824,0.000019172458,0.0000075876023,0.00006316063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936587,0.000027297923,0.00031934932,0.000099794925,0.00009652847,0.000091173526],"domain_scores_gemma":[0.9997091,0.00001519038,0.000032789332,0.00017208283,0.000032069325,0.00003876447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017978362,0.00007395637,0.00026298538,0.0003004869,0.000018837045,0.000009832242,0.00005864403,0.000054955497,0.000014539573],"category_scores_gemma":[0.0000032058842,0.00006394942,0.00003904061,0.00026704234,0.00001350838,0.00014444605,0.000005447253,0.000029241488,7.6870884e-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":[0.0000011670264,0.000018191227,0.0005809223,0.000060899652,0.000046531448,1.0556673e-7,0.00042960327,0.8821496,0.10635287,0.010254764,0.00000413445,0.00010124052],"study_design_scores_gemma":[0.00019254701,0.000021736636,0.004861477,0.000014971105,0.000057295,4.3747093e-7,0.00030170818,0.9830816,0.011386646,0.000005802302,0.0000049893874,0.00007081883],"about_ca_topic_score_codex":0.00038797574,"about_ca_topic_score_gemma":0.0008507481,"teacher_disagreement_score":0.100932,"about_ca_system_score_codex":0.000016500122,"about_ca_system_score_gemma":0.0000062083045,"threshold_uncertainty_score":0.26077816},"labels":[],"label_agreement":null},{"id":"W2142582324","doi":"10.1115/rtd2004-66026","title":"Optimization of Diesel Engine: Synchronous Alternator Group","year":2004,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Alternator; Automotive engineering; Diesel engine; Line (geometry); Electrical engineering; Relay; Power (physics); Diesel fuel; Engineering; Physics","score_opus":0.0033626258348955678,"score_gpt":0.1703941584692183,"score_spread":0.16703153263432272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142582324","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":[0.20191549,0.0003608195,0.77822715,0.000011277407,0.0004513343,0.000060512866,0.0000017879214,0.00027298037,0.018698659],"genre_scores_gemma":[0.99004495,0.00006099136,0.009699409,0.000005928737,0.000058321657,0.000005865719,0.0000035614853,0.000017463659,0.00010352634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995654,0.0000025099087,0.00015655407,0.000071308415,0.00008492475,0.00011925407],"domain_scores_gemma":[0.99980664,0.000006061564,0.000014884395,0.00012112045,0.00001691177,0.00003439116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004110465,0.000075126634,0.00010033466,0.000050044575,0.000015016974,0.000007749765,0.0000756437,0.00003624628,0.00005853067],"category_scores_gemma":[0.000005136365,0.00006617709,0.00003084066,0.000080037455,0.000011452527,0.00007232448,0.000008086893,0.000030687603,0.000011745753],"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":[5.4959867e-7,0.000014340836,0.00002483322,0.000033226057,0.000010736492,0.0000012085094,0.00007723996,0.99540013,0.0011949537,0.0024264106,0.000038626036,0.0007777278],"study_design_scores_gemma":[0.0005318916,0.00007015476,0.00031124757,0.00005293029,0.0000075036432,0.000009105919,0.000065805856,0.9882999,0.009183944,0.00005382566,0.0012020053,0.00021168229],"about_ca_topic_score_codex":0.00015424562,"about_ca_topic_score_gemma":0.000015274127,"teacher_disagreement_score":0.78812945,"about_ca_system_score_codex":0.000048842157,"about_ca_system_score_gemma":0.0000064013702,"threshold_uncertainty_score":0.26986235},"labels":[],"label_agreement":null},{"id":"W2144697227","doi":"10.1109/aps.2010.5561061","title":"Electric fields from RF tag interrogators underneath an urban rail train","year":2010,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Thales (Canada); University of Toronto","funders":"","keywords":"Ballast; Bogie; Train; Ground plane; Multiphysics; Electrical engineering; Antenna (radio); Engineering; Electrical conductor; Track (disk drive); Finite element method; Computer science; Mechanical engineering; Structural engineering","score_opus":0.0061395080627699656,"score_gpt":0.1902848948188921,"score_spread":0.18414538675612213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144697227","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":[0.9450644,0.00007803774,0.012058618,0.000040699444,0.0011128074,0.00005491182,0.0000030885583,0.0005004927,0.04108689],"genre_scores_gemma":[0.9975165,0.000005018337,0.00044217505,0.000062628016,0.00036577482,0.00000905549,0.000010728887,0.0000326531,0.001555431],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992028,0.000011276911,0.00019577783,0.00019579657,0.000117124204,0.00027725653],"domain_scores_gemma":[0.9994864,0.000030819032,0.000016046968,0.00031209772,0.0000139287085,0.00014071567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000095559284,0.00015687283,0.00015440135,0.000084397456,0.000044108896,0.00005662298,0.0002303398,0.00017354367,0.00070908025],"category_scores_gemma":[0.000013938263,0.00013511122,0.000059544927,0.00016857995,0.000013936617,0.00013904144,0.000012003611,0.0002791481,0.00006895645],"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":[0.000025248492,0.00038086568,0.008708972,0.00008024162,0.0002989637,0.00006733756,0.012224476,0.03458082,0.76400983,0.03688129,0.05696262,0.08577936],"study_design_scores_gemma":[0.0012231141,0.00035576386,0.009503402,0.000035606896,0.000041292322,0.00002833269,0.0013144809,0.80761266,0.0750339,0.001466116,0.1019242,0.0014611152],"about_ca_topic_score_codex":0.0008745274,"about_ca_topic_score_gemma":0.0023217455,"teacher_disagreement_score":0.77303183,"about_ca_system_score_codex":0.000021527523,"about_ca_system_score_gemma":0.000016417289,"threshold_uncertainty_score":0.7763931},"labels":[],"label_agreement":null},{"id":"W2148714761","doi":"10.1002/atr.193","title":"Railway passenger train delay prediction via neural network model","year":2012,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":140,"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":"Overfitting; Computer science; Artificial neural network; Test set; Artificial intelligence; Machine learning; Set (abstract data type); Train; Data mining; Test data; Data set; Time delay neural network","score_opus":0.00773148274247261,"score_gpt":0.20491453130199722,"score_spread":0.1971830485595246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148714761","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":[0.7361521,0.0010235624,0.26077956,0.000019945943,0.0016986574,0.0000616455,0.0000062929093,0.000060755334,0.00019745361],"genre_scores_gemma":[0.9922375,0.000106277424,0.006783903,0.00001677982,0.00077441667,0.0000051063316,0.000015253719,0.00003114253,0.000029610372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998835,0.000015756192,0.00056294695,0.00006548053,0.00023794944,0.00028281854],"domain_scores_gemma":[0.9995376,0.000021047437,0.00015265419,0.00007895138,0.000078356876,0.00013139276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025751605,0.00013426572,0.00019846048,0.00007727791,0.00004996094,0.000009483304,0.00007068355,0.00007792925,0.0000118465505],"category_scores_gemma":[0.0000039042193,0.000119487144,0.00011806966,0.00016703011,0.00001171256,0.00086806325,4.916564e-7,0.00019429107,0.0000018976581],"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":[0.00002589315,0.000023842234,0.00086027465,0.000024263438,0.00002637518,0.0000040742225,0.0011784352,0.9789929,0.010523115,0.000121169345,0.00019188292,0.00802781],"study_design_scores_gemma":[0.0013572058,0.00017835115,0.12987371,0.00011874532,0.00012053795,0.00008982274,0.00030079132,0.8616595,0.0011920221,0.00036073587,0.004395214,0.0003533272],"about_ca_topic_score_codex":0.0000014676048,"about_ca_topic_score_gemma":0.000011616673,"teacher_disagreement_score":0.2560854,"about_ca_system_score_codex":0.00005009324,"about_ca_system_score_gemma":0.000011115396,"threshold_uncertainty_score":0.48725444},"labels":[],"label_agreement":null},{"id":"W2152906398","doi":"10.3141/2448-06","title":"Deadlock Avoidance and Detection in Railway Simulation Systems","year":2014,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Deadlock prevention algorithms; Train; Reservation; Computer science; Scheduling (production processes); Deadlock; Real-time computing; Algorithm; Job shop scheduling; Mathematical optimization; Minification; Distributed computing; Schedule; Computer network; Mathematics","score_opus":0.039423212587778395,"score_gpt":0.31476070875512124,"score_spread":0.2753374961673428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152906398","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":[0.97853094,0.000500375,0.01924116,0.00016744035,0.00074789923,0.0005040865,0.000007757525,0.000041195908,0.00025911615],"genre_scores_gemma":[0.9988722,0.00047237135,0.00019147265,0.0000057718653,0.0001882062,0.000047148507,0.0000028934571,0.000046078203,0.00017381078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99536616,0.0008385564,0.0011291378,0.0002624647,0.001784057,0.00061960844],"domain_scores_gemma":[0.99740654,0.0008664128,0.00017227096,0.00028875266,0.0010448652,0.00022116496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045061535,0.00019208528,0.0003627424,0.0009437805,0.00029632653,0.00012876923,0.00041532383,0.00018288814,0.000018886663],"category_scores_gemma":[0.00014632985,0.00015242577,0.00012622503,0.0015402478,0.00021587261,0.0005101805,0.0000029101482,0.0013666813,0.000008399464],"study_design_candidate":"simulation_or_modeling","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":[0.00020673587,0.000052691335,0.049172677,0.00041509763,0.00003676162,0.00001619871,0.0014954616,0.92408884,0.0067911358,0.0011308023,0.00018600292,0.016407583],"study_design_scores_gemma":[0.0012198501,0.00032072683,0.78578204,0.0004928835,0.000014577769,8.556234e-7,0.0009365941,0.19625321,0.0012693871,0.00065085496,0.012846701,0.00021229881],"about_ca_topic_score_codex":0.0053760475,"about_ca_topic_score_gemma":0.03246555,"teacher_disagreement_score":0.7366094,"about_ca_system_score_codex":0.00020893231,"about_ca_system_score_gemma":0.000081742546,"threshold_uncertainty_score":0.98518944},"labels":[],"label_agreement":null},{"id":"W2169262009","doi":"10.1109/vetec.1990.110300","title":"An interactive train operations simulator for integrated applications in transit systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Block (permutation group theory); Computer science; Track (disk drive); SIGNAL (programming language); Set (abstract data type); Component (thermodynamics); Simulation; Window (computing); Real-time computing; Operating system; Programming language","score_opus":0.012787764202238244,"score_gpt":0.22971224241214733,"score_spread":0.2169244782099091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169262009","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":[0.14677007,0.00021352849,0.84480953,0.000027188436,0.00021178935,0.00082842185,0.00004880502,0.00036885653,0.006721808],"genre_scores_gemma":[0.9981631,0.0000055168875,0.0005949961,0.00000801453,0.00005452871,0.0005431276,0.000033678618,0.000023894867,0.0005731463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993861,0.000016325313,0.00024358157,0.00014446289,0.000051068666,0.00015850569],"domain_scores_gemma":[0.9996872,0.000044189845,0.00000734445,0.00016024367,0.00004160487,0.000059393926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006939463,0.000110588124,0.00014384995,0.000110973626,0.00005234721,0.00005991243,0.000102873484,0.00006289128,0.00006120613],"category_scores_gemma":[0.0000054463826,0.00009453489,0.00003523056,0.00023605958,0.0000098413175,0.00019707634,0.0000013235885,0.00007000679,0.000014083641],"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":[9.632523e-7,0.00005842371,0.0000154232,0.000019458485,0.000009380153,2.3138767e-7,0.0010405353,0.9900001,0.003317714,0.004224816,0.00014780257,0.0011651521],"study_design_scores_gemma":[0.00022913872,0.000031287174,0.000053863965,0.000015168127,0.0000038110772,0.0000013196466,0.0015842494,0.9863848,0.00034763064,0.0000053918952,0.011220636,0.00012271412],"about_ca_topic_score_codex":0.00027018192,"about_ca_topic_score_gemma":0.0006326083,"teacher_disagreement_score":0.85139304,"about_ca_system_score_codex":0.00006538853,"about_ca_system_score_gemma":0.000005358782,"threshold_uncertainty_score":0.38550207},"labels":[],"label_agreement":null},{"id":"W2170176569","doi":"10.5267/j.ijiec.2013.11.002","title":"Scheduling algorithm with controllable train speeds and departure times to decrease the total train tardiness","year":2014,"lang":"en","type":"article","venue":"International Journal of Industrial Engineering Computations","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Tardiness; Train; Scheduling (production processes); Schedule; Computer science; Real-time computing; Benchmark (surveying); Mathematical optimization; Algorithm; Job shop scheduling; Mathematics","score_opus":0.008309986096417513,"score_gpt":0.20757572844642955,"score_spread":0.19926574235001204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170176569","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":[0.33365571,0.00013775262,0.6629136,0.0011546235,0.0017032361,0.00011942737,0.00001664362,0.00006283969,0.0002361483],"genre_scores_gemma":[0.9849508,0.0000034553696,0.013463299,0.000043141845,0.0014704936,0.0000054222437,0.0000047483395,0.000024718765,0.000033939577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990527,0.000024523933,0.00035651767,0.00008819014,0.0003199897,0.00015805227],"domain_scores_gemma":[0.9991871,0.0002379734,0.00007515505,0.000067367495,0.0002235457,0.00020883724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036946769,0.0001470187,0.00020936801,0.00021505693,0.00005417323,0.00014598087,0.00020859185,0.00006615738,0.000014540391],"category_scores_gemma":[0.000307453,0.00010516241,0.00005896924,0.00020073388,0.00001982736,0.00014488412,0.000016466152,0.0002595662,0.0000018823384],"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":[0.000016186288,0.000011465816,0.000016663846,0.0000025996849,0.00014468214,0.000011719019,0.00020100301,0.97946113,0.0004201044,0.0006847911,0.00025584642,0.018773787],"study_design_scores_gemma":[0.0013327952,0.000103781604,0.00026231265,0.00015440756,0.000030039682,0.00040238604,0.000111461624,0.99204355,0.00018335257,0.000045081455,0.005181713,0.00014910514],"about_ca_topic_score_codex":0.000019926434,"about_ca_topic_score_gemma":0.0000029876865,"teacher_disagreement_score":0.65129507,"about_ca_system_score_codex":0.000059201087,"about_ca_system_score_gemma":0.00006754642,"threshold_uncertainty_score":0.4288399},"labels":[],"label_agreement":null},{"id":"W2205442059","doi":"","title":"경기도권 철도체계의 문제점과 개선 방안","year":2006,"lang":"ko","type":"article","venue":"한국철도학회 학술발표대회논문집","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Metropolitan area; Transport engineering; Quarter (Canadian coin); Population; Geography; Computer science; Engineering; Demography","score_opus":0.004051575636030345,"score_gpt":0.17157458019478122,"score_spread":0.16752300455875088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205442059","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":[0.5127822,0.011159105,0.0054567014,0.00047922315,0.008507971,0.000504106,0.000116951705,0.0011992474,0.4597945],"genre_scores_gemma":[0.9576542,0.00018047144,0.00045821167,0.00007989446,0.002870793,0.000037581478,0.000053463933,0.00017877195,0.038486622],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964501,0.000078440884,0.0009373868,0.0006958242,0.0005895046,0.0012487337],"domain_scores_gemma":[0.9984371,0.00009541147,0.00014875027,0.00095481257,0.000108059634,0.00025583422],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038030767,0.0006922691,0.00070121634,0.00026722893,0.00028289168,0.00024820998,0.0005689414,0.00047543988,0.0011377955],"category_scores_gemma":[0.000043106666,0.0006822515,0.00034703978,0.0007033445,0.00011425482,0.00024150076,0.00010834145,0.00046731776,0.002595232],"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":[0.000045861707,0.0007652341,0.012771323,0.0011932675,0.00036781168,0.0005986315,0.0016109766,0.6171322,0.018193956,0.051024623,0.27215493,0.024141207],"study_design_scores_gemma":[0.0018619407,0.00024884392,0.020099087,0.0006575046,0.0001711334,0.00014014615,0.0003606545,0.16302633,0.0057451925,0.0010119238,0.8042411,0.0024361],"about_ca_topic_score_codex":0.004180217,"about_ca_topic_score_gemma":0.00041240084,"teacher_disagreement_score":0.5320862,"about_ca_system_score_codex":0.00025054233,"about_ca_system_score_gemma":0.000085239066,"threshold_uncertainty_score":0.9997753},"labels":[],"label_agreement":null},{"id":"W2208505569","doi":"10.22004/ag.econ.207993","title":"Short-Haul Intermodal Service: Can Rail Compete with Truck?","year":2006,"lang":"en","type":"preprint","venue":"AgEcon Search (University of Minnesota, USA)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Truck; Service (business); Transport engineering; Business; Automotive engineering; Engineering; Marketing","score_opus":0.01575340097727872,"score_gpt":0.19350798006897138,"score_spread":0.17775457909169265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208505569","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":[0.97954345,0.0001656963,0.0032454839,0.00027656474,0.00036994982,0.00031676018,0.00023095764,0.00018915976,0.015661972],"genre_scores_gemma":[0.9957905,0.00009256148,0.0012316792,0.000016021253,0.00011077872,0.0000016923515,0.00021024488,0.00006333031,0.0024831996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979218,0.00008875823,0.0002850625,0.0005757036,0.0005313331,0.00059731526],"domain_scores_gemma":[0.9985936,0.000059714377,0.00009181956,0.00077527144,0.00024797724,0.00023158062],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028692713,0.00043964933,0.00073081494,0.00041436453,0.0001673231,0.000056140056,0.0010001395,0.0003762503,0.0002859804],"category_scores_gemma":[0.0000029584037,0.00050936034,0.00019779269,0.00036584042,0.00020220966,0.00016685962,0.0005168621,0.0007894684,0.000057758636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018786282,0.00027323063,0.018350454,0.0050943615,0.0012513249,0.001417556,0.0123245865,0.91216934,0.002911944,0.00080913794,0.012278153,0.03293203],"study_design_scores_gemma":[0.00521169,0.0007790749,0.43191272,0.0032763353,0.0005914918,0.00026057026,0.014433815,0.479631,0.0019200442,0.00013287617,0.056651343,0.0051990277],"about_ca_topic_score_codex":0.0141290445,"about_ca_topic_score_gemma":0.029303594,"teacher_disagreement_score":0.43253836,"about_ca_system_score_codex":0.00024538353,"about_ca_system_score_gemma":0.0001539159,"threshold_uncertainty_score":0.9997358},"labels":[],"label_agreement":null},{"id":"W2216786882","doi":"10.1109/vppc.2015.7352921","title":"Converter Design for a Railway Voltage Booster Using Two Simulators","year":2015,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Thévenin's theorem; Booster (rocketry); Sizing; Voltage; Computer science; Software; Electronic engineering; Electrical engineering; Engineering; Equivalent circuit","score_opus":0.061954711611985576,"score_gpt":0.26387707827895784,"score_spread":0.20192236666697227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216786882","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":[0.09209941,0.000120115896,0.90092325,0.000007232247,0.0008388761,0.00024008796,0.0000022390038,0.00026575287,0.005503017],"genre_scores_gemma":[0.9918644,5.6160457e-7,0.006216846,0.0000786283,0.00017581033,0.000019154064,0.0000018399895,0.00004757299,0.0015952335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921983,0.0000126028335,0.00020753575,0.00014740514,0.00011842331,0.0002941761],"domain_scores_gemma":[0.9995472,0.00005345681,0.000017592889,0.00018493627,0.000056464713,0.00014038412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024967158,0.00015049617,0.00017672742,0.000066496155,0.00003616951,0.0000470845,0.00010102195,0.000062099796,0.000042996646],"category_scores_gemma":[0.00001934078,0.00012464139,0.00006146273,0.000091534464,0.000015816273,0.00014807552,0.000014114859,0.000043294152,0.000048810743],"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":[0.000010242747,0.000009914902,0.000116283656,0.00002596011,0.000026860662,0.0000026829848,0.0003935605,0.98539835,0.005528706,0.0005710876,0.00659317,0.0013231674],"study_design_scores_gemma":[0.00081722863,0.000038221548,0.0000068735567,0.0000128084885,0.000010309371,0.000004856289,0.00010565769,0.9716471,0.004084665,0.00011165245,0.022960238,0.00020042274],"about_ca_topic_score_codex":0.00007602168,"about_ca_topic_score_gemma":0.000008365998,"teacher_disagreement_score":0.89976496,"about_ca_system_score_codex":0.000068870286,"about_ca_system_score_gemma":0.000027414319,"threshold_uncertainty_score":0.5082728},"labels":[],"label_agreement":null},{"id":"W2231724445","doi":"","title":"Évaluation de l’impact potentiel de l’implantation d’un réseau de bornes de recharge rapide pour véhicules électriques sur les autoroutes du Québec","year":2014,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Geology","score_opus":0.011944231637059773,"score_gpt":0.1943033651196322,"score_spread":0.18235913348257243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2231724445","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":[0.6774715,0.010980664,0.30739382,0.00051269657,0.0002848555,0.00012984066,0.000015314932,0.00007642808,0.0031348348],"genre_scores_gemma":[0.9837386,0.0019248539,0.013341669,0.000011068537,0.0002405658,0.0000012969417,0.000012431499,0.000025146986,0.0007043931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981766,0.0004786429,0.00034715305,0.00024746274,0.00026504308,0.00048515192],"domain_scores_gemma":[0.99892664,0.0002468825,0.00015368611,0.0002521863,0.00025272727,0.0001678928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010581608,0.00028856602,0.00036301685,0.00017047457,0.0006425073,0.000037246195,0.0005167354,0.0003221213,0.00006884591],"category_scores_gemma":[0.00033421075,0.00028839018,0.00038968626,0.00024242165,0.00032318328,0.00032007773,0.000084353494,0.00024672737,0.00001770285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000089223846,0.0003059896,0.054160073,0.0020438195,0.00030684235,0.000009739847,0.009865429,0.6909219,0.21447879,0.0094718635,0.0016058225,0.016740525],"study_design_scores_gemma":[0.0006517483,0.000085884014,0.32310432,0.00205744,0.00024450186,0.00015822443,0.0002303419,0.57352674,0.09472978,0.0005827419,0.0043668733,0.0002613747],"about_ca_topic_score_codex":0.15605786,"about_ca_topic_score_gemma":0.082363054,"teacher_disagreement_score":0.30626705,"about_ca_system_score_codex":0.0049900236,"about_ca_system_score_gemma":0.0011925905,"threshold_uncertainty_score":0.99995685},"labels":[],"label_agreement":null},{"id":"W2249504285","doi":"","title":"캐나다 무인 운전 전동차 제동 시스템 고찰","year":2007,"lang":"ko","type":"article","venue":"한국철도학회 학술발표대회논문집","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Brake; Automotive engineering; Control (management); Engineering; Computer science; Artificial intelligence","score_opus":0.0070031773832832595,"score_gpt":0.20742544561467519,"score_spread":0.20042226823139192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2249504285","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":[0.52903,0.010161894,0.025076473,0.00038284552,0.013130991,0.00057609787,0.000059623966,0.0011803785,0.4204017],"genre_scores_gemma":[0.9796817,0.00028749363,0.000686619,0.00016075534,0.0024465928,0.000014818574,0.000025879706,0.00019004966,0.016506106],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.995751,0.000062315725,0.0010921033,0.00073575357,0.0006976745,0.00166114],"domain_scores_gemma":[0.9979044,0.00018226718,0.00016802114,0.0010639619,0.00013393276,0.0005473882],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0012398504,0.0006919239,0.0007149843,0.00036041808,0.0002891937,0.00018791223,0.00064379047,0.00058792916,0.00096805097],"category_scores_gemma":[0.000116106596,0.0007127712,0.00035195574,0.0008488816,0.00012351768,0.0002470228,0.00013288413,0.0006406785,0.0029674466],"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":[0.00027144607,0.0015415185,0.021686539,0.0030200267,0.0015215595,0.002917183,0.014738869,0.21975085,0.044760507,0.077206805,0.14391777,0.4686669],"study_design_scores_gemma":[0.002328783,0.00045940088,0.035070326,0.00090423843,0.00020064304,0.00025443808,0.0017626934,0.04994121,0.011895189,0.0003404901,0.89377916,0.0030634082],"about_ca_topic_score_codex":0.0009751665,"about_ca_topic_score_gemma":0.00047031796,"teacher_disagreement_score":0.7498614,"about_ca_system_score_codex":0.00031677817,"about_ca_system_score_gemma":0.00008822327,"threshold_uncertainty_score":0.9999452},"labels":[],"label_agreement":null},{"id":"W2257650298","doi":"10.21702/rpj.2011.5.8","title":"Оценка готовности и способности проводников к корректному взаимодействию с пассажирами","year":2011,"lang":"en","type":"article","venue":"Российский психологический журнал","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Computer science","score_opus":0.018987000703129357,"score_gpt":0.14277961703118341,"score_spread":0.12379261632805405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257650298","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":[0.42779312,0.0039935457,0.009195403,0.00009496905,0.0060024452,0.0007368269,0.00006965266,0.003831976,0.548282],"genre_scores_gemma":[0.9909001,0.0003504998,0.00238078,0.00023764928,0.0009223636,0.00016337103,0.00004044791,0.000304018,0.004700764],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99529207,0.00011474371,0.0012041384,0.00092917855,0.00076506427,0.001694827],"domain_scores_gemma":[0.99737096,0.00008503803,0.00017723278,0.0015411534,0.0001702335,0.0006553678],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006063867,0.0009570842,0.0009051369,0.000511357,0.0003464245,0.00015119046,0.001170975,0.00054419885,0.0021568632],"category_scores_gemma":[0.000071087314,0.00093177817,0.00044892455,0.0010284889,0.00022841168,0.0006495776,0.0001880233,0.00071563653,0.0027834447],"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":[0.00057124044,0.003251074,0.029069653,0.0031286413,0.0029414836,0.0022168243,0.038146872,0.07290837,0.07363915,0.124218546,0.35591283,0.29399532],"study_design_scores_gemma":[0.006768919,0.0012281196,0.06851624,0.0012083808,0.00053007796,0.00087775686,0.0035422666,0.075466745,0.07465856,0.0065197856,0.7509971,0.009686049],"about_ca_topic_score_codex":0.00056160724,"about_ca_topic_score_gemma":0.00019765637,"teacher_disagreement_score":0.56310695,"about_ca_system_score_codex":0.00022803496,"about_ca_system_score_gemma":0.00009860613,"threshold_uncertainty_score":0.9993133},"labels":[],"label_agreement":null},{"id":"W2263106464","doi":"10.2172/911260","title":"NYPA/TH!NK Clean Commute Program Final Report - Inception through December 2004","year":2005,"lang":"en","type":"report","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Lease; Quarter (Canadian coin); Relocation; Train; Business; Transport engineering; Engineering; Geography; Finance; Archaeology; Computer science","score_opus":0.04571217797408735,"score_gpt":0.3040413403178908,"score_spread":0.2583291623438035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2263106464","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":[0.007585903,0.0030518658,0.0059500556,0.000041897343,0.0041361935,0.0006967536,0.000017649973,0.0024446612,0.976075],"genre_scores_gemma":[0.47403884,0.0112380665,0.031247897,0.0001311016,0.011838617,0.0011029808,0.002625585,0.0009811776,0.46679574],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962005,0.000034563232,0.0013271488,0.00059982727,0.0011042072,0.0007337689],"domain_scores_gemma":[0.998074,0.000026508296,0.00029849273,0.0011475409,0.00029851685,0.00015494754],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00065773254,0.00066811446,0.000812991,0.00015217155,0.00015087417,0.00014777218,0.00037534203,0.00067960593,0.0011913857],"category_scores_gemma":[0.000054028584,0.000600551,0.00035879973,0.00035113015,0.00008200251,0.0002832853,0.00008695468,0.000749545,0.00029390847],"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":[0.0000069844154,0.00025203475,0.00034218357,0.0008351638,0.0002840695,0.000725589,0.0002007297,0.07721741,0.000035640798,0.00018021911,0.66903424,0.25088575],"study_design_scores_gemma":[0.00019931262,0.000077833894,0.0007768735,0.00034620147,0.00007030285,0.0023710576,0.000061697465,0.003744928,0.00006923521,0.00002187766,0.99149424,0.00076643686],"about_ca_topic_score_codex":0.003460297,"about_ca_topic_score_gemma":0.002995624,"teacher_disagreement_score":0.50927925,"about_ca_system_score_codex":0.0007333319,"about_ca_system_score_gemma":0.0003909741,"threshold_uncertainty_score":0.99972165},"labels":[],"label_agreement":null},{"id":"W2290837137","doi":"","title":"127. Low Voltage Switchgear Design","year":2007,"lang":"en","type":"article","venue":"Tunnelling and Underground Space Technology","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Kyoto Protocol; Climate change; Conference of the parties; Action plan; Context (archaeology); Greenhouse gas; China; Commission; Engineering; Negotiation; Political science; International trade; Business; Law; Geography; Economics; Management","score_opus":0.008544636211487886,"score_gpt":0.19723005120598494,"score_spread":0.18868541499449706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290837137","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":[0.25318775,0.0019412045,0.74037665,0.00013859238,0.00035795482,0.00008950888,4.3460886e-7,0.001158279,0.0027496086],"genre_scores_gemma":[0.9949971,0.00041468834,0.0035064097,0.000020188549,0.000113250695,0.0000064688675,0.0000011335103,0.00004938093,0.00089137384],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987859,0.000008762022,0.0002531887,0.0002740474,0.00011679409,0.00056127564],"domain_scores_gemma":[0.99944013,0.00010259256,0.000037124442,0.00029363087,0.00002923572,0.00009725354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045447377,0.00022407387,0.00025188268,0.00036979152,0.00015541814,0.00004489724,0.00017138597,0.00034733972,0.000013263041],"category_scores_gemma":[0.000023484708,0.00021800904,0.000036965503,0.00048041387,0.00012026252,0.00008074477,0.000029624593,0.0002972682,0.000046186586],"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":[0.0000685891,0.00019951939,0.0024018958,0.00046490796,0.00029010113,0.00043730566,0.001444514,0.34516978,0.23819514,0.35000184,0.003275711,0.058050685],"study_design_scores_gemma":[0.0031343494,0.0010267558,0.0010047997,0.00059137354,0.00013442093,0.00099553,0.009657876,0.641661,0.10931924,0.12590654,0.10294468,0.0036234346],"about_ca_topic_score_codex":0.000053398067,"about_ca_topic_score_gemma":0.00006430009,"teacher_disagreement_score":0.74180937,"about_ca_system_score_codex":0.00006155042,"about_ca_system_score_gemma":0.000012605856,"threshold_uncertainty_score":0.8890151},"labels":[],"label_agreement":null},{"id":"W2312008557","doi":"10.1109/tpwrd.2015.2472994","title":"Power-Quality Impact Assessment for High-Speed Railway Associated With High-Speed Trains Using Train Timetable—Part I: Methodology and Modeling","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":116,"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 Natural Science Foundation of China; University of Alberta","keywords":"Train; Engineering; Traction power network; Traction substation; Waveform; Transformer; Electric power system; Traction (geology); Power (physics); Power flow; Voltage; Harmonic; Electronic engineering; Automotive engineering; Computer science; Electrical engineering; Mechanical engineering","score_opus":0.09649870824432084,"score_gpt":0.31873977021809746,"score_spread":0.22224106197377663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312008557","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":[0.49210584,0.00008261106,0.50603676,0.000026289023,0.0007830911,0.0002875845,0.00019534373,0.00021479651,0.00026770672],"genre_scores_gemma":[0.9867211,0.00002247164,0.012767081,0.00005214567,0.00004989852,0.000023120492,0.000028945591,0.00011888618,0.00021633695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720764,0.0003681413,0.0007068795,0.0005488184,0.00044017975,0.00072835374],"domain_scores_gemma":[0.9983965,0.0004483364,0.0001336084,0.00039448656,0.00027509945,0.00035194022],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018223011,0.0005158175,0.0008602263,0.00035327705,0.00023898504,0.000104498045,0.0001810383,0.00033129,0.00009523607],"category_scores_gemma":[0.000030358007,0.00045131403,0.00022181631,0.0004332323,0.00008689766,0.00041744692,0.0000023727046,0.0003996486,0.0000051173843],"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":[0.00017905461,0.00017607158,0.000008008299,0.000021860318,0.00057068624,0.000006915546,0.00083381165,0.9806374,0.016817017,0.00011220903,0.000117029034,0.00051998295],"study_design_scores_gemma":[0.0046364604,0.0009951202,0.00027653907,0.0001276444,0.00029460416,0.00003789544,0.0008025177,0.98725116,0.0043343473,0.00017971465,0.00013143473,0.00093254936],"about_ca_topic_score_codex":0.000981203,"about_ca_topic_score_gemma":0.00015737228,"teacher_disagreement_score":0.4946153,"about_ca_system_score_codex":0.0005943578,"about_ca_system_score_gemma":0.00020665483,"threshold_uncertainty_score":0.9997939},"labels":[],"label_agreement":null},{"id":"W2318530662","doi":"10.1115/jrc2013-2492","title":"A Study on Determination of Electrical Impedance and Frequency Characteristics of a Subway System Using Finite Element Analysis","year":2013,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Northern Alberta Institute of Technology","funders":"","keywords":"Track circuit; Finite element method; Electrical impedance; Track (disk drive); Traction power network; Engineering; Power (physics); Electrical engineering; Computer science; Electronic engineering; Structural engineering; Electronic circuit; Mechanical engineering; Physics","score_opus":0.011671713684266586,"score_gpt":0.22386052324124286,"score_spread":0.21218880955697628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2318530662","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":[0.93557364,0.00003518466,0.06360941,7.789998e-7,0.000040481707,0.00018668806,0.0000035473067,0.00003269689,0.000517572],"genre_scores_gemma":[0.9991691,0.00000390801,0.0007697478,0.0000010169376,0.000014450378,0.000017279597,0.0000015571891,0.000009127069,0.000013809771],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991359,0.000029494044,0.00042983887,0.000116481286,0.00016288228,0.00012536977],"domain_scores_gemma":[0.9995788,0.00005212968,0.000097450466,0.00015992917,0.000077860874,0.000033847133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014499093,0.00009816389,0.0003012549,0.00023374094,0.000019831217,0.000013445661,0.000058688904,0.00003484263,0.000010017176],"category_scores_gemma":[0.000017125622,0.000081496524,0.000046067536,0.00044842294,0.00000972628,0.000056418023,0.000009100637,0.00003861973,0.0000013187189],"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":[0.00002649449,0.0011928012,0.5950897,0.0017448702,0.0019058625,0.000029814983,0.0069085364,0.11505261,0.22625948,0.009527625,0.000015661786,0.042246584],"study_design_scores_gemma":[0.00014433391,0.00019302774,0.10663917,0.000031727595,0.00012736935,0.0000013125194,0.00037706282,0.89057326,0.0018158746,0.0000039679608,6.887347e-7,0.000092218506],"about_ca_topic_score_codex":0.00044628454,"about_ca_topic_score_gemma":0.000023121069,"teacher_disagreement_score":0.7755206,"about_ca_system_score_codex":0.000052549087,"about_ca_system_score_gemma":0.0000069057414,"threshold_uncertainty_score":0.33233318},"labels":[],"label_agreement":null},{"id":"W2322315326","doi":"10.2316/journal.203.2014.4.203-0108","title":"EVALUATION AND SELECTION FOR AVAILABILITY IMPROVEMENT OF FREQUENCY CONVERTERS IN ELECTRIFIED RAILWAY","year":2014,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Converters; Selection (genetic algorithm); Electrical engineering; Automotive engineering; Engineering; Telecommunications; Transport engineering; Computer science; Voltage; Artificial intelligence","score_opus":0.0066411606594232115,"score_gpt":0.22467693849134254,"score_spread":0.21803577783191933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322315326","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":[0.94739413,0.0012255708,0.047668397,0.000036084213,0.0020029524,0.00008595825,0.000003941879,0.000010563621,0.0015724245],"genre_scores_gemma":[0.99965674,0.00007242415,0.00007163124,0.000009129899,0.00013295017,0.000013431271,0.0000016944779,0.000008782323,0.000033214306],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889,0.00004986791,0.00053684704,0.00009088445,0.00033445936,0.00009795095],"domain_scores_gemma":[0.99920416,0.0000668286,0.00016655763,0.000040592244,0.00048048826,0.000041355775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013548691,0.00008608555,0.00019706316,0.00019556505,0.000014178801,0.000028995833,0.00008342438,0.000056695153,0.000006129284],"category_scores_gemma":[0.00008101416,0.00007469032,0.00004180736,0.000059163336,0.000018971175,0.00013708953,0.0000054786065,0.000052436113,1.07545546e-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":[0.00028437708,0.00025270478,0.02139303,0.00041741444,0.001053355,0.0000029491264,0.0025101448,0.23101717,0.53983814,0.023227943,0.0009721785,0.17903063],"study_design_scores_gemma":[0.0034720153,0.0008763212,0.0098115215,0.00029820175,0.000057801153,0.00011289663,0.00032778922,0.96729535,0.009644592,0.0012249043,0.006611283,0.0002673161],"about_ca_topic_score_codex":0.0004006411,"about_ca_topic_score_gemma":0.000078224635,"teacher_disagreement_score":0.7362782,"about_ca_system_score_codex":0.00012117496,"about_ca_system_score_gemma":0.000032259893,"threshold_uncertainty_score":0.30457827},"labels":[],"label_agreement":null},{"id":"W2323679649","doi":"10.1115/jrc2015-5629","title":"Radio Propagation Prediction for CBTC Data Communication Subsystem Design","year":2015,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"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":"Ultra high frequency; Radio propagation model; Radio propagation; Ray tracing (physics); Train; Radio frequency; Computer science; Process (computing); Electronic engineering; Radio Link Protocol; Engineering; Simulation; Telecommunications; Wireless; Optics; Physics","score_opus":0.12388011088324205,"score_gpt":0.25194639350286674,"score_spread":0.1280662826196247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2323679649","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":[0.0038287174,0.00046492903,0.9880796,0.00004268421,0.0004490583,0.00048204645,0.000019045157,0.000508244,0.0061256667],"genre_scores_gemma":[0.98283,0.000020445867,0.016081791,0.000004396926,0.00012769841,0.00010002997,0.00028410272,0.000023175038,0.0005283635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993851,0.000042414635,0.00021288633,0.00012452851,0.00011757608,0.00011749382],"domain_scores_gemma":[0.9991159,0.000039138424,0.000028142307,0.0006841798,0.00007501339,0.00005765124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078294106,0.00007678724,0.00009291849,0.00004234702,0.000051870895,0.000041188516,0.0002881783,0.000057430156,0.000003008357],"category_scores_gemma":[0.000046189863,0.00006700107,0.0000131797315,0.0001012958,0.000009576222,0.00033570407,0.000024422767,0.000036912854,0.000018231365],"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":[0.000017532418,0.00002913398,0.00014411028,0.00013151982,0.000036656154,2.4781525e-7,0.00060404034,0.88022786,0.0019363568,0.0039070924,0.106496304,0.006469134],"study_design_scores_gemma":[0.00029349254,0.000037253154,0.000065010536,0.000024489971,0.0000091598395,0.0000053315025,0.00017975044,0.9786725,0.0010214357,0.000055989007,0.019557176,0.00007839943],"about_ca_topic_score_codex":0.00005135234,"about_ca_topic_score_gemma":0.000022918835,"teacher_disagreement_score":0.9790013,"about_ca_system_score_codex":0.000078692356,"about_ca_system_score_gemma":0.000025274901,"threshold_uncertainty_score":0.27322242},"labels":[],"label_agreement":null},{"id":"W2358156957","doi":"","title":"Optimize the Occupation Plan of Arrival-departure Lines in Passenger Station based on Genetic Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Plan (archaeology); Crossover; Genetic algorithm; Fitness function; Operations research; Key (lock); Function (biology); Mutation; Arrival time; Integer programming; Chromosome; Engineering; Computer science; Algorithm; Transport engineering; Artificial intelligence; Geography; Machine learning; Archaeology; Computer security","score_opus":0.008161835085287335,"score_gpt":0.2146234231928678,"score_spread":0.20646158810758047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358156957","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":[0.38551328,0.00008302166,0.6082892,0.000058093345,0.0003696574,0.00017861572,0.000008892708,0.00010066618,0.005398566],"genre_scores_gemma":[0.9855928,0.00000855769,0.0141735915,0.000033906286,0.00008304299,0.000007835119,0.000014203314,0.000013061322,0.00007301198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999372,0.000017519518,0.00024189557,0.000085640706,0.00014866766,0.00013424369],"domain_scores_gemma":[0.99966794,0.0001077964,0.000029438414,0.00014367118,0.000027512964,0.000023643033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022986092,0.000086072054,0.00009256677,0.00009530976,0.000018639847,0.000009394382,0.000065236614,0.000060736358,0.000025287072],"category_scores_gemma":[0.000015864473,0.000056712728,0.00002588593,0.00017894342,0.000010428582,0.000029974728,0.0000028342533,0.000065530476,0.0000044481626],"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":[0.000007631188,0.000017382074,0.00074915495,0.000013783466,0.0000031805657,0.0000020867196,0.0001398884,0.9827302,0.00065918156,0.00004294769,0.000274628,0.0153599065],"study_design_scores_gemma":[0.0002472147,0.0000331936,0.046731055,0.000022199694,0.0000028015354,6.953036e-7,0.0001052637,0.9480228,0.004047057,0.000009047206,0.0007018247,0.00007684777],"about_ca_topic_score_codex":0.00012188198,"about_ca_topic_score_gemma":0.0001756685,"teacher_disagreement_score":0.60007954,"about_ca_system_score_codex":0.000025118707,"about_ca_system_score_gemma":0.000008548867,"threshold_uncertainty_score":0.2312678},"labels":[],"label_agreement":null},{"id":"W2360178671","doi":"","title":"Research on gusts caused by high-speed trains passing through tunnel","year":2015,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ministry of Education and Child Care","funders":"","keywords":"Train; Marshalling; Railway tunnel; Engineering; Marine engineering; Amplitude; Simulation; Structural engineering; Computer science; Physics","score_opus":0.10860202491109426,"score_gpt":0.31902559466320074,"score_spread":0.21042356975210647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2360178671","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":[0.68477607,0.0002568993,0.005857275,0.0004550183,0.0010139958,0.00015293653,0.000008916089,0.0006417803,0.30683714],"genre_scores_gemma":[0.9927989,0.000011882931,0.00028191356,0.00005084834,0.00022633738,0.000009222623,0.000009976489,0.000047206002,0.0065636723],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845254,0.000062712206,0.00022250974,0.00022549836,0.0005202946,0.0005164499],"domain_scores_gemma":[0.9993456,0.000070390386,0.000012826128,0.00031467277,0.00008561123,0.00017091019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000551606,0.00015192399,0.00018134213,0.00009595365,0.000099945624,0.000088237466,0.00019416917,0.00012230998,0.000074662574],"category_scores_gemma":[0.0000354377,0.00012468116,0.000035648434,0.00039544335,0.00004826095,0.00015064435,0.000022901457,0.00024710433,0.00028385874],"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":[0.000011626809,0.00011635136,0.000035261204,0.000046608082,0.0000462077,0.000031732285,0.002683522,0.36259705,0.025769148,0.018252416,0.5876315,0.002778571],"study_design_scores_gemma":[0.0049500098,0.0009975773,0.0021448531,0.00033447202,0.000021973312,0.000021806885,0.011815144,0.31018555,0.069405064,0.0021191328,0.59603953,0.0019648504],"about_ca_topic_score_codex":0.0011121143,"about_ca_topic_score_gemma":0.00011374687,"teacher_disagreement_score":0.3080229,"about_ca_system_score_codex":0.00018808676,"about_ca_system_score_gemma":0.000035607,"threshold_uncertainty_score":0.508435},"labels":[],"label_agreement":null},{"id":"W2362918992","doi":"","title":"Advantages and Application of Linear Induction Motor in Urban Transit System","year":2008,"lang":"en","type":"article","venue":"Converter Technology & Electric Traction","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"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":"Bogie; Linear induction motor; Urban rail transit; Urban rail; Urban transit; Linear motor; Automotive engineering; Traction motor; Rail transit; Transport engineering; Engineering; Transit system; Traction (geology); Transit (satellite); Induction motor; Public transport; Mechanical engineering; Electrical engineering; Voltage","score_opus":0.003909506238337504,"score_gpt":0.18077274912704885,"score_spread":0.17686324288871136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2362918992","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":[0.9384445,0.00086780003,0.05974642,0.000031533342,0.00015304268,0.0001801853,8.1756446e-7,0.0004206473,0.00015506659],"genre_scores_gemma":[0.99952847,0.00022068444,0.00011325745,0.0000019436375,0.000035368692,0.000061041545,0.0000026903294,0.000015495494,0.000021073898],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935627,0.000011085366,0.0002509561,0.00016488155,0.00006738083,0.00014943106],"domain_scores_gemma":[0.99975264,0.00001130487,0.0000498024,0.00014112865,0.000026675352,0.000018418492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000060776256,0.00010244398,0.00018220913,0.00054638134,0.00004533736,0.0000019814727,0.00006136434,0.00024657676,9.000651e-7],"category_scores_gemma":[0.000004118369,0.00010728208,0.000021543456,0.00061047636,0.0000364436,0.00012949298,0.0000029095852,0.0001949845,0.000004626321],"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":[0.00002425075,0.00005891443,0.009748881,0.00018477814,0.000026798949,0.0000074479494,0.00030662498,0.0016423746,0.87110096,0.0007420317,0.000035898986,0.11612104],"study_design_scores_gemma":[0.0008788893,0.0002900605,0.04297841,0.000058951515,0.00003005966,0.0006660382,0.00042321175,0.7837463,0.16911119,0.00003181713,0.0014761271,0.00030892092],"about_ca_topic_score_codex":0.000067888184,"about_ca_topic_score_gemma":0.000009197658,"teacher_disagreement_score":0.78210396,"about_ca_system_score_codex":0.000090568436,"about_ca_system_score_gemma":0.000009726239,"threshold_uncertainty_score":0.43748364},"labels":[],"label_agreement":null},{"id":"W2378250473","doi":"","title":"The Theory and Experiment Study on New Intelligent Electronically Controlled Pneumatic Brake for Freight Train","year":2001,"lang":"en","type":"article","venue":"Zhongguo tiedao kexue","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Brake; Air brake; Automotive engineering; Engineering; Rail freight transport","score_opus":0.013558788586139514,"score_gpt":0.24378261764541542,"score_spread":0.2302238290592759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2378250473","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":[0.9418559,0.00433403,0.036047164,0.00023219168,0.00083842047,0.002442652,0.0000027215763,0.0002913997,0.013955517],"genre_scores_gemma":[0.99520206,0.00013194412,0.00006034739,0.000055307584,0.00020979984,0.00035940888,0.000001846568,0.000051136336,0.003928162],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985092,0.000109687455,0.0004525799,0.00025334358,0.00020976989,0.00046536818],"domain_scores_gemma":[0.9987869,0.00064714404,0.00005303479,0.0003567236,0.000022829903,0.00013331191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008684738,0.0002559606,0.00037991744,0.0000612539,0.0002272767,0.00012633263,0.00023981123,0.00006840816,0.000036007525],"category_scores_gemma":[0.000095680705,0.00016322147,0.00011100488,0.000109085486,0.00003512088,0.00004745923,0.000019272844,0.0001440302,0.000020317608],"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":[0.0047075893,0.0025206548,0.00057488645,0.00022091094,0.0031225001,0.000060005175,0.033888917,0.113832176,0.030857675,0.5170826,0.016032273,0.27709982],"study_design_scores_gemma":[0.06872384,0.018704025,0.009002366,0.00076704734,0.00077574275,0.0001776458,0.04374521,0.24921216,0.026544545,0.041803345,0.53496623,0.0055778455],"about_ca_topic_score_codex":0.00002380234,"about_ca_topic_score_gemma":0.00008991977,"teacher_disagreement_score":0.51893395,"about_ca_system_score_codex":0.00008361188,"about_ca_system_score_gemma":0.00003778838,"threshold_uncertainty_score":0.66559786},"labels":[],"label_agreement":null},{"id":"W2383192576","doi":"","title":"COMPREHENSIVE DESCRIPTION OF HEAVY HAUL RAILWAYS ABROAD","year":2000,"lang":"en","type":"article","venue":"Tiedao gongcheng xuebao","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"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":"Train; Transport engineering; Work (physics); Heavy industry; Coal; China; Plan (archaeology); Engineering; Freight trains; Business; Geography; Waste management; Economics; Mechanical engineering","score_opus":0.015130149197893756,"score_gpt":0.2119333343492878,"score_spread":0.19680318515139403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2383192576","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":[0.88521457,0.0017720207,0.0007381247,0.000018474491,0.0006504119,0.00014267281,0.000010706925,0.0003971535,0.11105589],"genre_scores_gemma":[0.9931332,0.00017986142,0.00047815056,0.000030011863,0.00022581994,0.000018308807,0.000015903037,0.00005154067,0.005867235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986987,0.000033412747,0.0004338419,0.00022089698,0.0002321178,0.00038099018],"domain_scores_gemma":[0.99936545,0.000033856224,0.000042163916,0.0003848803,0.00006719238,0.00010648711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011261082,0.0002338966,0.00035053087,0.000111096364,0.000075667114,0.00003440265,0.0002046834,0.0001399968,0.0007190994],"category_scores_gemma":[0.00000674732,0.000232165,0.000119436874,0.00029056997,0.00007120007,0.00020724746,0.000014739658,0.00017102658,0.0003773712],"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":[0.00010559966,0.00019932255,0.0006862041,0.0007420716,0.00020816932,0.000020054926,0.0033903043,0.8038178,0.066767275,0.0021215673,0.03228084,0.08966079],"study_design_scores_gemma":[0.0021820865,0.0003209965,0.029011456,0.00042726187,0.00008643072,0.00008974075,0.0007991635,0.09569622,0.039544202,0.0006041814,0.8297889,0.0014493534],"about_ca_topic_score_codex":0.0003607692,"about_ca_topic_score_gemma":0.000023678014,"teacher_disagreement_score":0.79750806,"about_ca_system_score_codex":0.0000715194,"about_ca_system_score_gemma":0.000017456785,"threshold_uncertainty_score":0.9467414},"labels":[],"label_agreement":null},{"id":"W2383438001","doi":"","title":"Design Characteristics and Application of Basic Types of China Introduced High Power Locomotives","year":2008,"lang":"en","type":"article","venue":"Railway Locomotive & Car","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Engineering; Siemens; Corporation; Diesel locomotive; Service (business); China; Automotive engineering; Transport engineering; Manufacturing engineering; Power (physics); Electrical engineering; Business; Marketing; Finance","score_opus":0.006523717079831964,"score_gpt":0.1873983472168286,"score_spread":0.18087463013699664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2383438001","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":[0.91229916,0.0003615573,0.08582588,0.000015467898,0.00019160348,0.00029685756,0.00002377519,0.00007829076,0.00090739725],"genre_scores_gemma":[0.99862516,0.00013127225,0.0010193229,0.0000039189254,0.00007806969,0.000022982815,0.000012398942,0.000036900834,0.0000699639],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989382,0.000060621296,0.0003927127,0.00023002965,0.00017500094,0.0002034145],"domain_scores_gemma":[0.9993092,0.00007433962,0.00013409767,0.0002981592,0.000118060045,0.0000661905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018331791,0.00020463223,0.00043782816,0.00012861619,0.000055918637,0.000005770602,0.00014638757,0.00009458862,0.000023703178],"category_scores_gemma":[0.00003840846,0.00019055235,0.000054585806,0.00024398259,0.00017599371,0.00009225479,0.000030123916,0.000109234155,0.0000112144935],"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":[0.0001724775,0.0006451011,0.030954782,0.0010612879,0.0006266676,0.000028911969,0.0187524,0.15782726,0.75007755,0.009936106,0.000674073,0.029243404],"study_design_scores_gemma":[0.0011846763,0.00059253856,0.72521806,0.00015861412,0.000075584,0.000053217693,0.00023110025,0.03362522,0.23675612,0.00021592587,0.0011921974,0.0006967416],"about_ca_topic_score_codex":0.00007676835,"about_ca_topic_score_gemma":0.0000023272637,"teacher_disagreement_score":0.6942633,"about_ca_system_score_codex":0.00003500129,"about_ca_system_score_gemma":0.000022491296,"threshold_uncertainty_score":0.77704996},"labels":[],"label_agreement":null},{"id":"W2416075755","doi":"","title":"Case studies: results and synthesis projet 7FP CLOSER (Connecting LOng and Short-distance networks for Efficient Transport) Rapport de recherche Deliverable 5.2 project européen CLOSER.","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ministère des Transports","funders":"","keywords":"Deliverable; Computer science; Engineering; Systems engineering","score_opus":0.09281736668227844,"score_gpt":0.29341913779608964,"score_spread":0.2006017711138112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2416075755","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":[0.6077812,0.012179764,0.37155664,0.0002732174,0.00034499852,0.001383156,0.0001683618,0.00046460284,0.0058480827],"genre_scores_gemma":[0.9623251,0.003148945,0.033063356,0.000011485465,0.00006900604,0.00036255617,0.00011335055,0.0001059409,0.0008002616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949782,0.0025847799,0.00078215235,0.0008045039,0.00020675927,0.0006436002],"domain_scores_gemma":[0.9945577,0.0031347696,0.00025043808,0.0010709558,0.0007899661,0.0001961966],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.014776377,0.00047059183,0.00059662055,0.00015139596,0.00046751226,0.00018335675,0.00039658873,0.0004801431,0.000002688871],"category_scores_gemma":[0.0016892158,0.00047261376,0.00014665897,0.00029985863,0.00014405485,0.00010826618,0.0002628049,0.0007628368,6.286622e-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":[0.00034343192,0.0014323451,0.005285335,0.011082691,0.0018514908,0.0005844038,0.15649,0.26147443,0.002018673,0.007192186,0.00190988,0.55033517],"study_design_scores_gemma":[0.0008716714,0.0000023064163,0.0005979841,0.003940079,0.00033043706,0.00056182605,0.002178155,0.9773822,0.0070408992,0.0001268092,0.005771642,0.0011960062],"about_ca_topic_score_codex":0.00068667374,"about_ca_topic_score_gemma":0.0014892836,"teacher_disagreement_score":0.71590775,"about_ca_system_score_codex":0.00024831865,"about_ca_system_score_gemma":0.00015106949,"threshold_uncertainty_score":0.99977255},"labels":[],"label_agreement":null},{"id":"W2469326219","doi":"10.1109/ipemc.2016.7512645","title":"Impacts of traction transformers on power rating of Railway Power Quality Compensator","year":2016,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of New Brunswick","funders":"","keywords":"Transformer; Traction substation; Power quality; Correctness; Traction power network; Electric power system; Reliability engineering; Engineering; Automotive engineering; Ferroresonance in electricity networks; Grid; AC power; Computer science; Electrical engineering; Power (physics); Voltage; Mathematics","score_opus":0.011860447616472224,"score_gpt":0.24215716767721224,"score_spread":0.23029672006074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2469326219","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":[0.935525,0.000029307352,0.008465559,0.000046452464,0.000339714,0.000082695115,0.0000103595985,0.00007848042,0.055422407],"genre_scores_gemma":[0.9996939,0.000009017045,0.000095976735,0.000009954989,0.000012076208,0.0000024071942,6.938176e-7,0.000017767508,0.00015821689],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900067,0.000028673352,0.00046384498,0.000109240544,0.00021082915,0.00018672108],"domain_scores_gemma":[0.9995359,0.00012144101,0.00007581382,0.0001552342,0.00004713135,0.00006448925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033297433,0.00012203949,0.00025157878,0.000078908924,0.000019603212,0.0000045843954,0.0000668471,0.00007414906,0.00038929828],"category_scores_gemma":[0.00003796966,0.00007627152,0.00010360102,0.000109318964,0.00003249686,0.00014078067,0.0000027723597,0.000049779173,0.000014987443],"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":[0.000039982013,0.00006630779,0.00092343724,0.000085878324,0.00004598594,5.101512e-7,0.0007698246,0.002566679,0.98721284,0.00417252,0.00027245,0.003843575],"study_design_scores_gemma":[0.0018366557,0.0005547296,0.06157818,0.0003912628,0.00001378191,0.000005746062,0.0013545892,0.0011725264,0.92933273,0.000040387014,0.0032422347,0.00047715125],"about_ca_topic_score_codex":0.00016922504,"about_ca_topic_score_gemma":0.0000483011,"teacher_disagreement_score":0.06416886,"about_ca_system_score_codex":0.000042992597,"about_ca_system_score_gemma":0.000015290416,"threshold_uncertainty_score":0.4262543},"labels":[],"label_agreement":null},{"id":"W2495222067","doi":"10.1109/tpwrd.2015.2472961","title":"Power-Quality Impact Assessment for High-Speed Railway Associated With High-Speed Trains Using Train Timetable—Part II: Verifications, Estimations and Applications","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":58,"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 Natural Science Foundation of China; University of Alberta","keywords":"Harmonics; Train; Harmonic; Engineering; Electric power system; Reliability engineering; Power (physics); Harmonic analysis; Voltage; Power quality; Electronic engineering; Traction power network; Weibull distribution; Automotive engineering; Electrical engineering; Mathematics","score_opus":0.030779943794949763,"score_gpt":0.2776267494817535,"score_spread":0.24684680568680373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2495222067","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":[0.3826881,0.00008788591,0.6140527,0.000071622584,0.00039920703,0.0007863686,0.00082771556,0.0003641577,0.00072224136],"genre_scores_gemma":[0.9925501,0.000020529085,0.0064863404,0.00003682611,0.000044983317,0.00017587186,0.00013647713,0.0000932233,0.0004556677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978238,0.00010843621,0.0006548785,0.00048219188,0.0004226402,0.0005081038],"domain_scores_gemma":[0.99840015,0.0002407363,0.00016104542,0.0005305202,0.00033227715,0.00033526504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000650176,0.00042374886,0.00051780284,0.00032980688,0.0005892585,0.0001350586,0.00019715284,0.00021818228,0.00010724585],"category_scores_gemma":[0.000016444104,0.00039572813,0.00016093,0.0006764152,0.000122016536,0.0004688475,0.0000023245072,0.00028719686,0.000012884784],"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":[0.00005623777,0.00053421146,0.00001665273,0.000027809601,0.00047572138,0.0000014345898,0.00073754345,0.98661983,0.008868913,0.0010066428,0.00066044135,0.0009945439],"study_design_scores_gemma":[0.015958674,0.0030752653,0.012009374,0.00052988384,0.0014188376,0.00009869465,0.002877809,0.93570846,0.013169549,0.00086368923,0.010023412,0.0042663417],"about_ca_topic_score_codex":0.0003155905,"about_ca_topic_score_gemma":0.00009162252,"teacher_disagreement_score":0.60986197,"about_ca_system_score_codex":0.0006145457,"about_ca_system_score_gemma":0.00026283765,"threshold_uncertainty_score":0.99984944},"labels":[],"label_agreement":null},{"id":"W2505379252","doi":"10.1016/j.trb.2016.07.003","title":"A two-step linear programming model for energy-efficient timetables in metro railway networks","year":2016,"lang":"en","type":"article","venue":"Transportation Research Part B Methodological","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thales (Canada); University of Toronto","funders":"","keywords":"Train; Computer science; Linear programming; Compiler; Energy consumption; Energy (signal processing); Service (business); Mathematical optimization; Engineering; Operating system; Algorithm","score_opus":0.23574547800610535,"score_gpt":0.4021652464940827,"score_spread":0.16641976848797735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505379252","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":[0.10343385,0.0004840556,0.89486086,0.00009574666,0.00016537511,0.00045997475,0.000025894722,0.00022545736,0.0002487685],"genre_scores_gemma":[0.85312617,0.00012504672,0.14514895,0.000014559335,0.00017197082,0.0009074904,0.000035149507,0.000045050496,0.00042562102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966892,0.0006138655,0.00067241373,0.00049335416,0.00052713667,0.0010040106],"domain_scores_gemma":[0.9971442,0.002130967,0.00004673673,0.00027553737,0.00019496267,0.00020754432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063995956,0.00023459297,0.000462658,0.0003275149,0.00013512366,0.000032809665,0.00027923816,0.00022267253,0.000062444415],"category_scores_gemma":[0.00034833312,0.00015430147,0.0001664778,0.000732528,0.00014318043,0.00010051021,0.000009405658,0.00026459465,0.000007249255],"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":[0.00009918453,0.000081032296,0.0006089029,0.000042046162,0.000023609193,0.000009435661,0.00010504707,0.9550973,0.0018775602,0.011782286,0.0002226951,0.030050874],"study_design_scores_gemma":[0.0009860478,0.00010943788,0.0007799107,0.00007741817,0.000010207033,4.1508662e-7,0.00009629902,0.9882574,0.0010181634,0.00037561837,0.008057152,0.00023194778],"about_ca_topic_score_codex":0.00011033804,"about_ca_topic_score_gemma":0.0008065395,"teacher_disagreement_score":0.74971193,"about_ca_system_score_codex":0.00009608086,"about_ca_system_score_gemma":0.000044218366,"threshold_uncertainty_score":0.6292231},"labels":[],"label_agreement":null},{"id":"W2523094825","doi":"10.3390/en9100762","title":"Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution","year":2016,"lang":"en","type":"article","venue":"Energies","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":21,"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":"National Natural Science Foundation of China","keywords":"Regenerative brake; Mode (computer interface); Control theory (sociology); Brake; Point (geometry); Computer science; Energy (signal processing); Optimal control; Energy consumption; Automotive engineering; Mathematical optimization; Engineering; Control (management); Mathematics; Electrical engineering","score_opus":0.013353291720572716,"score_gpt":0.18535483076409792,"score_spread":0.17200153904352522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523094825","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":[0.32161516,0.00017934381,0.66617364,0.00003168019,0.00016447173,0.00010448797,0.00000408008,0.00059119065,0.011135979],"genre_scores_gemma":[0.9844459,0.000005851408,0.014660992,0.000011927297,0.00023390187,0.00008259717,0.0000182121,0.00005999146,0.00048059144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859613,0.00005169481,0.00026345524,0.0003676616,0.0003263605,0.00039468077],"domain_scores_gemma":[0.99928546,0.000043906344,0.00007292512,0.000410557,0.0000772152,0.000109923545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001648229,0.00026810518,0.00022512817,0.00016688228,0.00016442234,0.00005932417,0.00020082256,0.000105505635,0.000013999834],"category_scores_gemma":[0.0000084523435,0.00017202192,0.000056752124,0.00028796206,0.00006783778,0.00025028994,0.000019939494,0.000076900535,0.0000073790256],"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":[0.00006600509,0.00006049747,0.000017003216,0.000023238557,0.000020171683,0.0000059587605,0.0002689889,0.980159,0.004117475,0.007148132,0.00020106843,0.007912503],"study_design_scores_gemma":[0.00057378365,0.00015648275,0.00009180712,0.00010969123,0.000011763928,0.000023044697,0.0004449705,0.9766858,0.02032847,0.000014894228,0.0012070934,0.00035217454],"about_ca_topic_score_codex":0.00008706923,"about_ca_topic_score_gemma":0.00001100546,"teacher_disagreement_score":0.66283077,"about_ca_system_score_codex":0.00013908042,"about_ca_system_score_gemma":0.00005147626,"threshold_uncertainty_score":0.70148504},"labels":[],"label_agreement":null},{"id":"W2530791898","doi":"10.1109/tits.2016.2586938","title":"A Cognitive Control Method for Cost-Efficient CBTC Systems With Smart Grids","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Train; Computer science; Engineering; Traction power network; Wireless; Automotive engineering; Simulation; Power (physics); Telecommunications","score_opus":0.019776501171788363,"score_gpt":0.25244501836034855,"score_spread":0.2326685171885602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530791898","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":[0.010817879,0.00028374663,0.979945,0.000029967106,0.0037996424,0.0029614798,0.0013008976,0.00056947675,0.0002918839],"genre_scores_gemma":[0.99478555,0.000057229616,0.00039759872,0.000017988585,0.00013079085,0.0038192205,0.000025277788,0.000119205186,0.00064714643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973848,0.0001065569,0.0009445617,0.00051809783,0.0005016563,0.00054432935],"domain_scores_gemma":[0.99830997,0.00061217946,0.0001422699,0.0003031014,0.00039901896,0.00023348928],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000503839,0.0004552285,0.0005865307,0.00033682145,0.00020725522,0.00008668855,0.00016686856,0.00020460605,0.000033829147],"category_scores_gemma":[0.0000040176064,0.0003136885,0.00024153103,0.0003699901,0.00006369485,0.00014586831,9.1944266e-8,0.00015418054,0.00007900803],"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":[0.00027554703,0.00013013024,0.00003381475,0.0003148749,0.00036209504,0.0000071294016,0.0006085654,0.98598325,0.0018971468,0.0005287145,0.00005649075,0.009802266],"study_design_scores_gemma":[0.0053746053,0.00097187347,0.00016295639,0.0025270581,0.0005709434,0.00006346173,0.0028772014,0.9365307,0.036444332,0.000006005113,0.013186714,0.0012841597],"about_ca_topic_score_codex":0.00030943062,"about_ca_topic_score_gemma":0.00019145984,"teacher_disagreement_score":0.98396766,"about_ca_system_score_codex":0.00021733793,"about_ca_system_score_gemma":0.000057205554,"threshold_uncertainty_score":0.9999315},"labels":[],"label_agreement":null},{"id":"W2536571921","doi":"10.4230/oasics.atmos.2016.6","title":"Multi-Column Generation Model for the Locomotive Assignment Problem","year":2016,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Column generation; Train; Decomposition; Column (typography); Computer science; Set (abstract data type); Decomposition method (queueing theory); Generator (circuit theory); Heuristics; Assignment problem; Process (computing); Fraction (chemistry); Mathematical optimization; Algorithm; Mathematics; Power (physics)","score_opus":0.029924688836358893,"score_gpt":0.23247067837628183,"score_spread":0.20254598953992292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536571921","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":[0.022374252,0.00007381492,0.9729409,0.00019321108,0.00085692975,0.002016827,0.0004970365,0.00023038246,0.00081661396],"genre_scores_gemma":[0.97042197,0.000064456166,0.025559006,0.00016545031,0.00029836808,0.001431707,0.00012052931,0.00008560394,0.0018529224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980375,0.000012851386,0.0008452033,0.00018206034,0.00029326312,0.0006291003],"domain_scores_gemma":[0.9989449,0.00012405551,0.00017044785,0.00045910254,0.00018250487,0.000119011034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048794234,0.00033440813,0.00029495836,0.00009712882,0.0003513214,0.00016649243,0.00041940509,0.0001752457,0.000013496488],"category_scores_gemma":[0.000035854184,0.00019871246,0.00021219633,0.00010741257,0.00006666343,0.0006366774,0.00006576589,0.00012571618,0.000048536313],"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":[0.000052145402,0.00025184927,0.00040920367,0.00066618173,0.0004023864,6.248415e-7,0.00997136,0.9165153,0.005478085,0.0041744183,0.024277013,0.03780146],"study_design_scores_gemma":[0.0021181174,0.000087897075,0.000047071746,0.00009239162,0.00003259193,0.000005356805,0.00028779553,0.9643477,0.0019993861,0.00007269158,0.03058632,0.00032265406],"about_ca_topic_score_codex":0.000008833895,"about_ca_topic_score_gemma":0.000064630476,"teacher_disagreement_score":0.9480477,"about_ca_system_score_codex":0.00023732973,"about_ca_system_score_gemma":0.00003920093,"threshold_uncertainty_score":0.81032586},"labels":[],"label_agreement":null},{"id":"W2544852071","doi":"10.2495/tdi-v1-n3-501-510","title":"Freight train scheduling with minimum energy consumption","year":2017,"lang":"en","type":"article","venue":"International Journal of Transport Development and Integration","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Energy consumption; Scheduling (production processes); Computer science; Mathematical optimization; Engineering; Mathematics; Electrical engineering","score_opus":0.014981361056404576,"score_gpt":0.219826608695095,"score_spread":0.20484524763869041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544852071","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":[0.86165214,0.00019711671,0.13514322,0.000106593834,0.00080618216,0.000020981619,0.0000016941706,0.00001608023,0.002056007],"genre_scores_gemma":[0.9927566,0.00020901169,0.006713297,0.000015922156,0.00015888589,0.0000027785711,0.00001284024,0.000010312748,0.0001203182],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999215,0.0000048747725,0.00035401087,0.00007156899,0.0002716903,0.000082871935],"domain_scores_gemma":[0.99953955,0.000008922024,0.00016448043,0.000060109334,0.00017598081,0.000050948256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016088752,0.00010445942,0.00012576768,0.00013088409,0.00009671617,0.00009056527,0.00019448827,0.000039591676,0.000030322835],"category_scores_gemma":[0.0000065128193,0.00007697077,0.000029526575,0.00001489032,0.000036023826,0.00044936087,0.0000032833389,0.00008631888,0.0000012046967],"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":[0.00079070905,0.00028218896,0.08852002,0.00012085423,0.0016235643,0.0005052812,0.008623889,0.03002765,0.062042534,0.020512927,0.00046930826,0.7864811],"study_design_scores_gemma":[0.0068637477,0.0006717115,0.6452709,0.003057392,0.00014100313,0.0010902152,0.0009878438,0.054077443,0.20299695,0.0005985501,0.08281085,0.0014333957],"about_ca_topic_score_codex":0.00001657692,"about_ca_topic_score_gemma":0.0001674664,"teacher_disagreement_score":0.78504765,"about_ca_system_score_codex":0.00004930924,"about_ca_system_score_gemma":0.000043775082,"threshold_uncertainty_score":0.31387767},"labels":[],"label_agreement":null},{"id":"W2552892684","doi":"10.3103/s1068371216090042","title":"On statistical models of the amplitude and the duration of pulsed electromagnetic interference in automatic-control and telemechanic channels of subway lines","year":2016,"lang":"en","type":"article","venue":"Russian Electrical Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Bombardier (Canada)","funders":"","keywords":"Amplitude; Standard deviation; Interference (communication); Electromagnetic interference; Mathematics; Probability density function; Train; Mathematical analysis; Electromagnetic compatibility; Pulse duration; Acoustics; Control theory (sociology); Electronic engineering; Physics; Computer science; Engineering; Statistics; Electrical engineering; Optics","score_opus":0.0037353665225580467,"score_gpt":0.17239261761965197,"score_spread":0.16865725109709392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552892684","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":[0.60229766,0.00044148206,0.39678416,0.000086638225,0.000058793437,0.00022334422,0.0000057705774,0.000029930789,0.00007220252],"genre_scores_gemma":[0.99966043,0.00005911611,0.00022069945,0.000002998428,0.000013828383,0.000021659054,2.4011302e-7,0.000014724715,0.000006304309],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908495,0.000037998252,0.00043059344,0.00011464091,0.00012798715,0.00020382849],"domain_scores_gemma":[0.9993246,0.00041979871,0.000060061462,0.00014710898,0.000015245776,0.00003314722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022172865,0.00012574716,0.00030155136,0.00010963054,0.000013087376,0.000007530499,0.000117395495,0.000058446,0.0000031836687],"category_scores_gemma":[0.00016085917,0.00006467088,0.00003193115,0.00024996386,0.000047520283,0.000048056754,0.000013823349,0.00010134623,1.3332537e-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":[0.00008412338,0.000046865993,0.00005869727,0.00030804524,0.000054047563,0.000001055056,0.0003571102,0.20060289,0.30656317,0.4798664,0.000003778413,0.012053832],"study_design_scores_gemma":[0.00092130643,0.000145469,0.0020951764,0.00017338456,0.000011992161,0.000004437498,0.000002879492,0.98199487,0.011647223,0.0029238092,7.7471833e-7,0.00007869489],"about_ca_topic_score_codex":0.000033013534,"about_ca_topic_score_gemma":0.0000064211627,"teacher_disagreement_score":0.781392,"about_ca_system_score_codex":0.000026705302,"about_ca_system_score_gemma":0.000012993547,"threshold_uncertainty_score":0.2637202},"labels":[],"label_agreement":null},{"id":"W2568444998","doi":"10.1002/atr.1430","title":"Development of efficient stop planning optimization process for high‐speed rail systems","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Heuristics; Process (computing); Decomposition; Computer science; Integer programming; Mathematical optimization; Operations research; Linear programming; Speedup; Network planning and design; Engineering; Algorithm; Mathematics","score_opus":0.011354672509078475,"score_gpt":0.2355590435183579,"score_spread":0.22420437100927942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568444998","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":[0.5382385,0.0001645436,0.46100187,0.00000500546,0.00046394535,0.00009112921,0.000004752516,0.000016539067,0.000013713699],"genre_scores_gemma":[0.9807199,0.0000142144,0.01914664,9.881804e-7,0.00006317889,0.000008771984,0.000008291827,0.000020356067,0.000017622146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882185,0.000005831003,0.0007394528,0.00007479193,0.00023209573,0.00012599069],"domain_scores_gemma":[0.99923486,0.00003702422,0.00034058705,0.000053094504,0.00028784206,0.000046602756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020038217,0.00009961841,0.00022351636,0.00013324068,0.000036350928,0.0000062667687,0.00007495703,0.000046283618,0.0000027068852],"category_scores_gemma":[0.000013533002,0.00007068494,0.00004655416,0.00012120555,0.000009870685,0.0001754504,3.8568066e-7,0.000035695717,2.5213893e-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":[0.00006194855,0.00002058925,0.000050047787,0.00023416986,0.000028044795,0.0000014044431,0.0022041267,0.95117307,0.043478567,0.00013216744,0.0000038135404,0.0026120478],"study_design_scores_gemma":[0.019159526,0.0011240181,0.038185652,0.01018527,0.0003063383,0.00004708731,0.013894454,0.47291043,0.4374503,0.0001378314,0.004897491,0.00170159],"about_ca_topic_score_codex":6.2546707e-7,"about_ca_topic_score_gemma":0.0000010157149,"teacher_disagreement_score":0.47826263,"about_ca_system_score_codex":0.000063428786,"about_ca_system_score_gemma":0.000048048714,"threshold_uncertainty_score":0.2882448},"labels":[],"label_agreement":null},{"id":"W2584370355","doi":"10.1109/mepcon.2016.7836858","title":"Total traction package optimal performance — The future trends in automotive, rail, and aerospace industries","year":2016,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Energy conservation; Automotive industry; Efficient energy use; Energy consumption; Engineering; Aerospace; Renewable energy; Regenerative brake; Automotive engineering; Sustainable transport; Transport engineering; Sustainability; Ecology; Electrical engineering","score_opus":0.005620547454571189,"score_gpt":0.18510559033722418,"score_spread":0.179485042882653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584370355","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":[0.99059767,0.00020226525,0.00043793477,0.00065717765,0.0003540267,0.00003386606,0.0000023724822,0.000120926044,0.007593779],"genre_scores_gemma":[0.99329376,0.00010587121,0.000040681916,0.000007628048,0.00017545915,0.000009589383,9.1346243e-7,0.000012026784,0.006354068],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994949,0.000012312514,0.00011546985,0.00011003227,0.00008463772,0.00018260963],"domain_scores_gemma":[0.9998015,0.000022355121,0.000015038505,0.00011568778,0.000011853806,0.0000335364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010243444,0.00010740397,0.00009431187,0.00006696882,0.000055469274,0.000030282003,0.000055219498,0.000091816226,0.00009756607],"category_scores_gemma":[0.0000047982044,0.000054139466,0.000016863783,0.00021722402,0.00003695098,0.00027331364,0.000012152893,0.00011243026,0.000009172909],"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":[0.000030640273,0.000049750823,0.015282621,0.000040707335,0.000040687934,0.0000091375205,0.0024058798,0.07838854,0.013679881,0.0012866679,0.014626546,0.8741589],"study_design_scores_gemma":[0.0013984777,0.00019713669,0.8694644,0.000112679765,0.000011133224,0.00010453326,0.0027410903,0.0684852,0.015641158,0.0000036880429,0.04126936,0.0005711596],"about_ca_topic_score_codex":0.000027234,"about_ca_topic_score_gemma":0.00007860851,"teacher_disagreement_score":0.8735878,"about_ca_system_score_codex":0.00003524143,"about_ca_system_score_gemma":0.000005756042,"threshold_uncertainty_score":0.22077434},"labels":[],"label_agreement":null},{"id":"W2587485247","doi":"10.1002/atr.1441","title":"Train rescheduling model with train delay and passenger impatience time in urban subway network","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Schedule; Duration (music); Train; TRIPS architecture; Computer science; Travel time; Operations research; Variable (mathematics); Genetic algorithm; Passenger train; Transport engineering; Simulation; Engineering; Automotive engineering; Mathematics","score_opus":0.003965494322480957,"score_gpt":0.1808700264059197,"score_spread":0.17690453208343876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2587485247","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":[0.9111024,0.00036945075,0.08824995,0.000052542728,0.00007677993,0.000060646955,0.0000038333314,0.000019976753,0.00006440528],"genre_scores_gemma":[0.98740107,0.00012889675,0.012328563,0.0000074498794,0.000073256575,0.000003827279,0.0000015622102,0.000021809667,0.000033557168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990757,0.0000140973825,0.00043821216,0.000099921024,0.00017057084,0.000201473],"domain_scores_gemma":[0.99963975,0.000046698624,0.000119562326,0.00006304007,0.000053864143,0.000077065975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024044703,0.00011926216,0.00020763118,0.000090778325,0.00002550292,0.000009067034,0.00006065443,0.000051258077,0.000004337473],"category_scores_gemma":[0.000006709829,0.00007832086,0.000035307163,0.00016695887,0.000023867682,0.00046648466,4.538542e-7,0.00010893138,4.6879165e-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":[0.00006969826,0.000010571141,0.0013219418,0.00001799819,0.0000103555185,0.00002389336,0.0016061461,0.9645434,0.02636046,0.00011483899,0.000013708035,0.0059069917],"study_design_scores_gemma":[0.010311155,0.0014459185,0.35202032,0.003609354,0.000120447745,0.00015421533,0.0011106308,0.62185323,0.0050852024,0.0018654881,0.0010426678,0.0013813366],"about_ca_topic_score_codex":0.000002806733,"about_ca_topic_score_gemma":0.00012280028,"teacher_disagreement_score":0.35069838,"about_ca_system_score_codex":0.000042431315,"about_ca_system_score_gemma":0.000026516418,"threshold_uncertainty_score":0.31938317},"labels":[],"label_agreement":null},{"id":"W2592370412","doi":"10.1155/2017/6381718","title":"A Novel Space-Time-Speed Method for Increasing the Passing Capacity with Safety Guaranteed of Railway Station","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":16,"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":"State Key Laboratory of Rail Traffic Control and Safety; National Natural Science Foundation of China","keywords":"Variable (mathematics); Process (computing); Computer science; Mode (computer interface); Simulation; Engineering; Real-time computing; Mathematics","score_opus":0.013034718571368235,"score_gpt":0.24585582514545357,"score_spread":0.23282110657408533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592370412","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":[0.4796406,0.0000432749,0.5198459,0.00006673983,0.00014587237,0.00012010661,0.000024830873,0.000010318168,0.000102340506],"genre_scores_gemma":[0.8513141,0.000017294831,0.14854568,0.0000047814146,0.00007491459,0.0000024087044,0.0000063745665,0.000021815556,0.000012606751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989597,0.000024984549,0.0005472511,0.00008461921,0.00023984231,0.00014356713],"domain_scores_gemma":[0.9986138,0.00017299938,0.00072432327,0.00017137607,0.00027792482,0.000039628172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076312636,0.00012598961,0.00029654914,0.00007714456,0.00022110257,0.00003729302,0.00015321503,0.000047896996,0.0000028900395],"category_scores_gemma":[0.00007630571,0.00008461517,0.00010687263,0.000079015386,0.00004822673,0.00049548136,8.647865e-7,0.00012102147,1.4409085e-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":[0.00040820468,0.00001915187,0.00022857868,0.00008484755,0.00006159776,0.0000012077846,0.0018618166,0.71858335,0.27261326,0.0003887728,0.000004387408,0.0057448247],"study_design_scores_gemma":[0.011909126,0.00092646776,0.6052248,0.001999762,0.00056702073,0.00015731197,0.0033731316,0.24595352,0.12489807,0.00085210055,0.0034041163,0.0007346098],"about_ca_topic_score_codex":0.00011030012,"about_ca_topic_score_gemma":0.00016828574,"teacher_disagreement_score":0.6049962,"about_ca_system_score_codex":0.000048062906,"about_ca_system_score_gemma":0.000036864116,"threshold_uncertainty_score":0.34505063},"labels":[],"label_agreement":null},{"id":"W2592952515","doi":"10.1002/atr.1445","title":"Train movement simulation by element increment method","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McGill University","funders":"State Key Laboratory of Rail Traffic Control and Safety; National Natural Science Foundation of China","keywords":"Traction (geology); Process (computing); Computer science; Curve fitting; Discrete element method; Simulation; Polynomial; Movement (music); Engineering; Algorithm; Mechanical engineering; Mathematics; Mechanics; Mathematical analysis","score_opus":0.0064829284039353815,"score_gpt":0.24512466260940585,"score_spread":0.23864173420547047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592952515","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":[0.3115667,0.00021695957,0.6875986,0.00008466841,0.00033336636,0.0000746838,0.000011371432,0.000024433844,0.00008923154],"genre_scores_gemma":[0.98659515,0.00013228499,0.013086327,0.00002655832,0.00007590758,0.000005372057,0.0000062792583,0.000016620312,0.000055521698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890965,0.000018098013,0.0005769719,0.00007434086,0.00028696773,0.00013396994],"domain_scores_gemma":[0.99957204,0.00004598464,0.00016628258,0.00006719405,0.00008664918,0.00006184551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030177395,0.000100970676,0.0001547423,0.0000777166,0.000023354953,0.0000068370646,0.00006359082,0.000033159107,0.000046456924],"category_scores_gemma":[0.0000067358696,0.00007005913,0.00007151681,0.000085632666,0.0000057087323,0.00029011854,4.7129217e-7,0.000048948485,0.0000017345928],"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":[0.000017757093,0.00001905337,0.00007761874,0.000015075802,0.000025226274,0.0000020571736,0.00030448637,0.735646,0.20157057,0.00012897539,0.00010456711,0.06208861],"study_design_scores_gemma":[0.016408341,0.002221392,0.10201307,0.0013942673,0.00031786587,0.000011038798,0.0025359637,0.08640819,0.5542978,0.004084776,0.22863685,0.0016704163],"about_ca_topic_score_codex":0.0000034061904,"about_ca_topic_score_gemma":0.000012486315,"teacher_disagreement_score":0.67502844,"about_ca_system_score_codex":0.00011548017,"about_ca_system_score_gemma":0.000010531482,"threshold_uncertainty_score":0.28569284},"labels":[],"label_agreement":null},{"id":"W2593905957","doi":"10.3141/2648-04","title":"Redesigning Main Lines for Commuter Rail Electrification","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrification; Track (disk drive); Transport engineering; Installation; Overhead (engineering); Engineering; Electricity; Electrical engineering","score_opus":0.10682103193431441,"score_gpt":0.3734438256521494,"score_spread":0.266622793717835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593905957","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":[0.96763307,0.00035755357,0.026820732,0.0023638823,0.0011117231,0.0010469354,0.000059979942,0.000057412974,0.0005487013],"genre_scores_gemma":[0.9946114,0.0007544678,0.0029498923,0.000012878947,0.00042473702,0.00014401846,0.000016341026,0.00007275498,0.0010135497],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99555326,0.00038993798,0.0011507191,0.00026993683,0.0018243735,0.00081178837],"domain_scores_gemma":[0.9957836,0.0006840971,0.00036981257,0.0007760184,0.0021388861,0.00024758241],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005281218,0.00021690548,0.00038848052,0.00060177594,0.0015541093,0.0003621511,0.0015821479,0.00019455809,0.000056261022],"category_scores_gemma":[0.0002653106,0.00016733639,0.00035166327,0.0005481449,0.00037591238,0.00071436056,0.0000031191794,0.0012871388,0.000010200087],"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":[0.0037345807,0.00071037386,0.19338503,0.0028853214,0.0010737126,0.00019506236,0.012326234,0.12807584,0.3927117,0.025185227,0.13363267,0.10608423],"study_design_scores_gemma":[0.002838414,0.0007133364,0.87649834,0.00076153537,0.000068085814,0.0000014052451,0.0013004293,0.009455678,0.026818242,0.004392135,0.07670655,0.0004458464],"about_ca_topic_score_codex":0.0025867294,"about_ca_topic_score_gemma":0.014954267,"teacher_disagreement_score":0.6831133,"about_ca_system_score_codex":0.00019355984,"about_ca_system_score_gemma":0.00025784812,"threshold_uncertainty_score":0.9997457},"labels":[],"label_agreement":null},{"id":"W2604376850","doi":"","title":"RaIl transit returning to Kitchener-Waterloo : Ontario's first new LRT system takes shape","year":2017,"lang":"en","type":"article","venue":"Passenger train journal","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Light rail transit; Transport engineering; Transit (satellite); Rail transit; Light rail; Engineering; Rapid transit; Transit system; Public transport; Environmental science; Civil engineering; Geography","score_opus":0.017178049542727496,"score_gpt":0.2008650008113317,"score_spread":0.1836869512686042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604376850","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":[0.88323134,0.0010058762,0.064466216,0.0030496719,0.007573237,0.00037118315,0.000012032314,0.0006501336,0.03964033],"genre_scores_gemma":[0.99100465,0.000013330264,0.0030948378,0.00004036007,0.001748377,0.000012695854,0.0000022164088,0.00008726906,0.0039962754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978368,0.000040984865,0.0006261891,0.00030669407,0.00044393697,0.0007453679],"domain_scores_gemma":[0.99847645,0.000039178463,0.00014880499,0.00056121295,0.000072679766,0.0007016632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063366245,0.00038055846,0.0004928356,0.00017142469,0.00097487523,0.0009027994,0.0007589529,0.00017856713,0.0003675877],"category_scores_gemma":[0.00004159193,0.0003319779,0.00024203376,0.00008595007,0.000030540232,0.0004297642,0.000021527047,0.00064513367,0.00009680347],"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":[0.00021241122,0.00020646879,0.009805966,0.0014441641,0.0014094334,0.0032331801,0.19773348,0.45099586,0.13160431,0.00175261,0.08189103,0.119711086],"study_design_scores_gemma":[0.005614686,0.0005463424,0.08786759,0.004304935,0.00033582817,0.0051059914,0.009239843,0.087186225,0.0073060687,0.00015477056,0.78868145,0.0036562812],"about_ca_topic_score_codex":0.0034846803,"about_ca_topic_score_gemma":0.02154219,"teacher_disagreement_score":0.7067904,"about_ca_system_score_codex":0.00040328957,"about_ca_system_score_gemma":0.00013460843,"threshold_uncertainty_score":0.9999132},"labels":[],"label_agreement":null},{"id":"W2606872609","doi":"","title":"Capacity at Railway Stations","year":2011,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transport Canada","funders":"","keywords":"Geography; Transport engineering; Environmental science; Engineering","score_opus":0.02114890495335972,"score_gpt":0.17161108133103695,"score_spread":0.15046217637767723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606872609","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":[0.68802583,0.00036646627,0.05232074,0.00019095984,0.00036673195,0.00082062674,0.0002876052,0.0010766439,0.25654438],"genre_scores_gemma":[0.9795124,0.00042822098,0.015725866,0.00001587483,0.000028138651,4.0490139e-7,0.000021812062,0.00005334722,0.0042139357],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9969882,0.00016075406,0.00059581024,0.0007327468,0.0007366747,0.00078582246],"domain_scores_gemma":[0.9973778,0.00028928678,0.00034221326,0.0011410944,0.00034004496,0.00050954474],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055869244,0.00051541586,0.0009949392,0.00053995225,0.00044704136,0.0000121308285,0.0017570974,0.0008203133,0.003503171],"category_scores_gemma":[0.00010595925,0.00065483234,0.00062771473,0.0011557557,0.001343614,0.00048234384,0.0007208563,0.00075999415,0.00038410697],"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":[0.005611128,0.008476377,0.031391468,0.0036991707,0.0033796753,0.0035075166,0.010751048,0.018370822,0.5178507,0.126565,0.24811651,0.022280566],"study_design_scores_gemma":[0.020970643,0.0045497855,0.45647594,0.0027152677,0.003105121,0.0010936206,0.011445226,0.018176844,0.040067706,0.005310056,0.42522004,0.010869742],"about_ca_topic_score_codex":0.0014233013,"about_ca_topic_score_gemma":0.0029161216,"teacher_disagreement_score":0.47778302,"about_ca_system_score_codex":0.00066264666,"about_ca_system_score_gemma":0.000118617405,"threshold_uncertainty_score":0.9995903},"labels":[],"label_agreement":null},{"id":"W2611206265","doi":"","title":"Højhastighedsbaner i Skandinavien","year":2008,"lang":"da","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transport Canada","funders":"","keywords":"Computer science","score_opus":0.014220192465791384,"score_gpt":0.17979846286203416,"score_spread":0.1655782703962428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611206265","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":[0.69088197,0.0047129528,0.06858777,0.0011220969,0.001583078,0.002353239,0.00079774903,0.0016001423,0.22836101],"genre_scores_gemma":[0.9678858,0.004248849,0.009028834,0.000031647363,0.000153158,4.3906175e-7,0.00004788749,0.0001250514,0.01847836],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932002,0.00038287806,0.0012715431,0.0016750755,0.0017744723,0.0016958509],"domain_scores_gemma":[0.99431455,0.00076135626,0.00092501944,0.0022039514,0.00069082616,0.0011042765],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00087745645,0.0012107043,0.0025326004,0.0011305565,0.0010309041,0.000033453613,0.003840522,0.002224754,0.0044263816],"category_scores_gemma":[0.00027642952,0.0015714999,0.00161112,0.0026725077,0.0035712472,0.00082984916,0.0017334792,0.0020433848,0.0009656185],"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":[0.010604876,0.01581379,0.031646736,0.007838276,0.0057671345,0.02529964,0.0066008973,0.045661878,0.24991831,0.049334396,0.52209955,0.02941452],"study_design_scores_gemma":[0.026837628,0.007093823,0.21114893,0.0075819683,0.0048162458,0.0040519647,0.011509598,0.013949993,0.005888305,0.0010312046,0.6935191,0.012571197],"about_ca_topic_score_codex":0.0030906284,"about_ca_topic_score_gemma":0.0013679541,"teacher_disagreement_score":0.2770038,"about_ca_system_score_codex":0.0011955274,"about_ca_system_score_gemma":0.00054732437,"threshold_uncertainty_score":0.99981225},"labels":[],"label_agreement":null},{"id":"W2614791712","doi":"10.1109/tte.2017.2703583","title":"Comprehensive Topological Overview of Rolling Stock Architectures and Recent Trends in Electric Railway Traction Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":196,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Insulated-gate bipolar transistor; Power electronics; Traction motor; Traction substation; Standardization; Traction (geology); Engineering; Train; Transformer; Traction control system; Electric traction; Electrical engineering; Automotive engineering; Computer science; Mechanical engineering; Voltage","score_opus":0.044094675762256084,"score_gpt":0.2825435967612039,"score_spread":0.2384489209989478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614791712","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":[0.8502684,0.0016238508,0.14694422,0.00007308128,0.0003994057,0.00030000537,0.000020977817,0.00011695562,0.00025310143],"genre_scores_gemma":[0.9970163,0.0026850172,0.000071850634,0.000006141891,0.000026375039,0.00010029543,0.000019746343,0.000023883396,0.000050353672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986917,0.000058789115,0.0005351771,0.00027223674,0.0002134822,0.00022860871],"domain_scores_gemma":[0.9993318,0.00006390731,0.00017424872,0.00029140426,0.000079702564,0.000058940994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015807405,0.0001960547,0.0003021961,0.0004920351,0.00021942903,0.00004517903,0.00012972747,0.00017473016,0.00001920672],"category_scores_gemma":[0.0000033003535,0.00019446478,0.00007485809,0.00039267176,0.00004363092,0.00015121406,2.1570052e-8,0.00030199875,0.0000016641638],"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":[0.00008156575,0.000112682384,0.00012943843,0.00014119339,0.000035643698,0.0000016906642,0.00047753387,0.63226134,0.23490682,0.0003194784,0.000004745506,0.13152789],"study_design_scores_gemma":[0.0021353464,0.0005303852,0.37819105,0.0002448436,0.000127616,0.000020526366,0.00021568964,0.19924843,0.41754633,0.00016363258,0.00087332923,0.0007027948],"about_ca_topic_score_codex":0.00036092557,"about_ca_topic_score_gemma":0.0003807943,"teacher_disagreement_score":0.4330129,"about_ca_system_score_codex":0.00009566946,"about_ca_system_score_gemma":0.000016770928,"threshold_uncertainty_score":0.7930044},"labels":[],"label_agreement":null},{"id":"W2621274330","doi":"","title":"Crosstalk Inside a Bundle with Unshielded Twisted Pair Cables and a Single Wire","year":2012,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Twisted pair; Crosstalk; Bundle; Electrical engineering; Physics; Materials science; Optics; Engineering","score_opus":0.012257429451442139,"score_gpt":0.18762499423281917,"score_spread":0.17536756478137702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621274330","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":[0.90338194,0.006150527,0.0351824,0.0007423112,0.00031133043,0.0003869694,0.00004734654,0.00077905064,0.05301813],"genre_scores_gemma":[0.98655576,0.00021352297,0.009443895,0.000019270397,0.000035397155,0.00007132322,0.00014959872,0.00008301169,0.0034282468],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975382,0.000826946,0.00040888047,0.00048780625,0.00029127777,0.0004468898],"domain_scores_gemma":[0.99731797,0.00039859914,0.00018819675,0.0013106945,0.00055772153,0.00022684634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015186019,0.00038780144,0.00041153678,0.00014816629,0.00024399883,0.00042909704,0.00047856776,0.00030481434,0.00004340147],"category_scores_gemma":[0.00019346196,0.0003669628,0.00009010235,0.0002887187,0.00022050008,0.00015551008,0.00043139912,0.0004661394,0.000011108474],"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":[0.00019977304,0.00830015,0.064141534,0.014892377,0.0026099624,0.00014683609,0.27207217,0.11942564,0.12408013,0.12582774,0.012054412,0.25624928],"study_design_scores_gemma":[0.0056308694,0.00001253892,0.03736933,0.025087986,0.0005765993,0.0004730107,0.003565428,0.37180713,0.26789227,0.00319508,0.27691826,0.0074714697],"about_ca_topic_score_codex":0.0023543895,"about_ca_topic_score_gemma":0.0046051387,"teacher_disagreement_score":0.26850674,"about_ca_system_score_codex":0.00011892054,"about_ca_system_score_gemma":0.000096142394,"threshold_uncertainty_score":0.9998782},"labels":[],"label_agreement":null},{"id":"W2626613082","doi":"","title":"Locomotive assignment under consist busting and maintenance constraints","year":2014,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada); Concordia University","funders":"","keywords":"Column generation; Train; Heuristics; Scalability; Decomposition; String (physics); Optimization problem; Computer science; Engineering; Mathematical optimization; Algorithm; Mathematics","score_opus":0.004225027175911758,"score_gpt":0.1605987540331355,"score_spread":0.15637372685722375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2626613082","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":[0.61792696,0.00025340499,0.33611777,0.00021477527,0.00052989786,0.00012233041,0.000006093726,0.00025642215,0.04457232],"genre_scores_gemma":[0.99761444,0.000028084656,0.0017382983,0.00012709538,0.000104178886,0.000009939942,0.0000023250225,0.000022919736,0.00035273825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999228,0.000033424385,0.00017899483,0.00017958244,0.00010803063,0.00027198668],"domain_scores_gemma":[0.999609,0.00010512811,0.000033081546,0.00013161833,0.000024939436,0.000096244614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020085719,0.00015108648,0.00017775901,0.000044127883,0.0001447688,0.00004620478,0.000071427334,0.00009540338,0.000025541269],"category_scores_gemma":[0.000044275985,0.00013830686,0.000032099888,0.00007210644,0.00033298516,0.000050327533,0.00001334464,0.00015822082,0.000012959831],"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":[0.000013428474,0.000067051944,0.005463,0.00035881327,0.00025396686,0.0000639739,0.0049915034,0.17684644,0.012177452,0.67780834,0.0059847,0.11597133],"study_design_scores_gemma":[0.008288979,0.00054799055,0.07103459,0.001304507,0.00021175538,0.0010752585,0.018357772,0.69544184,0.010888535,0.022288937,0.16569379,0.0048660496],"about_ca_topic_score_codex":0.000033342712,"about_ca_topic_score_gemma":0.000012395886,"teacher_disagreement_score":0.6555194,"about_ca_system_score_codex":0.00009448468,"about_ca_system_score_gemma":0.000008071263,"threshold_uncertainty_score":0.563999},"labels":[],"label_agreement":null},{"id":"W2652595374","doi":"","title":"An Enhanced Optimization Model for Scheduling Freight Trains","year":2013,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada); Concordia University","funders":"","keywords":"Train; Schedule; Scalability; Scheduling (production processes); Computer science; Column generation; Freight trains; Track (disk drive); Mathematical optimization; Convergence (economics); Operations research; Engineering; Mathematics","score_opus":0.007685638485981742,"score_gpt":0.19071682088909567,"score_spread":0.18303118240311392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2652595374","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":[0.29936454,0.00007332011,0.69857496,0.000027464708,0.00018019073,0.00015937192,0.0000041199114,0.00022714984,0.0013888754],"genre_scores_gemma":[0.8324691,0.000023513323,0.16680993,0.000040609586,0.00016227832,0.00012233364,0.000025022819,0.00004068043,0.00030652434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992964,0.000009352043,0.00018849799,0.00016851563,0.000084495296,0.00025272436],"domain_scores_gemma":[0.9996335,0.000018830626,0.000024938823,0.00017875507,0.000052186453,0.00009180979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070267466,0.00013760944,0.0001397656,0.00006883157,0.00014598256,0.00006896563,0.00012602564,0.00014247367,0.000034358116],"category_scores_gemma":[0.000013258435,0.00013310583,0.000056608642,0.00009356097,0.00002955272,0.00028216292,0.000003071099,0.00009025485,0.000009042252],"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":[0.000001318985,0.000011764324,0.0000034535437,0.000027571677,0.000010921713,1.913142e-7,0.0016042724,0.9762803,0.013608568,0.0057184002,0.00010750278,0.002625753],"study_design_scores_gemma":[0.00022881373,0.000021171823,0.000014397198,0.00000974956,0.000005836621,0.0000011002135,0.0001882911,0.99671775,0.0019930976,0.00052445516,0.000114498755,0.00018084102],"about_ca_topic_score_codex":0.000030871575,"about_ca_topic_score_gemma":0.000009284599,"teacher_disagreement_score":0.53310454,"about_ca_system_score_codex":0.000064437154,"about_ca_system_score_gemma":0.000010833657,"threshold_uncertainty_score":0.5427898},"labels":[],"label_agreement":null},{"id":"W2726079540","doi":"","title":"Planning the most suitable travel speed for high frequency railway lines","year":2005,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transport Canada","funders":"","keywords":"Punctuality; Headway; Train; Transport engineering; Line (geometry); Spare part; Engineering; Travel time; Automotive engineering; Computer science; Operations management","score_opus":0.01509213296415616,"score_gpt":0.2227521327511114,"score_spread":0.20765999978695523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726079540","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":[0.68921953,0.0028533798,0.108997434,0.0014146427,0.0021440655,0.0007365571,0.00003987316,0.0010026234,0.1935919],"genre_scores_gemma":[0.98606145,0.000012755495,0.0031085748,0.00010702753,0.000854483,0.000029428349,0.000011725067,0.000040081362,0.009774444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990914,0.000008261113,0.0002641004,0.00014869733,0.00011849341,0.00036903692],"domain_scores_gemma":[0.9995551,0.00010060628,0.000022843997,0.0002273635,0.000038837792,0.00005521094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002187597,0.00015941418,0.00016735824,0.000048245216,0.00014378373,0.00006216757,0.00021501357,0.00008371059,0.00012862575],"category_scores_gemma":[0.000027781027,0.00010344205,0.000054307562,0.00016402779,0.000019802119,0.00013694217,0.000010958691,0.000093253984,0.00004620671],"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":[0.0000054439315,0.00003070442,0.00035633153,0.00006675016,0.000059546397,0.0000022996487,0.0009566049,0.898826,0.03667847,0.02853503,0.02793034,0.006552478],"study_design_scores_gemma":[0.0019241836,0.0001383863,0.006272523,0.00013231597,0.00005681627,0.00004132019,0.0018418188,0.76135737,0.06556089,0.00091349636,0.16055165,0.0012092259],"about_ca_topic_score_codex":0.0002779848,"about_ca_topic_score_gemma":0.000099382625,"teacher_disagreement_score":0.29684195,"about_ca_system_score_codex":0.000035218538,"about_ca_system_score_gemma":0.000014598236,"threshold_uncertainty_score":0.42182443},"labels":[],"label_agreement":null},{"id":"W2727324042","doi":"","title":"Deadlock Avoidance and Detection in Railway Simulation Systems","year":2013,"lang":"en","type":"article","venue":"Les Cahiers du GERAD","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Deadlock prevention algorithms; Computer science; Reservation; Scheduling (production processes); Train; Distributed computing; Deadlock; Real-time computing; Algorithm; Mathematical optimization; Computer network; Mathematics; Geography","score_opus":0.003984293277856587,"score_gpt":0.16653566412140278,"score_spread":0.16255137084354618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727324042","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":[0.96492624,0.0009613774,0.031816375,0.000014967772,0.00048115218,0.00017576202,7.35943e-7,0.00016282016,0.0014605402],"genre_scores_gemma":[0.999363,0.000042718388,0.0001371099,0.000009643255,0.0001166467,0.000049960345,0.0000013628078,0.00002105986,0.0002584912],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.00002965768,0.0002014607,0.00014034842,0.0000898069,0.00018880943],"domain_scores_gemma":[0.99973243,0.0000544873,0.000025083127,0.00011361083,0.000022767743,0.0000516315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104047154,0.00011515667,0.00013516807,0.000104319086,0.00007612845,0.00006805201,0.000049611986,0.00012954067,0.000006866677],"category_scores_gemma":[0.000020426747,0.00011206419,0.000019791929,0.00015820086,0.000030864965,0.00018722432,0.0000055049395,0.00013671316,0.000029103468],"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":[0.0000018302453,0.000005802697,0.0013485141,0.000085582564,0.0000074423006,0.0000024647438,0.000750514,0.9746933,0.006635786,0.0009014191,0.000051976884,0.015515392],"study_design_scores_gemma":[0.00024221711,0.00001762391,0.015691223,0.00003408409,0.0000026203627,0.000009838432,0.00024248697,0.97979265,0.0005352107,0.00012874398,0.003129457,0.000173856],"about_ca_topic_score_codex":0.0007095568,"about_ca_topic_score_gemma":0.0000954092,"teacher_disagreement_score":0.03443674,"about_ca_system_score_codex":0.0001081192,"about_ca_system_score_gemma":0.000003009739,"threshold_uncertainty_score":0.4569845},"labels":[],"label_agreement":null},{"id":"W2736372245","doi":"","title":"Modelling expected train passenger delays on large scale railway networks","year":2006,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"","keywords":"Scale (ratio); Computer science; Transport engineering; Engineering; Geography","score_opus":0.007184434365010746,"score_gpt":0.16594005939675094,"score_spread":0.1587556250317402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736372245","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":[0.36873662,0.0006244102,0.5478859,0.00021361721,0.00028969123,0.0007454308,0.00014779986,0.0011109456,0.08024556],"genre_scores_gemma":[0.987207,0.00037578956,0.009795002,0.000019892172,0.00010322229,5.7689465e-7,0.000053170792,0.00008227452,0.0023630934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99612516,0.00019987927,0.00072668004,0.0009403697,0.0008873789,0.0011205116],"domain_scores_gemma":[0.9974111,0.00042697688,0.00034598567,0.0011211389,0.0002887238,0.0004061208],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007036415,0.000666479,0.0012287904,0.0006256711,0.00045764004,0.000028199012,0.0016071834,0.0011922879,0.00084131607],"category_scores_gemma":[0.000036646594,0.0008339031,0.00077726017,0.0014357044,0.00067467376,0.000388391,0.0004219564,0.0011694932,0.0001156354],"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":[0.0007711642,0.0014552489,0.00065349514,0.0002382448,0.00023665624,0.00052282383,0.00027757484,0.9417794,0.014961381,0.009651777,0.028081674,0.0013705925],"study_design_scores_gemma":[0.0094832005,0.0013325587,0.011261503,0.0014225653,0.0007632453,0.00018345195,0.0034465757,0.882251,0.0021112694,0.0008825438,0.08292989,0.003932194],"about_ca_topic_score_codex":0.00081015134,"about_ca_topic_score_gemma":0.0014038457,"teacher_disagreement_score":0.6184704,"about_ca_system_score_codex":0.0005299152,"about_ca_system_score_gemma":0.00009442136,"threshold_uncertainty_score":0.99941117},"labels":[],"label_agreement":null},{"id":"W2738522480","doi":"10.1115/jrc2017-2342","title":"Improving Rail Connectivity Through 3GPP Technology","year":2017,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ericsson (Canada)","funders":"","keywords":"Telecommunications; Wireless; Public transport; Computer science; Cellular network; Wireless network; Computer network; Quality of service; Intelligent transportation system; 3rd Generation Partnership Project 2; Computer security; Transport engineering; Engineering","score_opus":0.010530334427115514,"score_gpt":0.21804912923426445,"score_spread":0.20751879480714894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2738522480","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":[0.68181556,0.00020562443,0.04237614,0.00022125366,0.0009882331,0.000071639566,0.0000012836621,0.0009212025,0.27339906],"genre_scores_gemma":[0.9975393,0.000011026537,0.0011319839,0.0000118152375,0.000091826696,0.000012788999,2.9837847e-7,0.000018640276,0.0011823092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994441,0.0000036402166,0.00011186893,0.00014488635,0.0000634694,0.0002319901],"domain_scores_gemma":[0.99933094,0.00001250625,0.000032711523,0.0005781117,0.000019838339,0.000025902264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000085105545,0.00010269411,0.00013969763,0.00003861299,0.00025436102,0.00007677283,0.0002855015,0.00011371427,0.000056860474],"category_scores_gemma":[0.000068999565,0.0000885728,0.00003399514,0.000046341152,0.00006248312,0.00023415417,0.00006139602,0.000100737205,0.00008156758],"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":[0.000009319341,0.0001128933,0.015958767,0.00033528468,0.00017214836,0.000082595856,0.0007488453,0.042234596,0.21721445,0.32835385,0.008192939,0.3865843],"study_design_scores_gemma":[0.002058449,0.00020380029,0.013074779,0.000120685676,0.000035643807,0.00012939061,0.00092787296,0.5289295,0.22724839,0.0077581466,0.21768396,0.0018294336],"about_ca_topic_score_codex":0.0005589447,"about_ca_topic_score_gemma":0.0001976658,"teacher_disagreement_score":0.48669487,"about_ca_system_score_codex":0.000026833628,"about_ca_system_score_gemma":0.000008212016,"threshold_uncertainty_score":0.3611894},"labels":[],"label_agreement":null},{"id":"W2757054860","doi":"10.1109/ictis.2017.8047758","title":"Data-driven models for predicting delay recovery in high-speed rail","year":2017,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of Waterloo","funders":"","keywords":"Dwell time; Reliability (semiconductor); Computer science; Predictive modelling; Linear regression; Random forest; Mean squared prediction error; Regression analysis; Regression; Performance prediction; Simulation; Statistics; Artificial intelligence; Machine learning; Mathematics","score_opus":0.041375347832145776,"score_gpt":0.24898449474840756,"score_spread":0.20760914691626178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757054860","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":[0.76470286,0.0001533883,0.17612664,0.00009346876,0.0017513632,0.00034556037,0.0001719374,0.00038623248,0.05626853],"genre_scores_gemma":[0.9939765,0.00003077844,0.0045677004,0.000010760194,0.00020339961,0.000016629017,0.00004746878,0.00003169475,0.0011151079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915576,0.0000066098264,0.00024833772,0.000227797,0.000094283045,0.00026723393],"domain_scores_gemma":[0.9989534,0.00004689599,0.000044468503,0.0008862923,0.000020479154,0.000048464524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002670095,0.00011513081,0.00018268893,0.00005815241,0.00013022913,0.000113612106,0.0005930318,0.00008200583,0.000014711869],"category_scores_gemma":[0.0000593917,0.0001042259,0.000031526615,0.000031373478,0.000014696021,0.00072670367,0.00010414594,0.000072180796,0.000009541069],"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":[0.000005158579,0.000008719892,0.00033887997,0.000034367622,0.000016658621,0.0000027452763,0.00005923114,0.9921694,0.00030607288,0.0013176773,0.0022126334,0.0035285042],"study_design_scores_gemma":[0.00042316283,0.000017785742,0.0005873385,0.000042160278,0.000005100319,0.0000021126866,0.000031801756,0.9956631,0.000104299594,0.0003327636,0.0026530172,0.00013735103],"about_ca_topic_score_codex":0.00085058436,"about_ca_topic_score_gemma":0.0011275742,"teacher_disagreement_score":0.2292736,"about_ca_system_score_codex":0.000039185954,"about_ca_system_score_gemma":0.000014668708,"threshold_uncertainty_score":0.42502087},"labels":[],"label_agreement":null},{"id":"W2788540669","doi":"10.1155/2018/8521576","title":"Testing the Generality of a Passenger Disregarded Train Dwell Time Estimation Model at Short Stops: Both Comparison and Theoretical Approaches","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":10,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Generality; Dwell time; Scheduling (production processes); Estimation; Computer science; Simulation; Engineering; Mathematics; Mathematical optimization","score_opus":0.02805424900524584,"score_gpt":0.2391802520568893,"score_spread":0.21112600305164347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788540669","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":[0.8931471,0.00015066958,0.10613339,0.00003348965,0.00007052305,0.00008111714,0.0000060015072,0.000018696604,0.00035901228],"genre_scores_gemma":[0.9766788,0.000007290448,0.02320396,0.0000036066856,0.000071786206,0.0000029882403,0.0000073432234,0.000015097395,0.000009129162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902666,0.00002683526,0.00053122744,0.000086542765,0.00021070777,0.000118047545],"domain_scores_gemma":[0.9995571,0.0000762794,0.00014443307,0.00008558016,0.0000880537,0.000048559297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003444214,0.00010800236,0.00023011536,0.000043064098,0.00006603344,0.000011141882,0.000072972856,0.000049769606,0.0000056686617],"category_scores_gemma":[0.000023871855,0.00007472728,0.000053118023,0.00010438803,0.00013689105,0.00016763322,0.0000016126771,0.000102897844,3.9426686e-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":[0.000041826002,0.000024860019,0.0004308131,0.000045406465,0.000021496819,0.0000011672105,0.0026067551,0.9490199,0.040304054,0.0019247329,0.000012603968,0.00556636],"study_design_scores_gemma":[0.00026570915,0.00010618165,0.033729438,0.00006220652,0.00005019299,0.000011601461,0.00022032419,0.9601444,0.004374643,0.00093353196,0.0000123711125,0.00008940143],"about_ca_topic_score_codex":0.0000022339125,"about_ca_topic_score_gemma":0.000013852651,"teacher_disagreement_score":0.08353169,"about_ca_system_score_codex":0.000032028496,"about_ca_system_score_gemma":0.000014126356,"threshold_uncertainty_score":0.304729},"labels":[],"label_agreement":null},{"id":"W2790747112","doi":"10.1139/cjce-2017-0642","title":"Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":42,"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":"National Natural Science Foundation of China; China Railway","keywords":"Support vector machine; Random forest; Artificial neural network; Dwell time; Computer science; Linear regression; Predictive modelling; Reliability (semiconductor); Regression; Regression analysis; Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics","score_opus":0.015119114212295698,"score_gpt":0.20112112155239337,"score_spread":0.18600200734009767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790747112","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":[0.9539192,0.0020872988,0.04099338,0.00001855135,0.0026216132,0.00012299143,0.000015264242,0.00004013955,0.00018157282],"genre_scores_gemma":[0.99478626,0.000021063499,0.0034293262,0.000010542572,0.0016422553,0.0000011381624,0.0000069864345,0.000086058644,0.000016388687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978641,0.0000393412,0.0010255,0.00018210322,0.00023290019,0.0006560454],"domain_scores_gemma":[0.99851453,0.00018784037,0.0003839022,0.00019145448,0.00020875128,0.00051351567],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007486211,0.00030698322,0.00061844144,0.00041563011,0.00018556752,0.00006136476,0.00020236042,0.00016108567,0.000033767134],"category_scores_gemma":[0.00041899242,0.00026490926,0.00014422105,0.00036208075,0.000070888076,0.00034375393,0.000026008922,0.00036900386,5.650237e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053946325,0.000003965752,0.0032671138,0.00014873175,0.000054468288,0.00009595345,0.00026070912,0.9856182,0.0066998624,0.000018132645,0.00027309227,0.003505822],"study_design_scores_gemma":[0.0007605746,0.00011675421,0.0016479655,0.0011329567,0.000039540795,0.00044584274,0.000034881115,0.99392486,0.00095264934,0.000023793706,0.00064050494,0.0002796549],"about_ca_topic_score_codex":0.0015054401,"about_ca_topic_score_gemma":0.030545782,"teacher_disagreement_score":0.040867053,"about_ca_system_score_codex":0.00016557172,"about_ca_system_score_gemma":0.00020490501,"threshold_uncertainty_score":0.99998033},"labels":[],"label_agreement":null},{"id":"W2794184436","doi":"10.1109/tits.2017.2777982","title":"Physics-Based Optimization of Access Point Placement for Train Communication Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":31,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software deployment; Point (geometry); Wireless; Computer science; Communications system; Optimization problem; Point-to-point; Engineering; Simulation; Mathematical optimization; Computer network; Telecommunications; Algorithm; Mathematics","score_opus":0.039018715835536705,"score_gpt":0.2704808224902892,"score_spread":0.2314621066547525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794184436","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":[0.009426559,0.00017932092,0.9858412,0.000015032618,0.0022804756,0.0012996269,0.00029928825,0.00028105272,0.00037741626],"genre_scores_gemma":[0.99749106,0.000071373455,0.0012178167,0.000010921474,0.00010919503,0.00076829357,0.00013726705,0.00006858713,0.00012546247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980721,0.00007001036,0.0009967565,0.0002634744,0.0003445096,0.000253189],"domain_scores_gemma":[0.99872875,0.00013644891,0.00020276438,0.0004458977,0.00039873587,0.000087427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036503744,0.00026517163,0.00036820682,0.00024167517,0.00018269906,0.000087450055,0.00029010756,0.00014409509,0.00004071257],"category_scores_gemma":[0.0000020007,0.0002703258,0.00017705977,0.000429747,0.00008092699,0.00027242667,1.8726413e-7,0.00010829062,0.000010676926],"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":[0.000073040275,0.00013441917,0.000008228157,0.00045810573,0.0000931707,1.897954e-7,0.00091914146,0.9956666,0.00092795276,0.00079695927,0.00012224079,0.0007999414],"study_design_scores_gemma":[0.0005660259,0.00023801244,0.00001637008,0.00041182953,0.00006996901,8.6939104e-7,0.00080150535,0.9491912,0.047525022,0.0000066702883,0.0009124383,0.00026008938],"about_ca_topic_score_codex":0.00030637791,"about_ca_topic_score_gemma":0.00010236365,"teacher_disagreement_score":0.9880645,"about_ca_system_score_codex":0.0001450948,"about_ca_system_score_gemma":0.0000448391,"threshold_uncertainty_score":0.9999749},"labels":[],"label_agreement":null},{"id":"W2796775962","doi":"10.1155/2018/8502819","title":"The Planners’ Perspective on Train Timetable Errors in Sweden","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":9,"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":"Trafikverket","keywords":"Punctuality; Train; Operations research; Transport engineering; Control (management); Transportation planning; Process (computing); Plan (archaeology); Computer science; Engineering","score_opus":0.008699316673827985,"score_gpt":0.24491002146194915,"score_spread":0.23621070478812117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796775962","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":[0.99305224,0.0004215414,0.0013566006,0.0001379705,0.0008070125,0.00006244554,0.0000027584633,0.000018715706,0.0041407007],"genre_scores_gemma":[0.9991982,0.00007952397,0.0004337703,0.000011024259,0.00015659885,0.0000017480783,0.0000011191222,0.000012389966,0.00010560556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99932736,0.00001304529,0.00030131335,0.000059303107,0.0001635273,0.00013544313],"domain_scores_gemma":[0.999686,0.000041397834,0.00008447778,0.000063287436,0.00009054501,0.000034304412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020402257,0.00007716921,0.00012056966,0.000093706396,0.00005012406,0.000011476708,0.000089204594,0.000032588378,0.000009027847],"category_scores_gemma":[0.000014358248,0.000053094238,0.000052175226,0.00017878137,0.000027701242,0.00018017937,2.634433e-7,0.00013402966,0.000005813654],"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":[0.0001009628,0.00002054266,0.00011613974,0.00000624695,0.000024127809,0.00001519787,0.0071496475,0.9813384,0.0054077767,0.0022507594,0.00014582198,0.0034243674],"study_design_scores_gemma":[0.008591976,0.004039043,0.73620176,0.0010966986,0.00013977315,0.00008142017,0.09053108,0.023616748,0.040028106,0.008731449,0.085666604,0.0012753218],"about_ca_topic_score_codex":0.000018347298,"about_ca_topic_score_gemma":0.00048768552,"teacher_disagreement_score":0.95772165,"about_ca_system_score_codex":0.000092922324,"about_ca_system_score_gemma":0.000016569853,"threshold_uncertainty_score":0.21651201},"labels":[],"label_agreement":null},{"id":"W2799346248","doi":"10.1155/2018/7905820","title":"Integrated Optimization on Train Control and Timetable to Minimize Net Energy Consumption of Metro Lines","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":64,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Train; Headway; Energy consumption; Regenerative brake; Automotive engineering; Automatic train control; Energy (signal processing); Engineering; Simulation; Efficient energy use; Optimal control; Computer science; Control system; Mathematical optimization; Brake; Electrical engineering","score_opus":0.00683449849518363,"score_gpt":0.21159412252682588,"score_spread":0.20475962403164225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799346248","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":[0.5276519,0.0002572492,0.47160852,0.000023157025,0.0003135104,0.000047288315,0.000014045337,0.000016601367,0.00006773757],"genre_scores_gemma":[0.9724307,0.00014830547,0.027244467,0.000027918484,0.00009515346,0.0000030574759,0.0000131361685,0.000015184943,0.000022086775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991795,0.000018957076,0.00048216275,0.00007605637,0.00014537892,0.00009794091],"domain_scores_gemma":[0.99943954,0.000043392985,0.0001637029,0.000056976027,0.00023139027,0.00006498387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013966835,0.00010156979,0.00024500245,0.00020622475,0.00002345612,0.000009088527,0.00004616169,0.0000497163,0.00002536739],"category_scores_gemma":[0.000021833817,0.00008621767,0.000044122127,0.00018706734,0.000023607385,0.0001925119,3.271718e-7,0.00005265553,6.5560516e-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":[0.00020207721,0.000020871168,0.00018798791,0.000023747754,0.0000358199,0.0000015680563,0.00032511566,0.9474626,0.042592585,0.00013034954,0.000033763823,0.00898347],"study_design_scores_gemma":[0.011821616,0.0038593484,0.11480528,0.0012270525,0.00043944095,0.000027959548,0.0014084663,0.71368694,0.1430814,0.00019628552,0.008570039,0.0008761576],"about_ca_topic_score_codex":0.000012856727,"about_ca_topic_score_gemma":0.000042264208,"teacher_disagreement_score":0.4447788,"about_ca_system_score_codex":0.00002271041,"about_ca_system_score_gemma":0.000012828294,"threshold_uncertainty_score":0.35158545},"labels":[],"label_agreement":null},{"id":"W2800293522","doi":"10.1155/2018/7284815","title":"PULSim: User-Based Adaptable Simulation Tool for Railway Planning and Operations","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"","keywords":"Workflow; Computer science; Scheduling (production processes); Software; Process (computing); Systems engineering; Simulation software; Simulation modeling; User interface; Software engineering; Field (mathematics); Engineering; Database; Operating system","score_opus":0.012547331536499867,"score_gpt":0.2559816982794608,"score_spread":0.24343436674296096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800293522","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":[0.5683053,0.00015452446,0.43110806,0.000014753334,0.00028433042,0.00007925122,0.0000064391443,0.000019996038,0.000027314392],"genre_scores_gemma":[0.9768753,0.000008145287,0.022820119,0.000015631234,0.0002070484,0.000007339492,0.000015855352,0.000017662287,0.000032894408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933875,0.0000060184702,0.00036391465,0.00006874838,0.00011120982,0.00011134915],"domain_scores_gemma":[0.9995808,0.0000536284,0.000069812115,0.000053026833,0.00020297087,0.00003974962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013046394,0.0000833201,0.00013425233,0.00009368728,0.00009189562,0.00002527825,0.000039313927,0.00004320303,0.0000077438435],"category_scores_gemma":[0.000019497726,0.000077286495,0.000041808813,0.000103698796,0.000015725082,0.0004557224,2.9157636e-7,0.000055406308,4.730352e-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":[0.00004721237,0.000009559728,0.00034671227,0.0000350805,0.000012026129,0.0000012202783,0.00052143063,0.98321265,0.012416784,0.00014349079,0.000023853096,0.0032299766],"study_design_scores_gemma":[0.00220062,0.00053466076,0.05019093,0.00022363442,0.00006023416,0.0000040788996,0.00035053614,0.91945094,0.010185027,0.000106273015,0.016446665,0.00024638305],"about_ca_topic_score_codex":0.000002182034,"about_ca_topic_score_gemma":0.00002875915,"teacher_disagreement_score":0.40857,"about_ca_system_score_codex":0.000023943227,"about_ca_system_score_gemma":0.000022874563,"threshold_uncertainty_score":0.3151652},"labels":[],"label_agreement":null},{"id":"W2801314480","doi":"10.1155/2018/5983250","title":"Defining Reserve Times for Metro Systems: An Analytical Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":25,"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":"Headway; Flexibility (engineering); Robustness (evolution); Computer science; Energy consumption; Context (archaeology); Exploit; Operations research; Energy (signal processing); Transport engineering; Simulation; Engineering; Computer security","score_opus":0.014826848208990854,"score_gpt":0.2594946885040685,"score_spread":0.24466784029507763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801314480","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":[0.7415319,0.0008455103,0.25551564,0.000009293208,0.00081140245,0.0001145602,0.000013915837,0.000052461502,0.0011053059],"genre_scores_gemma":[0.97576714,0.00001586331,0.023785075,0.000003991709,0.00033512386,0.000007542881,0.000022689064,0.000025036157,0.000037530233],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900407,0.0000146056755,0.0005191844,0.00009472619,0.00019972828,0.00016770719],"domain_scores_gemma":[0.9993741,0.000039829705,0.00013733264,0.00009962955,0.0002596195,0.00008946979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003312462,0.00010195739,0.00023761952,0.00014499614,0.000054235174,0.000026199052,0.00010754834,0.00005981086,0.0000048235465],"category_scores_gemma":[0.000021853297,0.00008685527,0.00009120041,0.0001819156,0.000022948669,0.00045504293,4.5977657e-7,0.00008745201,0.0000014079219],"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":[0.000071899034,0.000030587576,0.00036036776,0.00011872062,0.00005243936,0.000003064435,0.0007141149,0.98937714,0.0011314705,0.0065777143,0.0001235032,0.0014389893],"study_design_scores_gemma":[0.0032430901,0.0023971978,0.028377159,0.00040538114,0.00028971053,0.000049340495,0.0077871,0.9397093,0.0034277511,0.0006506665,0.013021318,0.00064196845],"about_ca_topic_score_codex":0.00000779467,"about_ca_topic_score_gemma":0.00002385479,"teacher_disagreement_score":0.23423523,"about_ca_system_score_codex":0.000041064446,"about_ca_system_score_gemma":0.000020752012,"threshold_uncertainty_score":0.35418552},"labels":[],"label_agreement":null},{"id":"W2804640572","doi":"10.1155/2018/7308058","title":"Optimal Operation of High-Speed Trains Using Hybrid Model Predictive Control","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":18,"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":"Model predictive control; Control theory (sociology); Train; Nonlinear system; Linearization; Controller (irrigation); Piecewise linear function; Hybrid system; Piecewise; Computer science; Computation; Process (computing); Mathematical optimization; Control engineering; Optimal control; Engineering; Control (management); Algorithm; Mathematics; Artificial intelligence","score_opus":0.008341301952758097,"score_gpt":0.22102561404740384,"score_spread":0.21268431209464575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804640572","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":[0.536951,0.000047529367,0.4626294,0.00000485648,0.0002489147,0.000047346235,0.00002454504,0.000011728639,0.00003468695],"genre_scores_gemma":[0.9804099,0.000024619103,0.019336736,0.000005696794,0.00018948081,8.864068e-7,0.000008570162,0.000018791423,0.000005320596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990412,0.000010558841,0.00055010227,0.00007158033,0.00021070789,0.00011583004],"domain_scores_gemma":[0.999392,0.000012694319,0.00019883875,0.000063856554,0.00028224292,0.00005037788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012839622,0.00009938828,0.0002349019,0.00010902026,0.000037917467,0.0000075354183,0.000066990295,0.000038058883,0.000007855529],"category_scores_gemma":[0.0000066810626,0.00009105881,0.000075190495,0.00008730917,0.000035202218,0.00048935396,3.521065e-7,0.0000863134,3.963282e-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":[0.000105926316,0.00001966981,0.000030378067,0.000019385221,0.000031173724,0.0000033949914,0.0010930253,0.84871197,0.14937122,0.00013122868,0.000005975481,0.00047665075],"study_design_scores_gemma":[0.0013916636,0.00029513708,0.004112471,0.00007836348,0.000059179692,0.000010764622,0.00023040964,0.9612678,0.03237496,0.00007185177,0.000017872828,0.00008947057],"about_ca_topic_score_codex":0.000008147534,"about_ca_topic_score_gemma":0.000011286704,"teacher_disagreement_score":0.4434589,"about_ca_system_score_codex":0.000050808485,"about_ca_system_score_gemma":0.000041435265,"threshold_uncertainty_score":0.37132707},"labels":[],"label_agreement":null},{"id":"W2807796274","doi":"10.1115/jrc2018-6266","title":"Solving Various Train Approach Speeds to Highway Crossings Using Innovative Technologies","year":2018,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Level crossing; Catenary; Axle; Track (disk drive); Transport engineering; Computer science; Range (aeronautics); Overhead (engineering); Automotive engineering; Lock (firearm); SAFER; Automatic train control; Acceleration; Control (management); Engineering; Electrical engineering; Computer security","score_opus":0.01932405513214107,"score_gpt":0.23482364207317985,"score_spread":0.21549958694103877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807796274","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":[0.48340982,0.00006359856,0.3926495,0.000041045536,0.00044048965,0.0001295786,0.0000020616687,0.0019104863,0.121353425],"genre_scores_gemma":[0.9394525,9.0733334e-7,0.05953093,0.000038915987,0.00015051673,0.000009591656,0.0000012473037,0.000038572896,0.0007768026],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988733,0.000007830949,0.0002717566,0.0002556225,0.00015394152,0.00043753293],"domain_scores_gemma":[0.9995129,0.000015105541,0.000027631237,0.0002758994,0.00012501485,0.000043412263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021451275,0.00020042153,0.0002146527,0.00027246887,0.00021491041,0.00015392234,0.0002491456,0.00014640209,0.00002364654],"category_scores_gemma":[0.000048459544,0.00017091502,0.000029919778,0.0013594655,0.00015495067,0.00015765674,0.00007347775,0.00013276824,0.000041405783],"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":[0.000017103652,0.0001354135,0.0005654546,0.00021404604,0.00018126484,0.000020958174,0.017821932,0.17899103,0.6624568,0.042941824,0.011022077,0.0856321],"study_design_scores_gemma":[0.00051664654,0.00023988266,0.00082145387,0.00014765006,0.0000144105525,0.00008303781,0.0060558375,0.78920645,0.15112272,0.00047464765,0.05000955,0.0013077143],"about_ca_topic_score_codex":0.00021547312,"about_ca_topic_score_gemma":0.000021882179,"teacher_disagreement_score":0.6102154,"about_ca_system_score_codex":0.00011176441,"about_ca_system_score_gemma":0.000026615608,"threshold_uncertainty_score":0.69697124},"labels":[],"label_agreement":null},{"id":"W2808082044","doi":"10.1115/jrc2018-6114","title":"Interoperability for Communications Based Train Control","year":2018,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Interoperability; Control (management); Computer science; Telecommunications; Operating system","score_opus":0.01909907060567694,"score_gpt":0.2411396379507878,"score_spread":0.22204056734511085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808082044","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":[0.049521808,0.000067532346,0.88930905,0.0004627776,0.00033614787,0.0002532374,0.000019232086,0.00039113255,0.05963909],"genre_scores_gemma":[0.99485254,7.3613177e-7,0.0046620225,0.00012257465,0.00006454357,0.00006662922,0.0000038113105,0.000010634144,0.00021650032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999658,0.000014528537,0.00012520886,0.00006387866,0.000030231919,0.000108146574],"domain_scores_gemma":[0.99944216,0.00005153795,0.0000063159773,0.0004201331,0.000048891612,0.000030951698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016684819,0.000053260355,0.00007832106,0.00002032188,0.0000617254,0.000014562267,0.00018829718,0.000027848917,0.00008854638],"category_scores_gemma":[0.000021847734,0.00004303726,0.000038807055,0.000048081143,0.000062494706,0.00003097253,0.0000059844765,0.000027701353,0.000024881574],"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":[0.00021449056,0.0012087061,0.0063425526,0.0008774279,0.00054458185,0.0000011433111,0.007092774,0.17793071,0.20599961,0.18893129,0.12766199,0.28319475],"study_design_scores_gemma":[0.00038348936,0.000052831674,0.000435057,0.0000062463905,0.000003377109,2.6406238e-7,0.000045539742,0.91512704,0.00096146524,0.000046668993,0.082868956,0.00006908189],"about_ca_topic_score_codex":0.000034624678,"about_ca_topic_score_gemma":0.00020086164,"teacher_disagreement_score":0.94533074,"about_ca_system_score_codex":0.000020984879,"about_ca_system_score_gemma":0.000007318232,"threshold_uncertainty_score":0.17550085},"labels":[],"label_agreement":null},{"id":"W2808115598","doi":"10.1115/jrc2018-6116","title":"CBTC DCS Based on LTE Unlicensed Wireless Access: Assessment of Coexistence Performance With Wi-Fi","year":2018,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"WSP (Canada)","funders":"","keywords":"Spectrum management; Computer science; Software deployment; LTE Advanced; Standardization; Femtocell; Computer network; Wireless broadband; Context (archaeology); Wireless; Telecommunications; Throughput; Broadband networks; Communications system; Broadband; Wireless network; Base station; Cognitive radio; Telecommunications link","score_opus":0.014494055074949816,"score_gpt":0.25213156089011046,"score_spread":0.23763750581516063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808115598","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":[0.8206805,0.0000072215403,0.019013971,0.000019496929,0.00022979485,0.00011265991,0.0000035138073,0.00017814904,0.15975472],"genre_scores_gemma":[0.9977544,0.0000105707295,0.0015272979,0.000075166696,0.0000837772,0.000019244231,0.000003821221,0.000029418095,0.0004962829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998956,0.00001527899,0.00024637705,0.00019738027,0.00032150684,0.00026340375],"domain_scores_gemma":[0.9993358,0.000036801135,0.00005760558,0.00039026892,0.00010875106,0.0000707455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015483105,0.00017645402,0.00022171454,0.00009337262,0.000056180288,0.000039855364,0.00028416133,0.000057217178,0.00010163305],"category_scores_gemma":[0.0000031138698,0.00012785546,0.000031873846,0.00033815554,0.00009066778,0.0001718614,0.00001940191,0.00009153732,0.00001660508],"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":[0.00007650079,0.00020754097,0.020230679,0.0006187199,0.000069016,0.000008455315,0.00031243608,0.95753336,0.010942384,0.002085653,0.0017766336,0.006138624],"study_design_scores_gemma":[0.00044051404,0.00047145822,0.019447021,0.00022182257,0.000008408829,0.0000022218421,0.000055229048,0.9620594,0.015314656,8.183084e-7,0.0017638794,0.00021461214],"about_ca_topic_score_codex":0.00009833785,"about_ca_topic_score_gemma":0.00007357995,"teacher_disagreement_score":0.17707394,"about_ca_system_score_codex":0.00005700331,"about_ca_system_score_gemma":0.000052460768,"threshold_uncertainty_score":0.5213795},"labels":[],"label_agreement":null},{"id":"W2809840499","doi":"10.1177/0361198118780830","title":"Stochastic Model of Train Running Time and Arrival Delay: A Case Study of Wuhan–Guangzhou High-Speed Rail","year":2018,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Waterloo","funders":"","keywords":"Punctuality; Computer science; Correctness; Quality of service; Track (disk drive); Stochastic modelling; Probability distribution; Service (business); Service quality; Distribution (mathematics); Real-time computing; Transport engineering; Algorithm; Telecommunications; Engineering; Mathematics; Statistics","score_opus":0.06698981994734515,"score_gpt":0.33215320860043174,"score_spread":0.2651633886530866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809840499","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":[0.99353623,0.00015639365,0.005115524,0.0000843845,0.00021853828,0.00075010565,0.000054795357,0.00002845333,0.00005559209],"genre_scores_gemma":[0.9986208,0.000058879614,0.0009489497,0.0000029068956,0.0001221937,0.000019395402,0.0000039536353,0.00006261644,0.00016031321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947613,0.00042223625,0.001570782,0.0002937753,0.0023288867,0.00062305795],"domain_scores_gemma":[0.9962372,0.00045116173,0.00033265632,0.00039739287,0.0022863122,0.00029528522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035510096,0.00024233012,0.00061463926,0.0010267971,0.0003327336,0.000046891208,0.0005516604,0.00014962198,0.00008206995],"category_scores_gemma":[0.0000753117,0.00019098005,0.00018754997,0.0013811821,0.0006173617,0.0003685175,0.000006751909,0.0010103422,0.0000034643342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013822565,0.00070940796,0.012256569,0.00068479363,0.0005295387,0.0005976402,0.048061427,0.8699625,0.0598385,0.0004965867,0.00047939096,0.0050013936],"study_design_scores_gemma":[0.012121448,0.009403231,0.25386736,0.0019688308,0.00041486984,0.00006280556,0.04890569,0.6623279,0.008100286,0.0017024423,0.00015110461,0.0009740678],"about_ca_topic_score_codex":0.011790813,"about_ca_topic_score_gemma":0.024103468,"teacher_disagreement_score":0.24161078,"about_ca_system_score_codex":0.000091815855,"about_ca_system_score_gemma":0.0002588443,"threshold_uncertainty_score":0.9947898},"labels":[],"label_agreement":null},{"id":"W2810367614","doi":"10.1155/2018/3690603","title":"Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":18,"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; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Dwell time; Scheduling (production processes); Computer science; Mathematical optimization; Integer programming; Operations research; Job shop scheduling; Traffic congestion; Branch and bound; Sensitivity (control systems); Schedule; Transport engineering; Engineering; Algorithm; Mathematics","score_opus":0.007520971127885448,"score_gpt":0.22773718687884345,"score_spread":0.220216215750958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810367614","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":[0.8794938,0.00062064576,0.11928296,0.000027968903,0.00035861137,0.00008506481,0.000010048799,0.00002665158,0.00009429081],"genre_scores_gemma":[0.9809324,0.0000899468,0.018691866,0.0000071501586,0.00017444344,0.000004927252,0.000015550806,0.000017603628,0.00006610601],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99936366,0.000007869791,0.00033913206,0.00006914762,0.00010965067,0.00011051993],"domain_scores_gemma":[0.9995156,0.000073229596,0.0001248904,0.000043124215,0.00019003674,0.000053147014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001774369,0.000079979,0.00015142762,0.000115536604,0.00009152954,0.000014683635,0.000030824784,0.000037778012,0.000015985184],"category_scores_gemma":[0.000019130364,0.00007333663,0.00004502762,0.00010581997,0.000019850608,0.0003503596,5.542615e-7,0.000049700317,0.0000022198305],"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":[0.000080377424,0.000013927266,0.0004067138,0.00007672392,0.00006292389,0.0000029327568,0.0011792725,0.77803195,0.21039508,0.00014360575,0.00010815179,0.009498329],"study_design_scores_gemma":[0.01324051,0.0027027999,0.46334374,0.0011803744,0.0011440865,0.00016968377,0.0018682984,0.2899183,0.17299075,0.0026773622,0.049049508,0.0017145857],"about_ca_topic_score_codex":0.000001376161,"about_ca_topic_score_gemma":0.000043521068,"teacher_disagreement_score":0.48811367,"about_ca_system_score_codex":0.000036505702,"about_ca_system_score_gemma":0.000009335083,"threshold_uncertainty_score":0.2990581},"labels":[],"label_agreement":null},{"id":"W2811448823","doi":"10.1155/2018/3179321","title":"Stop Plan of Express and Local Train for Regional Rail Transit Line","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":20,"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":"Natural Science Foundation of Guangdong Province; Shenzhen Technology University","keywords":"Train; Energy consumption; Urban rail transit; Transport engineering; Genetic algorithm; Transit (satellite); Plan (archaeology); Line (geometry); Computer science; Operations research; Engineering; Public transport; Mathematics","score_opus":0.012569102378760005,"score_gpt":0.22534485437655666,"score_spread":0.21277575199779666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811448823","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":[0.59501123,0.00033537182,0.4043214,0.00003279871,0.00019705667,0.000052178508,0.00001489936,0.000009375753,0.000025682679],"genre_scores_gemma":[0.9926751,0.00008289044,0.00700922,0.000009110361,0.00018123,0.0000028023906,0.000009710593,0.000014089108,0.000015826363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992987,0.0000055684336,0.0004067534,0.00006163899,0.0001313862,0.00009594368],"domain_scores_gemma":[0.999627,0.000033882618,0.00011204397,0.00004452491,0.00013117996,0.000051384992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011964879,0.0000793086,0.00018885068,0.00007462207,0.000024372843,0.0000036199144,0.000050580446,0.000047472946,0.000005281117],"category_scores_gemma":[0.0000036316121,0.000070176786,0.00006514857,0.000065639426,0.000052943982,0.00017444923,1.955864e-7,0.000059185128,1.0503261e-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":[0.00034203503,0.000034428067,0.00004403354,0.00021101531,0.000046028286,0.0000048422335,0.0057439697,0.8563062,0.109696835,0.00081972877,0.00009695227,0.026653921],"study_design_scores_gemma":[0.038003944,0.012638047,0.18142222,0.0037999675,0.0007652192,0.00039694546,0.022722483,0.14646225,0.43414578,0.008096362,0.14915067,0.0023961142],"about_ca_topic_score_codex":0.0000031906966,"about_ca_topic_score_gemma":0.000044385793,"teacher_disagreement_score":0.70984393,"about_ca_system_score_codex":0.000011242374,"about_ca_system_score_gemma":0.0000146464345,"threshold_uncertainty_score":0.28617263},"labels":[],"label_agreement":null},{"id":"W2887005990","doi":"10.1155/2018/5367295","title":"A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Train; Beijing; Headway; Urban rail transit; Scheduling (production processes); Integer programming; Engineering; Transport engineering; Computer science; Rail transit; Operations research; Mathematical optimization; Operations management; Algorithm; Mathematics","score_opus":0.01618374074154215,"score_gpt":0.26418907317118673,"score_spread":0.24800533242964456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887005990","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":[0.5916295,0.00022737884,0.4077992,0.000013251037,0.00017409249,0.00012588089,0.000006548127,0.000013136414,0.00001099727],"genre_scores_gemma":[0.9754997,0.000048032718,0.024092784,0.0000055812807,0.0002897368,0.00001070257,0.000020370742,0.000030043391,0.0000030809938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987614,0.00002473295,0.00081068405,0.00010493827,0.000126459,0.00017177446],"domain_scores_gemma":[0.9993568,0.00005976357,0.00017371254,0.000091990514,0.0002615031,0.00005624864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048247646,0.00013152896,0.00025226353,0.00016569353,0.00006787193,0.000018727094,0.00009753363,0.00009745588,0.0000048251522],"category_scores_gemma":[0.000018791197,0.000106081214,0.000095827665,0.00028484393,0.00003245633,0.00053157046,3.8942872e-7,0.00021316439,5.6993276e-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":[0.000099167075,0.00003188022,0.0001456487,0.00008460898,0.000019377585,0.000040155344,0.0039171516,0.95861554,0.02665938,0.00012870497,7.8472937e-7,0.010257599],"study_design_scores_gemma":[0.0013946348,0.0007744573,0.002862693,0.00033104903,0.00007182168,0.00010405125,0.004487483,0.97808266,0.01158936,0.00005989225,0.000046778652,0.00019511746],"about_ca_topic_score_codex":0.000009479075,"about_ca_topic_score_gemma":0.00069647585,"teacher_disagreement_score":0.38387015,"about_ca_system_score_codex":0.00003476351,"about_ca_system_score_gemma":0.000032115353,"threshold_uncertainty_score":0.43258664},"labels":[],"label_agreement":null},{"id":"W2888341068","doi":"10.1109/tdc.2018.8440349","title":"Tertiary Voltage Unbalance Compensation for 500kV Single Phase Autotransformer Banks","year":2018,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Hydro One (Canada)","funders":"","keywords":"Transformer; Autotransformer; Overvoltage; Electrical engineering; Engineering; Capacitive sensing; Voltage; Emtp; Energy efficient transformer; Delta-wye transformer; Electromagnetic coil; Capacitance; Ground; Distribution transformer; Physics; Electric power system","score_opus":0.014037432519346228,"score_gpt":0.23919265658916464,"score_spread":0.22515522406981842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888341068","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":[0.3193604,0.000066670254,0.63889045,0.000042000425,0.00076984416,0.00019907634,0.000011501713,0.00042169334,0.04023837],"genre_scores_gemma":[0.9969333,0.0000027521794,0.0012640954,0.0000803558,0.00031136908,0.000029374536,0.000018575567,0.000026826863,0.0013333424],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993429,0.000003979419,0.00019604657,0.000133569,0.00008406504,0.00023941355],"domain_scores_gemma":[0.9997195,0.000022487135,0.000014839728,0.00014568391,0.000046065317,0.000051428487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009212643,0.00011445018,0.00011680794,0.000047182162,0.00007447466,0.00003129797,0.00008551853,0.00006172037,0.00017204894],"category_scores_gemma":[0.0000061862825,0.000099750556,0.000048272814,0.00009014965,0.000033222736,0.00014375964,0.0000039242996,0.00003705583,0.00006695919],"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":[0.00004296159,0.00029457163,0.00036555046,0.000257509,0.00007876132,0.000002815666,0.0011894605,0.014556069,0.7852668,0.007892603,0.026045,0.16400789],"study_design_scores_gemma":[0.0015614638,0.0003314539,0.0011498713,0.000046089805,0.000014532421,0.000006869238,0.000041805048,0.6668108,0.13477269,0.00013744635,0.1947674,0.0003595855],"about_ca_topic_score_codex":0.00003072829,"about_ca_topic_score_gemma":0.0000527101,"teacher_disagreement_score":0.6775729,"about_ca_system_score_codex":0.00003635607,"about_ca_system_score_gemma":0.0000073345077,"threshold_uncertainty_score":0.40677094},"labels":[],"label_agreement":null},{"id":"W2889211502","doi":"10.1155/2018/7285148","title":"Urban Rail Transit Scheduling under Time-Varying Passenger Demand","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":7,"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; Fundamental Research Funds for the Central Universities; National Science Foundation","keywords":"Train; Passenger train; Scheduling (production processes); Rail transit; Computer science; Genetic algorithm; Urban rail transit; Transit system; Transport engineering; Travel time; Time value of money; Urban transit; Engineering; Operations research; Transit (satellite); Public transport; Automotive engineering; Operations management","score_opus":0.006533839768037124,"score_gpt":0.2113193998697892,"score_spread":0.20478556010175208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889211502","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":[0.81026036,0.00073876546,0.18738285,0.00006633907,0.00074873003,0.00005241645,0.0000023876833,0.00006139963,0.00068677106],"genre_scores_gemma":[0.9922222,0.00007705629,0.007097581,0.000019767836,0.0004805412,0.000001474891,0.000006012052,0.000031929725,0.000063441534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989676,0.0000129894925,0.0005124235,0.00009410035,0.00022601896,0.00018688696],"domain_scores_gemma":[0.9995156,0.000027631624,0.00012771515,0.000084876134,0.00015787366,0.00008632969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014957871,0.0001327091,0.00022145813,0.0001230911,0.00006945537,0.00001994647,0.000087807755,0.00007049499,0.000047078298],"category_scores_gemma":[0.0000053195176,0.0001208926,0.000105873165,0.0001921735,0.000028140477,0.00046452504,4.3179597e-7,0.00015256487,0.000013604697],"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":[0.00003513964,0.000015865835,0.00009173094,0.000037674974,0.000046713034,0.000015951922,0.0014833573,0.85620797,0.1397915,0.00013742878,0.00011535029,0.0020213395],"study_design_scores_gemma":[0.01959287,0.0030961123,0.22441258,0.004083688,0.0011526184,0.00059970684,0.005640993,0.26064083,0.41212094,0.004147952,0.06035057,0.004161134],"about_ca_topic_score_codex":0.0000014753399,"about_ca_topic_score_gemma":0.000014360604,"teacher_disagreement_score":0.5955671,"about_ca_system_score_codex":0.000039625236,"about_ca_system_score_gemma":0.000021970664,"threshold_uncertainty_score":0.4929857},"labels":[],"label_agreement":null},{"id":"W2889357795","doi":"10.1155/2018/3985302","title":"Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Key Research and Development Program of China","keywords":"Train; Beijing; Cluster analysis; Principal component analysis; Computer science; Principal (computer security); Transport engineering; Operations research; Engineering; Data mining; Artificial intelligence; China","score_opus":0.04082400270061062,"score_gpt":0.2572501141278168,"score_spread":0.21642611142720616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889357795","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":[0.82555264,0.00018346086,0.17391777,0.000002217924,0.00022174271,0.00004367693,0.000037753493,0.000015961192,0.000024750807],"genre_scores_gemma":[0.98826104,0.00013899976,0.011301416,0.0000024852982,0.00016669811,7.3743365e-7,0.00010680987,0.000020051482,0.0000017873576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99902654,0.000024063802,0.0005352961,0.00012974252,0.00017495707,0.00010940178],"domain_scores_gemma":[0.99936855,0.000015601398,0.00022838276,0.00017397881,0.00014270972,0.0000708072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026330675,0.000118280564,0.0002486313,0.00014512437,0.000044543292,0.000013142459,0.0001438108,0.00005218776,0.000002803106],"category_scores_gemma":[0.000012155317,0.00010294634,0.000029795092,0.00018793163,0.000034039364,0.0010828342,0.0000016142468,0.00008268344,7.430436e-8],"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":[0.00010481049,0.0001740095,0.031268083,0.0005235135,0.00013539889,0.000009770613,0.046251554,0.72383577,0.17095228,0.00020896956,0.0000055036567,0.02653036],"study_design_scores_gemma":[0.001632938,0.00038286624,0.85688436,0.00023622708,0.00008554427,0.000021224168,0.008779412,0.12559475,0.0059560393,0.000087400884,0.000044600234,0.00029460818],"about_ca_topic_score_codex":0.000014838427,"about_ca_topic_score_gemma":0.00014394757,"teacher_disagreement_score":0.8256163,"about_ca_system_score_codex":0.000015483087,"about_ca_system_score_gemma":0.0000149464395,"threshold_uncertainty_score":0.419803},"labels":[],"label_agreement":null},{"id":"W2889793362","doi":"10.1155/2018/6157192","title":"Robust Train Scheduling Problem with Optimized Maintenance Planning on High-Speed Railway Corridors: The China Case","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":13,"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":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Train; Computer science; Schedule; Scheduling (production processes); Mathematical optimization; Linearization; Integer programming; Real-time computing; Algorithm; Nonlinear system; Mathematics","score_opus":0.010417829339636869,"score_gpt":0.21121608905583694,"score_spread":0.20079825971620008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889793362","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":[0.8450999,0.00012881895,0.15356861,0.00009595113,0.00052948896,0.00015718264,0.000006112365,0.000057630405,0.0003562519],"genre_scores_gemma":[0.9560923,0.000025867323,0.043446224,0.000024425752,0.0003151347,0.000005264258,0.000005808766,0.000040914765,0.000044054934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987499,0.000025554644,0.0005518888,0.00013807703,0.00027745517,0.0002571405],"domain_scores_gemma":[0.9992651,0.000052870128,0.00028988166,0.00014020824,0.00016842327,0.00008354207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003401104,0.00019775602,0.00028061573,0.00010917028,0.00016379521,0.00003643338,0.00013765255,0.000058266836,0.000018382467],"category_scores_gemma":[0.0000136556355,0.00012179772,0.00007975418,0.00024920842,0.000064766966,0.00035338374,8.362907e-7,0.00030661272,0.0000026799107],"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":[0.00034371536,0.000023404818,0.000033694887,0.000034689714,0.00004960241,0.0004949918,0.0044961697,0.99037987,0.002040572,0.00021082195,0.00003731924,0.0018551273],"study_design_scores_gemma":[0.0361981,0.009385841,0.06547241,0.0104645565,0.00072750985,0.010284221,0.033426393,0.80335665,0.021447316,0.000970807,0.005133075,0.0031331438],"about_ca_topic_score_codex":0.000016912123,"about_ca_topic_score_gemma":0.00004343652,"teacher_disagreement_score":0.18702327,"about_ca_system_score_codex":0.00006154332,"about_ca_system_score_gemma":0.00003145739,"threshold_uncertainty_score":0.49667668},"labels":[],"label_agreement":null},{"id":"W2890981148","doi":"10.1155/2018/4613468","title":"Combinatorial Optimization of Service Order and Overtaking for Demand-Oriented Timetabling in a Single Railway Line","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":10,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Overtaking; Train; Service (business); Order (exchange); Operations research; Computer science; Interval (graph theory); Dwell time; Transport engineering; Engineering; Mathematical optimization; Mathematics; Economics","score_opus":0.007880787445287408,"score_gpt":0.2244091783468531,"score_spread":0.21652839090156567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890981148","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":[0.6459463,0.00018786232,0.35312438,0.000013517021,0.00060356193,0.000078370496,0.0000028775282,0.000009197761,0.00003391416],"genre_scores_gemma":[0.9640129,0.000034924116,0.03577564,0.000007859586,0.00013823438,0.0000024712951,0.00001026472,0.000015576592,0.0000020978719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992194,0.000008274658,0.00049443485,0.00006531173,0.00011545684,0.00009711419],"domain_scores_gemma":[0.9993014,0.00004082646,0.00019676886,0.00004167636,0.0003903083,0.000029030138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017858524,0.000077307624,0.0001984094,0.00014394426,0.000024438208,0.0000066029647,0.00003688179,0.00004956149,0.0000042093857],"category_scores_gemma":[0.00003029898,0.00007394975,0.000028976376,0.00033734716,0.000011663072,0.00030599104,6.726356e-7,0.00005444364,6.6415346e-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":[0.00012997529,0.000037670998,0.0001809267,0.00010478781,0.000015364554,9.017163e-7,0.0013066961,0.96566594,0.031093352,0.00031161107,0.000001177555,0.001151605],"study_design_scores_gemma":[0.009902029,0.0011924827,0.012979381,0.000997168,0.00011681537,0.00001425565,0.0009731761,0.9329175,0.038754717,0.00060829363,0.001191726,0.0003525002],"about_ca_topic_score_codex":0.000010713863,"about_ca_topic_score_gemma":0.00008065831,"teacher_disagreement_score":0.3180666,"about_ca_system_score_codex":0.000022749875,"about_ca_system_score_gemma":0.00001528607,"threshold_uncertainty_score":0.30155832},"labels":[],"label_agreement":null},{"id":"W2892715791","doi":"10.1155/2018/3802032","title":"Optimization of Classification Track Assignment Considering Block Sequence at Train Marshaling Yard","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":16,"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":"Marshalling; Train; Block (permutation group theory); Track (disk drive); Sequence (biology); Computer science; Yard; Process (computing); Plan (archaeology); Integer programming; Engineering; Algorithm; Simulation; Mathematical optimization; Mathematics","score_opus":0.021922041493257563,"score_gpt":0.2431622013678122,"score_spread":0.22124015987455464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892715791","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":[0.8523022,0.0001603849,0.1465825,0.000027694998,0.0004986458,0.00008174815,0.0000075805274,0.00003129184,0.00030793645],"genre_scores_gemma":[0.9745955,0.00013437799,0.025101226,0.0000053235435,0.000112245194,0.0000029803246,0.000011098575,0.000019491814,0.000017786764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876326,0.000017439545,0.00073048467,0.00009574747,0.00026249254,0.00013056486],"domain_scores_gemma":[0.99924016,0.0000325659,0.00035663391,0.00009313077,0.00021973459,0.000057774505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021262169,0.000110323126,0.00020649108,0.00011704783,0.000049112274,0.000008870505,0.00007565417,0.00006289188,0.000024133087],"category_scores_gemma":[0.00001548709,0.000107646985,0.00007545786,0.00017203695,0.000044384997,0.0003188555,8.0616815e-7,0.000083554936,0.000001279681],"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":[0.00002561593,0.000013078613,0.0001726049,0.000048264676,0.000017886874,0.0000040476543,0.0010643974,0.7527371,0.24393567,0.00007412296,0.00001118894,0.001896004],"study_design_scores_gemma":[0.0033954924,0.00096159097,0.062838696,0.0012775981,0.00021673534,0.0001740785,0.0033691952,0.5339627,0.39026392,0.00027850785,0.0024880064,0.0007734653],"about_ca_topic_score_codex":0.0000024951541,"about_ca_topic_score_gemma":0.000022641063,"teacher_disagreement_score":0.2187744,"about_ca_system_score_codex":0.00012207315,"about_ca_system_score_gemma":0.000025593275,"threshold_uncertainty_score":0.43897167},"labels":[],"label_agreement":null},{"id":"W2893693072","doi":"10.1155/2018/4530787","title":"Optimizing High-Speed Railroad Timetable with Passenger and Station Service Demands: A Case Study in the Wuhan-Guangzhou Corridor","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":7,"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":"China Railway","keywords":"Column generation; Computation; Scheduling (production processes); Branch and bound; Computer science; Mathematical optimization; Upper and lower bounds; Speedup; Integer programming; Algorithm; Mathematics","score_opus":0.009008137598488165,"score_gpt":0.22574145830376868,"score_spread":0.2167333207052805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893693072","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":[0.99198705,0.0002604251,0.007246111,0.000039244507,0.0001921024,0.00020658331,0.0000022987388,0.000019155585,0.00004702109],"genre_scores_gemma":[0.9958658,0.000030719628,0.003946024,0.000025468633,0.000095762734,0.000006364826,0.0000037991051,0.000018787923,0.000007227822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99910474,0.000035761586,0.00040509057,0.000097175114,0.00021777148,0.00013947443],"domain_scores_gemma":[0.99948394,0.000051646773,0.00015160294,0.00009156619,0.00018091923,0.00004031486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033941437,0.000119956174,0.00019571009,0.0001280552,0.000072899646,0.000035198173,0.0000659609,0.000031357304,0.0000051347874],"category_scores_gemma":[0.0000046813925,0.00007976294,0.00001996918,0.00036499833,0.00001578175,0.000506496,7.4056027e-7,0.00013332494,8.229932e-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":[0.000117681586,0.00008182728,0.0016217639,0.0000584313,0.00004454873,0.0006790312,0.03558559,0.95680445,0.0026632487,0.000017563176,0.000008799978,0.0023170817],"study_design_scores_gemma":[0.01880932,0.005425951,0.5898938,0.000991444,0.0007188049,0.0037152383,0.28076574,0.09384119,0.0034759757,0.00012891847,0.0010755769,0.0011580396],"about_ca_topic_score_codex":0.00024767895,"about_ca_topic_score_gemma":0.0060354825,"teacher_disagreement_score":0.86296326,"about_ca_system_score_codex":0.000025960468,"about_ca_system_score_gemma":0.000018124274,"threshold_uncertainty_score":0.33679408},"labels":[],"label_agreement":null},{"id":"W2895018934","doi":"","title":"Optimization of Locomotive Management and Fuel Consumption in Rail Freight Transport","year":2017,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Column generation; Fuel efficiency; Heuristics; Engineering; Scalability; Energy consumption; Optimization problem; Operations research; Computer science; Mathematical optimization; Automotive engineering; Algorithm","score_opus":0.01766078492155869,"score_gpt":0.24027078201720273,"score_spread":0.22260999709564405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895018934","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":[0.6931884,0.00065630337,0.0002867161,0.0000061798955,0.00052992464,0.0004214984,0.000008983662,0.000057823105,0.30484414],"genre_scores_gemma":[0.9682216,0.0023279325,0.000042688986,2.04251e-7,0.000052772193,0.000005139205,0.000090387584,0.000035629302,0.029223615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984725,0.000109311004,0.0002795988,0.00038000522,0.00039678867,0.00036177918],"domain_scores_gemma":[0.99925125,0.000042032378,0.0001178014,0.00039293594,0.00008474738,0.00011121634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029314408,0.00021676687,0.00036037678,0.0012410227,0.0001950768,0.000042999272,0.00036319962,0.0002707463,0.000030286667],"category_scores_gemma":[0.000006594165,0.0002582616,0.00008008175,0.0003263051,0.00014988222,0.00023787846,0.000024312863,0.0004427893,0.0000034708476],"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":[0.0018563914,0.0006537406,0.41404596,0.023513032,0.001841643,0.007157632,0.009495787,0.50458294,0.006913316,0.020456048,0.0008375611,0.0086459555],"study_design_scores_gemma":[0.0025603557,0.00029839648,0.9510766,0.0020144482,0.00015951807,0.000019447036,0.0040395292,0.02012691,0.009240011,0.0001855247,0.009285827,0.0009933842],"about_ca_topic_score_codex":0.0037814835,"about_ca_topic_score_gemma":0.010643594,"teacher_disagreement_score":0.5370307,"about_ca_system_score_codex":0.00032197885,"about_ca_system_score_gemma":0.00007803732,"threshold_uncertainty_score":0.99998695},"labels":[],"label_agreement":null},{"id":"W2895761822","doi":"10.1155/2018/9354297","title":"Transfer Robustness Optimization for Urban Rail Transit Timetables","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Robustness (evolution); Linearization; Mathematical optimization; Computer science; Transfer function; Transfer (computing); Nonlinear system; Travel time; Operations research; Engineering; Simulation; Transport engineering; Mathematics","score_opus":0.006839292344104899,"score_gpt":0.20405129575359537,"score_spread":0.19721200340949047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895761822","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":[0.27682748,0.00031900796,0.72190285,0.000027342885,0.00068673596,0.000099729565,0.000010934774,0.000037792033,0.000088137174],"genre_scores_gemma":[0.96422166,0.00010335503,0.035156652,0.000012347451,0.00039033862,0.000008470297,0.000024060831,0.000035526507,0.00004757465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990701,0.000008616854,0.0005071731,0.000087548004,0.00016481822,0.00016175602],"domain_scores_gemma":[0.9994988,0.000023998939,0.000062582534,0.00007053024,0.00028150057,0.00006262935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001522484,0.00012396032,0.0002256646,0.00012281214,0.00006129393,0.000016009351,0.00008814837,0.00006777603,0.00004567891],"category_scores_gemma":[0.0000050790472,0.00011248261,0.00012723898,0.00018926196,0.000025842131,0.00064279017,1.434475e-7,0.000069766065,6.922897e-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":[0.00008928475,0.000019973115,0.00001410987,0.000074793716,0.00003737108,0.0000018210645,0.00092581473,0.98136556,0.013404633,0.00030259724,0.00009368442,0.0036703553],"study_design_scores_gemma":[0.0074750935,0.0014472083,0.004255733,0.0005020278,0.00045147625,0.000038825903,0.0012967689,0.8497546,0.10194949,0.00022396243,0.031709474,0.0008953806],"about_ca_topic_score_codex":0.0000013817966,"about_ca_topic_score_gemma":0.000021502456,"teacher_disagreement_score":0.6873942,"about_ca_system_score_codex":0.00002742101,"about_ca_system_score_gemma":0.000021297647,"threshold_uncertainty_score":0.45869076},"labels":[],"label_agreement":null},{"id":"W2896408261","doi":"","title":"Celebrating Construction of the MATL (Montana-Alberta Tie Line) Transmission Line (1)","year":2010,"lang":"en","type":"article","venue":"The Mathematics Enthusiast","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Strong; U.S. Department of Energy","keywords":"Line (geometry); Telecommunications; Engineering; Mathematics; Geometry","score_opus":0.007864336978173085,"score_gpt":0.19653784927853837,"score_spread":0.18867351230036528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896408261","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":[0.92094904,0.00007973252,0.023334807,0.00020486928,0.0011291122,0.0002769614,0.000005566962,0.00010983083,0.053910058],"genre_scores_gemma":[0.99394715,0.00001283637,0.005322983,0.000009201245,0.00013767375,0.000010032664,0.0000016363898,0.00003306521,0.00052544003],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904287,0.000019549161,0.0004227276,0.00009783815,0.00023154895,0.00018549507],"domain_scores_gemma":[0.9992034,0.00011256961,0.000116856616,0.0004863794,0.00003778915,0.000043017495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002467871,0.00015768084,0.0001998519,0.0000358084,0.00013303795,0.00003086043,0.00034469145,0.00007567117,0.00011982064],"category_scores_gemma":[0.000044269196,0.0000845268,0.000102048354,0.00020635438,0.00010403488,0.000049602942,0.000034386936,0.00025872278,0.000015458432],"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":[0.000011214051,0.0002618128,0.000035517925,0.0014604918,0.00015407136,0.0000027110434,0.16167553,0.10100508,0.62262136,0.07188565,0.00063570536,0.040250845],"study_design_scores_gemma":[0.00044241568,0.00004897608,0.0001502973,0.00044336234,0.00009613772,0.00018821485,0.002098523,0.8234113,0.1654788,0.0007551908,0.0065109553,0.00037583616],"about_ca_topic_score_codex":0.000098524615,"about_ca_topic_score_gemma":0.00034170775,"teacher_disagreement_score":0.7224062,"about_ca_system_score_codex":0.0000129523405,"about_ca_system_score_gemma":0.000015557769,"threshold_uncertainty_score":0.3446903},"labels":[],"label_agreement":null},{"id":"W2899057210","doi":"10.1155/2018/1784789","title":"A Sparse Optimization Approach for Energy-Efficient Timetabling in Metro Railway Systems","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Mathematical optimization; Train; Computer science; Relaxation (psychology); Optimization problem; Energy (signal processing); Convex optimization; Efficient energy use; Algorithm; Regular polygon; Mathematics; Engineering","score_opus":0.00989139750317891,"score_gpt":0.21313672889533716,"score_spread":0.20324533139215825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899057210","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":[0.19318372,0.0007658254,0.8048119,0.0000035607002,0.0008215517,0.0001233716,0.0000055938217,0.000029584098,0.00025490642],"genre_scores_gemma":[0.9084833,0.000052664393,0.0910915,0.000004290868,0.00027310147,0.000021164966,0.000023697949,0.000029408668,0.000020832726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987291,0.00001841844,0.0007114004,0.00011989387,0.00021960064,0.00020155832],"domain_scores_gemma":[0.9993559,0.000033567598,0.00021701441,0.000090754475,0.00024081102,0.000061939],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036704275,0.00013358385,0.00028615014,0.0003471449,0.00004415552,0.000025021422,0.000098550176,0.00007077255,0.000004443723],"category_scores_gemma":[0.00001576645,0.00012115188,0.000093102484,0.00041590948,0.000018044653,0.00025060796,6.7730235e-7,0.0000798244,3.0057006e-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":[0.000059590668,0.000051265884,0.00006213433,0.0000748606,0.000023284305,0.00000250394,0.00049144943,0.99430877,0.00273668,0.00043897607,0.000013480479,0.0017369802],"study_design_scores_gemma":[0.001059145,0.00013828887,0.0006784445,0.0000953372,0.000029601064,0.0000054449297,0.00076052896,0.9953369,0.0011735883,0.000009791307,0.0005747553,0.00013816543],"about_ca_topic_score_codex":0.000013595102,"about_ca_topic_score_gemma":0.000024001401,"teacher_disagreement_score":0.7152996,"about_ca_system_score_codex":0.00008593477,"about_ca_system_score_gemma":0.000023900868,"threshold_uncertainty_score":0.49404302},"labels":[],"label_agreement":null},{"id":"W2904067141","doi":"10.26906/sunz.2018.5.065","title":"АНАЛІЗ КОНСТРУКЦІЙ РЕСОРНИХ ПІДВІШУВАНЬ РЕЙКОВОГО МІСЬКОГО ЕЛЕКТРОРУХОМОГО СКЛАДУ","year":2018,"lang":"uk","type":"article","venue":"Системи управління навігації та зв’язку Збірник наукових праць","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":4,"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":"Computer science","score_opus":0.006447721849903788,"score_gpt":0.19742490699843082,"score_spread":0.19097718514852705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904067141","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":[0.37516227,0.022632826,0.01347106,0.0029105914,0.04356007,0.005046424,0.0012404667,0.006671837,0.52930444],"genre_scores_gemma":[0.93443525,0.001862334,0.00281625,0.0012296849,0.015536851,0.0005327239,0.0002799201,0.0015179605,0.041789036],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9777205,0.0009626765,0.005294602,0.0048836097,0.0038166742,0.0073219603],"domain_scores_gemma":[0.9865169,0.0007750421,0.0014996742,0.006706034,0.0014062151,0.0030961444],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0033108627,0.0046916506,0.004407944,0.0021037685,0.0026523445,0.0017818202,0.005063273,0.003262503,0.008612492],"category_scores_gemma":[0.0009113496,0.0049160463,0.0022492514,0.0044737975,0.0024013652,0.0020985089,0.0011672147,0.0034966916,0.02611558],"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":[0.0015926906,0.0052933693,0.010225702,0.006611581,0.0069755367,0.0027274007,0.028279653,0.024101518,0.09388666,0.06819727,0.5691676,0.18294102],"study_design_scores_gemma":[0.008619831,0.003322732,0.014346659,0.0032798138,0.0014083079,0.00093017786,0.0036325555,0.045006715,0.017667204,0.0032292735,0.8876856,0.010871113],"about_ca_topic_score_codex":0.0055013783,"about_ca_topic_score_gemma":0.002693887,"teacher_disagreement_score":0.55927294,"about_ca_system_score_codex":0.001752996,"about_ca_system_score_gemma":0.0016003757,"threshold_uncertainty_score":0.9992544},"labels":[],"label_agreement":null},{"id":"W2905256011","doi":"10.1155/2018/7805168","title":"Joint Operating Revenue and Passenger Travel Cost Optimization in Urban Rail Transit","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Waterloo","funders":"Department of Science and Technology of Sichuan Province; China Scholarship Council; National Natural Science Foundation of China","keywords":"Revenue; Operations research; Genetic algorithm; Sorting; Scheduling (production processes); Operating cost; Transport engineering; Urban rail transit; Revenue management; Beijing; Computer science; Transit (satellite); Multi-objective optimization; Mathematical optimization; Engineering; Public transport; China; Economics; Mathematics; Finance","score_opus":0.008104294039653766,"score_gpt":0.20879648548471963,"score_spread":0.20069219144506587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905256011","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":[0.82083535,0.00034039505,0.17815325,0.0000468051,0.00036688845,0.00009367808,0.0000074161135,0.000011514875,0.0001446802],"genre_scores_gemma":[0.98955697,0.00018258239,0.010029435,0.000012267337,0.00016675949,0.0000034385962,0.000005937736,0.00001802849,0.000024606932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99919766,0.000014373908,0.00047644207,0.00007583591,0.00012027669,0.00011540364],"domain_scores_gemma":[0.99970794,0.000012111086,0.00009226847,0.00004752779,0.00009080456,0.00004937207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018061924,0.00009048655,0.0001814267,0.00011663559,0.00003633267,0.00001603616,0.000036179594,0.00004974614,0.000010420516],"category_scores_gemma":[0.000009552124,0.000085801585,0.000034080436,0.00015516144,0.000020264908,0.00035165003,3.1334577e-7,0.000117180236,4.55711e-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":[0.000013450811,0.000012168544,0.0004479713,0.000036592144,0.000007898563,0.0000109493385,0.0037182411,0.96384305,0.029069053,0.00005190188,0.00003360432,0.0027551434],"study_design_scores_gemma":[0.0068871644,0.000847951,0.6430049,0.0016661746,0.00008576134,0.00011509799,0.0039708563,0.30271405,0.03624465,0.00016100613,0.0034426122,0.0008597459],"about_ca_topic_score_codex":0.000009464956,"about_ca_topic_score_gemma":0.00017295516,"teacher_disagreement_score":0.66112894,"about_ca_system_score_codex":0.000034149667,"about_ca_system_score_gemma":0.000014267477,"threshold_uncertainty_score":0.3498887},"labels":[],"label_agreement":null},{"id":"W2907678656","doi":"10.1109/iecon.2018.8591768","title":"Railway Traction Supply with PV integration for Power Quality Issues","year":2018,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":12,"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":"Harmonics; AC power; Inverter; Three-phase; Engineering; Electrical engineering; Transformer; Grid; Voltage; Automotive engineering; Control theory (sociology); Computer science; Control (management)","score_opus":0.014401852958593841,"score_gpt":0.2684238126612189,"score_spread":0.25402195970262503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907678656","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":[0.5399388,0.000075529664,0.39603224,0.0002396511,0.000828457,0.00023807719,0.0000063175407,0.0004850979,0.06215581],"genre_scores_gemma":[0.9933763,0.0000043918717,0.002765861,0.000029928337,0.00023305767,0.000033476965,0.000010518138,0.000019319617,0.0035271537],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99945796,0.0000107927635,0.00016641618,0.00012081126,0.00009738554,0.00014662021],"domain_scores_gemma":[0.9996934,0.000024596953,0.000021083428,0.00014019078,0.000086426306,0.000034293334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001830682,0.00009734242,0.00010926267,0.00004121832,0.000060053397,0.000039378247,0.00005150351,0.000056862496,0.00021640222],"category_scores_gemma":[0.000015064313,0.00006775584,0.000031218653,0.00008965633,0.00002530764,0.00018640513,0.0000024500864,0.000039293973,0.000044040495],"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":[0.0005755369,0.00042167556,0.0034689559,0.00042615324,0.00035569436,0.0000034924703,0.016587822,0.04418821,0.4520688,0.15466405,0.21740976,0.10982985],"study_design_scores_gemma":[0.0017479786,0.0016027052,0.027813135,0.00013743804,0.000033305696,0.000022454737,0.0044665243,0.1548977,0.27916425,0.0005695895,0.5284378,0.0011071207],"about_ca_topic_score_codex":0.00022586812,"about_ca_topic_score_gemma":0.00063798577,"teacher_disagreement_score":0.45343748,"about_ca_system_score_codex":0.00003137406,"about_ca_system_score_gemma":0.000006969083,"threshold_uncertainty_score":0.2763003},"labels":[],"label_agreement":null},{"id":"W2912117457","doi":"10.1109/tits.2018.2846480","title":"An On-Line Optimal Controller for a Commuter Train","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Solver; Robustness (evolution); Optimal control; Track (disk drive); Convex optimization; Vehicle dynamics; Propulsion; Computation; Computer science; Control theory (sociology); Control engineering; Optimization problem; Engineering; Controller (irrigation); Mathematical optimization; Simulation; Regular polygon; Automotive engineering; Algorithm; Control (management)","score_opus":0.022389944112766763,"score_gpt":0.2524808852117469,"score_spread":0.23009094109898012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912117457","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":[0.17256479,0.00005862589,0.82252336,0.000017770371,0.0027596008,0.0010636384,0.00029616532,0.00035903582,0.00035703604],"genre_scores_gemma":[0.99790925,0.000028221482,0.00025315574,0.000051569157,0.000119703516,0.00055247976,0.000063891035,0.00008596352,0.00093574397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982838,0.00004319632,0.0007034244,0.00033548955,0.00029688506,0.00033721802],"domain_scores_gemma":[0.99921477,0.00012891466,0.00006250982,0.00034550703,0.00010221852,0.0001460717],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025560518,0.00030900683,0.00041491026,0.00024198629,0.00010969809,0.0000712554,0.0001955597,0.00017571147,0.00011668268],"category_scores_gemma":[5.888159e-7,0.00028665276,0.00024643316,0.00018849914,0.000022268023,0.00018115,3.979189e-8,0.00020234146,0.00018381217],"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":[0.00015344494,0.00017665731,0.000009430015,0.00015037027,0.000110838795,0.0000014429867,0.0011319874,0.9902231,0.0034791944,0.0007851732,0.000091913775,0.0036864663],"study_design_scores_gemma":[0.0014162745,0.0010365001,0.000053171796,0.00017807583,0.00005233135,0.000004413514,0.0011102591,0.96654207,0.019000366,0.000006686378,0.010153075,0.00044676036],"about_ca_topic_score_codex":0.00008515472,"about_ca_topic_score_gemma":0.00007671594,"teacher_disagreement_score":0.8253445,"about_ca_system_score_codex":0.000085379645,"about_ca_system_score_gemma":0.000017756613,"threshold_uncertainty_score":0.9999586},"labels":[],"label_agreement":null},{"id":"W2913443311","doi":"10.1155/2019/8526953","title":"Optimizing Train-Set Circulation Plan in High-Speed Railway Networks Using Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":10,"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":"Plan (archaeology); Computer science; Genetic algorithm; Mode (computer interface); Hotspot (geology); Set (abstract data type); Operations research; Integer programming; Transport engineering; Algorithm; Engineering; Machine learning","score_opus":0.009394941894264515,"score_gpt":0.21045088344707064,"score_spread":0.20105594155280612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913443311","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":[0.71969616,0.0004166236,0.27888152,0.000004021034,0.0008582247,0.00009374855,0.0000031620148,0.000020542935,0.00002596261],"genre_scores_gemma":[0.9635636,0.00009598613,0.03611725,0.000006609152,0.00015970621,9.537457e-7,0.00001992286,0.000031271487,0.000004694804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998771,0.00001949056,0.0006732917,0.00011124923,0.00021715919,0.00020779813],"domain_scores_gemma":[0.9995558,0.00002803855,0.00020294919,0.00008841059,0.0000652495,0.000059532427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016946252,0.00013986781,0.0002784428,0.00021817615,0.000024530651,0.000018631543,0.00008340436,0.00009124716,0.000017276498],"category_scores_gemma":[0.0000025467775,0.00014076679,0.00007996403,0.0002737918,0.000008513845,0.0003868325,6.521554e-7,0.00019689414,0.0000013861891],"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":[0.00001660839,0.000011176148,0.0012904808,0.00003105884,0.000017310644,0.000025872832,0.001153333,0.976518,0.011510491,0.000021148047,0.0000011781013,0.009403339],"study_design_scores_gemma":[0.0013792687,0.00006468892,0.17222321,0.00021638197,0.000021641838,0.000024851095,0.00043427752,0.8250099,0.00030559034,0.00004761427,0.000085578446,0.00018703513],"about_ca_topic_score_codex":0.000020329391,"about_ca_topic_score_gemma":0.00002133847,"teacher_disagreement_score":0.24386743,"about_ca_system_score_codex":0.00010788281,"about_ca_system_score_gemma":0.000021026099,"threshold_uncertainty_score":0.57403034},"labels":[],"label_agreement":null},{"id":"W2914159478","doi":"10.1155/2019/4076865","title":"Modelling and Simulation of Tramway Transportation Systems","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Computer science; Track (disk drive); Acceleration; Automotive engineering; Software; Simulation; Energy storage; Energy (signal processing); Simulation software; Line (geometry); Engineering; Operating system","score_opus":0.007343638980391208,"score_gpt":0.20688980490216136,"score_spread":0.19954616592177016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914159478","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":[0.79097027,0.00077365927,0.20762254,0.0000023048242,0.00045918458,0.00009001633,0.000006966249,0.000015907204,0.000059124774],"genre_scores_gemma":[0.9979347,0.00019057351,0.001787647,0.0000010374298,0.00004119245,0.0000013069456,0.00001225926,0.00001749264,0.000013766494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899673,0.000008398482,0.0006308339,0.0000693164,0.00020612968,0.00008857702],"domain_scores_gemma":[0.9994931,0.000038712904,0.00023175334,0.00005930535,0.00013599494,0.0000411408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012932887,0.000092517264,0.00023842977,0.00012163145,0.000013818356,0.0000067587957,0.000037869173,0.00005783716,0.00000414728],"category_scores_gemma":[0.0000015589352,0.00008595822,0.000061034963,0.00012322157,0.000009095136,0.0004292014,1.03175175e-7,0.00008323532,5.522403e-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":[0.00003499023,0.000010987538,0.0006175775,0.0002705726,0.000021027116,0.0000018344128,0.0015497853,0.9864541,0.009630778,0.00033479268,7.249284e-7,0.0010727951],"study_design_scores_gemma":[0.000821484,0.00014454666,0.013955654,0.00026592932,0.000041857133,0.0000027521987,0.00082083355,0.9819805,0.001308273,0.00006974387,0.00046692428,0.00012148271],"about_ca_topic_score_codex":0.000011947227,"about_ca_topic_score_gemma":0.0000066176312,"teacher_disagreement_score":0.20696443,"about_ca_system_score_codex":0.0000184452,"about_ca_system_score_gemma":0.0000107747655,"threshold_uncertainty_score":0.35052747},"labels":[],"label_agreement":null},{"id":"W2923933476","doi":"10.1155/2019/8639589","title":"Modeling the Influence of Disturbances in High-Speed Railway Systems","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":34,"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":"Department of Science and Technology of Sichuan Province; Southwest Jiaotong University; National Natural Science Foundation of China","keywords":"Train; Cluster analysis; Nonparametric statistics; Parametric statistics; Goodness of fit; Computer science; Disturbance (geology); Data mining; Probability distribution; Distribution (mathematics); Statistics; Algorithm; Simulation; Artificial intelligence; Mathematics; Geography","score_opus":0.004668963179794669,"score_gpt":0.19623899276009338,"score_spread":0.1915700295802987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923933476","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":[0.99529237,0.0012450926,0.0026377824,0.000012332938,0.000640961,0.00010200155,0.0000029531232,0.000010880065,0.000055617613],"genre_scores_gemma":[0.99955803,0.00018699408,0.00018277753,0.0000025010074,0.0000407348,0.0000017636631,0.000001934123,0.000011314542,0.000013962531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989558,0.000014141544,0.00063650473,0.000060362567,0.0002255039,0.00010772751],"domain_scores_gemma":[0.9995756,0.000034414978,0.00016394281,0.00009589937,0.00010703114,0.000023084325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002148557,0.00008422415,0.00023375337,0.00009084811,0.000013311029,0.000008702433,0.00012955412,0.000036952893,0.0000019892232],"category_scores_gemma":[0.0000068443846,0.000057648725,0.00005360783,0.00020515175,0.000011709402,0.00036768595,5.909074e-7,0.00012980733,0.0000010934381],"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":[0.000023297258,0.000008986086,0.0028197162,0.000112594345,0.000011770132,0.0000026856899,0.0010447837,0.97466946,0.020638885,0.0004442897,0.0000010525314,0.00022247832],"study_design_scores_gemma":[0.00229681,0.00023317372,0.47346306,0.001544091,0.00004104691,0.000014437597,0.0036714114,0.51580215,0.0019745314,0.00027872,0.0003546798,0.0003258959],"about_ca_topic_score_codex":0.00008883159,"about_ca_topic_score_gemma":0.00006235009,"teacher_disagreement_score":0.47064334,"about_ca_system_score_codex":0.00003138962,"about_ca_system_score_gemma":0.000015724834,"threshold_uncertainty_score":0.23508468},"labels":[],"label_agreement":null},{"id":"W2924893847","doi":"10.1155/2019/6090742","title":"Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":46,"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":"Headway; Particle swarm optimization; Genetic algorithm; Solver; Schedule; Mathematical optimization; Computer science; Algorithm; Simulation; Mathematics","score_opus":0.006056491929442636,"score_gpt":0.21042594670448725,"score_spread":0.20436945477504462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924893847","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":[0.76973104,0.000160571,0.22939615,0.0000061445116,0.00032525297,0.00031329572,0.0000027738456,0.000028224817,0.000036568945],"genre_scores_gemma":[0.8791047,0.000016686927,0.12076211,0.0000040808736,0.00004306713,0.000010793798,0.0000051938414,0.00002985207,0.000023532293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985944,0.00003716428,0.0007116524,0.000156022,0.00027413247,0.00022660627],"domain_scores_gemma":[0.9994643,0.000027376265,0.00013827156,0.00015032635,0.00010364957,0.00011611337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029700703,0.00014828364,0.0002823477,0.0001851676,0.000043921496,0.000032099088,0.00009514158,0.00004888578,0.0000071254863],"category_scores_gemma":[0.000009712619,0.00013939966,0.000058431564,0.0004933132,0.0000070712626,0.0002977167,0.0000011083977,0.00013412641,0.0000030050644],"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":[0.000059241043,0.00022801729,0.0019411081,0.00003519226,0.000021924097,0.0001787049,0.0015674791,0.99043775,0.0016025116,0.00000905795,0.0000020513105,0.0039169807],"study_design_scores_gemma":[0.003476971,0.0005353648,0.05407052,0.00006589092,0.000035519268,0.000058892667,0.002804752,0.9371777,0.0014950657,0.000006774734,0.000040287203,0.0002322206],"about_ca_topic_score_codex":0.00009183384,"about_ca_topic_score_gemma":0.00008160153,"teacher_disagreement_score":0.10937367,"about_ca_system_score_codex":0.000087353335,"about_ca_system_score_gemma":0.00003436214,"threshold_uncertainty_score":0.5684553},"labels":[],"label_agreement":null},{"id":"W2937897853","doi":"10.1177/0361198119840621","title":"Reasons for Commuter Rail Electrification: Early 20th Century and Since 2000","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrification; Train; Haven; The Renaissance; Engineering; History; Archaeology","score_opus":0.03569290788710889,"score_gpt":0.30832804970341926,"score_spread":0.2726351418163104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937897853","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":[0.9931776,0.0011461502,0.0021661418,0.0012650504,0.000630937,0.0010798224,0.000063013205,0.000044538672,0.00042676297],"genre_scores_gemma":[0.9955819,0.0018555982,0.001086175,0.00001813219,0.00016667403,0.00008356045,0.000017655131,0.00006193273,0.0011283373],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9959857,0.0003660865,0.0009283232,0.00029060597,0.0016600395,0.0007692376],"domain_scores_gemma":[0.99677306,0.0007297619,0.0001731117,0.00043794425,0.0015959507,0.00029019665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002852452,0.00020872772,0.0003731136,0.0005706637,0.00036803217,0.00014642357,0.00067993655,0.000175126,0.00010943896],"category_scores_gemma":[0.000053341926,0.00016263386,0.00023206908,0.0011403575,0.00023050743,0.0005178177,0.0000023521332,0.0012278906,0.000022585287],"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":[0.0046985974,0.00081359816,0.6326186,0.003889383,0.0011271436,0.0001018565,0.024957307,0.062007442,0.1141587,0.04476349,0.043876585,0.06698727],"study_design_scores_gemma":[0.0020094388,0.0007230228,0.87215257,0.00042260968,0.000038032682,0.0000013623472,0.0010884498,0.0022558786,0.0022849655,0.0008056475,0.11792092,0.00029709653],"about_ca_topic_score_codex":0.0020201206,"about_ca_topic_score_gemma":0.0044700666,"teacher_disagreement_score":0.23953395,"about_ca_system_score_codex":0.0001921978,"about_ca_system_score_gemma":0.00029646762,"threshold_uncertainty_score":0.6632017},"labels":[],"label_agreement":null},{"id":"W2942871875","doi":"10.1155/2019/1315638","title":"Optimization Method of Locomotive Working Diagram Layout","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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; National Science Foundation","keywords":"Diagram; Train; Engineering; Intersection (aeronautics); Point (geometry); Page layout; Scheduling (production processes); Schedule; Computer science; Engineering drawing; Transport engineering; Operations management; Mathematics","score_opus":0.005697891623979411,"score_gpt":0.2239424045623826,"score_spread":0.2182445129384032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942871875","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":[0.40841553,0.00020378763,0.5902957,0.0000059699114,0.00055594876,0.00006443424,0.0000015669892,0.000016141128,0.00044097492],"genre_scores_gemma":[0.92247474,0.00008263486,0.07734876,0.0000030490255,0.000047191133,0.0000011703031,0.0000061497117,0.000015418978,0.000020897249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992017,0.000015178928,0.0004610574,0.000057255165,0.00017463259,0.000090174646],"domain_scores_gemma":[0.99951345,0.00003828692,0.00023794262,0.00006589789,0.000111346424,0.00003308322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017031404,0.000079353464,0.00021257972,0.00011344538,0.00001121026,0.0000054524335,0.000066277345,0.000045089535,0.000022990183],"category_scores_gemma":[0.0000060146426,0.00007129948,0.00008444061,0.00017746985,0.0000061796372,0.00023931739,4.6127255e-7,0.00009553845,0.000001117624],"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":[0.000019824136,0.0000150285305,0.0015434939,0.000052057116,0.000026291487,0.0000022815805,0.0010494429,0.9770514,0.006592171,0.00017723927,0.0000029445685,0.0134678725],"study_design_scores_gemma":[0.0052261073,0.0009954178,0.1758076,0.0017930609,0.0002361597,0.00004664223,0.0042319065,0.7548983,0.052192606,0.0005086379,0.0032480636,0.0008155048],"about_ca_topic_score_codex":0.0000034616846,"about_ca_topic_score_gemma":0.0000061973096,"teacher_disagreement_score":0.5140592,"about_ca_system_score_codex":0.000027723367,"about_ca_system_score_gemma":0.000012157907,"threshold_uncertainty_score":0.29075083},"labels":[],"label_agreement":null},{"id":"W2942910330","doi":"10.1155/2019/7258986","title":"Discrete Train Speed Profile Optimization for Urban Rail Transit: A Data-Driven Model and Integrated Algorithms Based on Machine Learning","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":35,"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":"Beijing Municipal Natural Science Foundation; China National Funds for Distinguished Young Scientists; National Natural Science Foundation of China","keywords":"Energy consumption; Support vector machine; Heuristic; Energy (signal processing); Computer science; Algorithm; Regression analysis; Mathematical optimization; Engineering; Simulation; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.01071325137345116,"score_gpt":0.2251636260240443,"score_spread":0.21445037465059313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942910330","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":[0.20680119,0.0001229369,0.7922958,0.000037325408,0.0001927481,0.0002725135,0.00016794824,0.000046748843,0.00006278355],"genre_scores_gemma":[0.90922177,0.000044097997,0.08987676,0.000011156053,0.000044884455,0.0000044964913,0.0006935649,0.0000389449,0.000064311855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905676,0.000017157514,0.0004359421,0.0001585377,0.00018737839,0.00014423783],"domain_scores_gemma":[0.9995239,0.00004106515,0.00015531383,0.000116968506,0.00009734944,0.000065453045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020117377,0.00015173695,0.00025920593,0.00013859007,0.00004293489,0.000022104568,0.00010843941,0.00006275,0.000017532364],"category_scores_gemma":[0.000012828693,0.0001286895,0.000062924104,0.00013623499,0.000011500761,0.0004608556,6.703254e-7,0.00018680358,4.0272542e-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":[0.0001816596,0.000019765614,0.000084091014,0.0001072499,0.000022841716,0.0000022887327,0.000697494,0.9882093,0.0069632195,0.000034828747,0.000013948455,0.0036633138],"study_design_scores_gemma":[0.0020652323,0.0003113803,0.0005080767,0.00016020976,0.000042085136,0.0000022125105,0.00018684515,0.99585307,0.00040107593,0.000011517161,0.00031255974,0.00014574818],"about_ca_topic_score_codex":0.000004351879,"about_ca_topic_score_gemma":0.000019980507,"teacher_disagreement_score":0.7024206,"about_ca_system_score_codex":0.000033791817,"about_ca_system_score_gemma":0.000032165128,"threshold_uncertainty_score":0.5247806},"labels":[],"label_agreement":null},{"id":"W2943611693","doi":"10.1155/2019/7261726","title":"Adaptive Model Predictive Control for Cruise Control of High-Speed Trains with Time-Varying Parameters","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Cruise control; Train; Model predictive control; Control theory (sociology); Computer science; Adaptive control; PID controller; Bounded function; Engineering; Control engineering; Control (management); Temperature control; Mathematics; Artificial intelligence","score_opus":0.0061303689301161965,"score_gpt":0.19308599456017922,"score_spread":0.18695562563006302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943611693","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":[0.4922099,0.00007512442,0.50716764,0.000009711775,0.00011625892,0.00027295342,0.00007721793,0.000015883028,0.000055299628],"genre_scores_gemma":[0.986006,0.000013896091,0.013858088,0.00001214057,0.00003349562,0.000011398438,0.000009811697,0.000031270025,0.000023876537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896055,0.000011862148,0.0005271393,0.00010394117,0.00022980449,0.00016668701],"domain_scores_gemma":[0.9991454,0.00011162254,0.00032741704,0.00008262481,0.00026786828,0.00006510652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013465757,0.00014485093,0.00043985635,0.00011375906,0.000021434153,0.0000059881854,0.000080418584,0.000058517526,0.0000040340483],"category_scores_gemma":[0.0000076261263,0.0001169572,0.00013118761,0.00009672739,0.000022899141,0.0003856639,2.1458331e-7,0.00010977924,6.2467075e-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":[0.0015718645,0.000032457527,0.000089179106,0.00006250735,0.00019615423,0.0000029098553,0.0010512238,0.95590484,0.040187623,0.00021746452,0.000004206004,0.0006795415],"study_design_scores_gemma":[0.008927206,0.001289609,0.003283165,0.00023169571,0.00017760451,0.0000042922884,0.0003622542,0.98210853,0.003201532,0.00024411523,0.000011993796,0.00015798204],"about_ca_topic_score_codex":0.000005155434,"about_ca_topic_score_gemma":0.0000057284474,"teacher_disagreement_score":0.4937961,"about_ca_system_score_codex":0.000049254082,"about_ca_system_score_gemma":0.000043492684,"threshold_uncertainty_score":0.47693762},"labels":[],"label_agreement":null},{"id":"W2950193259","doi":"10.24843/spektrum.2019.v06.i01.p20","title":"ANALISIS TEGANGAN LANGKAH DAN TEGANGAN SENTUH SERTA PERENCANAAN SISTEM PEMBUMIAN PADA PEMBANGUNAN SUBSTATION VVIP DI BANDAR UDARA INTERNASIONAL I GUSTI NGURAH RAI BALI","year":2019,"lang":"en","type":"article","venue":"Jurnal SPEKTRUM","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Encana (Canada)","funders":"","keywords":"Ground; Ohm; Electrical engineering; Engineering; Grid; Volt; Earthing system; Voltage; Geology; Geodesy","score_opus":0.005315970269741523,"score_gpt":0.1915805117702847,"score_spread":0.1862645415005432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950193259","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":[0.9837753,0.00042891208,0.0003339656,0.0003013265,0.002069992,0.00024937498,0.000067270295,0.00031875836,0.012455109],"genre_scores_gemma":[0.99586564,0.00003653401,0.00008397345,0.00008516133,0.0006727407,0.000013458158,0.00016435697,0.000116608004,0.0029615103],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99716026,0.00008862918,0.00070083764,0.0005201598,0.0007604912,0.0007696386],"domain_scores_gemma":[0.9987016,0.0000684117,0.00017676418,0.0005599845,0.00012055842,0.00037273328],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033774125,0.00049985433,0.0005366853,0.00029930728,0.00020373776,0.00024185769,0.00052819325,0.00022623206,0.00030827578],"category_scores_gemma":[0.000031618714,0.00044493284,0.00028825703,0.0005213164,0.000054016182,0.00040193557,0.000068186506,0.00047719994,0.00017874486],"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":[0.00034506602,0.0006192058,0.5959032,0.0016926476,0.0017284777,0.00085445825,0.010995215,0.13401967,0.19216354,0.010982348,0.02436497,0.026331158],"study_design_scores_gemma":[0.0041020224,0.00074258423,0.70920676,0.0012140756,0.0003107351,0.00065476185,0.009817767,0.12684497,0.028792376,0.000117432195,0.114893086,0.0033034289],"about_ca_topic_score_codex":0.0019741212,"about_ca_topic_score_gemma":0.001702835,"teacher_disagreement_score":0.16337116,"about_ca_system_score_codex":0.00035510855,"about_ca_system_score_gemma":0.00008787944,"threshold_uncertainty_score":0.99980026},"labels":[],"label_agreement":null},{"id":"W2952894872","doi":"10.1007/s40534-019-0188-z","title":"Statistical delay distribution analysis on high-speed railway trains","year":2019,"lang":"en","type":"article","venue":"Journal of Modern Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":37,"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":"National Natural Science Foundation of China; China Railway","keywords":"Train; Log-normal distribution; Exponential distribution; Goodness of fit; Distribution fitting; Statistics; Logistic distribution; Gamma distribution; Probability distribution; Mathematics; Distribution (mathematics); Computer science; Logistic regression; Mathematical analysis","score_opus":0.005781184182445611,"score_gpt":0.2020242286007375,"score_spread":0.19624304441829188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952894872","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":[0.6232406,0.000051227853,0.3761315,0.000020850957,0.00025057155,0.000039035192,0.00010307783,0.000021926244,0.00014121417],"genre_scores_gemma":[0.9991608,0.000028605544,0.0003925841,0.000010512037,0.00007581761,8.4885863e-7,0.00026321565,0.00001684627,0.00005072133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987997,0.00002232075,0.00053662347,0.00010486934,0.00037558368,0.00016093165],"domain_scores_gemma":[0.99952805,0.000048535025,0.00013106699,0.000109060435,0.00009428058,0.000089016095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002194073,0.00012507464,0.00031082085,0.000169755,0.000026856025,0.000022188713,0.00008846753,0.00008147209,0.00010544749],"category_scores_gemma":[0.0000064315986,0.00010669234,0.00016670684,0.00028797996,0.000011628144,0.00016194728,3.995664e-7,0.00016727508,0.00001552996],"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":[0.00004619607,0.000044218,0.0021577368,0.000027731972,0.00026540755,0.00002059365,0.00039654746,0.9875071,0.0031594967,0.0023150174,0.00011456207,0.0039453628],"study_design_scores_gemma":[0.0013757218,0.00037166185,0.51454425,0.00006718475,0.00065074104,0.000010008694,0.000095534684,0.4795453,0.0013434102,0.00065303704,0.0010347763,0.00030839202],"about_ca_topic_score_codex":0.000022915918,"about_ca_topic_score_gemma":0.00006394319,"teacher_disagreement_score":0.5123865,"about_ca_system_score_codex":0.00008364242,"about_ca_system_score_gemma":0.000020517928,"threshold_uncertainty_score":0.43507874},"labels":[],"label_agreement":null},{"id":"W2956928365","doi":"10.1155/2019/4364162","title":"Cost-Effective, Time-Efficient Passenger Rail System for the Eastern United States","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Transport engineering; Track (disk drive); Ticket; Metropolitan area; Point (geometry); Park and ride; Service (business); Transfer (computing); Computer science; Engineering; Operations research; Public transport; Business; Geography; Computer network","score_opus":0.006821570540292256,"score_gpt":0.21727115090096125,"score_spread":0.210449580360669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956928365","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":[0.91260797,0.0004757854,0.08452157,0.000039865285,0.0013308729,0.0008673088,0.00002175484,0.000055969485,0.00007890312],"genre_scores_gemma":[0.9991461,0.00004229331,0.00045408655,0.00001063346,0.00010264228,0.00006189675,0.00003052814,0.00003081727,0.00012100214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906677,0.000019327035,0.00043873047,0.00008599732,0.00021670604,0.00017246792],"domain_scores_gemma":[0.99920964,0.0002136047,0.00018846219,0.00011398312,0.00022219481,0.000052116968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026782116,0.00012979264,0.00022065944,0.00012394419,0.00005033372,0.000021949558,0.00011391953,0.000045730416,0.0000084927515],"category_scores_gemma":[0.0000063987422,0.00008725745,0.00012629697,0.00021634884,0.000011820071,0.00013500433,7.3045453e-7,0.000115850424,0.000015185049],"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":[0.00007752016,0.000016738139,0.00017565541,0.0001765658,0.00006468909,0.0000038828834,0.0010233215,0.98793846,0.005259542,0.00008216131,0.000042401207,0.0051390594],"study_design_scores_gemma":[0.0027407266,0.00034005125,0.018536456,0.00047942123,0.00012563448,0.000023611237,0.0042978274,0.9186994,0.003911192,0.000011844506,0.050559983,0.00027389431],"about_ca_topic_score_codex":0.000006479052,"about_ca_topic_score_gemma":0.0000060477564,"teacher_disagreement_score":0.08653813,"about_ca_system_score_codex":0.000081921375,"about_ca_system_score_gemma":0.000012160876,"threshold_uncertainty_score":0.35582557},"labels":[],"label_agreement":null},{"id":"W2964514245","doi":"10.1155/2019/1382394","title":"Increasing Robustness by Reallocating the Margins in the Timetable","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities","keywords":"Punctuality; Headway; Unavailability; Robustness (evolution); Operations research; Computer science; Heuristic; Engineering; Transport engineering; Simulation; Reliability engineering","score_opus":0.0038842170808593194,"score_gpt":0.19178051405803037,"score_spread":0.18789629697717106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964514245","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":[0.9933752,0.00071058766,0.004762683,0.00008545887,0.00029551197,0.00008551808,0.00000156913,0.000009969643,0.0006735326],"genre_scores_gemma":[0.9988762,0.00009558626,0.0009088263,0.00001939396,0.000048376856,0.0000027908882,0.0000049122737,0.000011961113,0.00003196323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99924564,0.0000371555,0.00035269905,0.000049706978,0.00019442619,0.00012036618],"domain_scores_gemma":[0.99963593,0.00009439108,0.00011604499,0.00009562785,0.000041451218,0.000016574984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059282366,0.000072739706,0.00012749045,0.00004683898,0.000035843343,0.00002009608,0.00015190543,0.000028091848,0.000009153136],"category_scores_gemma":[0.000009685825,0.000042129526,0.00004510222,0.0002272252,0.000008471001,0.00028017204,4.820553e-7,0.00017598557,0.0000015427923],"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":[0.0000119684455,0.000010339234,0.0015198708,0.0000243894,0.0000076544,0.000003058625,0.0014768217,0.9810819,0.013883599,0.000097427546,0.0000444539,0.0018385106],"study_design_scores_gemma":[0.004322316,0.0003712276,0.7624862,0.0011601937,0.00014459439,0.0002490508,0.030188331,0.16269355,0.0054193875,0.00028880566,0.03187215,0.00080419006],"about_ca_topic_score_codex":0.000053621396,"about_ca_topic_score_gemma":0.00007653598,"teacher_disagreement_score":0.81838834,"about_ca_system_score_codex":0.000028629767,"about_ca_system_score_gemma":0.000012217093,"threshold_uncertainty_score":0.17179921},"labels":[],"label_agreement":null},{"id":"W2967281967","doi":"10.1109/itec.2019.8790539","title":"Dynamic (Transient) Modeling and Study of Light Rail Transit System","year":2019,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Stantec (Canada)","funders":"","keywords":"Inrush current; Transient (computer programming); Emtp; Traction power network; Engineering; Electric power system; Automotive engineering; Train; Traction motor; Traction (geology); Computer science; Power (physics); Electrical engineering; Voltage; Mechanical engineering; Physics","score_opus":0.004136049928733909,"score_gpt":0.17127252981787086,"score_spread":0.16713647988913696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967281967","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":[0.9612481,0.00028795574,0.022492794,0.000004781629,0.0002584748,0.00022830968,9.376063e-7,0.00018294179,0.015295684],"genre_scores_gemma":[0.9996137,0.000006892374,0.000076462675,0.0000012163022,0.0000069794105,0.000006297776,5.3746425e-7,0.000021311129,0.00026658253],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993083,0.000011804332,0.00025950652,0.00014556127,0.0001307282,0.00014411219],"domain_scores_gemma":[0.9997333,0.000007833215,0.000012184211,0.00018524918,0.000020620972,0.000040779723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010540592,0.00011053619,0.0002256015,0.00007288861,0.00002252988,0.000012457387,0.00007436589,0.000044889362,0.000012948088],"category_scores_gemma":[4.7954376e-7,0.00009140527,0.000029990995,0.00010520838,0.0000035283424,0.00006808332,0.0000057524526,0.000050871153,0.000011603214],"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":[0.0000042377833,0.000035102945,0.00014508497,0.0003391808,0.000034569992,0.0000016787553,0.0022038294,0.98652226,0.010059467,0.00042051775,0.000005132829,0.00022894665],"study_design_scores_gemma":[0.00051737163,0.00010697266,0.00014069096,0.000060278962,0.000013451444,0.0000056848176,0.005230211,0.99362165,0.00014335375,0.000001366239,0.0000459325,0.00011305219],"about_ca_topic_score_codex":0.00013783507,"about_ca_topic_score_gemma":0.00010157555,"teacher_disagreement_score":0.038365602,"about_ca_system_score_codex":0.000022507975,"about_ca_system_score_gemma":0.0000039458605,"threshold_uncertainty_score":0.37273988},"labels":[],"label_agreement":null},{"id":"W2969003068","doi":"10.1109/access.2019.2935106","title":"Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal","year":2019,"lang":"en","type":"review","venue":"IEEE Access","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":70,"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":"State Key Laboratory of Rail Traffic Control and Safety; National Natural Science Foundation of China; China Railway","keywords":"Key (lock); Computer science; Operations research; Data science; Industrial engineering; Engineering; Computer security","score_opus":0.1993119771729817,"score_gpt":0.3599851209676399,"score_spread":0.1606731437946582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969003068","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":[0.000005207246,0.9907188,0.0034650308,0.000008502698,0.0005586802,0.0013732938,0.000111618465,0.0001679028,0.003590998],"genre_scores_gemma":[0.000044882097,0.99855244,0.00040058306,0.00003167944,0.00015938036,0.00023398975,0.0003336985,0.0001291102,0.00011421011],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801725,0.00008652128,0.00055469596,0.00071280426,0.0002582119,0.0003705196],"domain_scores_gemma":[0.99831295,0.000118756616,0.00017852022,0.0012685655,0.000017546468,0.0001036365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001841754,0.0005896982,0.0019594629,0.00013626713,0.000057869373,0.00019319207,0.0014035306,0.00014828968,0.000010415087],"category_scores_gemma":[0.0000039931047,0.00041417684,0.00011563151,0.00036246033,0.000044097524,0.0003975383,0.00028710152,0.00035065218,0.00003238948],"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":[5.497089e-7,0.000014109762,0.0000010567211,0.32960802,0.0004688978,0.000023131195,0.000034927165,0.0029938098,1.8536785e-8,0.00007268115,0.0026820446,0.66410077],"study_design_scores_gemma":[0.00012516821,0.000012957096,0.0000040463,0.044342197,0.0015748556,0.00006400828,0.000019818794,0.0031421846,2.3865171e-8,8.659355e-7,0.9501764,0.0005374869],"about_ca_topic_score_codex":0.00003010527,"about_ca_topic_score_gemma":0.0000144183405,"teacher_disagreement_score":0.9474943,"about_ca_system_score_codex":0.000044942335,"about_ca_system_score_gemma":0.000030333875,"threshold_uncertainty_score":0.999831},"labels":[],"label_agreement":null},{"id":"W2980207453","doi":"10.1155/2019/2689648","title":"Reliability Evaluation for LTE Based CBTC Train Ground Communication Systems","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Beijing Jiaotong University","keywords":"Reliability (semiconductor); Transmission (telecommunications); Urban rail transit; Wireless; Communications system; Data transmission; Computer science; Engineering; Real-time computing; Reliability engineering; Computer network; Telecommunications; Transport engineering","score_opus":0.011773069146847106,"score_gpt":0.2472525799349544,"score_spread":0.23547951078810728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980207453","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":[0.9354296,0.0007929444,0.061897367,0.0000312415,0.0010783374,0.00052386057,0.000012863193,0.000034984365,0.00019881978],"genre_scores_gemma":[0.99456877,0.000041841336,0.0051699914,0.000006536448,0.00006280676,0.000036297362,0.00006894755,0.00002140107,0.000023405637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987762,0.000050260147,0.000638577,0.00008997657,0.00032583758,0.00011915283],"domain_scores_gemma":[0.9989327,0.00011767648,0.00025925596,0.00020045976,0.00044572883,0.000044186578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011049502,0.000102864804,0.00021841416,0.00010159257,0.00004009306,0.000021996095,0.00011537744,0.000066478315,0.000012408079],"category_scores_gemma":[0.000030094927,0.000094766445,0.00011160973,0.00014120899,0.000011123374,0.00047877393,3.455114e-7,0.00010562752,0.0000020857333],"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":[0.00005240902,0.00002470222,0.00020349536,0.00016847516,0.000014242987,2.2275354e-7,0.00047006868,0.98455423,0.010835989,0.00034706172,0.000026097567,0.0033030303],"study_design_scores_gemma":[0.0042186286,0.00039761222,0.1250331,0.0004814473,0.00013535535,0.000004931745,0.0014784951,0.8585934,0.0012373992,0.00062937057,0.007485871,0.00030438934],"about_ca_topic_score_codex":0.000012257802,"about_ca_topic_score_gemma":0.00002438456,"teacher_disagreement_score":0.1259608,"about_ca_system_score_codex":0.00014695106,"about_ca_system_score_gemma":0.000048275222,"threshold_uncertainty_score":0.38644633},"labels":[],"label_agreement":null},{"id":"W2981207057","doi":"10.1016/j.ssci.2019.104510","title":"A hybrid model to improve the train running time prediction ability during high-speed railway disruptions","year":2019,"lang":"en","type":"article","venue":"Safety Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":56,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Support vector machine; Benchmark (surveying); Train; Machine learning; Computer science; Predictive modelling; Kalman filter; Artificial intelligence; Data mining; Simulation","score_opus":0.005028233187755179,"score_gpt":0.19672990219506944,"score_spread":0.19170166900731428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981207057","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":[0.9810277,0.000019034223,0.01133983,0.00020711565,0.00080997276,0.00040052834,0.000051178256,0.00030259864,0.0058420184],"genre_scores_gemma":[0.9985112,0.0000029273472,0.00039264644,0.000023797671,0.00009317176,0.00001894075,0.0000028839304,0.00001807713,0.00093638076],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983401,0.000015182895,0.00030303383,0.0004195251,0.00043206717,0.0004901303],"domain_scores_gemma":[0.9991311,0.000033125696,0.00003325356,0.00059056864,0.00006157358,0.00015038032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010002082,0.00015734824,0.00015855282,0.00010887452,0.00044627071,0.00008404265,0.0004992262,0.000033973105,0.000051157305],"category_scores_gemma":[0.000056012963,0.00011794963,0.00006146393,0.0005971737,0.00014922349,0.0004542628,0.00008944525,0.0001581576,0.00024815847],"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":[0.000004702373,0.0000066360167,0.00010366278,0.00001137596,0.0000024033852,2.6697697e-7,0.0005194674,0.6629726,0.3353674,0.00046412268,0.000014211073,0.0005331416],"study_design_scores_gemma":[0.00018838356,0.00002930446,0.03256925,0.000028536457,0.000004044035,0.0000070095084,0.0000798064,0.96088034,0.0057000457,0.0001404453,0.00021362938,0.0001592138],"about_ca_topic_score_codex":0.00006279873,"about_ca_topic_score_gemma":0.0000048556794,"teacher_disagreement_score":0.32966736,"about_ca_system_score_codex":0.00024811534,"about_ca_system_score_gemma":0.000071471586,"threshold_uncertainty_score":0.48098463},"labels":[],"label_agreement":null},{"id":"W2982323084","doi":"10.1109/apusncursinrsm.2019.8888560","title":"Configuration of Network Level Algorithms for Wireless Train Control Systems using Physics-Based Propagation Models","year":2019,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Toronto","funders":"","keywords":"RSS; Handover; Computer science; Channel (broadcasting); Wireless; Margin (machine learning); Set (abstract data type); Real-time computing; Control system; Wireless network; Algorithm; Computer network; Engineering; Telecommunications; Electrical engineering; Machine learning","score_opus":0.04180046471183598,"score_gpt":0.23019189382731658,"score_spread":0.1883914291154806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982323084","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":[0.08702023,0.00007009124,0.91039926,0.000004039654,0.00060030003,0.00078461494,0.00002571186,0.00009813089,0.0009976384],"genre_scores_gemma":[0.99742126,7.439989e-7,0.0020728346,0.00000761564,0.000236568,0.000060014438,0.000024915988,0.000031638032,0.0001443767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991502,0.000022456155,0.00033559473,0.00013616619,0.000148596,0.00020694315],"domain_scores_gemma":[0.99955946,0.00005469784,0.00008503642,0.00014724763,0.00012263167,0.00003089953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023430727,0.00012856736,0.00027303453,0.000030969197,0.00003617056,0.000028326443,0.00006761477,0.00007992331,0.000004344208],"category_scores_gemma":[0.0000021672508,0.00011470422,0.0000667624,0.000116343705,0.00001483754,0.00016273561,0.0000017493057,0.00003803222,0.0000022672664],"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":[0.000008328853,0.000011151326,0.000011316855,0.00024347709,0.000022499233,6.836239e-8,0.000050630246,0.93818766,0.04407059,0.0154368235,0.000029642448,0.001927791],"study_design_scores_gemma":[0.00089818734,0.000047287464,0.000016396416,0.00011101541,0.000014290866,5.7894147e-7,0.00006182165,0.99096626,0.0076089143,0.00009475921,0.000035602665,0.00014490052],"about_ca_topic_score_codex":0.00012876937,"about_ca_topic_score_gemma":0.0000043866944,"teacher_disagreement_score":0.91040105,"about_ca_system_score_codex":0.000051473333,"about_ca_system_score_gemma":0.000037284553,"threshold_uncertainty_score":0.46775025},"labels":[],"label_agreement":null},{"id":"W2991524930","doi":"10.24385/lincoln.24326056","title":"Advanced Robust Control Design For High Speed Tilting Trains","year":2018,"lang":"en","type":"dissertation","venue":"Lincoln Repository (University of Lincoln)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Tilt (camera); Engineering; Context (archaeology); Track (disk drive); Automotive engineering; Computer science; Simulation; Mechanical engineering","score_opus":0.00974995016887407,"score_gpt":0.18287645733108318,"score_spread":0.17312650716220912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991524930","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":[0.6955461,0.0013642944,0.24581413,0.00003125505,0.012760157,0.0036163358,0.00019985801,0.00115356,0.039514296],"genre_scores_gemma":[0.9435397,0.00004842418,0.0254197,0.000007099496,0.0009200744,0.000009158975,0.0002306654,0.00012062789,0.029704558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814856,0.0000746829,0.0004958509,0.00050802453,0.00033624034,0.00043664358],"domain_scores_gemma":[0.998316,0.00020810512,0.00045654137,0.00044279726,0.00041326796,0.00016326926],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032007226,0.0004294361,0.0008275556,0.00024092622,0.00046719532,0.00004009439,0.0005453994,0.00066751183,0.000034902776],"category_scores_gemma":[0.00003625557,0.00055331585,0.00034014395,0.00026508156,0.00012308326,0.00023043496,0.000017867507,0.00027690703,0.000015492125],"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":[0.0012855547,0.00023088262,0.000062575,0.002361197,0.00082569965,0.00013758037,0.0056873355,0.9288939,0.043581787,0.0007195163,0.004741078,0.0114728715],"study_design_scores_gemma":[0.013337878,0.0023351181,0.003470538,0.0032465567,0.0017846792,0.000050461713,0.025382714,0.8766594,0.026427899,0.00021589754,0.043244418,0.0038444418],"about_ca_topic_score_codex":0.00018992169,"about_ca_topic_score_gemma":0.00024346149,"teacher_disagreement_score":0.24799357,"about_ca_system_score_codex":0.00026486805,"about_ca_system_score_gemma":0.00020193566,"threshold_uncertainty_score":0.99969184},"labels":[],"label_agreement":null},{"id":"W2993024578","doi":"10.1155/2019/4213095","title":"Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"City, University of London; University of Hong Kong; National Natural Science Foundation of China; University College London; Fundamental Research Funds for the Central Universities; China Railway; City University of Hong Kong","keywords":"Train; Schedule; Energy consumption; Running time; Engineering; Energy (signal processing); Beijing; Computer science; Simulation; Transport engineering; Real-time computing; Algorithm","score_opus":0.004132319409095897,"score_gpt":0.1972896812499555,"score_spread":0.1931573618408596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993024578","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":[0.9058549,0.002092884,0.09013515,0.000026586747,0.0015327295,0.00013115874,0.000025012441,0.00004473926,0.00015687894],"genre_scores_gemma":[0.9803425,0.00013489331,0.01859527,0.000016603972,0.00016471994,0.0000075058356,0.000053003398,0.000040253955,0.0006452516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988725,0.000008713811,0.0005355098,0.00011993163,0.0002368836,0.0002264178],"domain_scores_gemma":[0.9994415,0.000037902315,0.00019644834,0.000106267915,0.00013555125,0.00008231875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001544419,0.00015255595,0.00030151234,0.000104188184,0.000036392285,0.00001698191,0.00011233454,0.00006962465,0.00005659725],"category_scores_gemma":[0.000005038214,0.00013967088,0.00013360697,0.00013405412,0.000008365689,0.0005040846,8.85517e-7,0.000100855956,0.000004620492],"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":[0.000053024847,0.000029901006,0.00003852797,0.00005671725,0.000048289134,0.0000014757622,0.00019895815,0.8246977,0.17037487,0.0006009792,0.00032014167,0.0035794356],"study_design_scores_gemma":[0.02306071,0.0034316615,0.034514524,0.001203271,0.0006251672,0.000048401176,0.0035531262,0.14797783,0.4817065,0.00063113373,0.30084312,0.0024045566],"about_ca_topic_score_codex":0.000009524397,"about_ca_topic_score_gemma":0.000007225108,"teacher_disagreement_score":0.67671984,"about_ca_system_score_codex":0.000075460346,"about_ca_system_score_gemma":0.000024348117,"threshold_uncertainty_score":0.56956136},"labels":[],"label_agreement":null},{"id":"W2993413053","doi":"10.1155/2019/5174961","title":"A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":15,"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":"Train; Schedule; Genetic algorithm; Energy (signal processing); Energy consumption; Dwell time; Adaptability; Computer science; Real-time computing; Artificial neural network; Reuse; Simulation; Engineering; Mathematical optimization; Artificial intelligence","score_opus":0.0040304385274701465,"score_gpt":0.21214620311589505,"score_spread":0.2081157645884249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993413053","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":[0.122565165,0.00062841165,0.87480867,0.000017238044,0.000780806,0.00016498264,0.000014695269,0.00010585692,0.00091416936],"genre_scores_gemma":[0.6361188,0.000152512,0.36269963,0.000007055194,0.00021414172,0.000016382794,0.00006598652,0.0000664961,0.00065896113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855834,0.000035270565,0.00074429996,0.00016208032,0.0002737897,0.00022621838],"domain_scores_gemma":[0.99907637,0.00012856946,0.00035147407,0.00013746323,0.0002270203,0.00007910913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041355353,0.00018229011,0.00045570225,0.00019007632,0.00005580816,0.00003148372,0.00012814176,0.000092533235,0.000036699563],"category_scores_gemma":[0.000008903041,0.00016236643,0.00018556189,0.0002857613,0.0000075943017,0.0005718291,0.0000010355857,0.00008389322,0.000006592028],"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":[0.00008129462,0.00001508331,0.000019156947,0.00016637484,0.000084219304,0.0000021708809,0.00014134927,0.9151917,0.08255956,0.0009271148,0.000037754082,0.00077421253],"study_design_scores_gemma":[0.0013628365,0.00021517949,0.00052010466,0.0003305549,0.00014780874,0.000011046509,0.0006024234,0.9836442,0.011478336,0.0001025687,0.0013005126,0.00028441328],"about_ca_topic_score_codex":0.000023583974,"about_ca_topic_score_gemma":0.000006713221,"teacher_disagreement_score":0.5135537,"about_ca_system_score_codex":0.00013067701,"about_ca_system_score_gemma":0.000033922086,"threshold_uncertainty_score":0.6621111},"labels":[],"label_agreement":null},{"id":"W2994808218","doi":"10.1002/for.2639","title":"A predictive model of train delays on a railway line","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":67,"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":"Department of Science and Technology of Sichuan Province; China Scholarship Council","keywords":"Computer science; Artificial neural network; Python (programming language); Random forest; Predictive modelling; Artificial intelligence; Machine learning","score_opus":0.02369394031512286,"score_gpt":0.201390566858223,"score_spread":0.17769662654310014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994808218","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":[0.94158745,0.00015834549,0.046050332,0.0000123612945,0.0003876247,0.00005911412,0.000006666842,0.000019343655,0.01171875],"genre_scores_gemma":[0.99761504,0.000010182396,0.0020470754,0.000008301729,0.00017634392,9.4540144e-7,3.8188026e-7,0.000022731141,0.00011897834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989864,0.000013197028,0.0005183666,0.00006659755,0.00024366827,0.00017173796],"domain_scores_gemma":[0.9994295,0.00007667595,0.0002259503,0.000094921474,0.0001085218,0.00006437684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041505505,0.00010888074,0.0002835632,0.00016039613,0.000018536623,0.000009410122,0.00012654233,0.000061429455,0.000015027182],"category_scores_gemma":[0.000067068155,0.00008609586,0.00012995015,0.00013000618,0.00001202495,0.0001204251,0.000009342201,0.00020978737,0.0000033230335],"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":[0.000034843262,0.000022167722,0.0001015567,0.00006558938,0.000041249285,0.0000051869156,0.0007772128,0.97886413,0.012745527,0.00011252519,0.00010252537,0.0071275034],"study_design_scores_gemma":[0.00045550556,0.0004865551,0.00006160069,0.0004084885,0.0000132631485,0.00006566342,0.00020044265,0.9949042,0.003143899,0.00010624795,0.00007511489,0.000078999066],"about_ca_topic_score_codex":0.0000043194414,"about_ca_topic_score_gemma":0.0000023711004,"teacher_disagreement_score":0.056027595,"about_ca_system_score_codex":0.000050799714,"about_ca_system_score_gemma":0.000033353892,"threshold_uncertainty_score":0.35108873},"labels":[],"label_agreement":null},{"id":"W2997124273","doi":"10.1155/2019/9120239","title":"Optimization of the Shunting Operation Plan at Electric Multiple Units Depots","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":13,"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":"Technische Universiteit Delft; National Natural Science Foundation of China","keywords":"Engineering; Time horizon; Yard; Integer programming; Scheduling (production processes); Schedule; Simulation; Operations research; Transport engineering; Reliability engineering; Computer science; Mathematical optimization; Operations management; Mathematics","score_opus":0.005405571089226464,"score_gpt":0.17625512412300082,"score_spread":0.17084955303377436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997124273","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":[0.9785799,0.00018425357,0.020403987,0.0000086081545,0.00056797377,0.00009782597,0.0000023289124,0.0000137740235,0.00014138181],"genre_scores_gemma":[0.99815196,0.000066152104,0.0016805588,0.000004408644,0.000039962455,0.0000011984722,0.000010185099,0.000014052858,0.00003153764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992506,0.00001333909,0.00041173783,0.000050994473,0.00018610773,0.00008723177],"domain_scores_gemma":[0.9995178,0.000028480965,0.00021635568,0.00007322771,0.000142709,0.000021438807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000097587574,0.00007014906,0.00012768818,0.00007650434,0.000037542875,0.000006475331,0.00007816558,0.00004045481,0.000011608944],"category_scores_gemma":[0.00001658399,0.000052602412,0.000046413545,0.0003019362,0.0000044285534,0.00028076922,8.576554e-7,0.00008257614,0.0000010354349],"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":[0.00001697551,0.000006300418,0.008119617,0.000036826565,0.000009770657,5.04306e-7,0.00049825845,0.8640986,0.12654026,0.000029369963,0.000004197255,0.000639316],"study_design_scores_gemma":[0.0017242604,0.00016547221,0.29438806,0.00028017338,0.00004793615,0.000019419322,0.00047446752,0.5632429,0.13898271,0.00001256442,0.00045721183,0.00020481097],"about_ca_topic_score_codex":0.0000056487593,"about_ca_topic_score_gemma":0.0000525358,"teacher_disagreement_score":0.30085567,"about_ca_system_score_codex":0.000048232567,"about_ca_system_score_gemma":0.000021281265,"threshold_uncertainty_score":0.2145064},"labels":[],"label_agreement":null},{"id":"W2997344316","doi":"10.1155/2020/3474020","title":"Integrated Optimization on Energy Saving and Quality of Service of Urban Rail Transit System","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Train; Urban rail transit; Energy consumption; Sorting; Genetic algorithm; Energy (signal processing); Service quality; Service (business); Transport engineering; Beijing; Regenerative brake; Quality (philosophy); Mathematical optimization; Quality of service; Computer science; Engineering; Automotive engineering; Operations research; Mathematics; Computer network; Economics; Electrical engineering","score_opus":0.012715451083295115,"score_gpt":0.2079001109548587,"score_spread":0.19518465987156358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997344316","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":[0.74932986,0.00033911283,0.24997097,0.000045044533,0.00013670328,0.00003745167,0.000014896808,0.000024198065,0.00010177798],"genre_scores_gemma":[0.99602526,0.00007968828,0.00380849,0.000018643075,0.000036241814,9.312066e-7,0.000013897747,0.000015083657,0.0000017632893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886024,0.000028158947,0.0007706415,0.00007282339,0.00019756811,0.00007059355],"domain_scores_gemma":[0.9993033,0.000034550754,0.00032533926,0.000052983385,0.00022274244,0.000061102655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012666837,0.00009780875,0.00031987936,0.00007695222,0.0000149425105,0.0000042134184,0.000060164915,0.000051779283,0.0000033707627],"category_scores_gemma":[0.000009213447,0.00008768098,0.000061359526,0.00027855943,0.000010433832,0.00019459792,4.0224734e-7,0.00007814451,6.1468455e-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":[0.00014970644,0.00001336539,0.00012292119,0.0006190715,0.00003090577,0.0000018516911,0.0025030274,0.9157142,0.07871098,0.0011343326,0.0000025646484,0.0009970638],"study_design_scores_gemma":[0.008557745,0.0022138604,0.072128035,0.004593053,0.00034840955,0.000019178406,0.019608475,0.70383006,0.18672061,0.00004973656,0.000998623,0.0009321908],"about_ca_topic_score_codex":0.000052105956,"about_ca_topic_score_gemma":0.00006313106,"teacher_disagreement_score":0.24669541,"about_ca_system_score_codex":0.000024592453,"about_ca_system_score_gemma":0.00001757661,"threshold_uncertainty_score":0.35755265},"labels":[],"label_agreement":null},{"id":"W2998971297","doi":"10.1155/2020/5081315","title":"Improving Synchronization in an Air and High-Speed Rail Integration Service via Adjusting a Rail Timetable: A Real-World Case Study in China","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":35,"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":"Higher Education Discipline Innovation Project; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Synchronization (alternating current); Subnetwork; Transport engineering; Service (business); Transfer (computing); Transfer station; Focus (optics); Rail network; China; Operations research; Computer science; Engineering; Computer network; Business","score_opus":0.008266838730882303,"score_gpt":0.22613895146642624,"score_spread":0.21787211273554394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998971297","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":[0.96810025,0.00012682773,0.031277414,0.000049450744,0.00015826295,0.00023040983,0.0000025483307,0.00004175559,0.000013061432],"genre_scores_gemma":[0.9976772,0.000034376608,0.002123851,0.000018558942,0.000092601775,0.0000051184766,0.00001752069,0.000028604567,0.0000021823946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986961,0.000049906794,0.0007652356,0.00016042498,0.00017648227,0.00015185997],"domain_scores_gemma":[0.99946785,0.000026267167,0.00023351939,0.00006960614,0.0001137671,0.00008900931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028262293,0.00015365718,0.00029661853,0.00027644215,0.000041295698,0.000024885832,0.00006326809,0.00004723273,0.000004412141],"category_scores_gemma":[0.000020315838,0.00014982189,0.000022936218,0.0009164573,0.000006174841,0.0011360744,0.0000014497933,0.00024819886,2.739547e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000064115084,0.00006282406,0.009980759,0.0001564445,0.000009123339,0.00046328435,0.017023606,0.9217446,0.022334574,0.0000075035828,3.6243998e-7,0.028152816],"study_design_scores_gemma":[0.0028637468,0.00041499798,0.3241579,0.00021536178,0.000052473093,0.000077138975,0.009989908,0.6609997,0.0009786437,0.000015346453,0.000004906099,0.00022984589],"about_ca_topic_score_codex":0.002392987,"about_ca_topic_score_gemma":0.0352622,"teacher_disagreement_score":0.31417713,"about_ca_system_score_codex":0.00010330508,"about_ca_system_score_gemma":0.00003236918,"threshold_uncertainty_score":0.98234177},"labels":[],"label_agreement":null},{"id":"W3002764693","doi":"10.1155/2020/3809734","title":"A Mixed Integer Linear Programming Model for Rolling Stock Deadhead Routing before the Operation Period in an Urban Rail Transit Line","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Technische Universität Braunschweig; Technische Universiteit Delft; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Integer programming; Idle; Stock (firearms); Routing (electronic design automation); Linear programming; Urban rail transit; Computation; Transport engineering; Computer science; Operations research; Mathematical optimization; Engineering; Mathematics; Algorithm; Computer network","score_opus":0.025354895228223307,"score_gpt":0.24844755272843774,"score_spread":0.22309265750021443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3002764693","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":[0.49536857,0.00013790774,0.50398535,0.00018457622,0.00010694114,0.00018463455,0.0000043012174,0.00002619609,0.0000015097189],"genre_scores_gemma":[0.9738923,0.000009354451,0.02569026,0.000029219527,0.00027944767,0.000025181022,0.000032621552,0.000037182348,0.0000044704634],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875623,0.00001760419,0.0007201411,0.00012616742,0.00018500096,0.00019483006],"domain_scores_gemma":[0.9995488,0.000016151493,0.00014148049,0.000068555964,0.00014267347,0.00008236213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002727721,0.00014754376,0.00025159676,0.00006950923,0.000084813706,0.000037179252,0.00012281803,0.000069026246,0.0000013227962],"category_scores_gemma":[0.000019390855,0.000113326874,0.00011779079,0.00019106087,0.000011988916,0.000602527,6.343771e-7,0.00024676722,2.1523748e-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":[0.000103645376,0.000023666944,0.00033034937,0.000072999865,0.000016120608,0.000004403792,0.031872403,0.94908446,0.008197865,0.00012277307,0.0000014026667,0.010169925],"study_design_scores_gemma":[0.0010713512,0.0003478434,0.0010005005,0.00011265396,0.000035263583,0.00000333124,0.0023521564,0.9937821,0.0010169505,0.000025288198,0.00014137766,0.00011115726],"about_ca_topic_score_codex":0.0000035918792,"about_ca_topic_score_gemma":0.00046544682,"teacher_disagreement_score":0.47852367,"about_ca_system_score_codex":0.000046576537,"about_ca_system_score_gemma":0.000036070305,"threshold_uncertainty_score":0.4621336},"labels":[],"label_agreement":null},{"id":"W3004154305","doi":"10.1155/2020/3831915","title":"An Adjustment Method for the Customized Trains of the Railway","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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; Natural Science Foundation of Inner Mongolia; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Train; Headway; Revenue; Service (business); Transport engineering; Plan (archaeology); Set (abstract data type); Operations research; Computer science; Travel time; Passenger train; Engineering; Automotive engineering","score_opus":0.01181631063814786,"score_gpt":0.25254395632576654,"score_spread":0.24072764568761867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004154305","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":[0.25814703,0.00085424416,0.7391042,0.0006094468,0.0008939212,0.00029437387,0.000022674534,0.00002205347,0.000052067495],"genre_scores_gemma":[0.98037773,0.00009130522,0.019256335,0.00006426042,0.0001763421,0.0000091903985,0.0000023643279,0.000015856647,0.0000066252537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922204,0.00002454452,0.00043836844,0.00005518552,0.00017206966,0.000087793924],"domain_scores_gemma":[0.9994816,0.00009149307,0.00019498832,0.00008991723,0.000094170966,0.00004782209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022524896,0.0000787738,0.00018702305,0.000021982023,0.000039766735,0.0000050416847,0.0001727545,0.000030133588,0.0000070252845],"category_scores_gemma":[0.00001824368,0.000042897802,0.00017493228,0.00013929285,0.000013780031,0.00014888616,4.3672617e-7,0.00009515081,1.3761188e-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":[0.00009031128,0.000013262228,0.000014134894,0.000045676716,0.000040352494,4.075569e-7,0.0035675624,0.9057045,0.07026061,0.0003897622,0.00004987853,0.01982353],"study_design_scores_gemma":[0.016557261,0.0017534266,0.17179967,0.00037981683,0.0010180512,0.000023872504,0.01575213,0.56404763,0.16414076,0.00087687455,0.06295809,0.00069238956],"about_ca_topic_score_codex":0.0000032276153,"about_ca_topic_score_gemma":0.000020558653,"teacher_disagreement_score":0.7222307,"about_ca_system_score_codex":0.000016805088,"about_ca_system_score_gemma":0.000022899922,"threshold_uncertainty_score":0.17493217},"labels":[],"label_agreement":null},{"id":"W3004266749","doi":"10.1155/2020/4735397","title":"Virtual Line Shafting-Based Total-Amount Coordinated Control of Multi-Motor Traction Power","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":10,"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; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Control theory (sociology); Traction (geology); Torque; Traction control system; Robustness (evolution); Block diagram; Control engineering; Control system; Computer science; Controller (irrigation); Sliding mode control; Lyapunov function; Engineering; Automotive engineering; Control (management); Nonlinear system","score_opus":0.00738479949975385,"score_gpt":0.21696557270726666,"score_spread":0.2095807732075128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004266749","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":[0.7156656,0.00022398867,0.28344506,0.00009126792,0.00039357643,0.00009164623,0.00002849614,0.00004070643,0.000019714726],"genre_scores_gemma":[0.9982389,0.000013619023,0.0015778476,0.00003380882,0.00008665254,0.0000027283668,0.000011574979,0.000025109619,0.000009750689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988414,0.000015862917,0.0007165855,0.0000856778,0.00021749214,0.000122972],"domain_scores_gemma":[0.9992535,0.000042969845,0.00032392162,0.000056077988,0.00022434644,0.000099158635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011733288,0.00012710238,0.00030016896,0.00009212471,0.00002312346,0.0000076109477,0.00006881776,0.00006926783,0.0000321197],"category_scores_gemma":[0.000035271776,0.000117002135,0.00014280819,0.00019626689,0.000015432706,0.00028736817,3.0629727e-7,0.00017383919,0.0000018790522],"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":[0.00020846851,0.000044573284,0.00010743733,0.00004226316,0.000030529023,0.000010930989,0.0005079738,0.7338103,0.26358047,0.00001932519,0.0000066360803,0.0016311018],"study_design_scores_gemma":[0.011453218,0.002349705,0.11292205,0.0003134422,0.0001641772,0.0000136537365,0.0012472762,0.8011621,0.06881732,0.000006548384,0.0011201515,0.00043031803],"about_ca_topic_score_codex":0.000004528199,"about_ca_topic_score_gemma":0.0000072043235,"teacher_disagreement_score":0.28257334,"about_ca_system_score_codex":0.000038472666,"about_ca_system_score_gemma":0.000032031872,"threshold_uncertainty_score":0.47712085},"labels":[],"label_agreement":null},{"id":"W3007601616","doi":"10.37367/jpi.v2i1.47","title":"Analysis of the Carrying Capacity of Kranji Traction Substation in the Operation of Soekarno-Hatta Airport Train","year":2018,"lang":"en","type":"article","venue":"Jurnal Perkeretaapian Indonesia (Indonesian Railway Journal)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Encana (Canada)","funders":"","keywords":"Headway; Engineering; Carrying capacity; Transformer; Traction (geology); Automotive engineering; Load factor; Electrical engineering; Voltage; Structural engineering; Transport engineering; Mechanical engineering","score_opus":0.014970100604311278,"score_gpt":0.22282626020652538,"score_spread":0.2078561596022141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007601616","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":[0.9872889,0.0002621638,0.010242346,0.00022039271,0.0006434415,0.00032431466,0.000020682239,0.000025350173,0.0009723887],"genre_scores_gemma":[0.9994844,0.00007454724,0.00009405906,0.000025757161,0.00025032964,0.000012422146,0.0000073217698,0.000039892613,0.0000112410935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99596167,0.0005123419,0.0018408418,0.00025247896,0.0010175916,0.00041505313],"domain_scores_gemma":[0.99784404,0.00013834934,0.000980986,0.00057732593,0.00035699803,0.00010231367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025244984,0.00033559997,0.00083967525,0.0010778892,0.0003064056,0.00007901849,0.0007162487,0.00023541925,0.00004504402],"category_scores_gemma":[0.00006181602,0.00022350662,0.000620439,0.0028004807,0.0003125507,0.00053087063,0.000018766408,0.00065286807,0.0000016574711],"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":[0.00019871347,0.00037257923,0.3558329,0.00033752492,0.0016234475,0.000026819394,0.037982505,0.42118979,0.1752392,0.0015586661,0.00013229041,0.005505535],"study_design_scores_gemma":[0.0011266156,0.00016434256,0.87367874,0.00024024928,0.0007182307,0.00030371462,0.0043358495,0.104772404,0.014205987,0.000073379975,0.000069208894,0.00031125324],"about_ca_topic_score_codex":0.00027592795,"about_ca_topic_score_gemma":0.0011403145,"teacher_disagreement_score":0.51784587,"about_ca_system_score_codex":0.00020220675,"about_ca_system_score_gemma":0.0001464943,"threshold_uncertainty_score":0.9114335},"labels":[],"label_agreement":null},{"id":"W3008790764","doi":"10.1016/j.trc.2020.02.021","title":"A Bayesian network model to predict the effects of interruptions on train operations","year":2020,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":70,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Train; Bayesian network; Computer science; Line (geometry); Domain (mathematical analysis); Bayesian probability; Domain knowledge; Data mining; Artificial intelligence; Mathematics","score_opus":0.04830770220749815,"score_gpt":0.3087129091733793,"score_spread":0.26040520696588115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008790764","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":[0.5279903,0.00042516802,0.45470497,0.010522391,0.00031160956,0.0014220802,0.00006217755,0.0029220018,0.0016393233],"genre_scores_gemma":[0.99793,0.00013686746,0.0013826707,0.000035100595,0.000042816995,0.000387614,0.000012415767,0.000022849268,0.00004968851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987929,0.000039099574,0.0002947049,0.00019453718,0.00034105493,0.0003377444],"domain_scores_gemma":[0.99944305,0.00013149242,0.000013994419,0.00027577596,0.00007676082,0.000058913018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027466103,0.000119017815,0.00015407355,0.00015232687,0.00020738904,0.000023100984,0.00038598763,0.000084158935,0.000008159394],"category_scores_gemma":[0.00019474984,0.00009075642,0.00005623968,0.0010107114,0.00010988897,0.00006982061,0.000011281887,0.00034426138,0.000009608689],"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":[0.0000066921484,0.000014104201,0.00007232569,0.00010555649,0.000023384784,0.000002393971,0.0025407777,0.97286165,0.0030675086,0.012295342,0.006413773,0.0025965143],"study_design_scores_gemma":[0.00017937459,0.00031825295,0.0006582386,0.00026058243,0.000011335234,1.3826498e-7,0.0023477257,0.9847833,0.006635361,0.0005137192,0.0041404287,0.000151523],"about_ca_topic_score_codex":0.00003509562,"about_ca_topic_score_gemma":0.00021202191,"teacher_disagreement_score":0.46993968,"about_ca_system_score_codex":0.000022570723,"about_ca_system_score_gemma":0.000024349833,"threshold_uncertainty_score":0.3700939},"labels":[],"label_agreement":null},{"id":"W3010687601","doi":"10.1155/2020/3602727","title":"A Cost and Passenger Responsible Optimization Method for the Operation Plan of Additional High-Speed Trains in a Peak Period","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University","keywords":"Train; Plan (archaeology); Solver; Computation; Set (abstract data type); Computer science; Integer programming; Mathematical optimization; Service (business); Operations research; Transport engineering; Simulation; Engineering; Algorithm; Mathematics","score_opus":0.019438434069765732,"score_gpt":0.2508028586630653,"score_spread":0.23136442459329956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010687601","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":[0.123884745,0.00028347853,0.8742598,0.00082710304,0.00016171775,0.00029042584,0.00026192827,0.000013860531,0.000016926948],"genre_scores_gemma":[0.89967036,0.00012350835,0.099934645,0.000027457618,0.00009004039,0.000016149865,0.00011933761,0.0000140041175,0.0000044817302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993015,0.000022747503,0.00040698863,0.00006563242,0.00012959426,0.0000735318],"domain_scores_gemma":[0.9995586,0.00015136503,0.00012203346,0.00003392519,0.00009646614,0.000037576145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001933492,0.00007119969,0.00016059967,0.00007344249,0.000033677214,0.000011917414,0.00004512695,0.000037846905,0.00004628461],"category_scores_gemma":[0.00004444155,0.000055626264,0.00004296573,0.00014390537,0.000011802584,0.00025345746,4.6307028e-7,0.00008163926,8.3870475e-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":[0.00019201964,0.0000114598115,0.000015059798,0.000047083824,0.00001734036,0.0000020516059,0.0031779332,0.9741419,0.01930995,0.0003945688,0.00006602907,0.0026246086],"study_design_scores_gemma":[0.0022384026,0.00029870492,0.024448235,0.00011418523,0.000045174173,0.000010325062,0.0023580636,0.9647825,0.0028554401,0.000060531776,0.0026625511,0.00012590655],"about_ca_topic_score_codex":0.000004000058,"about_ca_topic_score_gemma":0.00006244383,"teacher_disagreement_score":0.7757856,"about_ca_system_score_codex":0.00001997528,"about_ca_system_score_gemma":0.000039113635,"threshold_uncertainty_score":0.22683732},"labels":[],"label_agreement":null},{"id":"W3017123605","doi":"","title":"An evaluation of energy-absorbing guide rail terminals in New Brunswick","year":2011,"lang":"en","type":"article","venue":"Mathematical Systems Theory","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"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":"Transport engineering; Energy (signal processing); Telecommunications; Engineering; Aeronautics; Mathematics; Statistics","score_opus":0.03643648361101048,"score_gpt":0.2554141609915973,"score_spread":0.21897767738058682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017123605","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":[0.79760563,0.0031798405,0.07877713,0.0000032180442,0.00062473456,0.00036267657,0.0000012876203,0.00023252603,0.11921295],"genre_scores_gemma":[0.99834454,0.000010382223,0.00069518865,0.0000049935643,0.000109052344,0.000027724273,0.0000016941331,0.000041754774,0.00076469925],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786144,0.00034402258,0.00088212395,0.00019437599,0.0004384585,0.0002795529],"domain_scores_gemma":[0.9990296,0.00015435739,0.00010619576,0.0004844312,0.0000951716,0.00013028768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003895619,0.00018953734,0.00046156076,0.00017114771,0.00002163939,0.000019678331,0.00026005422,0.00013306667,0.00016565739],"category_scores_gemma":[0.00017556481,0.00015715703,0.00006937685,0.00020841444,0.000045084627,0.00017302277,0.000017329048,0.000074799114,0.00003885167],"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":[0.000013295787,0.00014176364,0.00017092943,0.000891435,0.00006918259,0.000008624542,0.006408956,0.027949288,0.0047097085,0.94755596,0.00020179554,0.011879081],"study_design_scores_gemma":[0.0012255784,0.00017060191,0.0006662795,0.002546732,0.00014430528,0.00010559368,0.0047271424,0.7402883,0.012249125,0.23648842,0.00064144,0.0007464417],"about_ca_topic_score_codex":0.0006050341,"about_ca_topic_score_gemma":0.00008695954,"teacher_disagreement_score":0.71233904,"about_ca_system_score_codex":0.00011233108,"about_ca_system_score_gemma":0.0001244265,"threshold_uncertainty_score":0.64086777},"labels":[],"label_agreement":null},{"id":"W3022309789","doi":"","title":"Analysis of GHG Emissions for city passenger trains: is electricity an obvious option for Montreal commuter trains?","year":2012,"lang":"en","type":"article","venue":"Transportation Research Board 91st Annual MeetingTransportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University; McGill University","funders":"","keywords":"Train; Electrification; Electricity; Greenhouse gas; Diesel fuel; Renewable energy; Environmental science; Capital cost; Transport engineering; Engineering; Automotive engineering; Electrical engineering","score_opus":0.07924191067854185,"score_gpt":0.37657152650990966,"score_spread":0.2973296158313678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022309789","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":[0.95487976,0.00045204087,0.037905294,0.00028824442,0.00022000753,0.0024832876,0.002931931,0.0002999784,0.0005394292],"genre_scores_gemma":[0.99191076,0.00032708648,0.0032358414,0.000031655843,0.0003210033,0.0015485298,0.0021025178,0.00013475299,0.00038786384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99248147,0.000535478,0.0015876512,0.00079283083,0.00245164,0.0021509228],"domain_scores_gemma":[0.9934692,0.0014279794,0.00020081284,0.0007094636,0.00330326,0.00088931696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0071164374,0.00048054126,0.0009563725,0.0023971824,0.00087491097,0.00011745623,0.0006714268,0.000512914,0.00016530878],"category_scores_gemma":[0.00025019574,0.0004829262,0.0006994828,0.0039112037,0.00038486542,0.0011222413,0.000006063471,0.0009048659,0.000005717769],"study_design_candidate":"observational","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":[0.0053172545,0.006028157,0.25243273,0.0063212174,0.0074566356,0.000035798723,0.21957217,0.28217164,0.11213264,0.022739254,0.023663675,0.06212883],"study_design_scores_gemma":[0.0029806413,0.0013462269,0.85573757,0.0002077787,0.00077071064,4.0350164e-7,0.011659122,0.09858724,0.011845367,0.0006166022,0.01519969,0.0010486302],"about_ca_topic_score_codex":0.007418609,"about_ca_topic_score_gemma":0.019250348,"teacher_disagreement_score":0.60330486,"about_ca_system_score_codex":0.0002513044,"about_ca_system_score_gemma":0.00023599845,"threshold_uncertainty_score":0.99976224},"labels":[],"label_agreement":null},{"id":"W3033137653","doi":"10.1155/2020/5609524","title":"A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":27,"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":"Heuristic; Mathematical optimization; Integer programming; Discretization; Track (disk drive); Computer science; Linear programming; Process (computing); Engineering; Algorithm; Mathematics","score_opus":0.012678191785682013,"score_gpt":0.21754813666156284,"score_spread":0.2048699448758808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033137653","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":[0.5573034,0.00012544738,0.44230634,0.000010645871,0.000054325923,0.000111766596,0.000019235513,0.0000073615115,0.0000614539],"genre_scores_gemma":[0.9323675,0.000038040744,0.067500845,0.0000045198803,0.000041216623,0.0000061078263,0.0000216527,0.000018388846,0.0000016993961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998838,0.0000085130505,0.0008452515,0.00007638173,0.000121934136,0.000109887704],"domain_scores_gemma":[0.99946445,0.000036840425,0.00029738335,0.000045212684,0.00010273307,0.000053353815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020208729,0.00009616993,0.00031065513,0.00013887744,0.000013897768,0.0000030521426,0.0000610599,0.000059149865,0.0000027363274],"category_scores_gemma":[0.00002426414,0.000092513095,0.000109029075,0.00028165657,0.000013991274,0.00031499186,3.9669348e-7,0.000093840594,2.7052765e-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":[0.00011482016,0.000031861015,0.000067921486,0.00041365903,0.00001989198,0.00002732868,0.010236648,0.950263,0.02759062,0.00018080536,0.0000034966072,0.011049909],"study_design_scores_gemma":[0.005866669,0.0009325089,0.002973314,0.0003824245,0.00010115714,0.00017212897,0.019739484,0.9460497,0.023190279,0.0001519132,0.00010564794,0.00033478256],"about_ca_topic_score_codex":0.000008802455,"about_ca_topic_score_gemma":0.00005046468,"teacher_disagreement_score":0.3750641,"about_ca_system_score_codex":0.000023852548,"about_ca_system_score_gemma":0.00002385028,"threshold_uncertainty_score":0.37725744},"labels":[],"label_agreement":null},{"id":"W3041460529","doi":"10.1155/2020/7527294","title":"Adaptive Output Feedback Control for the Trajectory Tracking of High-Speed Trains with Disturbance Uncertainties on the Basis of Neural Network Observers","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Controller (irrigation); Artificial neural network; Tracking error; Adaptive control; Trajectory; Bounded function; Observer (physics); Computer science; Nonlinear system; Estimator; Basis function; Mathematics; Artificial intelligence; Control (management)","score_opus":0.019801572448765344,"score_gpt":0.19878636659652762,"score_spread":0.17898479414776228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041460529","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":[0.9295672,0.0006848203,0.06842892,0.00054774963,0.00040574305,0.00028690626,0.000044165197,0.000015263802,0.000019212705],"genre_scores_gemma":[0.9989665,0.000047427293,0.00072927144,0.000053957036,0.00016421569,0.0000068910917,0.0000030275376,0.000022108856,0.0000065971567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989928,0.000025461184,0.0005041264,0.00008264268,0.00025025674,0.00014469231],"domain_scores_gemma":[0.9989417,0.00037511415,0.00038413683,0.000083449704,0.00017672674,0.00003887438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001656257,0.000135485,0.00032654149,0.000024295137,0.000059537342,0.000007597334,0.00015825122,0.000033576267,0.0000027895699],"category_scores_gemma":[0.000021485377,0.00007486176,0.00016912176,0.0001935464,0.00006446821,0.00016544583,3.869897e-7,0.00015491067,5.2204257e-8],"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":[0.00085418404,0.000016213513,0.00030442444,0.00007132107,0.00015406289,0.0000019201564,0.0033878922,0.99031484,0.0013888733,0.00068958505,0.000056145316,0.0027605516],"study_design_scores_gemma":[0.009075981,0.004545876,0.63360983,0.0010792587,0.0007592733,0.0000061314945,0.019443065,0.3178064,0.011859032,0.000240265,0.0010087063,0.0005661572],"about_ca_topic_score_codex":0.000014745072,"about_ca_topic_score_gemma":0.00008047281,"teacher_disagreement_score":0.6725084,"about_ca_system_score_codex":0.000024314997,"about_ca_system_score_gemma":0.000024362043,"threshold_uncertainty_score":0.30527738},"labels":[],"label_agreement":null},{"id":"W3044811479","doi":"10.1016/j.tre.2020.102022","title":"Modeling train operation as sequences: A study of delay prediction with operation and weather data","year":2020,"lang":"en","type":"article","venue":"Transportation Research Part E Logistics and Transportation Review","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":99,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Artificial neural network; Computer science; Sensitivity (control systems); Net (polyhedron); Performance prediction; Predictive modelling; State (computer science); Machine learning; Data mining; Artificial intelligence; Simulation; Engineering; Algorithm; Mathematics","score_opus":0.1680081681407262,"score_gpt":0.34573713162202196,"score_spread":0.17772896348129577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044811479","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":[0.8102264,0.0152686015,0.1701289,0.00061216624,0.00007260039,0.002610845,0.0006820456,0.00016966523,0.00022879803],"genre_scores_gemma":[0.9730927,0.025190182,0.0004073759,0.00006314235,0.00003665875,0.000105276195,0.001072616,0.000025154195,0.000006922698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981424,0.00009158271,0.0006734169,0.00039560036,0.00051365414,0.00018331438],"domain_scores_gemma":[0.9993466,0.000041075662,0.00005077041,0.0002261087,0.00019194181,0.00014351872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006680443,0.00016963268,0.00032352246,0.00006862035,0.0001357504,0.000047532354,0.0001349349,0.00006212105,0.000047400164],"category_scores_gemma":[0.0000191907,0.00013955781,0.000018084418,0.00029286102,0.00007645987,0.00036633332,0.0000018728628,0.00019218605,0.0000023755283],"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":[0.00005897116,0.0001276891,0.0027020948,0.004133126,0.00010476286,0.000030505144,0.006015733,0.9797325,0.00039534754,0.0029471035,0.00013152149,0.0036206604],"study_design_scores_gemma":[0.0011891238,0.0011107507,0.0046062726,0.0011054561,0.0002450521,0.0000027562198,0.002506074,0.9865538,0.000034407658,0.00004870912,0.0023143261,0.00028325626],"about_ca_topic_score_codex":0.00066061504,"about_ca_topic_score_gemma":0.002837945,"teacher_disagreement_score":0.16972151,"about_ca_system_score_codex":0.000009908633,"about_ca_system_score_gemma":0.000056652454,"threshold_uncertainty_score":0.56910026},"labels":[],"label_agreement":null},{"id":"W3045091765","doi":"10.1115/jrc2020-8003","title":"Access Point Placement Optimization for a CBTC System Wireless Data Communication Network","year":2020,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"WSP (Canada)","funders":"","keywords":"Computer science; Wireless; Heuristic; Communications system; Software deployment; Convex optimization; Computer network; Wireless network; Transceiver; Wireless site survey; Real-time computing; Wi-Fi array; Telecommunications; Regular polygon","score_opus":0.05121623855553708,"score_gpt":0.2538742226471371,"score_spread":0.20265798409160002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045091765","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":[0.0015808344,0.00026980674,0.9890933,0.00029565685,0.00024057667,0.00037847078,0.000020113632,0.0005300084,0.007591214],"genre_scores_gemma":[0.9841717,0.00005881286,0.014903604,0.00009778499,0.00022552857,0.000080131664,0.00038558134,0.00003357663,0.000043269392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992626,0.000022329568,0.000267276,0.00017609038,0.000098478515,0.0001732417],"domain_scores_gemma":[0.9992236,0.00004118044,0.000041436346,0.00058808527,0.00003645131,0.000069267066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021309785,0.000103763654,0.00014993352,0.000016471693,0.00009376078,0.000107642976,0.0006771498,0.00004744401,0.000028077338],"category_scores_gemma":[0.000009926105,0.00009552659,0.000022071223,0.00017417209,0.000007356298,0.00029573546,0.0001790595,0.00004675549,0.000007336681],"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":[0.0000066266416,0.0000047826475,0.000018647954,0.00020076684,0.000020617834,1.7242873e-7,0.00008186219,0.97878426,0.000018531244,0.002504903,0.01781726,0.0005415667],"study_design_scores_gemma":[0.00024286547,0.000016874583,0.000007345672,0.00006537258,0.000011896243,9.244633e-7,0.00026882053,0.993081,0.00006335448,0.0000018110094,0.0061222566,0.000117512434],"about_ca_topic_score_codex":0.000046137662,"about_ca_topic_score_gemma":0.00002327961,"teacher_disagreement_score":0.98259085,"about_ca_system_score_codex":0.00004990754,"about_ca_system_score_gemma":0.000011015142,"threshold_uncertainty_score":0.38954616},"labels":[],"label_agreement":null},{"id":"W3046828055","doi":"10.1155/2020/7085809","title":"Research on the Utilization of Metro Regenerative Braking Energy Based on an Improved Differential Evolution Algorithm","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":13,"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":"Nanjing Institute of Technology; Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Regenerative brake; Headway; Train; Energy consumption; Traction (geology); Automotive engineering; Energy (signal processing); Computer science; Engine braking; Integer programming; Running time; Total energy; Algorithm; Engineering; Simulation; Mathematics; Electrical engineering; Brake","score_opus":0.03401045335243373,"score_gpt":0.2844240299519225,"score_spread":0.25041357659948876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046828055","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":[0.459078,0.00008267473,0.5402566,0.000116960444,0.00027932724,0.000072584866,0.000011034942,0.00002024792,0.000082548584],"genre_scores_gemma":[0.99792236,0.000024248118,0.0017437908,0.000025894691,0.00022850881,0.0000058374044,0.00002348843,0.000021857239,0.0000039877054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986852,0.00011596182,0.00045376623,0.00011658666,0.00048572023,0.00014278715],"domain_scores_gemma":[0.99922657,0.000100399324,0.00018858438,0.000106438,0.00030591805,0.00007208065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026854177,0.00011052531,0.0001828426,0.00018624136,0.00008321875,0.000015188304,0.0001260365,0.000058945836,0.000014392549],"category_scores_gemma":[0.000029317782,0.00007978073,0.00008243201,0.00045617632,0.000028945897,0.00022207099,8.332064e-7,0.00021862134,3.071134e-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":[0.00013377222,0.000047481713,0.000017455657,0.00002129504,0.000017765447,0.0000024394383,0.00054275134,0.89781684,0.08589657,0.0025203004,0.0000131969,0.012970158],"study_design_scores_gemma":[0.0007467062,0.0013278542,0.008305477,0.00012312246,0.000020237085,4.829021e-7,0.0011512935,0.8821564,0.10576238,0.00013974392,0.00016016093,0.0001061864],"about_ca_topic_score_codex":0.000016202195,"about_ca_topic_score_gemma":0.000041307812,"teacher_disagreement_score":0.53884435,"about_ca_system_score_codex":0.00008144517,"about_ca_system_score_gemma":0.000039280763,"threshold_uncertainty_score":0.3253364},"labels":[],"label_agreement":null},{"id":"W3084069757","doi":"10.1109/tpwrd.2020.3022750","title":"Real-Time Hierarchical Neural Network Based Fault Detection and Isolation for High-Speed Railway System Under Hybrid AC/DC Grid","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Field-programmable gate array; Fault detection and isolation; Artificial neural network; Grid; Real-time computing; Computer science; Transient (computer programming); Fault (geology); Engineering; Embedded system; Artificial intelligence","score_opus":0.009189231743255524,"score_gpt":0.18879908204648368,"score_spread":0.17960985030322815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084069757","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":[0.37375763,0.00004797937,0.6232429,0.00012939273,0.0016793915,0.00031624598,0.00006850106,0.0005978686,0.00016004988],"genre_scores_gemma":[0.99884725,0.00002000965,0.0005743189,0.000090875095,0.00024566572,0.000050784525,0.000016168624,0.00007315631,0.00008179652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986327,0.00006534034,0.00037910175,0.0003701525,0.00018676785,0.00036590884],"domain_scores_gemma":[0.999355,0.00016562881,0.00004990769,0.00019655348,0.000064727996,0.00016818586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012681384,0.00027549337,0.00030030494,0.000113652626,0.000272554,0.000073660456,0.00010077168,0.00014146014,0.000040335508],"category_scores_gemma":[0.0000027765009,0.00027973662,0.00014221542,0.00025624555,0.000036784964,0.00020872241,0.0000012092189,0.00024606066,0.000037484122],"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":[0.00015898023,0.000022018681,0.000001053428,0.000098378936,0.00005574126,0.0000037150921,0.00007872076,0.94574046,0.05122017,0.000018142051,0.00048285464,0.002119789],"study_design_scores_gemma":[0.00094194175,0.00028292614,0.00012879336,0.00006168817,0.00006371175,0.00001633925,0.000052518397,0.9814245,0.01621374,0.0000054629177,0.0004977123,0.00031063054],"about_ca_topic_score_codex":0.00007125608,"about_ca_topic_score_gemma":0.000014681688,"teacher_disagreement_score":0.6250896,"about_ca_system_score_codex":0.00012630726,"about_ca_system_score_gemma":0.000021794473,"threshold_uncertainty_score":0.9999655},"labels":[],"label_agreement":null},{"id":"W3087780323","doi":"10.1155/2020/8867404","title":"Equity-Oriented Train Timetabling with Collaborative Passenger Flow Control: A Spatial Rebalance of Service on an Oversaturated Urban Rail Transit Line","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Train; Solver; Weighting; Computer science; Tabu search; Service (business); Service level; Integer programming; Heuristic; Transport engineering; Urban rail transit; Operations research; Mathematical optimization; Engineering; Algorithm","score_opus":0.007321100585265899,"score_gpt":0.2177197759545639,"score_spread":0.21039867536929802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087780323","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":[0.7601704,0.00024524404,0.23850985,0.000347074,0.00024932125,0.00021269578,0.00012226542,0.000061136554,0.00008202198],"genre_scores_gemma":[0.9937516,0.000033023007,0.0057996684,0.00013083933,0.00018567638,0.0000037287405,0.00005763929,0.00003479019,0.000003052772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985529,0.000048274633,0.00065642677,0.0001530049,0.00040473617,0.00018461331],"domain_scores_gemma":[0.9988256,0.000048749458,0.00032953656,0.000092404414,0.0005489838,0.00015472672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001760812,0.00019828205,0.00046939807,0.00009467587,0.000039773902,0.000014022747,0.00010659544,0.000075458556,0.000013801212],"category_scores_gemma":[0.000017326525,0.00016317714,0.000069886955,0.000709374,0.00001908611,0.00048512444,5.0053364e-7,0.00024900376,6.2372527e-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":[0.0016537742,0.000057418987,0.000041161606,0.00010413709,0.00010226699,0.000033291384,0.008018814,0.8394073,0.148359,0.000055659846,0.000008676365,0.0021585065],"study_design_scores_gemma":[0.029574078,0.0091753295,0.037376378,0.001402418,0.0006475091,0.00002820853,0.007787454,0.8088707,0.10053573,0.00004648097,0.0033809722,0.0011747442],"about_ca_topic_score_codex":0.000016508375,"about_ca_topic_score_gemma":0.00027895396,"teacher_disagreement_score":0.23358119,"about_ca_system_score_codex":0.000037151298,"about_ca_system_score_gemma":0.00007326107,"threshold_uncertainty_score":0.6654171},"labels":[],"label_agreement":null},{"id":"W3088031960","doi":"10.1007/s12469-020-00253-x","title":"Railway timetabling: a maximum bottleneck path algorithm for finding an additional train path","year":2020,"lang":"en","type":"article","venue":"Public Transport","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":16,"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":"Trafikverket; Horizon 2020 Framework Programme; Linköpings Universitet; H2020 Societal Challenges; Canadian Centre for Applied Research in Cancer Control","keywords":"Bottleneck; Train; Dijkstra's algorithm; Algorithm; Path (computing); Robustness (evolution); Computer science; Shortest path problem; Graph; Mathematical optimization; Engineering; Mathematics; Theoretical computer science; Geography","score_opus":0.028088267761812623,"score_gpt":0.21132104487830697,"score_spread":0.18323277711649436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088031960","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":[0.030561812,0.00042073737,0.9509288,0.00088209525,0.0009000779,0.000654608,0.007589762,0.0016312808,0.0064308206],"genre_scores_gemma":[0.9601908,0.00002248157,0.03363545,0.00027703677,0.0010779607,0.00035475593,0.0040772464,0.00013592916,0.0002283756],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822295,0.000017273736,0.0004426037,0.000410437,0.0003202577,0.0005864511],"domain_scores_gemma":[0.99920565,0.000049081795,0.00004756984,0.00020454872,0.000068084,0.000425037],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027605324,0.0002816191,0.00034330334,0.000115506926,0.00013917046,0.00007436496,0.00029560222,0.00014761259,0.0015738878],"category_scores_gemma":[0.000021450809,0.00028215602,0.00020359048,0.00039394968,0.000031771335,0.00047567926,0.000005265253,0.00017848182,0.00003072727],"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":[0.00004756612,0.0006200427,0.000762612,0.0007951474,0.00054461416,0.00017200022,0.006051587,0.042383455,0.0053013084,0.009666197,0.058942564,0.8747129],"study_design_scores_gemma":[0.0010769119,0.00017568926,0.003279319,0.00003701938,0.000035021814,0.000021714632,0.00021787152,0.37924412,0.00015444463,0.00016746832,0.61497825,0.0006121744],"about_ca_topic_score_codex":0.000013205899,"about_ca_topic_score_gemma":0.000010854609,"teacher_disagreement_score":0.92962897,"about_ca_system_score_codex":0.000040842584,"about_ca_system_score_gemma":0.000088134366,"threshold_uncertainty_score":0.99996305},"labels":[],"label_agreement":null},{"id":"W3095778641","doi":"10.1155/2020/1390764","title":"Consistent Total Traction Torque-Oriented Coordinated Control of Multimotors with Input Saturation for Heavy-Haul Locomotives","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Control theory (sociology); Traction (geology); Saturation (graph theory); Torque; MATLAB; Sliding mode control; Traction motor; Traction control system; Engineering; Computer science; Automotive engineering; Control (management); Mathematics; Physics; Nonlinear system","score_opus":0.005678918288655975,"score_gpt":0.20080256548384817,"score_spread":0.1951236471951922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095778641","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":[0.6825986,0.0001301165,0.31655768,0.0001120582,0.00029610356,0.00024112676,0.000023150687,0.000027370843,0.000013824649],"genre_scores_gemma":[0.99608546,0.000019706691,0.0037266144,0.000015381072,0.000087076936,0.0000103800985,0.000030467125,0.00001977567,0.0000051339644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999039,0.000014809372,0.00057299493,0.00008958271,0.00017536765,0.00010823428],"domain_scores_gemma":[0.9991341,0.00005231787,0.00033636653,0.00004442745,0.0003543459,0.00007838981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000079166406,0.00012120938,0.00028667593,0.000071691924,0.0000316578,0.000007356004,0.000037525973,0.000053530315,0.0000039618035],"category_scores_gemma":[0.000023648568,0.00010042319,0.000102414364,0.00016916716,0.000022845441,0.0003849787,2.3756544e-7,0.000106274965,1.8938182e-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":[0.00093319593,0.00004207499,0.0003477969,0.00013648102,0.00008613037,0.000004728629,0.001773309,0.88701224,0.10745233,0.00009349655,0.000012341982,0.002105854],"study_design_scores_gemma":[0.032239825,0.0094914595,0.27981132,0.0011583136,0.0007605233,0.0000923477,0.01112724,0.37799168,0.28162667,0.00005330577,0.0045505445,0.0010967721],"about_ca_topic_score_codex":0.0000057895336,"about_ca_topic_score_gemma":0.000018765244,"teacher_disagreement_score":0.50902057,"about_ca_system_score_codex":0.000044234203,"about_ca_system_score_gemma":0.00002831525,"threshold_uncertainty_score":0.40951386},"labels":[],"label_agreement":null},{"id":"W3095823701","doi":"10.1155/2020/8974315","title":"Passenger Demand-Oriented High-Speed Train Stop Planning with Service-Node Features Analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Beijing; Plan (archaeology); Service (business); Process (computing); Cluster analysis; Markov decision process; Operations research; Computer science; Transport engineering; Engineering; Real-time computing; Markov process; Artificial intelligence; China","score_opus":0.006765439890321015,"score_gpt":0.20862934366718233,"score_spread":0.2018639037768613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095823701","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":[0.909667,0.0004754146,0.089197956,0.00030628237,0.00016175647,0.000055645916,0.000011429016,0.00006491797,0.00005958221],"genre_scores_gemma":[0.99385935,0.000036156096,0.0057878406,0.0000979705,0.00013953981,0.0000015854544,0.00004322312,0.000025430663,0.000008929198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9989472,0.000015972164,0.00044359584,0.00012058902,0.0003076384,0.0001650191],"domain_scores_gemma":[0.999416,0.000025361629,0.00019274942,0.000075642296,0.00015596097,0.00013430593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087294065,0.00015596217,0.00035466647,0.00016480219,0.000044327535,0.00001777188,0.00009226523,0.000058925354,0.000015287615],"category_scores_gemma":[0.0000054603843,0.00012306348,0.00011272809,0.0009271439,0.000008938856,0.0003245182,5.724442e-7,0.00020654025,8.709097e-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":[0.00013835095,0.000013489379,0.001537508,0.000067399495,0.00037467803,0.000074252705,0.0060512708,0.9753761,0.015758345,0.00006582278,0.000032953114,0.00050981244],"study_design_scores_gemma":[0.004185008,0.00053929683,0.9215019,0.0003275761,0.0015689813,0.000027728818,0.0065366775,0.05218708,0.008649416,0.000029504841,0.00378037,0.00066645315],"about_ca_topic_score_codex":0.000013870122,"about_ca_topic_score_gemma":0.00008555158,"teacher_disagreement_score":0.92318904,"about_ca_system_score_codex":0.00002817232,"about_ca_system_score_gemma":0.00001805905,"threshold_uncertainty_score":0.5018383},"labels":[],"label_agreement":null},{"id":"W3097691141","doi":"","title":"Ligne à 735 kV Micoua-Saguenay : study area","year":2017,"lang":"fr","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Geology","score_opus":0.02164003051560964,"score_gpt":0.24078116456010373,"score_spread":0.2191411340444941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097691141","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":[0.6430689,0.004001744,0.0027687442,0.0012133304,0.008408682,0.000339249,0.000006832508,0.00024124981,0.33995128],"genre_scores_gemma":[0.8312023,0.000102091486,0.00029583342,0.000019397943,0.00071922125,0.000025106527,0.0000013857343,0.00005554569,0.16757911],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9981587,0.000039785715,0.0004418028,0.00040926726,0.00029050358,0.00065993075],"domain_scores_gemma":[0.9980746,0.00003943284,0.000109075685,0.0014832623,0.000068656926,0.00022497975],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00040480492,0.00035540582,0.00042006638,0.00008043233,0.00055625377,0.00047073484,0.00072741916,0.00019426094,0.0011595484],"category_scores_gemma":[0.00007344104,0.00032777543,0.00014176356,0.00007996463,0.000093058385,0.00037576124,0.00015340507,0.00021923504,0.0012452416],"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":[0.00004625506,0.0052019684,0.18203935,0.0011859505,0.0018179589,0.0015396137,0.019216247,0.27196312,0.014929788,0.043415286,0.24033606,0.2183084],"study_design_scores_gemma":[0.00398446,0.0012492212,0.26484093,0.0007839921,0.00034195234,0.00012336622,0.007185161,0.17306745,0.004451136,0.00049160485,0.5407295,0.0027511937],"about_ca_topic_score_codex":0.014597806,"about_ca_topic_score_gemma":0.0037120185,"teacher_disagreement_score":0.30039346,"about_ca_system_score_codex":0.00010404048,"about_ca_system_score_gemma":0.00003594271,"threshold_uncertainty_score":0.99991745},"labels":[],"label_agreement":null},{"id":"W3104616299","doi":"10.1155/2020/8894174","title":"A Stochastic Programming Approach for Scheduling Extra Metro Trains to Serve Passengers from Uncertain Delayed High-Speed Railway Trains","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Natural Science Foundation of China; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Train; Schedule; Scheduling (production processes); Computer science; Operator (biology); Flow network; Mathematical optimization; Operations research; Stochastic programming; Transport engineering; Engineering; Mathematics","score_opus":0.02015355165748619,"score_gpt":0.2366914996059259,"score_spread":0.2165379479484397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104616299","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":[0.46415216,0.00020083462,0.53484315,0.00014067182,0.00023043714,0.00030220812,0.000054880282,0.0000688268,0.0000068250633],"genre_scores_gemma":[0.7725754,0.0000079536685,0.22682923,0.00005323966,0.00033606164,0.000030144365,0.000107637505,0.00005754391,0.0000028225331],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810153,0.000022982169,0.00089077436,0.00025710903,0.00036269444,0.00036492938],"domain_scores_gemma":[0.9990257,0.000101942765,0.00024534835,0.00010620854,0.000188767,0.00033202904],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002301594,0.00027081388,0.0005082677,0.00016112126,0.00008076205,0.00004699243,0.00020310968,0.00010852101,0.0000060779457],"category_scores_gemma":[0.00006346598,0.000258173,0.0002497889,0.0004873807,0.000016837217,0.0003976178,0.0000012964654,0.00025880305,8.822342e-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":[0.00023924102,0.00004420791,0.000014806948,0.0001076052,0.00015320518,0.00001277879,0.0072847814,0.9150725,0.06357323,0.00010995631,0.0000108821905,0.013376815],"study_design_scores_gemma":[0.01049773,0.002567369,0.009045763,0.00052367226,0.0007301429,0.000014020569,0.028795157,0.9362871,0.007564554,0.0003062075,0.002053346,0.0016149394],"about_ca_topic_score_codex":0.00003052317,"about_ca_topic_score_gemma":0.00008177643,"teacher_disagreement_score":0.3084232,"about_ca_system_score_codex":0.00010532667,"about_ca_system_score_gemma":0.00005377788,"threshold_uncertainty_score":0.99998707},"labels":[],"label_agreement":null},{"id":"W3108500205","doi":"10.1155/2020/8892372","title":"Discrete Optimization on Train Rescheduling on Single-Track Railway: Clustering Hierarchy and Heuristic Search","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Natural Science Foundation of Gansu Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China; China Railway","keywords":"Punctuality; Heuristic; Computer science; Track (disk drive); Mathematical optimization; Cluster analysis; Function (biology); Discrete event simulation; Hierarchy; Discrete optimization; Discrete time and continuous time; Engineering; Optimization problem; Simulation; Algorithm; Mathematics; Artificial intelligence; Transport engineering","score_opus":0.015466876434470093,"score_gpt":0.22834286435488493,"score_spread":0.21287598792041484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108500205","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":[0.72050375,0.00018751202,0.2783204,0.00030952244,0.0002744924,0.000098068136,0.000007910545,0.00005482514,0.00024349969],"genre_scores_gemma":[0.98598766,0.00015922834,0.013533686,0.00006154536,0.00020002006,0.000001866964,0.000012937217,0.000036224606,0.0000068008094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989015,0.000026293497,0.00047632548,0.00014221221,0.0002850564,0.00016861867],"domain_scores_gemma":[0.9995546,0.000064663844,0.00010488242,0.00006570073,0.000064470565,0.00014573132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001481474,0.00014492283,0.00022009695,0.00012108571,0.00005903905,0.000034532084,0.000073048875,0.000055358316,0.000007785627],"category_scores_gemma":[0.000034901666,0.00013379517,0.000054563454,0.00018941605,0.000020217345,0.00027615478,0.0000011470913,0.00026195776,0.0000012668912],"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":[0.00017821298,0.000016322168,0.000022796208,0.00013383247,0.000017316164,0.0000315057,0.0022633427,0.9694066,0.020241015,0.000094611845,0.000005379539,0.007589038],"study_design_scores_gemma":[0.002674835,0.0024693941,0.00817191,0.00090717094,0.00006976356,0.000023807506,0.0014364449,0.97363216,0.009344644,0.00006032434,0.0007305358,0.00047900682],"about_ca_topic_score_codex":0.0000013521501,"about_ca_topic_score_gemma":0.0000052997493,"teacher_disagreement_score":0.26548392,"about_ca_system_score_codex":0.00004532813,"about_ca_system_score_gemma":0.0000128862275,"threshold_uncertainty_score":0.5456009},"labels":[],"label_agreement":null},{"id":"W3109486263","doi":"10.1049/iet-est.2020.0070","title":"Energy management strategy to optimise regenerative braking in a hybrid dual‐mode locomotive","year":2020,"lang":"en","type":"article","venue":"IET Electrical Systems in Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Université du Québec à Trois-Rivières","funders":"Government of Canada","keywords":"Regenerative brake; Automotive engineering; Energy management; Engineering; Energy (signal processing); Engine braking; Energy management system; Energy storage; Energy consumption; Supercapacitor; Energy recovery; Catenary; Accumulator (cryptography); Computer science; Electrical engineering; Brake; Power (physics)","score_opus":0.010793126395883259,"score_gpt":0.21579592608890613,"score_spread":0.20500279969302287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109486263","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":[0.7140093,0.001429913,0.27763194,0.00012416457,0.00043168632,0.0009896121,0.00004470654,0.0003582719,0.0049804393],"genre_scores_gemma":[0.99918866,0.000068304944,0.00021517793,0.000049228638,0.00010478745,0.00023081263,0.000046022982,0.00004014373,0.000056838984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981661,0.000072701856,0.0006618995,0.0003906124,0.0002876531,0.00042102492],"domain_scores_gemma":[0.99960726,0.000031492596,0.00005051144,0.00012120675,0.00003214588,0.00015739833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013021518,0.00023630749,0.00035844967,0.0002471604,0.000030418829,0.000045360775,0.00013343789,0.00008634543,0.0000073739093],"category_scores_gemma":[0.0000050028157,0.00024772927,0.00005451137,0.0011495617,0.000008281262,0.00013971559,0.0000028403847,0.00017444823,0.000009045197],"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":[0.000030261583,0.00003885242,0.0002972715,0.000116038034,0.000024817637,0.00022998433,0.001135424,0.9824539,0.0021639871,0.0065396302,0.00018359798,0.0067862268],"study_design_scores_gemma":[0.0009319888,0.0002900359,0.0058743637,0.00023657786,0.000015944295,0.000005884785,0.00031002867,0.98653144,0.003937911,0.00006551539,0.0012947778,0.00050553796],"about_ca_topic_score_codex":0.0012289755,"about_ca_topic_score_gemma":0.0008812703,"teacher_disagreement_score":0.2851794,"about_ca_system_score_codex":0.00022330848,"about_ca_system_score_gemma":0.000019069912,"threshold_uncertainty_score":0.9999975},"labels":[],"label_agreement":null},{"id":"W3112650549","doi":"10.1155/2020/8882554","title":"A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Energy consumption; Genetic algorithm; Energy (signal processing); Computer science; Engineering; Real-time computing; Dwell time; Artificial intelligence; Simulation; Machine learning","score_opus":0.008121857747041916,"score_gpt":0.22434785527488454,"score_spread":0.21622599752784263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112650549","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":[0.014758815,0.00066475745,0.98368794,0.000070313945,0.00034656734,0.00014300752,0.000006011496,0.00008580947,0.00023679981],"genre_scores_gemma":[0.8501203,0.00014479671,0.14929418,0.000026141079,0.00024153107,0.00001912828,0.00006138329,0.000052501604,0.000040004943],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985796,0.000080058446,0.00069495203,0.00015949752,0.0002901822,0.00019567038],"domain_scores_gemma":[0.9990588,0.00027241764,0.00032522166,0.00006722633,0.00016209403,0.000114265764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042478958,0.00017828119,0.0004474092,0.00014203986,0.00007946392,0.000040534323,0.00009803904,0.000077616394,0.000012535375],"category_scores_gemma":[0.000059051163,0.00015834293,0.00018076091,0.00031277194,0.00000874088,0.00032382252,4.8126054e-7,0.00014060587,6.4603506e-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":[0.00043398413,0.000017241553,0.000010905422,0.00012400952,0.000059215105,0.000004094389,0.00035357324,0.9813523,0.015658434,0.00023350237,0.000012249793,0.0017404516],"study_design_scores_gemma":[0.0025212409,0.00031355134,0.000136561,0.00013347185,0.000089811925,0.0000013084947,0.00047836453,0.9932601,0.0019777308,0.000022268514,0.0008983812,0.00016723902],"about_ca_topic_score_codex":0.000012501552,"about_ca_topic_score_gemma":0.000005628531,"teacher_disagreement_score":0.83536154,"about_ca_system_score_codex":0.0000655051,"about_ca_system_score_gemma":0.000027532591,"threshold_uncertainty_score":0.64570373},"labels":[],"label_agreement":null},{"id":"W3119111530","doi":"10.51501/jotnafe.v33i2.36","title":"The Impact of the Lack of Marine and Rail Standards on the Transportation of Large Power Transformers","year":2016,"lang":"en","type":"article","venue":"Journal of the National Academy of Forensic Engineers","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakeridge Health","funders":"","keywords":"Transformer; Engineering; Documentation; Forensic engineering; Marine engineering; Electrical engineering; Computer science; Voltage","score_opus":0.011949764664777974,"score_gpt":0.26517520810707695,"score_spread":0.25322544344229897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119111530","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":[0.9961015,0.00025046855,0.00047325416,0.0018644203,0.00016397626,0.00011320739,0.00018206224,0.000003402954,0.0008476954],"genre_scores_gemma":[0.9996756,0.00017604227,0.000030661115,0.00001656593,0.000036616173,9.816175e-7,2.2769206e-7,0.000011280208,0.000052051553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9984121,0.00003199618,0.0005787582,0.00004622908,0.0008075537,0.00012337988],"domain_scores_gemma":[0.99901336,0.0003341197,0.00034426255,0.000052308824,0.00022723274,0.00002868561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011161427,0.00010963887,0.00022021256,0.00007540489,0.00004390382,0.0000029950095,0.00026313486,0.00007380491,0.000020110467],"category_scores_gemma":[0.00015141293,0.000038283706,0.00030826856,0.0002088857,0.00016017037,0.00008287026,0.00000620196,0.00019533954,6.511674e-8],"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":[0.0005146018,0.0001232992,0.0043132696,0.00025980623,0.0016440355,3.8899424e-7,0.0030084546,0.7953353,0.11139768,0.06294383,0.012263779,0.008195573],"study_design_scores_gemma":[0.004322944,0.0006661613,0.7452957,0.0014252163,0.00021824149,0.000043852593,0.00078743906,0.0128903305,0.22278698,0.005961327,0.0052537117,0.00034809802],"about_ca_topic_score_codex":0.00000996502,"about_ca_topic_score_gemma":0.0000044224794,"teacher_disagreement_score":0.78244495,"about_ca_system_score_codex":0.0000804138,"about_ca_system_score_gemma":0.00006108673,"threshold_uncertainty_score":0.15611643},"labels":[],"label_agreement":null},{"id":"W3126439126","doi":"10.1155/2021/6653334","title":"A Benders Decomposition Algorithm for the Passenger Train Service Planning","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Schedule; Benders' decomposition; Service (business); Passenger train; Computer science; Decomposition; Key (lock); Linear programming; Passenger transport; Beijing; Transport engineering; Algorithm; Mathematical optimization; Operations research; Engineering; Automotive engineering; Mathematics","score_opus":0.010707530619600733,"score_gpt":0.25062760845582505,"score_spread":0.23992007783622432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126439126","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":[0.22896191,0.0015275601,0.7682129,0.00026602673,0.000866454,0.00007419934,0.000012167384,0.000025517053,0.000053246324],"genre_scores_gemma":[0.9645828,0.00010503229,0.034959946,0.00006718836,0.00021374694,0.0000095792375,0.000029141167,0.000019876592,0.000012666947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938405,0.000008487363,0.00030918026,0.00005727846,0.00013303742,0.00010797568],"domain_scores_gemma":[0.9995556,0.00008680534,0.00008964589,0.00005536067,0.00017837186,0.000034237084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011894713,0.00007485489,0.00011947456,0.000038468515,0.000063273714,0.00001695043,0.000055729317,0.000035758527,0.0000060300067],"category_scores_gemma":[0.000003999075,0.000058595164,0.000087479384,0.00015133903,0.000004736839,0.00021186506,3.1044823e-7,0.000095116215,3.3156726e-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":[0.000009364038,0.000010743248,0.00001200725,0.000036886573,0.000045043522,0.000016560793,0.0017558817,0.93464386,0.016001476,0.000057960144,0.000048248403,0.04736197],"study_design_scores_gemma":[0.006354347,0.00038583085,0.15607034,0.0009487986,0.0005012218,0.00032759813,0.02923772,0.7225318,0.0470113,0.0012804709,0.03452965,0.0008209539],"about_ca_topic_score_codex":0.0000024033216,"about_ca_topic_score_gemma":0.0000365721,"teacher_disagreement_score":0.7356209,"about_ca_system_score_codex":0.000027652612,"about_ca_system_score_gemma":0.000023211363,"threshold_uncertainty_score":0.23894414},"labels":[],"label_agreement":null},{"id":"W3128400016","doi":"10.1155/2021/6663022","title":"Fitting Method of Optimal Energy-Running Time Curve Based on Train Operation Data of an Urban Rail Section","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Curve fitting; Energy consumption; Outlier; Energy (signal processing); Smoothness; Data point; Algorithm; Data set; Computer science; Simulation; Mathematical optimization; Engineering; Mathematics; Statistics; Artificial intelligence","score_opus":0.013136387226656306,"score_gpt":0.25702859869808853,"score_spread":0.24389221147143222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128400016","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":[0.4854289,0.00013689327,0.51387733,0.000014587817,0.000323927,0.00003241057,0.000031589083,0.000018430927,0.00013593314],"genre_scores_gemma":[0.9050666,0.000017990891,0.09447585,0.000008026091,0.00015239968,0.000001240143,0.00023342662,0.000022533057,0.000021894966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986442,0.0000651946,0.000747799,0.00014190187,0.00029304458,0.0001078635],"domain_scores_gemma":[0.9991173,0.00007220649,0.0003318049,0.00020966043,0.0002178285,0.000051186285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000561117,0.000114712224,0.0002896644,0.0001471511,0.000034304263,0.000011411993,0.00012667608,0.00006944201,0.000026996278],"category_scores_gemma":[0.00003320186,0.000114219256,0.00007901115,0.00024166383,0.000010391439,0.00065821095,0.0000013689659,0.00012770985,1.5198079e-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":[0.00005147884,0.000049459868,0.0000632011,0.000056571967,0.000022740345,0.000009152477,0.00072473264,0.7780542,0.21304502,0.00004374655,0.000012439809,0.007867256],"study_design_scores_gemma":[0.0010109735,0.00031287994,0.0049532093,0.00028745175,0.000056508856,0.000010276215,0.0006670974,0.83318615,0.1589401,0.000010698986,0.00043092735,0.00013371928],"about_ca_topic_score_codex":0.000014164399,"about_ca_topic_score_gemma":0.000041962656,"teacher_disagreement_score":0.41963774,"about_ca_system_score_codex":0.000035660127,"about_ca_system_score_gemma":0.00006708254,"threshold_uncertainty_score":0.4657726},"labels":[],"label_agreement":null},{"id":"W3130000260","doi":"10.1287/trsc.2020.1030","title":"The Locomotive Assignment Problem with Distributed Power at the Canadian National Railway Company","year":2021,"lang":"en","type":"article","venue":"Transportation Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Université de Montréal; HEC Montréal","funders":"","keywords":"Fleet management; Integer programming; Time horizon; Mathematical optimization; Decomposition; Linear programming; Assignment problem; Reduction (mathematics); Constraint (computer-aided design); Operations research; Power (physics); Computer science; State (computer science); Constraint programming; Engineering; Transport engineering; Algorithm; Stochastic programming; Mathematics","score_opus":0.008333230380190881,"score_gpt":0.20577232988551913,"score_spread":0.19743909950532826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130000260","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":[0.92630213,0.00033622928,0.024207624,0.0037390566,0.00064942864,0.00054091454,0.00029981247,0.00015991714,0.043764867],"genre_scores_gemma":[0.9992551,0.00000473204,0.000105662235,0.00004681609,0.000010709617,0.000031864416,0.0000439314,0.0000069959074,0.0004941857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9987035,0.000014606496,0.00015044305,0.0001732747,0.00067783357,0.0002803573],"domain_scores_gemma":[0.99936205,0.00005133283,0.000030430785,0.00014989215,0.00028887525,0.00011739207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042521005,0.00008892834,0.00006465072,0.000033001415,0.0010028776,0.00013326462,0.00022219589,0.000021550883,0.000068077665],"category_scores_gemma":[0.000010039143,0.00004927493,0.00002267643,0.00074862025,0.00031811657,0.00013747509,0.0000025872694,0.000079454265,0.00001975104],"study_design_candidate":"simulation_or_modeling","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":[0.00000471329,0.0000173954,0.0034402397,0.000008205114,0.000022007966,0.000015755684,0.0022693863,0.96463054,0.002174039,0.025366193,0.0015008515,0.0005506527],"study_design_scores_gemma":[0.000553978,0.000046893696,0.8127346,0.0000614398,0.00001760613,0.000026671998,0.0018736457,0.0273298,0.011265882,0.00026527562,0.14539574,0.00042847733],"about_ca_topic_score_codex":0.002574219,"about_ca_topic_score_gemma":0.31999785,"teacher_disagreement_score":0.93730074,"about_ca_system_score_codex":0.0003684206,"about_ca_system_score_gemma":0.00057351106,"threshold_uncertainty_score":0.7713426},"labels":[],"label_agreement":null},{"id":"W3133611954","doi":"10.1007/s13177-020-00246-x","title":"Fitting Knock-on Delay Duration Distributions using High-Speed Train Operation Records","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Transportation Systems Research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Train; Duration (music); Statistics; Probability distribution; Distribution (mathematics); Simulation; Statistical analysis; Arrival time; Mathematics; Computer science; Engineering; Physics; Acoustics; Transport engineering; Geography","score_opus":0.07234724013519596,"score_gpt":0.34919014923902864,"score_spread":0.2768429091038327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133611954","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":[0.74467003,0.0005685727,0.24984379,0.00019926977,0.003913233,0.00019166175,0.00010147296,0.000038923317,0.00047301486],"genre_scores_gemma":[0.9978513,0.00030337117,0.0004892368,0.000009828846,0.00091583934,0.000011964118,0.00017780067,0.000032374828,0.00020830009],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963503,0.00024403293,0.0014181042,0.000203068,0.0014980698,0.00028642884],"domain_scores_gemma":[0.99638,0.00022293354,0.00021557407,0.00015664217,0.002887314,0.00013758583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015808163,0.00016786084,0.00026800312,0.00053995755,0.00016551751,0.00029616745,0.0003200698,0.00013231068,0.00013470872],"category_scores_gemma":[0.00018978672,0.00015936278,0.0001579963,0.0004760041,0.000040379335,0.00042936206,0.0000059942877,0.00046734884,0.00002891412],"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":[0.000056268414,0.00011136593,0.00045555775,0.000055507,0.00022606726,0.00026127926,0.0008952258,0.9295869,0.05210706,0.007544491,0.00055304024,0.00814724],"study_design_scores_gemma":[0.002499514,0.000663086,0.012878857,0.0031858797,0.00010034663,0.0012377761,0.009177388,0.6494845,0.26572675,0.00062452164,0.053356934,0.0010644597],"about_ca_topic_score_codex":0.00023178825,"about_ca_topic_score_gemma":0.00012365551,"teacher_disagreement_score":0.2801024,"about_ca_system_score_codex":0.0005973111,"about_ca_system_score_gemma":0.00020001599,"threshold_uncertainty_score":0.6498626},"labels":[],"label_agreement":null},{"id":"W3134789742","doi":"10.1155/2021/6611289","title":"Optimizing Train Timetable Based on Departure Time Preference of Passengers for High-Speed Rails","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Train; Computer science; Operations research; Bilevel optimization; Transport engineering; Service (business); Simulation; Mathematical optimization; Engineering; Optimization problem; Algorithm","score_opus":0.01002576247584262,"score_gpt":0.20695340316044553,"score_spread":0.19692764068460292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134789742","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":[0.8485746,0.0006088267,0.1492146,0.00009913923,0.00063728687,0.00018515004,0.00006563335,0.0000452636,0.0005695562],"genre_scores_gemma":[0.95812225,0.000038871825,0.041594643,0.000016776767,0.00006413932,0.000003183298,0.00004628385,0.000023645513,0.000090187714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99901056,0.000016933584,0.00050906686,0.000098757955,0.00021607116,0.00014862152],"domain_scores_gemma":[0.9993077,0.00010752847,0.00019931803,0.00010050844,0.00022652648,0.00005841174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016845949,0.00011976881,0.00030860025,0.0000974971,0.00002542172,0.000010490358,0.00007359109,0.00006723891,0.000040502437],"category_scores_gemma":[0.000028030301,0.00011014215,0.00013267575,0.00018719147,0.000010105823,0.00021551317,3.347685e-7,0.00011003729,9.664071e-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":[0.00007128396,0.000033628574,0.0000091929505,0.00009728117,0.000029215247,0.000008460807,0.00027072782,0.80393255,0.19387013,0.00011324571,0.00004372463,0.001520561],"study_design_scores_gemma":[0.007034353,0.0010420524,0.012944232,0.0012481144,0.00029586587,0.000014279711,0.00071335025,0.2250667,0.745348,0.00046853977,0.0051720366,0.00065249327],"about_ca_topic_score_codex":0.0000014237945,"about_ca_topic_score_gemma":0.000009307119,"teacher_disagreement_score":0.5788658,"about_ca_system_score_codex":0.000033012628,"about_ca_system_score_gemma":0.000056086712,"threshold_uncertainty_score":0.44914666},"labels":[],"label_agreement":null},{"id":"W3136074968","doi":"10.1155/2021/6679008","title":"Simultaneous Optimization of Train Timetabling and Platforming Problems for High-Speed Multiline Railway Network","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":13,"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":"Fundamental Research Funds for Central Universities of the Central South University; Fundamental Research Funds for the Central Universities","keywords":"Train; Track (disk drive); Integer programming; Computer science; Set (abstract data type); Resource (disambiguation); Resource allocation; Operations research; Engineering; Mathematical optimization; Transport engineering; Algorithm; Computer network; Mathematics","score_opus":0.007219839826094196,"score_gpt":0.2096770929239005,"score_spread":0.2024572530978063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136074968","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":[0.5003321,0.0013517713,0.49781272,0.000011183329,0.00034436153,0.00010724609,0.000010168568,0.000017791162,0.000012662041],"genre_scores_gemma":[0.8724652,0.0003220896,0.12699504,0.0000038820413,0.00012662966,0.0000020945874,0.00004073692,0.000022458049,0.000021863032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989676,0.0000075907055,0.0006579086,0.0000874582,0.00013841804,0.00014106536],"domain_scores_gemma":[0.9992363,0.00013998777,0.00024223875,0.000055591576,0.00027750252,0.000048375063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019810376,0.00010565112,0.00029042427,0.00007001062,0.000041074105,0.000011274096,0.000040366034,0.00006013478,0.000006379392],"category_scores_gemma":[0.00004483137,0.00010032048,0.000067917514,0.00019459397,0.000012252167,0.00029034176,6.9361533e-7,0.00008174766,5.241101e-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":[0.00004672921,0.000019900934,0.00002908279,0.0001871843,0.000041774576,0.000009672277,0.00073351775,0.94736713,0.03585586,0.00011274183,0.0000026493083,0.015593727],"study_design_scores_gemma":[0.0024583992,0.00018844768,0.000931228,0.00041718112,0.00010021616,0.00003435439,0.0005076951,0.9811672,0.0131231565,0.00023418985,0.00064767036,0.0001902619],"about_ca_topic_score_codex":0.0000026130544,"about_ca_topic_score_gemma":0.000028114298,"teacher_disagreement_score":0.3721331,"about_ca_system_score_codex":0.000018785406,"about_ca_system_score_gemma":0.000023419794,"threshold_uncertainty_score":0.40909505},"labels":[],"label_agreement":null},{"id":"W3142953050","doi":"10.1109/mper.2002.4312231","title":"A Robust Phase-Coordinates Frequency Dependent Underground Cable Model (Zcable) for the EMTP","year":2002,"lang":"en","type":"article","venue":"IEEE Power Engineering Review","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Emtp; Transformation (genetics); Frequency domain; Context (archaeology); Control theory (sociology); Matrix (chemical analysis); Transient (computer programming); Modal; Transformation matrix; Phase (matter); Stability (learning theory); Discretization; Computer science; Mathematics; Mathematical analysis; Physics","score_opus":0.03938655317808288,"score_gpt":0.2283084111671017,"score_spread":0.18892185798901884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142953050","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":[0.0018133662,0.35880625,0.63312113,0.0002533246,0.0017732207,0.00086756004,0.000027431795,0.0006139313,0.0027237516],"genre_scores_gemma":[0.9364709,0.05594681,0.0039584874,0.00020468309,0.00025286904,0.00078293943,0.000008274652,0.00021380793,0.002161207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983601,0.0000108685745,0.0005091196,0.00029569754,0.00023668956,0.00058754726],"domain_scores_gemma":[0.99904877,0.00014970562,0.00005270201,0.0005641592,0.0000654773,0.000119158525],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036982514,0.00037010724,0.00047165478,0.00007049654,0.00012089932,0.00008260743,0.0004191256,0.00009857073,0.00018638503],"category_scores_gemma":[0.00005226211,0.00028115851,0.00021792279,0.0003085714,0.000017417937,0.00021184157,0.000016682994,0.00021967027,0.000063725456],"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":[4.992373e-7,0.000035587007,0.0000013660283,0.0016729088,0.00007542194,0.0000037656835,0.0000719927,0.9822742,0.001274694,0.0005115353,0.012569859,0.0015082195],"study_design_scores_gemma":[0.0003540393,0.000033344208,0.0000015168424,0.0011646753,0.000086736276,0.000032379063,0.0000074327113,0.9679163,0.00022513227,0.000026163256,0.029772753,0.00037949873],"about_ca_topic_score_codex":0.000035422152,"about_ca_topic_score_gemma":0.0000064208734,"teacher_disagreement_score":0.9346576,"about_ca_system_score_codex":0.00014140195,"about_ca_system_score_gemma":0.0000110345045,"threshold_uncertainty_score":0.99996406},"labels":[],"label_agreement":null},{"id":"W314995906","doi":"","title":"Remote Control Locomotive Operations: Results of Focus Groups with Remote Control Operators in the United States and Canada","year":2006,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"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":"Focus group; Control (management); Business; Computer science; Marketing; Artificial intelligence","score_opus":0.0028505424385728575,"score_gpt":0.16405963515513453,"score_spread":0.16120909271656167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W314995906","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":[0.9699136,0.00025611173,0.025590464,0.00048797013,0.000056759745,0.00039741222,0.00006808072,0.000043325454,0.0031862597],"genre_scores_gemma":[0.9994226,0.000028278331,0.00025784483,0.000114102295,0.00003067348,0.000003886689,0.000023897624,0.00001581253,0.00010289273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990004,0.00008438642,0.00037263424,0.00014723284,0.00018288135,0.00021247858],"domain_scores_gemma":[0.99948907,0.00015742556,0.000029089168,0.00019862918,0.00008934631,0.000036440302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002827786,0.00015223655,0.00022708725,0.000092156995,0.00006311462,0.000040222807,0.000116384144,0.000042954733,0.0000029109156],"category_scores_gemma":[0.000026302749,0.00009091537,0.000013792921,0.00030888667,0.000044899498,0.000070588845,0.0000048414067,0.00010321283,3.5030624e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043245735,0.000010578949,0.00037720366,0.000012534043,0.000024595729,0.000017520986,0.00029701274,0.9973286,0.00023092839,0.00068434345,0.0006124146,0.00036105243],"study_design_scores_gemma":[0.003885524,0.00011123515,0.0052354834,0.00006481759,0.000015632713,0.000014836096,0.0011662657,0.98707724,0.0005470238,0.000034074874,0.0016616941,0.00018617221],"about_ca_topic_score_codex":0.9552968,"about_ca_topic_score_gemma":0.95887816,"teacher_disagreement_score":0.029508995,"about_ca_system_score_codex":0.00006243927,"about_ca_system_score_gemma":0.000062774634,"threshold_uncertainty_score":0.3707421},"labels":[],"label_agreement":null},{"id":"W3151965183","doi":"","title":"Metamodeling Transfer Capability of Manitoba-Ontario Electrical Interconnections","year":2011,"lang":"en","type":"article","venue":"机械工程与自动化：英文版","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Metamodeling; Computer science; Software engineering","score_opus":0.028816234176790363,"score_gpt":0.18208926388274477,"score_spread":0.1532730297059544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151965183","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":[0.9279064,0.00022250423,0.0457657,0.000003685843,0.00055548263,0.00011029297,0.0000038704748,0.00019896489,0.025233129],"genre_scores_gemma":[0.9989245,0.000010661922,0.0007776238,0.000008352731,0.00006232895,0.000024689121,0.000002035342,0.00002885857,0.0001609725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889225,0.000029143594,0.00042569867,0.00021170605,0.00014353359,0.00029769426],"domain_scores_gemma":[0.9994944,0.000029719107,0.0000162033,0.00031189746,0.0000610368,0.00008675096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020910839,0.00017273742,0.00028489224,0.00011896973,0.000053439584,0.000011123461,0.00018428045,0.00011448734,0.00030086862],"category_scores_gemma":[0.000015897966,0.00016034466,0.00016077777,0.00022166404,0.00003970158,0.0001323603,0.000012475385,0.0002250233,0.000032172382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034951518,0.0025557855,0.0708117,0.0012300843,0.0023822598,0.00008174827,0.20921342,0.36715028,0.21959773,0.07323121,0.0030629833,0.05033329],"study_design_scores_gemma":[0.003931721,0.001612297,0.121450126,0.0004415263,0.0010252114,0.00029164538,0.012766204,0.5718179,0.24543719,0.003981916,0.033014514,0.0042297407],"about_ca_topic_score_codex":0.06118425,"about_ca_topic_score_gemma":0.17096432,"teacher_disagreement_score":0.20466763,"about_ca_system_score_codex":0.00014147075,"about_ca_system_score_gemma":0.000035915666,"threshold_uncertainty_score":0.9450674},"labels":[],"label_agreement":null},{"id":"W3154087512","doi":"10.1155/2021/5589185","title":"A Timetable Optimization Model for Urban Rail Transit with Express/Local Mode","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government of Jiangsu Province","keywords":"Mode (computer interface); Genetic algorithm; Urban rail transit; Transport engineering; Minification; Transit (satellite); Computer science; Service (business); Urban transit; Travel time; Rail transit; Passenger transport; Operations research; Public transport; Engineering; Mathematical optimization; Business; Mathematics","score_opus":0.005988452679685277,"score_gpt":0.19925644433293682,"score_spread":0.19326799165325154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154087512","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":[0.0626024,0.0005357811,0.93646145,0.00002227226,0.00014898228,0.00007949522,0.000018625396,0.000032478663,0.00009853293],"genre_scores_gemma":[0.8218519,0.00009134637,0.17775686,0.000010255435,0.0000555529,0.000011046806,0.00004033754,0.000030267223,0.00015242113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918246,0.000006663278,0.00038455377,0.000095979056,0.00018600683,0.00014434112],"domain_scores_gemma":[0.9994809,0.00001733843,0.00009841533,0.00008028876,0.00025866763,0.000064411244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006652732,0.00011294397,0.00022099122,0.000064489956,0.000040556686,0.000015722058,0.000055107972,0.00005161618,0.0000090437115],"category_scores_gemma":[0.0000033536037,0.00009899872,0.000091924056,0.00014854583,0.000011709931,0.00047542257,2.614657e-7,0.000084850944,2.0237192e-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":[0.00010237922,0.000029561219,0.000009808224,0.00008056412,0.000041154093,0.000010458388,0.0013445277,0.9837438,0.013439283,0.00022282798,0.000050740615,0.00092491554],"study_design_scores_gemma":[0.0012940739,0.00008020092,0.00007862762,0.000095741576,0.000068785084,0.000013497169,0.00029939055,0.9896953,0.0076591442,0.00009027954,0.0004985148,0.00012644693],"about_ca_topic_score_codex":0.0000018990256,"about_ca_topic_score_gemma":0.00003454547,"teacher_disagreement_score":0.7592495,"about_ca_system_score_codex":0.000034702865,"about_ca_system_score_gemma":0.000058949376,"threshold_uncertainty_score":0.40370503},"labels":[],"label_agreement":null},{"id":"W3156703450","doi":"10.1155/2021/6652531","title":"Integrated Train Rescheduling and Rerouting during Multidisturbances under a Quasi-Moving Block System","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"State Key Laboratory of Traction Power; National Natural Science Foundation of China","keywords":"Retiming; Train; Computer science; Scheduling (production processes); Block (permutation group theory); Mathematical optimization; Integer programming; Set (abstract data type); Schedule; Lexicographical order; Blocking (statistics); Real-time computing; Algorithm; Mathematics; Computer network","score_opus":0.00604446842198643,"score_gpt":0.20152903897841487,"score_spread":0.19548457055642843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156703450","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":[0.98168206,0.0027055226,0.014857368,0.000021563752,0.0005342844,0.00004314304,0.00000383792,0.00007101987,0.0000812058],"genre_scores_gemma":[0.9912329,0.00015686767,0.008439031,0.0000032940377,0.00011364873,0.0000021040491,0.000005223457,0.000025060852,0.000021908669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988524,0.000027460179,0.0006319729,0.00012602173,0.0001895499,0.00017259736],"domain_scores_gemma":[0.9994582,0.000044489603,0.00018407975,0.00007406654,0.00016531491,0.000073854855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019326495,0.00013603253,0.0002701862,0.00009444189,0.00008903155,0.000034933073,0.00005302852,0.00006123547,0.0000020539942],"category_scores_gemma":[0.000025783938,0.00012573042,0.00007564807,0.00023890268,0.000014503814,0.00032250353,0.0000011918187,0.00020707524,3.53089e-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":[0.000021501515,0.000014087356,0.00053359556,0.0003399785,0.00004225043,0.000106067106,0.0022067055,0.82620144,0.16862006,0.000240071,8.4816696e-7,0.0016734117],"study_design_scores_gemma":[0.008428418,0.0003709913,0.4706748,0.011702887,0.000374913,0.0015779053,0.18415925,0.12325228,0.19676538,0.00015400613,0.0009117299,0.0016274299],"about_ca_topic_score_codex":0.000011163584,"about_ca_topic_score_gemma":0.00010321038,"teacher_disagreement_score":0.70294917,"about_ca_system_score_codex":0.00009350397,"about_ca_system_score_gemma":0.000037881513,"threshold_uncertainty_score":0.5127138},"labels":[],"label_agreement":null},{"id":"W3157372210","doi":"10.1155/2021/5595065","title":"A Monitoring Approach Based on Fuzzy Stochastic P-Timed Petri Nets of a Railway Transport Network","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Petri net; Train; Stochastic Petri net; Computer science; Context (archaeology); Fuzzy logic; Stability (learning theory); Distributed computing; Artificial intelligence","score_opus":0.008148063338936145,"score_gpt":0.20939821402170755,"score_spread":0.2012501506827714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157372210","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":[0.74950933,0.0012846781,0.24679744,0.00002400506,0.0014035063,0.00011628334,0.0000096927,0.000049443934,0.0008055998],"genre_scores_gemma":[0.9836162,0.000067724504,0.015959343,0.000009106437,0.00026062108,0.000006881453,0.000019163772,0.00003711508,0.000023879371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983392,0.000021612479,0.00081907056,0.0001398622,0.00044334968,0.00023691634],"domain_scores_gemma":[0.9991402,0.00006719827,0.00025612462,0.00015383892,0.0002781069,0.00010450716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022272496,0.00018042057,0.00041603847,0.00014323911,0.00004074544,0.000007712277,0.00010674945,0.00008415976,0.000009847757],"category_scores_gemma":[0.00001876347,0.0001731594,0.00021087928,0.0005125939,0.000017733179,0.00019449407,4.6817047e-7,0.00023684445,6.2101276e-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":[0.00010445393,0.000087020286,0.00055778056,0.00014003638,0.000046747235,0.000049999915,0.0005655929,0.98714685,0.008016895,0.00006066789,0.000012490995,0.00321146],"study_design_scores_gemma":[0.014456154,0.0018082635,0.6619472,0.0059234262,0.00074051647,0.0001472808,0.0038932571,0.2635486,0.042903423,0.00055621285,0.0022173196,0.0018583374],"about_ca_topic_score_codex":0.0000030685464,"about_ca_topic_score_gemma":0.000009404996,"teacher_disagreement_score":0.72359824,"about_ca_system_score_codex":0.00005242922,"about_ca_system_score_gemma":0.000081057646,"threshold_uncertainty_score":0.70612353},"labels":[],"label_agreement":null},{"id":"W3163144070","doi":"10.1109/tits.2021.3071153","title":"Computationally Efficient Dynamic Traffic Optimization of Railway Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":5,"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":"Rio Tinto; Australian Centre for Field Robotics","keywords":"Computation; Train; Computer science; Mathematical optimization; Deadlock; Key (lock); Optimization problem; Distributed computing; Algorithm; Mathematics","score_opus":0.009723398541311375,"score_gpt":0.2069437901305934,"score_spread":0.19722039158928203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163144070","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":[0.16314368,0.00055118004,0.83000374,0.00000816892,0.00459154,0.00068232656,0.00042487646,0.00038635553,0.00020811858],"genre_scores_gemma":[0.9985926,0.000062765524,0.00023092824,0.0000061273113,0.000030366731,0.000498403,0.00015726611,0.000081362065,0.0003401656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971035,0.00013578407,0.0012343155,0.0003576011,0.00086022256,0.00030855226],"domain_scores_gemma":[0.99909824,0.00011714762,0.00020245438,0.000295329,0.00017331606,0.000113485454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003847765,0.00031045085,0.0004377958,0.0005602019,0.00031311475,0.000047562487,0.000254141,0.00009784378,0.0001621899],"category_scores_gemma":[0.0000010485223,0.00034201908,0.00022052306,0.0008249298,0.00004770452,0.00009149497,3.7210538e-7,0.00028238926,0.000020546515],"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":[0.000036221267,0.00022577756,0.0000030772974,0.00028531151,0.00011856015,0.0000052740274,0.0018410844,0.99569607,0.00056491274,0.00032642076,0.000051041978,0.00084627303],"study_design_scores_gemma":[0.00037418958,0.00016262896,0.00004596171,0.0000922919,0.000054568995,0.000018865358,0.0021290262,0.9953706,0.0005427184,7.7317196e-7,0.0008960024,0.00031232124],"about_ca_topic_score_codex":0.0001365754,"about_ca_topic_score_gemma":0.000027361795,"teacher_disagreement_score":0.8354489,"about_ca_system_score_codex":0.00032287248,"about_ca_system_score_gemma":0.00006321413,"threshold_uncertainty_score":0.9999032},"labels":[],"label_agreement":null},{"id":"W3166546887","doi":"10.1115/jrc2021-58468","title":"A Systems Approach for the Evaluation and Rebuilding of the Rogers Pass Systems on Canadian Pacific","year":2021,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Installation; Plan (archaeology); Process (computing); Reliability (semiconductor); Track (disk drive); Computer science; Operations research; Operations management; Reliability engineering; Aeronautics; Engineering; Telecommunications; Transport engineering; Architectural engineering; Operating system","score_opus":0.02249834108588516,"score_gpt":0.2144232043075532,"score_spread":0.19192486322166805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166546887","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":[0.60208845,0.028551713,0.06361114,0.0008110078,0.014013282,0.0067372117,0.00013584975,0.00032377694,0.2837276],"genre_scores_gemma":[0.99907887,0.000020753929,0.000047520673,0.0000061022924,0.00008150772,0.00016926098,0.0000044014682,0.000016835826,0.00057477684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992058,0.00006383834,0.00019907659,0.00013518044,0.0002143002,0.0001817526],"domain_scores_gemma":[0.9994107,0.00010066442,0.00003339621,0.00030050383,0.000100905454,0.000053811586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069469324,0.00009215991,0.00013299004,0.000041700117,0.00015023455,0.00007824403,0.000103512735,0.000060654063,0.0000027293997],"category_scores_gemma":[0.000050987288,0.00005131098,0.000045721437,0.00019481198,0.000018160801,0.000030678664,0.0000077314535,0.00005646831,5.239321e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.3972293e-7,0.0000039042184,0.00016744218,0.00018565642,0.000044257748,1.9840266e-7,0.00026444648,0.9856919,0.0007152926,0.010762989,0.001381321,0.0007817576],"study_design_scores_gemma":[0.00012901089,0.0000086244445,0.00037796664,0.00007166728,0.000026096088,0.000011606981,0.004497586,0.9881337,0.0002755077,0.0000036343695,0.006389943,0.000074630276],"about_ca_topic_score_codex":0.015566616,"about_ca_topic_score_gemma":0.0030263045,"teacher_disagreement_score":0.39699042,"about_ca_system_score_codex":0.00011849694,"about_ca_system_score_gemma":0.00008495763,"threshold_uncertainty_score":0.9909888},"labels":[],"label_agreement":null},{"id":"W3167234986","doi":"10.1080/23248378.2021.1937355","title":"Predicting the effectiveness of supplement time on delay recoveries: a support vector regression approach","year":2021,"lang":"en","type":"article","venue":"International Journal of Rail Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Waterloo","funders":"National Natural Science Foundation of China","keywords":"Support vector machine; Computer science; Regression; Regression analysis; Data mining; Machine learning; Statistics; Mathematics","score_opus":0.006953929639345831,"score_gpt":0.22356111058107142,"score_spread":0.2166071809417256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167234986","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":[0.98425996,0.00012777692,0.012416989,0.00009293821,0.0013675997,0.00010364434,0.000068309775,0.000019024557,0.0015437336],"genre_scores_gemma":[0.99906504,0.000057640624,0.0005049838,0.000010277761,0.00017064474,0.0000092708615,0.000106727035,0.000015485079,0.000059901784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986976,0.00008311959,0.0005053451,0.00008788107,0.0005343733,0.00009168263],"domain_scores_gemma":[0.9991243,0.0001918119,0.00019800088,0.00008167588,0.00037102256,0.000033228494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066013244,0.000100986384,0.00017697335,0.00008753705,0.000027841814,0.000021605156,0.00018121225,0.00004605668,0.00010285806],"category_scores_gemma":[0.000045290595,0.000070315255,0.00013405965,0.00011198243,0.000019183606,0.00015808313,0.0000026501643,0.00013299093,0.0000023738908],"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":[0.0007387074,0.0002839854,0.008256065,0.00030288304,0.00073461124,0.00017548032,0.0033981607,0.865352,0.110829875,0.002346389,0.00089723186,0.006684626],"study_design_scores_gemma":[0.0071655465,0.0014426956,0.37093407,0.0041417126,0.00033194458,0.0007576842,0.0027085927,0.04779382,0.54249096,0.00061750686,0.020799529,0.00081597024],"about_ca_topic_score_codex":0.000014041,"about_ca_topic_score_gemma":0.000009437634,"teacher_disagreement_score":0.81755817,"about_ca_system_score_codex":0.00007888536,"about_ca_system_score_gemma":0.00005857242,"threshold_uncertainty_score":0.2867373},"labels":[],"label_agreement":null},{"id":"W3169906434","doi":"10.1155/2021/7763126","title":"Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":36,"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":"Fundamental Research Funds for the Central Universities; Huaiyin Institute of Technology","keywords":"Extreme learning machine; Computer science; Particle swarm optimization; Decision tree; Artificial intelligence; Algorithm; Machine learning; Artificial neural network","score_opus":0.014554558475829297,"score_gpt":0.2092041216335242,"score_spread":0.1946495631576949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169906434","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":[0.78384286,0.00055881194,0.21476577,0.000003825608,0.00054835004,0.000033000324,0.000025324143,0.000018038847,0.00020404207],"genre_scores_gemma":[0.97964305,0.00010770459,0.02004926,0.000002694821,0.0001362219,0.0000010242594,0.000029998984,0.000018818897,0.000011221682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894404,0.000016376052,0.00060424407,0.000078850826,0.00024169382,0.00011479868],"domain_scores_gemma":[0.9994936,0.000013498047,0.00018459214,0.00007851457,0.00017630264,0.000053509433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001428472,0.00009345379,0.00022185287,0.00009450769,0.00002659033,0.0000071688646,0.000054699776,0.000040351402,0.000008691186],"category_scores_gemma":[0.0000091075035,0.00009125601,0.00012001992,0.00018741348,0.000014056822,0.000315146,4.8855327e-7,0.00012536606,1.6898358e-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":[0.000014907258,0.000032664546,0.00022475515,0.00008846006,0.00003517374,0.000026080286,0.00064065057,0.83246213,0.16388525,0.00007385679,0.000009499449,0.002506563],"study_design_scores_gemma":[0.0065593473,0.00061555504,0.17258056,0.0011691052,0.00056937744,0.0013714257,0.0075236033,0.4265382,0.37634087,0.00078484457,0.005101666,0.00084546645],"about_ca_topic_score_codex":0.000003122143,"about_ca_topic_score_gemma":0.000004313863,"teacher_disagreement_score":0.40592393,"about_ca_system_score_codex":0.00004358036,"about_ca_system_score_gemma":0.000046022556,"threshold_uncertainty_score":0.3721312},"labels":[],"label_agreement":null},{"id":"W3175350881","doi":"10.1609/socs.v12i1.18576","title":"Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Simon Fraser University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Monash University; University of Southern California; Simon Fraser University; National Science Foundation","keywords":"Scalability; Computer science; Train; Distributed computing; Deadlock; Software; Simulated annealing; Plan (archaeology); Motion planning; Graph; Mathematical optimization; Artificial intelligence; Theoretical computer science; Robot; Algorithm; Mathematics","score_opus":0.012409772200874402,"score_gpt":0.22937554500457774,"score_spread":0.21696577280370333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175350881","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":[0.9217496,0.00046017388,0.000005831636,0.0035783702,0.0034281781,0.00011517877,0.0000035116295,0.00006461022,0.07059458],"genre_scores_gemma":[0.99845487,0.00008362522,0.000021264914,0.000034234145,0.00070332823,0.000013909877,0.0000017153478,0.000024395398,0.00066268066],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865013,0.000010526782,0.00025480142,0.0002291506,0.0006300168,0.00022538571],"domain_scores_gemma":[0.9993831,0.00011631664,0.00005996027,0.000115219176,0.00026894748,0.000056481997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004768945,0.00013966694,0.0001614584,0.00003612371,0.00019490617,0.00016790527,0.00050645706,0.00007775625,0.000016285634],"category_scores_gemma":[0.00010117871,0.00009459298,0.00006456606,0.00021477135,0.00006491453,0.00012788642,0.00022142526,0.00031213666,0.0000038199355],"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":[0.00039849637,0.0004022756,0.09211958,0.0007120917,0.00083185436,0.00004104452,0.011090903,0.070857875,0.4500259,0.33503258,0.036931247,0.0015561464],"study_design_scores_gemma":[0.007299251,0.00084124994,0.031659964,0.0037257827,0.00013505746,0.0004796667,0.005289,0.1954223,0.529384,0.013938615,0.20999525,0.0018298187],"about_ca_topic_score_codex":0.00003801233,"about_ca_topic_score_gemma":7.224675e-7,"teacher_disagreement_score":0.32109398,"about_ca_system_score_codex":0.00007605835,"about_ca_system_score_gemma":0.000023260103,"threshold_uncertainty_score":0.385739},"labels":[],"label_agreement":null},{"id":"W3175444034","doi":"10.1155/2021/4664010","title":"Linear Programming Model and Online Algorithm for Customer-Centric Train Calendar Generation","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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; Train; Benchmark (surveying); Heuristic; Algorithm; Computation; Linear programming; Computational complexity theory; Constant (computer programming); Time complexity; Quality (philosophy); Heuristics; State (computer science); Divide and conquer algorithms; Artificial intelligence","score_opus":0.013292091452083715,"score_gpt":0.2439118864741464,"score_spread":0.2306197950220627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175444034","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":[0.4188883,0.0010352551,0.5796019,0.000026835552,0.00028290675,0.00007033099,0.000071639064,0.000019541829,0.000003307168],"genre_scores_gemma":[0.58576465,0.0005747859,0.41310698,0.000012658119,0.00033466006,0.000005820458,0.00012665015,0.000025891482,0.000047910573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992398,0.000006635738,0.00040154994,0.00008720769,0.00013764792,0.00012718225],"domain_scores_gemma":[0.99957883,0.000013631048,0.000095584386,0.000044631735,0.00020353176,0.000063798754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009758364,0.00009153224,0.00016827094,0.00007855059,0.000042223775,0.000009274118,0.000030202687,0.000049883205,0.0000015810349],"category_scores_gemma":[0.000008645772,0.00008790181,0.00007022134,0.00014317415,0.000008623624,0.00024041536,4.2512974e-7,0.00009026199,1.2146863e-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":[0.0000041996313,0.000029615245,0.000008701223,0.00004341894,0.000015201799,0.000009714205,0.00039136547,0.73773587,0.015921298,0.00003609829,0.000016035452,0.24578847],"study_design_scores_gemma":[0.0010627515,0.000074335294,0.0008483918,0.000053840664,0.000049628303,0.000026814994,0.0003537817,0.9877251,0.004218691,0.000036127512,0.005423302,0.00012726514],"about_ca_topic_score_codex":8.713078e-7,"about_ca_topic_score_gemma":0.00004353777,"teacher_disagreement_score":0.24998918,"about_ca_system_score_codex":0.000031117655,"about_ca_system_score_gemma":0.000050139115,"threshold_uncertainty_score":0.35845318},"labels":[],"label_agreement":null},{"id":"W3183728565","doi":"","title":"Control superiority of DC in railway system: A facility to depict the on-board disinterring systems","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"","keywords":"Pantograph; Context (archaeology); Voltage; Train; Interoperability; Ripple; Bandwidth (computing); Electric power system; Waveform; Engineering; Electrical engineering; Computer science; Power (physics); Telecommunications; Engineering drawing","score_opus":0.005210205692327509,"score_gpt":0.19384216852149797,"score_spread":0.18863196282917047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183728565","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":[0.97417384,0.0037302952,0.019518934,0.00013870223,0.0009996265,0.00020436449,0.00001474946,0.000062123305,0.0011573832],"genre_scores_gemma":[0.99953645,0.00009634057,0.0000055535565,0.000011559644,0.00016023833,0.000021451324,9.821441e-7,0.00002030766,0.00014708744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728346,0.00021450875,0.00062410854,0.00020197735,0.00032909168,0.0013468616],"domain_scores_gemma":[0.9993091,0.00009834836,0.000066492496,0.00034079907,0.000087533655,0.000097687625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017612798,0.0002018805,0.00044567956,0.000114599505,0.00010538441,0.000077029144,0.0003148651,0.0000829578,0.000005237309],"category_scores_gemma":[0.000078411846,0.0001466531,0.00015771783,0.00027582044,0.000023188884,0.00008316578,0.000024009772,0.001053789,0.000025096517],"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":[0.00010024406,0.00010119896,0.014458043,0.00036306216,0.00039204245,0.00006683416,0.0012754312,0.91074485,0.013972078,0.055210475,0.000055654415,0.003260096],"study_design_scores_gemma":[0.026039204,0.003970611,0.12677927,0.010131431,0.0007097424,0.013552672,0.18864028,0.5563332,0.02491773,0.004113823,0.038002945,0.006809084],"about_ca_topic_score_codex":0.000653547,"about_ca_topic_score_gemma":0.004708076,"teacher_disagreement_score":0.35441163,"about_ca_system_score_codex":0.0012986688,"about_ca_system_score_gemma":0.0004394283,"threshold_uncertainty_score":0.59803396},"labels":[],"label_agreement":null},{"id":"W3186877296","doi":"10.14311/app.2021.31.0001","title":"Energy saving in rail transport","year":2021,"lang":"en","type":"article","venue":"Acta Polytechnica CTU Proceedings","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"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":"Track (disk drive); Electrification; Automotive engineering; Computer science; Simulation; Envelope (radar); Energy (signal processing); Power (physics); Engineering; Electrical engineering; Mechanical engineering; Electricity; Telecommunications","score_opus":0.005761264656400169,"score_gpt":0.18260213550284224,"score_spread":0.17684087084644207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186877296","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":[0.83273065,0.0014987848,0.0011441394,0.00040077095,0.0004648813,0.00010442044,0.0000051865522,0.0013436881,0.16230747],"genre_scores_gemma":[0.9978978,0.00028243815,0.000657986,0.000092163325,0.00012278491,0.000047472276,0.000006526213,0.000063990774,0.0008288127],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856305,0.0000031297086,0.00040214203,0.00032952442,0.00022496586,0.0004772116],"domain_scores_gemma":[0.99960446,0.000018295354,0.000036356636,0.00017801396,0.00005840005,0.00010447168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015488538,0.0002270716,0.0002930888,0.00017051786,0.000051143485,0.00004126937,0.00026346138,0.00019875546,0.000075979144],"category_scores_gemma":[0.00002444436,0.00024067606,0.00009120602,0.0007486234,0.000031284973,0.00026110365,0.000034229364,0.00022656583,0.000007334444],"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":[0.00001389028,0.00013471892,0.0047535608,0.0003054669,0.000056796383,0.00013159952,0.0010281022,0.0025395658,0.9516172,0.01794552,0.008059838,0.013413775],"study_design_scores_gemma":[0.001685288,0.000118684715,0.048102424,0.00088588585,0.000060181916,0.00051689177,0.0011466236,0.03781201,0.27165204,0.00084552675,0.6349585,0.0022158956],"about_ca_topic_score_codex":0.00013083521,"about_ca_topic_score_gemma":0.00011303265,"teacher_disagreement_score":0.67996514,"about_ca_system_score_codex":0.000098234734,"about_ca_system_score_gemma":0.000041743737,"threshold_uncertainty_score":0.9814485},"labels":[],"label_agreement":null},{"id":"W3193446884","doi":"10.1177/03611981211036878","title":"Effects of Hazmat Train Speed Restrictions on Train Delay Performance and Railroad Line Capacity: Comparative Study with Two Railway Simulation Tools","year":2021,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"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":"Train; Transport engineering; Range (aeronautics); Track (disk drive); Speed limit; Level crossing; Computer science; Automotive engineering; Simulation; Engineering","score_opus":0.09026962134724298,"score_gpt":0.3501334502359241,"score_spread":0.25986382888868115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193446884","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":[0.99540067,0.00017326484,0.0025662526,0.00016963671,0.00025086483,0.0011432202,0.000037980342,0.000035334306,0.00022275561],"genre_scores_gemma":[0.99869376,0.000294768,0.0005779499,0.000007065067,0.00010319654,0.000040912888,0.000012165939,0.000045954926,0.00022423793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9945555,0.000993223,0.0011654792,0.00034272711,0.0023749568,0.0005681609],"domain_scores_gemma":[0.9953309,0.001766093,0.000251799,0.0003387781,0.0020491465,0.0002632262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022711358,0.00027578583,0.00060812826,0.0007648878,0.00041982555,0.00011296816,0.00036689587,0.00011647352,0.00004259171],"category_scores_gemma":[0.0001154686,0.00020213722,0.00017003152,0.0021428377,0.00034877626,0.00057253515,0.0000035400144,0.001442816,0.0000034075658],"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":[0.0012929452,0.0006730448,0.02600357,0.0006257491,0.0003512596,0.00016221662,0.014209822,0.93480235,0.01486371,0.00041882516,0.00021661892,0.0063798632],"study_design_scores_gemma":[0.0052408534,0.0034420213,0.9334938,0.0009482609,0.00011968338,0.000003056065,0.006667878,0.033648808,0.015217536,0.00012031309,0.0007911291,0.00030664494],"about_ca_topic_score_codex":0.001245568,"about_ca_topic_score_gemma":0.014950527,"teacher_disagreement_score":0.90749025,"about_ca_system_score_codex":0.00015206772,"about_ca_system_score_gemma":0.0002707834,"threshold_uncertainty_score":0.83427453},"labels":[],"label_agreement":null},{"id":"W3197506852","doi":"10.32920/ryerson.14645691.v1","title":"Development of simulation model for evaluating operational performance of railroad networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Plan (archaeology); Visual Basic for Applications; Computer science; Scheduling (production processes); Software; State (computer science); Blocking (statistics); Operations research; Simulation; Engineering; Computer network; Operations management; Operating system","score_opus":0.05526813631224106,"score_gpt":0.29207267532116155,"score_spread":0.23680453900892048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197506852","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":[0.46735328,0.00013143064,0.53168744,7.860157e-7,0.00017984977,0.00013578415,0.0000021393962,0.00002166562,0.0004875927],"genre_scores_gemma":[0.83640623,0.00001099764,0.16318671,0.0000022461759,0.00004202237,0.00006723596,0.00011076734,0.000018284853,0.00015553286],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886334,0.000008134827,0.0006337712,0.00015996677,0.00021187527,0.00012289424],"domain_scores_gemma":[0.99939865,0.00004144527,0.000099378514,0.00016846253,0.0002706196,0.000021468377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038748793,0.00013983829,0.00027140428,0.000054874377,0.000039150633,0.000015770127,0.000105909494,0.00015182445,0.000021294183],"category_scores_gemma":[0.00001945701,0.00013650904,0.00007058867,0.000057047335,0.0000082452825,0.000054699754,0.00007307856,0.00009345619,2.6830716e-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":[0.0000042492397,0.0000114156355,0.00003131755,0.0005743003,0.000040403185,1.4437301e-8,0.0007499849,0.98956287,0.0012544007,0.000072968964,0.000007639031,0.0076904264],"study_design_scores_gemma":[0.00013521247,0.000009282793,0.00017028637,0.0002449228,0.000010355072,1.1979783e-7,0.000041020492,0.9970209,0.002207775,0.000003706965,0.000017832885,0.00013856612],"about_ca_topic_score_codex":0.0000051605025,"about_ca_topic_score_gemma":0.000012963037,"teacher_disagreement_score":0.36905292,"about_ca_system_score_codex":0.000055085886,"about_ca_system_score_gemma":0.00019560996,"threshold_uncertainty_score":0.5566677},"labels":[],"label_agreement":null},{"id":"W3197589824","doi":"10.1155/2021/8555372","title":"Application of Regenerative Braking with Optimized Speed Profiles for Sustainable Train Operation","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":9,"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":"Regenerative brake; Acceleration; Automotive engineering; Schedule; Limit (mathematics); Energy consumption; Speed limit; Computer science; Propulsion; Kinematics; Energy (signal processing); Genetic algorithm; Dynamic braking; Simulation; Engineering; Transport engineering","score_opus":0.00525560283612566,"score_gpt":0.2197123045315902,"score_spread":0.21445670169546455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197589824","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":[0.44251767,0.0002745048,0.5568043,0.00002742157,0.000060426224,0.00018781303,0.000004971218,0.000013091302,0.00010978301],"genre_scores_gemma":[0.9367517,0.00003822673,0.062965214,0.000004032978,0.00007051976,0.000017035562,0.00005505453,0.00001812695,0.000080053294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999215,0.000012831133,0.0004344865,0.000082143626,0.00014614948,0.00010940376],"domain_scores_gemma":[0.99905294,0.000029250696,0.0002105691,0.000067021625,0.00060999434,0.00003019845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015116928,0.0000860631,0.00021380202,0.00007249375,0.00004212865,0.000014410511,0.000043868353,0.00003874821,0.0000037336633],"category_scores_gemma":[0.000014288228,0.00007326569,0.0000613823,0.00018002365,0.0000120245895,0.00041793322,3.7914734e-7,0.000057926733,7.7408615e-8],"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":[0.00010248001,0.000022357366,0.000041028947,0.00015701567,0.000030979118,0.0000068888207,0.0010907718,0.8363957,0.15747799,0.0027133508,0.000008483354,0.0019530049],"study_design_scores_gemma":[0.005217137,0.0005531984,0.007095705,0.00028316292,0.00013566419,0.000036897636,0.011467962,0.11702981,0.85582304,0.00044789453,0.0015878829,0.00032164337],"about_ca_topic_score_codex":0.0000044814055,"about_ca_topic_score_gemma":0.000025077634,"teacher_disagreement_score":0.71936584,"about_ca_system_score_codex":0.000044100536,"about_ca_system_score_gemma":0.000067470944,"threshold_uncertainty_score":0.29876885},"labels":[],"label_agreement":null},{"id":"W3198376353","doi":"10.1609/icaps.v31i1.15994","title":"Scalable Rail Planning and Replanning: Winning the 2020 Flatland Challenge","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Automated Planning and Scheduling","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Simon Fraser University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada; Monash University; National Science Foundation; Deutsche Bahn; University of Southern California; Simon Fraser University","keywords":"Scalability; Computer science; Train; Distributed computing; Deadlock; Simulated annealing; Software; Motion planning; Plan (archaeology); Graph; Mathematical optimization; Operations research; Engineering; Robot; Artificial intelligence; Theoretical computer science; Algorithm; Mathematics","score_opus":0.02591758923968751,"score_gpt":0.2511828116355023,"score_spread":0.2252652223958148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198376353","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":[0.9704375,0.0016054923,0.00006273169,0.0015420927,0.00045625525,0.000071330534,0.000005078355,0.00032041722,0.025499105],"genre_scores_gemma":[0.9988547,0.00008731157,0.00055134733,0.0000650068,0.00012406005,0.000009637234,0.00000417816,0.000022444832,0.00028131128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881977,0.000009684366,0.00032318334,0.0002801394,0.00033243926,0.00023481053],"domain_scores_gemma":[0.99940157,0.00009401662,0.00013462052,0.00009773963,0.00020836903,0.00006369296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000382186,0.00019862707,0.0002220986,0.00007061094,0.00025485415,0.00028599805,0.00031329514,0.00010244321,0.000013082052],"category_scores_gemma":[0.00017409396,0.00013685544,0.00004376313,0.00017218251,0.00007455957,0.00016591484,0.00012917261,0.00034387223,0.0000013986667],"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":[0.0002056142,0.000119859265,0.18765229,0.0010451466,0.0009812032,0.0001028664,0.023600398,0.45797002,0.24568403,0.07246553,0.0069669485,0.0032060992],"study_design_scores_gemma":[0.00035295862,0.00003551831,0.00878494,0.0016248387,0.000022401118,0.00015249653,0.0033865855,0.977812,0.006346699,0.00035332845,0.0008995116,0.00022873403],"about_ca_topic_score_codex":0.000011170425,"about_ca_topic_score_gemma":5.8379595e-7,"teacher_disagreement_score":0.51984197,"about_ca_system_score_codex":0.000024802628,"about_ca_system_score_gemma":0.000030131005,"threshold_uncertainty_score":0.55808026},"labels":[],"label_agreement":null},{"id":"W3200231357","doi":"10.1007/s40534-021-00254-x","title":"Preface to special issue on hybrid and hydrogen technologies for railway operations","year":2021,"lang":"en","type":"article","venue":"Railway Engineering Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Engineering; Construction engineering","score_opus":0.0068177002058170625,"score_gpt":0.21244721754089377,"score_spread":0.2056295173350767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200231357","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":[0.93890035,0.0005735818,0.051203445,0.00058403524,0.0020428784,0.00054955925,0.000045515564,0.001473013,0.004627626],"genre_scores_gemma":[0.9868559,0.00004250561,0.011433659,0.00003401499,0.00076299877,0.00012658334,0.0000040064333,0.00004247279,0.0006978405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985027,0.000004393352,0.00022848681,0.00046776087,0.0002737509,0.0005229189],"domain_scores_gemma":[0.9992899,0.00005795583,0.000010858459,0.00040136804,0.00009534959,0.00014460979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027494322,0.00022316829,0.00021814975,0.0002684485,0.00023078307,0.00019154567,0.00035352612,0.000059246526,0.000021205038],"category_scores_gemma":[0.00035049414,0.000222694,0.00004488385,0.0008115277,0.0000835915,0.00023508524,0.00008855623,0.00012370553,0.000043382835],"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":[0.0000014851834,0.000011476778,0.0000066390426,0.000039417137,0.000007785138,0.000006291072,0.00018410367,0.87884223,0.10415733,0.0018892896,0.0009640215,0.01388992],"study_design_scores_gemma":[0.00021515711,0.00007478449,0.00015695473,0.00008745598,0.000007057783,0.000042691874,0.00016208595,0.36290556,0.46840283,0.000027492038,0.16748463,0.00043331622],"about_ca_topic_score_codex":0.000004087192,"about_ca_topic_score_gemma":0.000008743007,"teacher_disagreement_score":0.5159367,"about_ca_system_score_codex":0.00010885791,"about_ca_system_score_gemma":0.00006531565,"threshold_uncertainty_score":0.90811974},"labels":[],"label_agreement":null},{"id":"W3200483757","doi":"10.3390/s21186129","title":"Development and Validation of a Railway Safety System for Nordic Trains in Isolated Territories of Northern Quebec Based on IEEE 802.15.4 Protocol","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Cegep de Sept Iles; Université du Québec à Rimouski","funders":"","keywords":"Train; Context (archaeology); Transport engineering; Telecommunications; Track (disk drive); Intelligent transportation system; Computer science; Engineering; Geography; Archaeology","score_opus":0.009907278438614962,"score_gpt":0.21539322460771507,"score_spread":0.2054859461691001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200483757","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":[0.99120206,0.000006890445,0.0025884798,0.000013126584,0.00016998117,0.0055677327,0.000020523239,0.00005304523,0.00037818975],"genre_scores_gemma":[0.9964902,2.538229e-7,0.0008315922,0.0000019876722,0.00002980041,0.002517144,0.000014765852,0.000022449562,0.00009180556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990777,0.0000381482,0.00045847896,0.00014733906,0.00013393417,0.00014444691],"domain_scores_gemma":[0.9996188,0.000058783906,0.00007372601,0.00013600818,0.00008085802,0.00003181873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018183369,0.00012377296,0.0002584388,0.000099943594,0.000028767397,0.000009500781,0.000044171444,0.00007854093,0.000002500001],"category_scores_gemma":[0.000022446222,0.000115512164,0.000039920982,0.00019318296,0.00001794889,0.000030322588,0.000004304174,0.000047078807,5.7885984e-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":[0.0001383566,0.00008487666,0.0031109785,0.0028111308,0.00004431805,0.00000732924,0.0039968914,0.95981133,0.025513826,0.00011832355,0.0000095652795,0.0043530473],"study_design_scores_gemma":[0.0030336531,0.00016112614,0.007487486,0.0015248517,0.000016362454,0.000005457409,0.0021948102,0.47875726,0.49819374,0.0000023239745,0.008218806,0.00040413436],"about_ca_topic_score_codex":0.0007273498,"about_ca_topic_score_gemma":0.011400577,"teacher_disagreement_score":0.4810541,"about_ca_system_score_codex":0.00010241753,"about_ca_system_score_gemma":0.00009732944,"threshold_uncertainty_score":0.636179},"labels":[],"label_agreement":null},{"id":"W3206598549","doi":"10.7939/r3-3q7y-ev03","title":"Canadian Rail Research Laboratory Report on Enhanced Train Control","year":2018,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"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":"Control (management); Aeronautics; Engineering; Computer science; Artificial intelligence","score_opus":0.007653550014490941,"score_gpt":0.18793247541360011,"score_spread":0.18027892539910917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206598549","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":[0.61254084,0.000028843495,0.0000833972,0.00042153525,0.00019671606,0.00007546555,0.000009695386,0.000056789948,0.38658673],"genre_scores_gemma":[0.9824848,0.000008936933,0.00009191392,0.00004787122,0.00010729194,1.8420279e-7,0.0000110630635,0.000017458971,0.017230457],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992469,0.00005230863,0.00010265281,0.00016330904,0.00015028259,0.00028457365],"domain_scores_gemma":[0.9993334,0.00010212117,0.00002105924,0.00028570037,0.000053618136,0.0002041001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014480355,0.00008226269,0.00013589325,0.0002555052,0.00014388436,0.00001357792,0.00024506802,0.00009674083,0.00074816245],"category_scores_gemma":[0.000027874476,0.00009508565,0.000032261174,0.00034653494,0.00012822465,0.0002570707,0.000015829872,0.00013055478,0.0001431629],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003790047,0.00021460076,0.0071276515,0.00028518098,0.00052843493,0.001676257,0.035024527,0.014944725,0.03235801,0.03325166,0.8679424,0.0062675546],"study_design_scores_gemma":[0.00091341074,0.00031440266,0.00523311,0.00012244466,0.000010180138,0.000008689746,0.0015195233,0.004725708,0.012260499,0.0000767002,0.9744696,0.00034572836],"about_ca_topic_score_codex":0.067582436,"about_ca_topic_score_gemma":0.07917854,"teacher_disagreement_score":0.369944,"about_ca_system_score_codex":0.0000648028,"about_ca_system_score_gemma":0.0002594565,"threshold_uncertainty_score":0.9386266},"labels":[],"label_agreement":null},{"id":"W3210888909","doi":"10.18757/ejtir.2021.21.4.5453","title":"Multi-objective railway timetabling including energy-efficient train trajectory optimization","year":2021,"lang":"en","type":"article","venue":"European journal of transport and infrastructure research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transport Canada","funders":"","keywords":"Energy consumption; Train; Computer science; Robustness (evolution); Efficient energy use; Mathematical optimization; Running time; Energy (signal processing); Multi-objective optimization; Overtaking; Pareto principle; Real-time computing; Operations research; Transport engineering; Engineering; Mathematics; Algorithm","score_opus":0.029125116081483833,"score_gpt":0.2616144348418901,"score_spread":0.2324893187604063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210888909","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":[0.5270237,0.006088144,0.4555911,0.000045667228,0.0007545332,0.00007788527,0.00001174746,0.000060870465,0.010346378],"genre_scores_gemma":[0.98377264,0.0005921007,0.015071349,0.000011035008,0.00035136298,8.2812596e-7,0.000008146948,0.00005867142,0.00013389769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786353,0.00038685664,0.0005988094,0.00020779984,0.0005507257,0.00039228942],"domain_scores_gemma":[0.99903685,0.00006859026,0.000083610554,0.0001528523,0.00043870346,0.00021941049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019646636,0.00018943813,0.00031617758,0.00042235065,0.00021981695,0.00007198234,0.00021253625,0.000060054623,0.00012346516],"category_scores_gemma":[0.00005523068,0.00016138545,0.00012588174,0.00062393013,0.00010172047,0.00016805199,0.000023631395,0.00065368466,0.0000014271702],"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":[0.000020500142,0.000028511273,0.00026226896,0.000055195847,0.00007580074,0.0004098666,0.001713599,0.95806736,0.026674587,0.000047188958,0.00012550085,0.012519634],"study_design_scores_gemma":[0.004521138,0.00050103286,0.11904337,0.0008940973,0.0001146388,0.0018430302,0.004721848,0.8194769,0.017685777,0.000042133146,0.030138003,0.0010180459],"about_ca_topic_score_codex":0.0000053184317,"about_ca_topic_score_gemma":0.000005342361,"teacher_disagreement_score":0.45674893,"about_ca_system_score_codex":0.00007793977,"about_ca_system_score_gemma":0.000111612426,"threshold_uncertainty_score":0.6581108},"labels":[],"label_agreement":null},{"id":"W3214935581","doi":"10.1016/j.cor.2021.105629","title":"A time–space formulation for the locomotive routing problem at the Canadian National Railways","year":2021,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Operations research; Routing (electronic design automation); Space (punctuation); Transport engineering; Mathematical optimization; Engineering; Mathematics; Computer network","score_opus":0.043692456857560076,"score_gpt":0.30290484525562456,"score_spread":0.2592123883980645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214935581","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":[0.09329377,0.002705755,0.76059234,0.04851384,0.0019854787,0.006120674,0.00023340127,0.00044932755,0.08610541],"genre_scores_gemma":[0.9921025,0.000009433977,0.0020458158,0.00007153056,0.0002671627,0.00025673574,0.00009324971,0.000022803051,0.005130776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872905,0.00010525213,0.00017624118,0.00018117318,0.00042093644,0.0003873364],"domain_scores_gemma":[0.9983484,0.00053427275,0.000007829392,0.00022932435,0.00079408544,0.00008608722],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011481051,0.00009152804,0.000082175764,0.000109963425,0.002394104,0.00038399518,0.00025016343,0.000056935314,0.00006714664],"category_scores_gemma":[0.00013728172,0.00006196931,0.000053743774,0.00055647676,0.000060693146,0.00012617522,0.000078130615,0.00020583264,0.00009348956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000010806369,0.000006223492,0.000011636886,0.000008536774,0.000029515757,0.000001084033,0.0011573532,0.9551902,0.0007755404,0.021122776,0.02007655,0.0016195469],"study_design_scores_gemma":[0.00014259561,0.0000139233125,0.00026413662,0.000021331876,0.0000027627339,0.000010324136,0.0001848547,0.94945425,0.00066840596,0.00007045252,0.04909141,0.00007557734],"about_ca_topic_score_codex":0.0066884994,"about_ca_topic_score_gemma":0.17391132,"teacher_disagreement_score":0.8988087,"about_ca_system_score_codex":0.0006844269,"about_ca_system_score_gemma":0.0005477849,"threshold_uncertainty_score":0.99992603},"labels":[],"label_agreement":null},{"id":"W344991833","doi":"","title":"Rail Shuttles: Concepts and Case Studies","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Control reconfiguration; Interim; Service (business); Urban transit; Transport engineering; Transit (satellite); Rail transit; Public transport; Line (geometry); Transit system; Business; Operations research; Computer science; Engineering; Geography; Marketing","score_opus":0.08467940461300916,"score_gpt":0.38387217560009096,"score_spread":0.2991927709870818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W344991833","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":[0.9903741,0.004379188,0.0003959333,0.00032218965,0.0003443846,0.0010543759,0.00024056787,0.00055928517,0.0023300103],"genre_scores_gemma":[0.99080676,0.0058744303,0.0009994546,0.00003236851,0.00026090522,0.0004889349,0.00010781775,0.0001285445,0.0013007638],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9925984,0.00065047515,0.0012294503,0.0009491351,0.0028104945,0.0017620251],"domain_scores_gemma":[0.99458385,0.0012308314,0.00008963592,0.0005416955,0.002772766,0.00078124617],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0039722123,0.0004888983,0.0007226781,0.0011747967,0.0016089458,0.000107784086,0.00040600332,0.00032447246,0.00014113884],"category_scores_gemma":[0.00033535223,0.00047749473,0.00017093489,0.0020328616,0.001889617,0.00080342084,0.000014198094,0.0014954662,0.00010043029],"study_design_candidate":"qualitative","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":[0.0016616244,0.001127121,0.18317774,0.010093534,0.0020926315,0.065333135,0.411627,0.12998955,0.017100075,0.027435992,0.12067724,0.02968438],"study_design_scores_gemma":[0.012184046,0.0036620325,0.43870848,0.0023083824,0.00023046522,0.00079613685,0.3094516,0.013796593,0.012768234,0.0024803022,0.19838615,0.0052275583],"about_ca_topic_score_codex":0.0041930163,"about_ca_topic_score_gemma":0.008211954,"teacher_disagreement_score":0.25553074,"about_ca_system_score_codex":0.00019454399,"about_ca_system_score_gemma":0.00024665045,"threshold_uncertainty_score":0.99976766},"labels":[],"label_agreement":null},{"id":"W40582142","doi":"","title":"The ALP-45 Dual Power Locomotive","year":2011,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Pantograph; Engineering; Automotive engineering; Axle; Catenary; Electric locomotive; Powertrain; Transit (satellite); Diesel locomotive; Overhead (engineering); Crowds; Electrical engineering; Power (physics); Aeronautics; Marine engineering; Transport engineering; Public transport; Mechanical engineering; Computer science; Torque","score_opus":0.010684210961825362,"score_gpt":0.16947187886096945,"score_spread":0.15878766789914409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W40582142","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":[0.16808918,0.00021946449,0.0031337726,0.000020796351,0.00079046784,0.000051470444,6.3935516e-7,0.00025037557,0.82744384],"genre_scores_gemma":[0.9948186,0.000013365549,0.00013052326,0.000016496944,0.000039036728,0.0000072737776,2.4249331e-7,0.000013115393,0.004961346],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995786,0.000007897926,0.00010120718,0.00006551844,0.000071647795,0.00017515593],"domain_scores_gemma":[0.9997557,0.000017676984,0.000007533609,0.00016301886,0.000015690946,0.000040388404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009380469,0.000070697526,0.000055882963,0.000014846297,0.00006769282,0.000016465647,0.00009486471,0.000032958076,0.00030936912],"category_scores_gemma":[0.0000054826287,0.00004092228,0.000030940286,0.00006453859,0.000025051984,0.000043664248,0.000014300254,0.000054025906,0.00026726854],"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":[0.000034999488,0.0002387162,0.0024577344,0.000058592475,0.00051797193,0.00011874038,0.025722211,0.036666807,0.0074825226,0.6760351,0.2014861,0.04918052],"study_design_scores_gemma":[0.0009235986,0.00035965006,0.05738041,0.000053456097,0.000027026397,0.000093703864,0.005242885,0.053551104,0.03332212,0.0021745139,0.84553283,0.0013387146],"about_ca_topic_score_codex":0.00008958787,"about_ca_topic_score_gemma":0.00005170435,"teacher_disagreement_score":0.8267294,"about_ca_system_score_codex":0.000011465396,"about_ca_system_score_gemma":0.0000039508514,"threshold_uncertainty_score":0.34352854},"labels":[],"label_agreement":null},{"id":"W4200442198","doi":"10.3390/en15010037","title":"Energy Recovering Using Regenerative Braking in Diesel–Electric Passenger Trains: Economical and Technical Analysis of Fuel Savings and GHG Emission Reductions","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Université du Québec à Rimouski; Cegep de Sept Iles","funders":"","keywords":"Train; Regenerative brake; Automotive engineering; Diesel fuel; Brake; Fuel efficiency; Brake specific fuel consumption; Greenhouse gas; Powertrain; Engineering; Energy consumption; Efficient energy use; Computer science; Electrical engineering","score_opus":0.010234848293535591,"score_gpt":0.2150868719984478,"score_spread":0.2048520237049122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200442198","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":[0.992362,0.005234671,0.00086319173,0.000026203663,0.000089274006,0.000017724307,0.0000034750876,0.000053492186,0.0013499709],"genre_scores_gemma":[0.99746144,0.0010343377,0.001269438,0.000004590628,0.000057215297,0.000008124087,0.0000070758106,0.000018958233,0.00013880401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990891,0.000041266718,0.00033940127,0.00026241553,0.000073686926,0.00019410651],"domain_scores_gemma":[0.9996413,0.00008004484,0.000049872167,0.00014601713,0.000030883573,0.000051898947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011666267,0.00014264794,0.0003684289,0.0004047327,0.00006321705,0.000041643696,0.000048338366,0.00010889686,0.000017454426],"category_scores_gemma":[0.000048817474,0.00014507512,0.000067121066,0.0008465122,0.000042582775,0.00015247245,0.00004657763,0.00009294035,6.3187294e-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":[0.00000393974,0.000014715854,0.0003915962,0.000029611334,0.00013129659,0.000006445726,0.0004393697,0.5308326,0.46449316,0.0011229563,0.000030009016,0.0025043355],"study_design_scores_gemma":[0.0003947135,0.000044088785,0.011965668,0.00020585497,0.00027492072,0.000088908404,0.0011359279,0.8397274,0.14349553,0.00023998391,0.0018747542,0.0005522301],"about_ca_topic_score_codex":0.00028987898,"about_ca_topic_score_gemma":0.00040021766,"teacher_disagreement_score":0.32099763,"about_ca_system_score_codex":0.00009773987,"about_ca_system_score_gemma":0.000030809395,"threshold_uncertainty_score":0.59159917},"labels":[],"label_agreement":null},{"id":"W4212882334","doi":"10.1155/2022/7580267","title":"Prediction of Train Station Delay Based on Multiattention Graph Convolution Network","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Real-time computing; Graph; Process (computing); Simulation","score_opus":0.007843949554219456,"score_gpt":0.19340608312653326,"score_spread":0.1855621335723138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212882334","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":[0.7710563,0.00017135438,0.22716206,0.000017736898,0.001310691,0.00011666642,0.000066018256,0.00003676254,0.00006244561],"genre_scores_gemma":[0.9972505,0.00003658279,0.0024268313,0.000011019494,0.00008800579,0.000013713324,0.00014876033,0.00001775021,0.000006844726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873096,0.000048951646,0.00062623277,0.00007937432,0.00039507644,0.00011941522],"domain_scores_gemma":[0.9994025,0.000035906203,0.00033651636,0.00007026774,0.00011703798,0.00003777468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035935044,0.000093055045,0.00016458544,0.00020520213,0.000086604625,0.0000042615925,0.000059610553,0.000034095454,0.000027141388],"category_scores_gemma":[0.0000063057796,0.0000969102,0.00011943246,0.00033937188,0.0000124683165,0.00022970172,4.6273954e-7,0.00016669085,2.5402306e-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":[0.0001422441,0.000060238915,0.0008759865,0.000045498397,0.000020855576,0.0000044226913,0.00046112624,0.98036665,0.014065155,0.00019583582,0.00009769295,0.0036642724],"study_design_scores_gemma":[0.0039526667,0.0017759446,0.41190958,0.00022399369,0.00012405036,0.000014201032,0.0015019268,0.57542795,0.0020887777,0.00057393755,0.0021610325,0.00024591887],"about_ca_topic_score_codex":0.0000074357627,"about_ca_topic_score_gemma":0.00001699369,"teacher_disagreement_score":0.4110336,"about_ca_system_score_codex":0.000102552476,"about_ca_system_score_gemma":0.000024848745,"threshold_uncertainty_score":0.39518833},"labels":[],"label_agreement":null},{"id":"W4213285145","doi":"10.1007/978-3-030-91589-6_20","title":"Advanced Analytics for Energy-Efficiency Improvement in Mine-Railway Operation","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Engineering; Automotive engineering; Energy consumption; Fuel efficiency; Diesel fuel; Artificial neural network; Computer science; Artificial intelligence","score_opus":0.010753364526083086,"score_gpt":0.20572968944997372,"score_spread":0.19497632492389064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213285145","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":[0.000400799,0.0012398383,0.042094663,0.000044606488,0.0015752668,0.0005935673,0.00007809527,0.0002802347,0.9536929],"genre_scores_gemma":[0.10616807,0.00064955495,0.0012639735,0.00012151408,0.00032166755,0.0004659087,0.00040017636,0.0002405781,0.8903686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99826765,0.0000046428404,0.0006567583,0.0004177203,0.00029515038,0.00035804903],"domain_scores_gemma":[0.9993817,0.00004320226,0.00007771806,0.00037819974,0.000047231293,0.000071920345],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001866389,0.00039595173,0.00043700068,0.00034196078,0.000085590196,0.00003928688,0.00025011814,0.00019953509,0.0009397076],"category_scores_gemma":[0.000008069999,0.000392673,0.00017021074,0.00007560253,0.000018305898,0.00010286614,0.000052728734,0.00018691608,0.000009990347],"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":[0.0000067670912,0.000022334465,8.048034e-7,0.00009900319,0.000033846845,0.000005265388,0.000060192535,0.73289573,0.0010665738,0.24465862,0.0015578845,0.019593],"study_design_scores_gemma":[0.0005076913,0.00023396825,0.000001743224,0.000047454196,0.000021307102,0.0000020695873,0.000041172574,0.29440203,0.00039036918,0.0004918251,0.7032979,0.00056248874],"about_ca_topic_score_codex":0.000108313325,"about_ca_topic_score_gemma":0.0006311241,"teacher_disagreement_score":0.70174,"about_ca_system_score_codex":0.00039482187,"about_ca_system_score_gemma":0.0000543672,"threshold_uncertainty_score":0.9999736},"labels":[],"label_agreement":null},{"id":"W4214679893","doi":"10.2139/ssrn.4043348","title":"A Novel Model for Transfer Synchronization in Transit Networks and a Lagrangian-Based Heuristic Solution Method","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristic; Lagrangian; Lagrangian relaxation; Transfer (computing); Computer science; Synchronization (alternating current); Mathematical optimization; Transit (satellite); Mathematics; Applied mathematics; Public transport; Engineering; Computer network; Parallel computing; Transport engineering","score_opus":0.007392716733897753,"score_gpt":0.20765091970544908,"score_spread":0.20025820297155134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214679893","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":[0.014452602,0.003233529,0.9818358,0.00009638761,0.00012547433,0.00017253408,0.000007389867,0.000045247867,0.00003105183],"genre_scores_gemma":[0.99808395,0.0002267312,0.0014312636,0.000023908782,0.00006661871,0.000060883227,0.00001076727,0.000036636928,0.000059249654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984487,0.000043426287,0.00024728331,0.00013820377,0.0001322089,0.0009901477],"domain_scores_gemma":[0.9998141,0.000040018276,0.000019013474,0.000063257,0.000021786778,0.000041806077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012083489,0.00012261969,0.0001601172,0.00014898476,0.00020311408,0.000023583474,0.000091337766,0.000053080515,0.000003925805],"category_scores_gemma":[0.000006359941,0.00012954584,0.00006816898,0.00023352524,0.0000088772695,0.00007131495,0.0000036067418,0.00068688265,1.04042435e-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":[0.00003432228,0.00003099479,0.0000117946265,0.000023175235,0.00002376611,5.7188015e-7,0.00019096612,0.98309857,0.0015873701,0.0064807637,0.000007202214,0.008510482],"study_design_scores_gemma":[0.0012400472,0.00012801531,0.000022428405,0.000013967473,0.000026680409,0.00010541913,0.00014566768,0.9964035,0.000014731825,0.0016674796,0.000086440974,0.00014561677],"about_ca_topic_score_codex":0.000052345655,"about_ca_topic_score_gemma":0.0010607098,"teacher_disagreement_score":0.9836313,"about_ca_system_score_codex":0.0006916243,"about_ca_system_score_gemma":0.0003185053,"threshold_uncertainty_score":0.52827257},"labels":[],"label_agreement":null},{"id":"W4226182767","doi":"10.1177/03611981221085561","title":"Using Delay Logs and Machine Learning to Support Passenger Railway Operations","year":2022,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Train; Computer science; Leverage (statistics); Data science; Machine learning; Data mining","score_opus":0.09183245065545798,"score_gpt":0.3474221614683148,"score_spread":0.2555897108128568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226182767","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":[0.9909197,0.00034317488,0.006457743,0.0007644772,0.00061357376,0.00058921543,0.000071493494,0.000049986113,0.00019061177],"genre_scores_gemma":[0.99639547,0.00032271683,0.0021314074,0.000034348526,0.00012731066,0.00009037083,0.000020796124,0.00007362792,0.000803941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99439424,0.00092231,0.00105102,0.00030919578,0.0025652116,0.000758008],"domain_scores_gemma":[0.99784786,0.0003281016,0.00010435249,0.0002900883,0.0010231942,0.00040639966],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0042369263,0.00021633251,0.00036450033,0.0010855946,0.001430063,0.00014377965,0.0006538581,0.000087915825,0.00061390776],"category_scores_gemma":[0.000070605936,0.00018119226,0.00019102135,0.0020201104,0.00017990026,0.00040051254,0.000015648318,0.0023626515,0.000009532349],"study_design_candidate":"simulation_or_modeling","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":[0.0002086899,0.000086574946,0.0336217,0.00011576527,0.00009951691,0.00012811502,0.0047684605,0.9374304,0.016646247,0.0007995016,0.0018530396,0.004241995],"study_design_scores_gemma":[0.004606783,0.004294947,0.47064912,0.00042022133,0.00017448433,0.000038108737,0.017027983,0.17734835,0.0041839117,0.0007086076,0.3192286,0.0013189116],"about_ca_topic_score_codex":0.0068917032,"about_ca_topic_score_gemma":0.020428915,"teacher_disagreement_score":0.76008207,"about_ca_system_score_codex":0.00031113994,"about_ca_system_score_gemma":0.00030597518,"threshold_uncertainty_score":0.99993896},"labels":[],"label_agreement":null},{"id":"W4229372830","doi":"10.1080/23248378.2022.2071346","title":"Train operation conflict detection for high-speed railways: a naïve Bayes approach","year":2022,"lang":"en","type":"article","venue":"International Journal of Rail Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Bayes' theorem; Naive Bayes classifier; Bernoulli's principle; Computer science; Robustness (evolution); Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Bayesian probability; Engineering","score_opus":0.01202797125390702,"score_gpt":0.21644412189573795,"score_spread":0.2044161506418309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229372830","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":[0.610546,0.00026727523,0.38302457,0.0001453751,0.0049293996,0.00026962324,0.00016039431,0.000081432896,0.00057593203],"genre_scores_gemma":[0.99725235,0.000033558375,0.0017195567,0.000040756237,0.0005832386,0.000047386715,0.0001975081,0.000030776944,0.00009489913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985322,0.000034652658,0.00062118476,0.00012404443,0.0005551253,0.00013283058],"domain_scores_gemma":[0.99928045,0.000048531565,0.0002044346,0.00006350113,0.00035213208,0.00005094401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004668944,0.00012952041,0.00018675157,0.00028408624,0.000113663555,0.00005357551,0.00024793818,0.000049116275,0.00008682225],"category_scores_gemma":[0.00002613097,0.00013192472,0.00015258731,0.00012579118,0.000016102986,0.00026084398,0.0000024496092,0.0001797525,0.0000012376494],"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":[0.00017399872,0.00007936919,0.00007147073,0.000027793967,0.00018203584,0.000008717343,0.0020142733,0.9442485,0.038475085,0.0026184379,0.000523567,0.01157678],"study_design_scores_gemma":[0.015042503,0.0019979936,0.026843246,0.00014098285,0.000315586,0.00043808116,0.007952883,0.65126926,0.07101258,0.0014924948,0.22207163,0.0014227421],"about_ca_topic_score_codex":0.000050064238,"about_ca_topic_score_gemma":0.000041317566,"teacher_disagreement_score":0.38670632,"about_ca_system_score_codex":0.00018785757,"about_ca_system_score_gemma":0.000042231077,"threshold_uncertainty_score":0.5379734},"labels":[],"label_agreement":null},{"id":"W4237065130","doi":"10.1109/ojits.2019.2948830","title":"IEEE OPEN JOURNAL OF THE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY","year":2020,"lang":"en","type":"article","venue":"IEEE Open Journal of Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Canadian Standards Association","funders":"","keywords":"Intelligent transportation system; Computer science; Operations research; Transport engineering; Engineering","score_opus":0.046201294741638824,"score_gpt":0.26008913427205976,"score_spread":0.21388783953042095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237065130","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":[0.5525289,0.0058311517,0.41577408,0.00044106838,0.021926489,0.0022861725,0.0001751441,0.000058046768,0.0009789381],"genre_scores_gemma":[0.9975417,0.00081710395,0.0002951453,0.000090902955,0.0008975486,0.000024769932,0.000011388709,0.000087332846,0.0002340835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99416405,0.00021974178,0.0038048164,0.0002670344,0.0011569762,0.00038736526],"domain_scores_gemma":[0.99612296,0.000109961664,0.0019331161,0.0003301192,0.0010909251,0.0004129118],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001952639,0.0004310631,0.0011555405,0.00013464442,0.0001832844,0.00069464935,0.0026535115,0.0002219395,0.000055985845],"category_scores_gemma":[0.000023259716,0.0003055503,0.0007284001,0.00073366606,0.00007080111,0.0012200788,0.000008670942,0.0006752695,0.000018654153],"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":[0.00013518908,0.00009554354,0.0010485331,0.0005740454,0.0006009045,0.000056286317,0.01317391,0.96518683,0.00762017,0.00047547443,0.010182656,0.00085042324],"study_design_scores_gemma":[0.009748746,0.0040369728,0.00823418,0.01658215,0.0025231251,0.0014796413,0.12574142,0.20283577,0.1973214,0.00014183753,0.42724785,0.0041068858],"about_ca_topic_score_codex":0.00046012594,"about_ca_topic_score_gemma":0.00012043173,"teacher_disagreement_score":0.7623511,"about_ca_system_score_codex":0.00022847803,"about_ca_system_score_gemma":0.00032922713,"threshold_uncertainty_score":0.9999397},"labels":[],"label_agreement":null},{"id":"W4246911732","doi":"10.18280/ijsse.100414","title":"Interpretative Structural Modelling on Generation Mechanism of Train Operation Conflicts in High Speed Railway","year":2020,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Mechanism (biology); High speed train; Computer science; Transport engineering; Engineering; Forensic engineering","score_opus":0.012992792246733227,"score_gpt":0.20746337108271215,"score_spread":0.19447057883597893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246911732","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":[0.73457956,0.00014551036,0.26429555,0.000169768,0.00062581914,0.000048929178,0.00001397041,0.000014718463,0.000106149535],"genre_scores_gemma":[0.9985464,0.00014797345,0.0009690517,0.000035451223,0.00027831542,4.7511116e-7,0.000008121674,0.000012759854,0.0000014037915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999055,0.000017435168,0.0005071862,0.000087509405,0.00024207948,0.00009075717],"domain_scores_gemma":[0.99967545,0.000039345705,0.000089037414,0.00003378821,0.00009996751,0.000062414765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017813047,0.00011638002,0.00022116119,0.00015350702,0.00001315665,0.000028168935,0.00012619804,0.000058375215,0.000012503693],"category_scores_gemma":[0.000031844913,0.0001111298,0.000048889262,0.000071581846,0.000009916724,0.00024560923,0.000012935786,0.00020677497,4.6866904e-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":[0.00005708511,0.00000574817,0.0000075626344,0.000023591478,0.000052122057,0.000012778015,0.0056673675,0.9577596,0.018544134,0.017268384,0.0000024298777,0.0005991954],"study_design_scores_gemma":[0.00060122967,0.000090763264,0.00006795909,0.00011564108,0.0000049633386,0.0000199021,0.00012657949,0.98771733,0.010931809,0.00013100795,0.00009601005,0.000096829266],"about_ca_topic_score_codex":0.00002027827,"about_ca_topic_score_gemma":0.000005447705,"teacher_disagreement_score":0.26396686,"about_ca_system_score_codex":0.000062869396,"about_ca_system_score_gemma":0.00001101926,"threshold_uncertainty_score":0.45317414},"labels":[],"label_agreement":null},{"id":"W4280649579","doi":"10.1155/2022/1876579","title":"Energy Consumption Analysis of High-Speed Trains under Real Vehicle Test Conditions","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Energy consumption; Train; Automotive engineering; Traction (geology); Energy (signal processing); Engineering; Total energy; Electric energy consumption; Simulation; Computer science; Electric energy; Mechanical engineering; Electrical engineering; Mathematics; Statistics","score_opus":0.008959472039674154,"score_gpt":0.22680508547802555,"score_spread":0.2178456134383514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280649579","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":[0.9848084,0.00013398664,0.014386568,0.000026732705,0.00031896093,0.000028799983,0.00015239835,0.000027704667,0.0001164799],"genre_scores_gemma":[0.9991955,0.0002056072,0.00034939923,0.00001103274,0.00003224755,0.0000041689923,0.00015735129,0.0000142757035,0.000030428497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99894214,0.000021362826,0.0005699317,0.00007580524,0.00028391945,0.000106814245],"domain_scores_gemma":[0.9994397,0.00007276787,0.00025536155,0.00008511911,0.000096683034,0.000050358176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011769335,0.00008490241,0.00027260068,0.00039405876,0.00007587776,0.00000527047,0.00008046385,0.000028786453,0.00017891142],"category_scores_gemma":[0.000003422876,0.0000885738,0.00016461355,0.0006227357,0.000022883973,0.00018746249,9.507282e-7,0.000106902226,2.9481845e-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":[0.000018253422,0.00006596106,0.0012695369,0.000014274041,0.00024233274,0.000008140656,0.00054074347,0.91049045,0.08456399,0.0021148329,0.000021633523,0.0006498436],"study_design_scores_gemma":[0.0014742464,0.00033718222,0.9418242,0.00002886573,0.00094851555,0.00001070753,0.0017571334,0.047552858,0.0048476537,0.00042556605,0.00057044165,0.00022262521],"about_ca_topic_score_codex":0.00007725847,"about_ca_topic_score_gemma":0.00026952487,"teacher_disagreement_score":0.9405547,"about_ca_system_score_codex":0.00008767281,"about_ca_system_score_gemma":0.000023532652,"threshold_uncertainty_score":0.36119345},"labels":[],"label_agreement":null},{"id":"W4281649338","doi":"10.1155/2022/8674538","title":"An Experimental Analysis of Hierarchical Rail Traffic and Train Control in a Stochastic Environment","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Innosuisse - Schweizerische Agentur für Innovationsförderung; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Punctuality; Train; Energy (signal processing); Computer science; Monte Carlo method; Control (management); Simulation; Operations research; Engineering; Transport engineering","score_opus":0.003940704496328995,"score_gpt":0.20556076731944178,"score_spread":0.2016200628231128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281649338","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":[0.977789,0.00059600134,0.021441953,0.000009815305,0.000071635775,0.00006084451,0.00002073181,0.000006207111,0.0000038325957],"genre_scores_gemma":[0.9994714,0.000015019686,0.00046441695,0.0000050215613,0.000011882932,0.000010191614,0.000011894636,0.000008876233,0.0000012468331],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914664,0.000032572567,0.00044315375,0.000073998715,0.00021504537,0.00008858154],"domain_scores_gemma":[0.9997591,0.000026623122,0.000103559694,0.000051398285,0.000007663171,0.00005167446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017657313,0.00007030222,0.00025992582,0.00031048487,0.000025032768,0.0000032044265,0.000054840868,0.000017674805,0.00003723138],"category_scores_gemma":[0.0000014181738,0.00007180105,0.00007748611,0.00021785455,0.000019497284,0.0001199326,5.060159e-7,0.000121861,3.2243758e-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":[0.000095057956,0.00010757816,0.00017979756,0.0000059375548,0.00007708598,0.000011814124,0.004803846,0.92511815,0.067246266,0.000038583195,3.290523e-7,0.002315538],"study_design_scores_gemma":[0.0044705914,0.0012377154,0.2272773,0.000025450094,0.00035826134,0.000015883443,0.008397173,0.75710195,0.0007586442,0.00003050351,0.00009238334,0.00023417437],"about_ca_topic_score_codex":0.0000038974117,"about_ca_topic_score_gemma":0.00002401693,"teacher_disagreement_score":0.2270975,"about_ca_system_score_codex":0.00005782028,"about_ca_system_score_gemma":0.000009032296,"threshold_uncertainty_score":0.2927962},"labels":[],"label_agreement":null},{"id":"W4281703073","doi":"10.1155/2022/8579354","title":"First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Gansu Education Department; National Natural Science Foundation of China","keywords":"Train; Headway; Mathematical optimization; Computer science; Scheduling (production processes); Genetic algorithm; Interval (graph theory); Synchronization (alternating current); Operations research; Nonlinear system; Line (geometry); Connection (principal bundle); Real-time computing; Simulation; Engineering; Computer network; Mathematics","score_opus":0.005858509347814601,"score_gpt":0.21426539512163378,"score_spread":0.2084068857738192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281703073","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":[0.6465499,0.000636699,0.35154006,0.000062493215,0.0007459491,0.00022302083,0.000010239397,0.000043445198,0.0001882077],"genre_scores_gemma":[0.99818015,0.000058174162,0.0015219792,0.000013578337,0.00007041095,0.00003668954,0.00006143868,0.000020839156,0.00003676284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901974,0.00002994139,0.0004888073,0.00008157696,0.0002356646,0.00014428473],"domain_scores_gemma":[0.9996071,0.000050371193,0.00016701435,0.000060812657,0.00007407111,0.00004065775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024953828,0.000094333394,0.00016627663,0.00024055794,0.00010612577,0.000013370456,0.00007607136,0.000032134514,0.00014838883],"category_scores_gemma":[0.0000073610554,0.00010060444,0.00005398121,0.000508821,0.000010043979,0.00041726293,0.0000010665273,0.00019915283,0.0000013356806],"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":[0.000013328472,0.000038810715,0.001598672,0.000024638297,0.000016017108,0.000012591057,0.00031953581,0.99627703,0.0003686937,0.0008814753,0.00009054323,0.00035868862],"study_design_scores_gemma":[0.0029007767,0.000302569,0.26687437,0.00017236409,0.00007871011,0.000032841966,0.0020006502,0.7189374,0.00023009852,0.0016959873,0.006391526,0.00038272367],"about_ca_topic_score_codex":0.00001085717,"about_ca_topic_score_gemma":0.00029370232,"teacher_disagreement_score":0.35163024,"about_ca_system_score_codex":0.00023493449,"about_ca_system_score_gemma":0.00002926968,"threshold_uncertainty_score":0.410253},"labels":[],"label_agreement":null},{"id":"W4283168610","doi":"10.1155/2022/1647306","title":"Collaborative Optimization of Passenger Control Strategy and Train Operation Plan with Variable Formations for a Rail Transit Network","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Natural Science Foundation of Hunan Province; China Postdoctoral Science Foundation","keywords":"Train; Integer programming; Schedule; Operator (biology); Variable (mathematics); Operations research; Simulated annealing; Control variable; Control (management); Engineering; Plan (archaeology); Computer science; Scheduling (production processes); Transport engineering; Operations management","score_opus":0.0038677052670944225,"score_gpt":0.18552577609954393,"score_spread":0.1816580708324495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283168610","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":[0.16058072,0.0002890839,0.83845425,0.000031324067,0.00013446053,0.00029912917,0.00011993139,0.000013897153,0.0000771852],"genre_scores_gemma":[0.97328794,0.00004745589,0.0264522,0.000009334947,0.000045678535,0.000051707466,0.0000835924,0.000015314581,0.000006765256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992122,0.000025519483,0.00044411077,0.000060225866,0.00015559439,0.00010234188],"domain_scores_gemma":[0.99948555,0.00005107719,0.00020159608,0.000038912567,0.00018796412,0.000034876168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021920392,0.000086336135,0.00021070433,0.00007386998,0.00011678961,0.0000137491,0.00003905358,0.000026907665,0.000014790437],"category_scores_gemma":[0.0000030907536,0.00007730074,0.000029447168,0.00023709507,0.0000120470495,0.00042897655,2.6399087e-7,0.00008302903,1.4809724e-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":[0.00020878132,0.000022749668,0.000033648004,0.000059211037,0.000059435268,0.0000015085052,0.0026923846,0.9916359,0.0028288336,0.0019067614,0.000025533242,0.0005252066],"study_design_scores_gemma":[0.007810349,0.0018834241,0.0040404727,0.000112676375,0.00022223504,0.00003886555,0.011413107,0.9709653,0.0005158587,0.00029632598,0.002409632,0.0002917554],"about_ca_topic_score_codex":0.0000029477949,"about_ca_topic_score_gemma":0.000038038725,"teacher_disagreement_score":0.81270725,"about_ca_system_score_codex":0.000030928855,"about_ca_system_score_gemma":0.00006355957,"threshold_uncertainty_score":0.31522328},"labels":[],"label_agreement":null},{"id":"W4283716176","doi":"10.1115/jrc2022-77801","title":"Power Over CTC, A Novel Way to Control Signal Power Supplies","year":2022,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Redundancy (engineering); SIGNAL (programming language); Electric power system; Power (physics); Computer science; Electrical engineering; Engineering; Reliability engineering; Telecommunications","score_opus":0.003985373833882871,"score_gpt":0.17841913757937267,"score_spread":0.1744337637454898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283716176","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":[0.67686933,0.00022725921,0.07076794,0.00032502395,0.001777403,0.0003555786,0.000111800364,0.0007029033,0.24886276],"genre_scores_gemma":[0.994287,4.4573576e-7,0.00019157444,0.00046966126,0.000049823102,0.000090621186,0.0000030806716,0.000038110957,0.004869683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989721,0.000010476922,0.00020632682,0.00018634732,0.00028554566,0.00033918096],"domain_scores_gemma":[0.99961686,0.000033830387,0.000013199226,0.00021053402,0.000017666871,0.00010790402],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014609302,0.00015345463,0.00018252163,0.00010309081,0.00012559947,0.000038076272,0.00018913021,0.000033813627,0.012803384],"category_scores_gemma":[0.0000049944083,0.0001382198,0.00007627009,0.0001999882,0.000010917916,0.000060943672,0.000059418475,0.0001263977,0.0001230462],"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":[0.000024663723,0.0000899122,0.0002272462,0.000009729616,0.000068318004,0.000013101248,0.0010238687,0.8406201,0.08022813,0.01586954,0.06158338,0.00024198875],"study_design_scores_gemma":[0.0017512111,0.0003452093,0.005758824,0.000009300643,0.000011731655,0.000038541217,0.0011327094,0.06096715,0.0020677957,0.000045324978,0.9271164,0.0007557963],"about_ca_topic_score_codex":0.00009564787,"about_ca_topic_score_gemma":0.000015871767,"teacher_disagreement_score":0.86553305,"about_ca_system_score_codex":0.00008420465,"about_ca_system_score_gemma":0.000010412686,"threshold_uncertainty_score":0.98809904},"labels":[],"label_agreement":null},{"id":"W4285303486","doi":"10.1109/tpel.2022.3189006","title":"Admittance Decomposition for Assessment of APF and STATCOM Impact on the Low-Frequency Stability of Railway Vehicle-Grid Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Power Electronics","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Admittance; Electrical impedance; Phasor; Control theory (sociology); Stability (learning theory); Grid; Coupling (piping); Engineering; Electric power system; Power (physics); Electronic engineering; Computer science; Control engineering; Electrical engineering; Mathematics; Control (management); Physics","score_opus":0.010013048074731863,"score_gpt":0.2548870244951179,"score_spread":0.24487397642038605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285303486","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":[0.81224483,0.0005460226,0.18539625,0.000041909134,0.0005919023,0.0005130214,0.00035661613,0.000053591593,0.00025583323],"genre_scores_gemma":[0.9995304,0.00007913249,0.00008155839,0.000010031979,0.000010827135,0.00023681643,0.0000069910698,0.000031312335,0.000012973521],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870014,0.00010469493,0.00040472992,0.00019891841,0.00028981784,0.00030172605],"domain_scores_gemma":[0.9991658,0.00026496514,0.000100292724,0.00035294128,0.00006618039,0.00004986064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004872651,0.0001792169,0.0002770266,0.00009284715,0.00020199473,0.00001653226,0.0001625569,0.00004834401,0.000057847956],"category_scores_gemma":[0.0000024787537,0.00014169006,0.00014755932,0.00024717403,0.00003928639,0.00007163004,0.000001244066,0.00031329543,3.4736448e-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":[0.00007741737,0.00027712205,0.000057259076,0.00014972706,0.000132975,3.9106013e-7,0.00027573356,0.93209696,0.064433336,0.0020138894,0.000078058554,0.00040715118],"study_design_scores_gemma":[0.0022279657,0.0073764054,0.002099645,0.00019285575,0.0001532927,0.000030874882,0.0011379713,0.7931136,0.1910615,0.00037590793,0.0014365268,0.0007934706],"about_ca_topic_score_codex":0.00007259105,"about_ca_topic_score_gemma":0.0000309407,"teacher_disagreement_score":0.1872855,"about_ca_system_score_codex":0.00047383824,"about_ca_system_score_gemma":0.00012795159,"threshold_uncertainty_score":0.5777953},"labels":[],"label_agreement":null},{"id":"W4293057857","doi":"10.1155/2022/7025130","title":"Impact Assessment of Interlocking Systems on Single-Track Railway Lines as a Measure Leading to Resilient Railway System","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"European Social Fund; European Regional Development Fund; Univerzita Pardubice","keywords":"Interlocking; Automatic train control; Track (disk drive); Computer science; Presumption; Reliability engineering; Block (permutation group theory); Blocking (statistics); Engineering; Control (management); Artificial intelligence","score_opus":0.012152102927082362,"score_gpt":0.26863773991481193,"score_spread":0.2564856369877296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293057857","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":[0.9832724,0.0005032229,0.012968654,0.000026767782,0.0023277458,0.00028143305,0.000024347106,0.0000709275,0.0005245291],"genre_scores_gemma":[0.9989947,0.000010889516,0.0006767867,0.0000063188445,0.000171626,0.00002633816,0.000010759411,0.000052248008,0.000050329203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99737185,0.000087543565,0.0012129466,0.0001820561,0.00087973475,0.00026587496],"domain_scores_gemma":[0.9987584,0.000082161074,0.0005419556,0.00019466231,0.00027570894,0.00014715186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068305014,0.00023461803,0.00056163035,0.0004833194,0.00011293425,0.00003257489,0.00022593222,0.000052777323,0.00001335006],"category_scores_gemma":[0.000024857658,0.00020808681,0.00025137627,0.00049513957,0.000011078822,0.0002575561,0.0000040087048,0.0003366476,0.0000018148237],"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":[0.00011871728,0.00008509233,0.00060447934,0.0002778748,0.00009620243,0.00004708843,0.0022158101,0.88080096,0.11298261,0.00037331518,0.000050665927,0.0023472088],"study_design_scores_gemma":[0.02161603,0.04224224,0.42539564,0.03555955,0.0014606935,0.0023514156,0.15107815,0.19376737,0.0940335,0.00011100971,0.026294962,0.00608943],"about_ca_topic_score_codex":0.000034197783,"about_ca_topic_score_gemma":0.000021328642,"teacher_disagreement_score":0.6870336,"about_ca_system_score_codex":0.00077740825,"about_ca_system_score_gemma":0.000078104815,"threshold_uncertainty_score":0.8485534},"labels":[],"label_agreement":null},{"id":"W4293067612","doi":"10.1155/2022/5964010","title":"An Optimization Method of High-Speed Railway Rescheduling to Meet Unexpected Large Passenger Flow","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities","keywords":"Train; Revenue; Transport engineering; Plan (archaeology); Schedule; Integer programming; Operations research; Beijing; Engineering; Computer science; Business","score_opus":0.005846278796274416,"score_gpt":0.2393624060134965,"score_spread":0.2335161272172221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293067612","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":[0.45057917,0.00009232428,0.54856026,0.000032819204,0.00055772724,0.00007847334,0.000028353737,0.000035682267,0.000035235753],"genre_scores_gemma":[0.75146794,0.0000143889265,0.24834748,0.0000125619035,0.00007546462,0.0000054587476,0.000040623505,0.000028585891,0.0000075233843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985198,0.000075378186,0.00071352953,0.0001223873,0.00039576233,0.00017314307],"domain_scores_gemma":[0.9992675,0.000038622344,0.00024794624,0.0001372722,0.00020985337,0.00009876738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000472166,0.00012490684,0.0003023731,0.00027943516,0.000092602626,0.000010367157,0.00014580668,0.00004133505,0.00008714075],"category_scores_gemma":[0.000018325223,0.0001274192,0.00008957947,0.0005547715,0.0000047119333,0.00036755198,0.0000019728707,0.00014095406,2.276577e-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":[0.00009866239,0.00006272379,0.000038677805,0.000028991575,0.00003276006,0.000011183228,0.002654355,0.87145203,0.123960465,0.0003729671,0.000026298983,0.0012608516],"study_design_scores_gemma":[0.0031518715,0.0009852188,0.009207817,0.00012227883,0.00011955366,0.000030018144,0.005722039,0.9362713,0.0419137,0.00019382578,0.0018089908,0.0004734079],"about_ca_topic_score_codex":0.000011930596,"about_ca_topic_score_gemma":0.00004451356,"teacher_disagreement_score":0.30088878,"about_ca_system_score_codex":0.000078832665,"about_ca_system_score_gemma":0.000028957758,"threshold_uncertainty_score":0.51960045},"labels":[],"label_agreement":null},{"id":"W4299923899","doi":"10.71781/10524","title":"The load planning problem for double-stack intermodal trains","year":2020,"lang":"en","type":"preprint","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Montréal; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Stack (abstract data type); Train; Computer science; Geography; Operating system; Cartography","score_opus":0.013173026311255703,"score_gpt":0.1830845245188288,"score_spread":0.16991149820757312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299923899","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":[0.49512535,0.07224036,0.09270724,0.0025485938,0.011428465,0.0039206287,0.00046916708,0.0027870992,0.3187731],"genre_scores_gemma":[0.99273944,0.00018595599,0.00098914,0.000033246022,0.0004855087,0.00015656435,0.00007225623,0.00005791915,0.0052799424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99816376,0.000032004053,0.00040699143,0.00048434734,0.00043742754,0.00047548182],"domain_scores_gemma":[0.99895483,0.00009170711,0.00015712185,0.00040896668,0.00014766914,0.00023971891],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00021245576,0.0004039188,0.0003566477,0.00008763474,0.0035979913,0.00015317036,0.00066857523,0.00033599813,0.0000032669902],"category_scores_gemma":[0.000017937653,0.00037046213,0.00034714083,0.00013114148,0.00015412402,0.00012736343,0.00036776406,0.0005455879,0.000012079643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045195245,0.000035919307,0.00025808642,0.00047350104,0.0005188078,0.00042085157,0.022651127,0.94168735,0.0039436985,0.021376,0.0051927287,0.0029899874],"study_design_scores_gemma":[0.0027443964,0.00015187055,0.0013609269,0.0007734924,0.00026986387,0.00042231719,0.01055153,0.33938977,0.0037563345,0.001685941,0.63749254,0.0014010206],"about_ca_topic_score_codex":0.0030917136,"about_ca_topic_score_gemma":0.0008555232,"teacher_disagreement_score":0.6322998,"about_ca_system_score_codex":0.004083113,"about_ca_system_score_gemma":0.00087620807,"threshold_uncertainty_score":0.9998747},"labels":[],"label_agreement":null},{"id":"W4302009578","doi":"10.1155/2022/4092011","title":"Synchronous Optimization for Demand-Driven Train Operation Plan in Rail Transit Network Using Nondominated Sorting Coevolutionary Memetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Sorting; Computer science; Mathematical optimization; Genetic algorithm; Train; Memetic algorithm; Operations research; Algorithm; Engineering; Mathematics; Machine learning","score_opus":0.00794776747389083,"score_gpt":0.21294898291407496,"score_spread":0.20500121544018413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302009578","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":[0.35383368,0.00046508457,0.6449394,0.000013264,0.00047969923,0.00022130932,0.000018565106,0.00002252669,0.000006421157],"genre_scores_gemma":[0.88628864,0.000053172585,0.113307014,0.000009552395,0.00014049004,0.00003321745,0.00013318351,0.00003168322,0.0000030202555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985353,0.000052695577,0.00083164376,0.00012177656,0.00023726486,0.00022132414],"domain_scores_gemma":[0.99951124,0.00005316988,0.00024392696,0.00005500424,0.000089949506,0.000046729256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044261376,0.0001352809,0.00028178445,0.0001955663,0.00018455455,0.0000127208305,0.00008810048,0.00005018991,0.000026600203],"category_scores_gemma":[0.000007932046,0.00015002837,0.00010155153,0.00032739842,0.000013847238,0.00038872668,0.0000011251326,0.00017982245,8.961151e-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":[0.000099388126,0.00003859529,0.00007460337,0.00005136504,0.000028776438,0.000018339251,0.0022527769,0.9869659,0.0054375995,0.000043895012,0.000007844836,0.004980944],"study_design_scores_gemma":[0.0022152616,0.00018884928,0.001196062,0.00008385817,0.000048426027,0.000036927744,0.0015758795,0.99415326,0.00018730071,0.00006523796,0.00009046818,0.00015844515],"about_ca_topic_score_codex":0.000011391704,"about_ca_topic_score_gemma":0.000057498062,"teacher_disagreement_score":0.53245497,"about_ca_system_score_codex":0.000256907,"about_ca_system_score_gemma":0.0000637124,"threshold_uncertainty_score":0.6117979},"labels":[],"label_agreement":null},{"id":"W4307099086","doi":"10.1155/2022/9776845","title":"Energy-Saving Metro Train Timetable Optimization Method Based on a Dynamic Passenger Flow Distribution","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":9,"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":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Train; Energy consumption; Regenerative brake; Automotive engineering; Engineering; Energy flow; Particle swarm optimization; Efficient energy use; Energy (signal processing); Simulation; Computer science; Brake","score_opus":0.0038778906408317043,"score_gpt":0.21007750038779413,"score_spread":0.20619960974696241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307099086","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":[0.03767148,0.000244198,0.96102744,0.000049786373,0.00070469116,0.00006234977,0.000076979384,0.000057645375,0.00010539888],"genre_scores_gemma":[0.92681175,0.000029480232,0.07267925,0.000025168214,0.00004905664,0.000016285167,0.00032263956,0.00003259831,0.0000337764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864584,0.00008127813,0.0005353918,0.00012632039,0.00042902803,0.00018214589],"domain_scores_gemma":[0.9994421,0.000073924304,0.00021971682,0.00010702674,0.00009213041,0.000065086024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045632568,0.00014633125,0.00024558033,0.00020510978,0.00013699154,0.000017971222,0.00010485729,0.000041502244,0.00010536767],"category_scores_gemma":[0.000012657411,0.00014653549,0.00014853702,0.00047651856,0.000006481419,0.00028663344,0.0000010952233,0.00021167674,4.3391913e-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":[0.000065941415,0.00005424334,0.000017451805,0.000022098664,0.000026160022,0.000015232329,0.00015425473,0.98099065,0.005193496,0.00009037264,0.000046960835,0.01332313],"study_design_scores_gemma":[0.0007639006,0.00021102843,0.0015772174,0.00003379853,0.000051431714,0.0000064444707,0.00026452597,0.9933355,0.0007520143,0.00004717395,0.0028043403,0.00015263047],"about_ca_topic_score_codex":0.0000066958423,"about_ca_topic_score_gemma":0.000017339662,"teacher_disagreement_score":0.88914025,"about_ca_system_score_codex":0.00027970792,"about_ca_system_score_gemma":0.000037064132,"threshold_uncertainty_score":0.5975543},"labels":[],"label_agreement":null},{"id":"W4308717649","doi":"10.3917/rindu1.224.0101","title":"Le train à hydrogène","year":2022,"lang":"fr","type":"article","venue":"Annales des Mines - Réalités industrielles","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-Québec","funders":"","keywords":"Political science; Humanities; Rail transportation; Art; Transport engineering; Engineering","score_opus":0.03298328446399465,"score_gpt":0.22131574009532218,"score_spread":0.18833245563132753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308717649","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":[0.81206864,0.12250113,0.00075800886,0.008002899,0.0040422487,0.00032720427,0.00036772556,0.0003613336,0.051570818],"genre_scores_gemma":[0.89510083,0.00084109174,0.00040238828,0.00014743095,0.001039788,0.00013906468,0.000101816695,0.00013815262,0.10208942],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967032,0.00031522426,0.0008014374,0.0005697897,0.0005261168,0.0010841972],"domain_scores_gemma":[0.9986946,0.00018369555,0.00014321154,0.00059425004,0.00008121982,0.00030303674],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00065935403,0.00054414244,0.00055395,0.00027394213,0.001002114,0.00012661927,0.0006824348,0.00035306008,0.0016373397],"category_scores_gemma":[0.00008603826,0.00065755873,0.00031122478,0.0012112763,0.00043496265,0.0003022641,0.0002860168,0.00075420504,0.00010473966],"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":[0.000045475,0.00077045703,0.0013215271,0.0003602535,0.00037408742,0.0005124732,0.010826981,0.47900224,0.002910295,0.06587501,0.21481958,0.22318165],"study_design_scores_gemma":[0.0005731306,0.00026754555,0.0002851444,0.00014294888,0.000059716385,0.00036833377,0.009515774,0.010414749,0.0017251893,0.000867953,0.9750319,0.0007475817],"about_ca_topic_score_codex":0.0059891,"about_ca_topic_score_gemma":0.0006390966,"teacher_disagreement_score":0.76021236,"about_ca_system_score_codex":0.00022911686,"about_ca_system_score_gemma":0.00040645586,"threshold_uncertainty_score":0.99958754},"labels":[],"label_agreement":null},{"id":"W4311519325","doi":"10.1002/net.22133","title":"Two new mixed‐integer programming models for the integrated train formation and shipment path optimization problem","year":2022,"lang":"en","type":"article","venue":"Networks","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Train; Integer programming; Arc routing; Computer science; Routing (electronic design automation); Path (computing); Mathematical optimization; Sorting; Yard; Integer (computer science); Linear programming; Operations research; Mathematics; Algorithm; Computer network","score_opus":0.012625914508705316,"score_gpt":0.194890541610702,"score_spread":0.18226462710199667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311519325","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":[0.0010107415,0.0015404867,0.99586517,0.000083717525,0.00036082056,0.0005837087,0.000003234319,0.00015449546,0.00039762713],"genre_scores_gemma":[0.98179656,0.00008728535,0.017037794,0.000034412038,0.00015999196,0.00056342303,0.00008348917,0.000033039818,0.0002040012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933535,0.00002589549,0.00020386121,0.00011158865,0.000108753244,0.00021453208],"domain_scores_gemma":[0.9997564,0.000043003693,0.000038326565,0.0000982694,0.00002167756,0.00004232456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032761958,0.000108253764,0.000096098855,0.000033079603,0.00023172285,0.0000758676,0.000093558985,0.00003358308,0.000011094538],"category_scores_gemma":[0.0000030138776,0.000080881466,0.0000352117,0.00014914329,0.000008893368,0.00014517292,0.000023799637,0.00014129789,1.6069058e-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":[0.000006120767,0.0000066450884,0.0000016722197,0.000013712473,0.000011789083,2.4019093e-7,0.0007866447,0.8884019,0.00000570716,0.0010705371,0.0013955618,0.10829946],"study_design_scores_gemma":[0.00029880874,0.000044175787,0.000002376714,0.000020599902,0.0000120330005,0.0000056330837,0.0007321344,0.98185426,0.0000049104933,0.00013208769,0.016793635,0.0000993274],"about_ca_topic_score_codex":0.0000777969,"about_ca_topic_score_gemma":0.000056938814,"teacher_disagreement_score":0.98078585,"about_ca_system_score_codex":0.00007888899,"about_ca_system_score_gemma":0.000013962572,"threshold_uncertainty_score":0.32982507},"labels":[],"label_agreement":null},{"id":"W4311813654","doi":"10.1155/2022/9604362","title":"Train Scheduling Optimization for an Urban Rail Transit Line: A Simulated-Annealing Algorithm Using a Large Neighborhood Search Metaheuristic","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":32,"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":"Chengdu Science and Technology Program; Sichuan Province Science and Technology Support Program; National Key Research and Development Program of China; Jiangsu Development and Reform Commission; National Natural Science Foundation of China; China Railway","keywords":"Headway; Simulated annealing; Urban rail transit; Dwell time; Train; Schedule; Metaheuristic; Scheduling (production processes); Interval (graph theory); Urban transit; Simulation; Computer science; Heuristic; Mathematical optimization; Engineering; Algorithm; Mathematics; Transport engineering; Public transport","score_opus":0.017673334775953887,"score_gpt":0.26307372965500053,"score_spread":0.24540039487904663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311813654","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":[0.31944883,0.00057483,0.6792082,0.000010534564,0.00043737786,0.00018608554,0.00007798176,0.000050492683,0.000005648431],"genre_scores_gemma":[0.8744349,0.000032910655,0.12510554,0.000012555298,0.00021503757,0.000009188007,0.00012216397,0.000062754465,0.0000049540695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982553,0.000059521404,0.00079837104,0.00017289944,0.00039953226,0.0003143565],"domain_scores_gemma":[0.9992799,0.000060669936,0.00020110435,0.0001079337,0.00022892923,0.00012144653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006642042,0.00018178667,0.00034169026,0.00027442642,0.00027946668,0.000034168643,0.00014901912,0.000060624003,0.00002798349],"category_scores_gemma":[0.000014679645,0.00019550706,0.00018680659,0.00042152047,0.000009942973,0.00057264563,0.0000015063059,0.00030643158,1.0180894e-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":[0.00009202197,0.000077725854,0.000021384847,0.00009243268,0.000073808245,0.000016526992,0.0039349874,0.98275566,0.008650895,0.000102056925,0.0000013050758,0.004181195],"study_design_scores_gemma":[0.0020252024,0.00041039046,0.00009750415,0.000055561304,0.00012972785,0.000021695274,0.0021302197,0.99396926,0.000714753,0.000070223345,0.00016954487,0.00020589812],"about_ca_topic_score_codex":0.0000067850488,"about_ca_topic_score_gemma":0.0000067944306,"teacher_disagreement_score":0.55498606,"about_ca_system_score_codex":0.0001272405,"about_ca_system_score_gemma":0.00007491255,"threshold_uncertainty_score":0.7972547},"labels":[],"label_agreement":null},{"id":"W4312133923","doi":"10.1155/2022/7035214","title":"Headway Optimisation for Metro Lines Based on Timetable Simulation and Simulated Annealing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Anhui Provincial Key Research and Development Plan","keywords":"Headway; Simulated annealing; Train; Simulation; Block (permutation group theory); Randomness; Monte Carlo method; Interval (graph theory); Line (geometry); Blocking (statistics); Computer science; Engineering; Automotive engineering; Algorithm; Mathematics; Statistics","score_opus":0.011841193968400261,"score_gpt":0.24669067447674828,"score_spread":0.23484948050834803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312133923","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":[0.82065934,0.0002701823,0.17841606,0.000034050227,0.0004052769,0.00014015632,0.00001804077,0.0000352628,0.000021647664],"genre_scores_gemma":[0.9918589,0.000008940757,0.0079420265,0.000022329074,0.00007220004,0.000007131792,0.000051972493,0.000021608723,0.0000149059415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921334,0.000016299236,0.0003896188,0.000079545396,0.00019928436,0.00010192074],"domain_scores_gemma":[0.9995093,0.00013609928,0.00015393559,0.00005292292,0.00010905597,0.000038730566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025357152,0.000088646004,0.00016248104,0.00018339722,0.000113742855,0.000012683304,0.00004027525,0.000026140771,0.000013127236],"category_scores_gemma":[0.000019665056,0.000087441615,0.00006387118,0.00018409886,0.000005022531,0.0002145513,4.8879133e-7,0.00009396861,1.1725621e-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":[0.00014725132,0.000020267507,0.00014936992,0.00004408394,0.000014378154,0.000001684693,0.0002762565,0.99073243,0.0050708763,0.000027574803,0.0000071164295,0.0035087217],"study_design_scores_gemma":[0.001106081,0.00032943417,0.0032139632,0.000027508466,0.000031905365,7.588235e-7,0.00021293935,0.9915266,0.0014705203,0.00006135536,0.0019261118,0.00009283624],"about_ca_topic_score_codex":0.000003412627,"about_ca_topic_score_gemma":0.0000039991187,"teacher_disagreement_score":0.17119956,"about_ca_system_score_codex":0.000058461406,"about_ca_system_score_gemma":0.000014618939,"threshold_uncertainty_score":0.35657656},"labels":[],"label_agreement":null},{"id":"W4312647746","doi":"10.1109/tte.2022.3218762","title":"Thermal Constrained Energy Optimization of Railway Cophase Systems With ESS Integration—An FRA-Pruned DQN Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Strategic Innovation Fund","keywords":"Thermal; Computer science; Energy (signal processing); Mathematical optimization; Mathematics; Physics; Thermodynamics; Statistics","score_opus":0.007665702016002022,"score_gpt":0.18529760947559834,"score_spread":0.1776319074595963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312647746","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":[0.06614923,0.00009945248,0.9317706,0.0000139776685,0.00034160778,0.00040961857,0.00015871285,0.00034867157,0.0007081183],"genre_scores_gemma":[0.9964409,0.00003856721,0.0017672586,0.000014812558,0.000030714033,0.00073808647,0.00074240327,0.00007001171,0.00015724677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998095,0.00014546499,0.00065278466,0.00035746055,0.00049653585,0.00025276782],"domain_scores_gemma":[0.99913895,0.000046122277,0.00019196783,0.00034515277,0.00018410146,0.000093710216],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002528611,0.00026673387,0.00029203706,0.0004046539,0.00035781352,0.000037682352,0.00019791602,0.000116818715,0.00010264821],"category_scores_gemma":[0.0000010949778,0.0002667254,0.00008631809,0.0010049917,0.00006834209,0.0003488755,2.6469689e-8,0.00027797162,0.000001118317],"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":[0.00014620725,0.00034574774,0.0000028898191,0.000044462697,0.000061628954,0.0000011614777,0.0009728419,0.8745442,0.11853198,0.001968136,0.0000078788935,0.0033728806],"study_design_scores_gemma":[0.0010552601,0.0004715624,0.00008072691,0.000019481258,0.00008245504,0.000008288619,0.0011614261,0.84872943,0.14798632,0.0000051527245,0.00008346107,0.00031643838],"about_ca_topic_score_codex":0.00024176225,"about_ca_topic_score_gemma":0.000056939494,"teacher_disagreement_score":0.93029165,"about_ca_system_score_codex":0.00015059685,"about_ca_system_score_gemma":0.00009961234,"threshold_uncertainty_score":0.9999785},"labels":[],"label_agreement":null},{"id":"W4321995222","doi":"10.5194/egusphere-egu23-9660","title":"Modeling the Impact of Geomagnetically Induced Currents on Electrified Railway Signalling Systems in the United Kingdom","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Geomagnetically induced current; Earth's magnetic field; Storm; Line (geometry); Ground; Space weather; Geomagnetic storm; Electric power system; Current (fluid); Interference (communication); Ionosphere; Meteorology; Power (physics); Electrical engineering; Physics; Engineering; Geophysics; Magnetic field; Mathematics","score_opus":0.08192640601600165,"score_gpt":0.2961528304321119,"score_spread":0.21422642441611023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321995222","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":[0.9842224,0.0002880321,0.011431474,0.000034429755,0.0012292896,0.0007417285,0.000013673359,0.00029715427,0.001741777],"genre_scores_gemma":[0.9993631,0.00007011776,0.000015434725,0.000007392279,0.00022106065,0.00014877613,0.000049155347,0.0000897163,0.000035229976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971543,0.00027885527,0.0009739338,0.00040290056,0.0006018829,0.0005881558],"domain_scores_gemma":[0.99837404,0.00051172014,0.00011847993,0.0008114749,0.000118115946,0.000066161214],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015136839,0.0004884452,0.000589081,0.0005904778,0.00008339163,0.00018348424,0.0011035039,0.00040824673,0.000010802911],"category_scores_gemma":[0.00013552226,0.00026129035,0.00030285507,0.0009907869,0.000025484203,0.00003423471,0.000116327305,0.0013838386,0.000020352567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013445948,0.000042229494,0.00004579194,0.00017351979,0.000097090524,0.0000052214805,0.00053559,0.9948054,0.002423792,0.0015822985,0.00011425552,0.00016137717],"study_design_scores_gemma":[0.0002485284,0.00011108292,0.00031890618,0.0006998506,0.000022543703,0.00000289958,0.00022416464,0.99782765,0.00009575849,0.00013352766,0.000016952898,0.00029815198],"about_ca_topic_score_codex":0.0245873,"about_ca_topic_score_gemma":0.00011211172,"teacher_disagreement_score":0.024475187,"about_ca_system_score_codex":0.00017636668,"about_ca_system_score_gemma":0.00010582074,"threshold_uncertainty_score":0.9999839},"labels":[],"label_agreement":null},{"id":"W4322825545","doi":"10.5281/zenodo.7693839","title":"Wideband Modeling of Power SiC MOSFET Module and Conducted EMI Prediction of MVDC Railway Electrification System","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"EMI; Electrification; Wideband; Electrical engineering; Engineering; Power (physics); Electronic engineering; Electromagnetic interference; Engineering physics; Physics; Electricity","score_opus":0.02508229958555893,"score_gpt":0.18947129084799558,"score_spread":0.16438899126243664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322825545","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":[0.853197,0.0004914838,0.106712684,0.000104482926,0.00013126322,0.00044521905,0.00022740033,0.0011524732,0.037538003],"genre_scores_gemma":[0.99922925,0.000049575294,0.00009447042,0.0000053952026,0.00003889856,4.637597e-8,0.00019622654,0.00035922992,0.000026879656],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990116,0.000081630955,0.0003391423,0.00020058185,0.00020858661,0.00015846726],"domain_scores_gemma":[0.99933,0.000008798469,0.00007248071,0.00018992033,0.00030261077,0.000096207346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026121436,0.00009757086,0.0001688644,0.00010865416,0.00023213789,0.00006928708,0.00022834147,0.000058745652,0.00017464873],"category_scores_gemma":[0.00012977097,0.00010250819,0.00002710589,0.00041045705,0.00004413767,0.00015878805,0.00006308168,0.00011091082,0.00006214682],"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":[0.00005620439,0.000052422318,0.000011332807,0.00079735304,0.00009408601,0.0000020193113,0.0037377721,0.27671114,0.69891435,0.004890097,0.006411693,0.008321532],"study_design_scores_gemma":[0.00050182495,0.00019620189,0.00017604037,0.00008832768,0.000017407408,0.000023142537,0.0006663798,0.9590389,0.0142140975,0.00001070232,0.02493849,0.00012849718],"about_ca_topic_score_codex":0.000014393576,"about_ca_topic_score_gemma":6.207317e-8,"teacher_disagreement_score":0.68470025,"about_ca_system_score_codex":0.000047854348,"about_ca_system_score_gemma":0.0000022896083,"threshold_uncertainty_score":0.41801625},"labels":[],"label_agreement":null},{"id":"W4324046659","doi":"10.1155/2023/3196066","title":"Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"","keywords":"NetLogo; Train; Schedule; Headway; Computer science; Simulation; Platoon; Service (business); Transport engineering; Engineering","score_opus":0.00679168958152558,"score_gpt":0.2251212150674624,"score_spread":0.21832952548593684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324046659","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":[0.32704931,0.000040484087,0.67234284,0.000019132272,0.00017707131,0.00024000189,0.000011511563,0.00011565335,0.0000039717042],"genre_scores_gemma":[0.95871603,0.000021745234,0.04097199,0.000009499314,0.00006954191,0.000049050013,0.00010933818,0.000042901556,0.000009883979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990224,0.0000056539616,0.00047148459,0.0001059901,0.00023176086,0.00016266445],"domain_scores_gemma":[0.99926794,0.00012983468,0.00017261843,0.000079878264,0.00028888506,0.000060818707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020050153,0.00013777408,0.00020755298,0.00021611944,0.00007350736,0.000020239204,0.000081648366,0.000072910996,0.0000081361595],"category_scores_gemma":[0.000009158179,0.000119454475,0.00008406113,0.0003957601,0.000011171666,0.0003606475,2.943681e-7,0.00008941215,8.3037366e-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":[0.00015821982,0.00002438789,0.000088845925,0.00022558738,0.000030971394,0.000006773768,0.00045195612,0.9879639,0.0105834855,0.00003633036,0.0000016141065,0.0004279388],"study_design_scores_gemma":[0.001632249,0.0002659292,0.00096108095,0.00022141743,0.00004396595,6.9972833e-7,0.0005046313,0.9873531,0.008627218,0.000013810375,0.0002311241,0.00014476835],"about_ca_topic_score_codex":0.0000026052332,"about_ca_topic_score_gemma":0.000029770132,"teacher_disagreement_score":0.6316667,"about_ca_system_score_codex":0.000042969805,"about_ca_system_score_gemma":0.000038467737,"threshold_uncertainty_score":0.48712122},"labels":[],"label_agreement":null},{"id":"W4360618234","doi":"10.1155/2023/1217352","title":"Study on Energy-Saving Train Trajectory Optimization Based on Coasting Control in Metro Lines","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":9,"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":"Energy consumption; Punctuality; Trajectory; Energy (signal processing); Computer science; Control theory (sociology); Optimal control; Trajectory optimization; Control (management); Efficient energy use; Mathematical optimization; Engineering; Mathematics; Artificial intelligence","score_opus":0.011077073106824792,"score_gpt":0.2312474449250868,"score_spread":0.22017037181826202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360618234","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":[0.89565164,0.000046573045,0.103352524,0.00002932206,0.00059290795,0.000111556976,0.000006279508,0.000090251815,0.000118921685],"genre_scores_gemma":[0.9989042,0.000016391868,0.00087226246,0.000026168318,0.000114482624,0.000013123803,0.000013450204,0.00003285701,0.000007065033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987375,0.000045929515,0.00061143,0.00011420537,0.00031995232,0.0001709584],"domain_scores_gemma":[0.99947846,0.00016453832,0.000159886,0.00007757083,0.0000701704,0.00004939562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039215953,0.0001416428,0.0002608703,0.0005646503,0.00003913401,0.000013947476,0.00007556935,0.000048141268,0.0000042385936],"category_scores_gemma":[0.000037510898,0.00013076849,0.000080357466,0.00058604416,0.0000062674003,0.00018662972,2.7603502e-7,0.00015467741,7.202828e-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":[0.00009634494,0.00009805871,0.002402046,0.000019855734,0.000015869105,0.00007208445,0.00083843834,0.99030834,0.0030493243,0.000014608828,0.0000067046553,0.0030783478],"study_design_scores_gemma":[0.0037612095,0.0007585731,0.16133632,0.00030166327,0.000029808127,8.1960394e-7,0.003226731,0.82980716,0.0005253452,0.00000995378,0.000052734704,0.00018967366],"about_ca_topic_score_codex":0.00000858954,"about_ca_topic_score_gemma":0.00022602992,"teacher_disagreement_score":0.16050115,"about_ca_system_score_codex":0.00007681233,"about_ca_system_score_gemma":0.000022216125,"threshold_uncertainty_score":0.53325844},"labels":[],"label_agreement":null},{"id":"W4362599771","doi":"10.1155/2023/3897353","title":"Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"National Natural Science Foundation of China","keywords":"Train; Plan (archaeology); Multi-objective optimization; Genetic algorithm; Computer science; Pareto principle; Mathematical optimization; Pareto optimal; Urban rail transit; Integer programming; Integer (computer science); Nonlinear programming; Operations research; Simulation; Nonlinear system; Transport engineering; Engineering; Algorithm; Mathematics","score_opus":0.010375909444599348,"score_gpt":0.25840457953337115,"score_spread":0.2480286700887718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362599771","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":[0.06382488,0.0002584242,0.9344681,0.000097064134,0.00083110906,0.00036200482,0.000039695533,0.000080027596,0.00003870267],"genre_scores_gemma":[0.8992079,0.0001895312,0.1001399,0.000014348029,0.00024839278,0.00005254928,0.000076070406,0.000033344895,0.000037958333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990904,0.000033813907,0.00046759483,0.000091501184,0.00017362139,0.0001430846],"domain_scores_gemma":[0.9991738,0.00027737377,0.00018464499,0.000062430336,0.00026585173,0.000035864414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004920577,0.00011204834,0.00017391593,0.00012685223,0.00014904793,0.000029254174,0.00007994006,0.000054837732,0.0000039505057],"category_scores_gemma":[0.000059428006,0.00008392559,0.0000814848,0.000425241,0.000012146605,0.00037712845,5.8021425e-7,0.00010525794,8.55063e-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":[0.000056373883,0.000008456202,0.000037767353,0.000024130306,0.000047234756,0.0000020538164,0.011495967,0.9655649,0.01130322,0.00015545396,0.00010097585,0.01120346],"study_design_scores_gemma":[0.0013788623,0.0001234158,0.0063860407,0.000062136154,0.000049555914,0.0000029359458,0.008886863,0.9770651,0.004531148,0.00002725528,0.0013597236,0.00012696242],"about_ca_topic_score_codex":0.000006000102,"about_ca_topic_score_gemma":0.000054376775,"teacher_disagreement_score":0.83538306,"about_ca_system_score_codex":0.00005810778,"about_ca_system_score_gemma":0.000033586522,"threshold_uncertainty_score":0.34223863},"labels":[],"label_agreement":null},{"id":"W4365140859","doi":"10.1109/mei.2023.10101727","title":"Editorial","year":2023,"lang":"en","type":"editorial","venue":"IEEE Electrical Insulation Magazine","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Presentation (obstetrics); Engineering; Electrical engineering; Library science; Computer science; Medicine","score_opus":0.008241248302886745,"score_gpt":0.23275017946533388,"score_spread":0.22450893116244713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365140859","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000079483274,0.00028681668,0.0015818189,0.000008703787,0.99308705,0.00015319885,0.000066151144,0.0017862968,0.002950473],"genre_scores_gemma":[0.0007192519,0.00030140937,0.000041800784,0.0000026286098,0.9939765,0.000057612393,0.00043872296,0.00020723032,0.004254854],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99705344,0.000045652065,0.0006345727,0.00041922822,0.0012464222,0.0006006731],"domain_scores_gemma":[0.9983198,0.00075711985,0.00009800866,0.00037610586,0.00028731645,0.00016164385],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031777826,0.0004308895,0.00052182534,0.0003774861,0.000085654545,0.00011440304,0.00031965374,0.0015434866,0.000028804552],"category_scores_gemma":[0.000763389,0.0004318121,0.0001616402,0.001201175,0.000024502322,0.00012320129,0.000018925659,0.0011466135,0.002430954],"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":[0.0000102554795,0.000016416616,6.2571496e-7,0.000058232643,0.00004024109,0.00000558673,0.00001177317,0.041427583,0.0015296359,0.000029007668,0.95560694,0.0012636922],"study_design_scores_gemma":[0.00034094532,0.00006988655,0.000020156078,0.000036073987,0.000026913503,3.9006653e-7,2.8178934e-7,0.056864988,0.00005563347,0.00009365397,0.942054,0.00043707673],"about_ca_topic_score_codex":0.000037658818,"about_ca_topic_score_gemma":0.000013920849,"teacher_disagreement_score":0.015437406,"about_ca_system_score_codex":0.00031894533,"about_ca_system_score_gemma":0.00012440629,"threshold_uncertainty_score":0.9998134},"labels":[],"label_agreement":null},{"id":"W4377030597","doi":"10.1016/j.cie.2023.109302","title":"A novel Markov model for near-term railway delay prediction","year":2023,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Concordia University","funders":"","keywords":"Markov chain; Interpretability; Computer science; Benchmark (surveying); Markov model; Train; Kernel (algebra); Variable-order Markov model; Markov process; Term (time); Algorithm; Mathematical optimization; Machine learning; Mathematics; Statistics","score_opus":0.03081535407908533,"score_gpt":0.21012992051397536,"score_spread":0.17931456643489002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377030597","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":[0.14469402,0.000049553917,0.847456,0.000026935177,0.0052035498,0.00036489373,0.000067118475,0.0019989004,0.0001390271],"genre_scores_gemma":[0.98740053,0.000010439859,0.010215082,0.00001418623,0.0016540597,0.00016803658,0.00010433984,0.00013947987,0.00029386583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859685,0.0000050533854,0.00039218552,0.00027917622,0.00018730614,0.000539425],"domain_scores_gemma":[0.99943244,0.00009904786,0.00003244562,0.00025325507,0.000034474313,0.00014835651],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026719156,0.00027164642,0.00028082493,0.0002158663,0.000107949185,0.000117994205,0.00024316277,0.0002569834,0.0000031360105],"category_scores_gemma":[0.000040723917,0.0002993537,0.00014111248,0.0005120447,0.000015713134,0.0001742125,0.00005353414,0.00024767235,0.000014691204],"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":[0.000007661241,0.0000086719365,0.000017940612,0.000052684827,0.000053152333,0.0000031737034,0.00019602395,0.9754992,0.0058049536,0.00013030366,0.00649469,0.011731548],"study_design_scores_gemma":[0.0010935551,0.000035925535,0.000096108255,0.00011140488,0.00001623571,0.000012698887,0.00000804929,0.99129504,0.00025889804,0.0000032989742,0.006788017,0.0002807513],"about_ca_topic_score_codex":0.000010249255,"about_ca_topic_score_gemma":0.0000012186727,"teacher_disagreement_score":0.8427065,"about_ca_system_score_codex":0.0001228453,"about_ca_system_score_gemma":0.000039123664,"threshold_uncertainty_score":0.9999459},"labels":[],"label_agreement":null},{"id":"W4378193914","doi":"10.1155/2023/8989644","title":"A Rescheduling Approach for Freight Railway considering Equity and Efficiency by an Integrated Genetic Algorithm","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Chinese Academy of Engineering; China Scholarship Council; U.S. Department of Transportation","keywords":"Equity (law); Computer science; Operations research; Genetic algorithm; Track (disk drive); Benchmark (surveying); Rail freight transport; Transport engineering; Engineering","score_opus":0.014532723846783925,"score_gpt":0.24787108127715873,"score_spread":0.23333835743037482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378193914","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":[0.44869804,0.0005086211,0.55034095,0.0000067395786,0.00022103828,0.00010122988,0.000019941353,0.00006872687,0.000034709945],"genre_scores_gemma":[0.8151208,0.00029116197,0.18437722,0.000007082188,0.0000815703,0.000013677954,0.00006265672,0.000032049495,0.0000137543675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891335,0.000014120738,0.0005136954,0.00013893693,0.00019879175,0.00022111746],"domain_scores_gemma":[0.99953145,0.000039282142,0.00012615709,0.00007824412,0.00011816749,0.00010670589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031927787,0.00013655832,0.00023636126,0.00015531636,0.00007752506,0.00003486497,0.00009719363,0.00006829869,0.000001857393],"category_scores_gemma":[0.000019005174,0.0001226785,0.000061722625,0.0002991902,0.000025426538,0.00030096015,0.0000023532657,0.0001327913,3.2031988e-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":[0.000016687492,0.00002093718,0.00004614929,0.000100625344,0.000019621953,0.000007658165,0.0008945072,0.91302943,0.028309193,0.00003656535,0.000036503094,0.057482094],"study_design_scores_gemma":[0.0019134426,0.0003930607,0.006653987,0.00014396387,0.00006179299,0.000037307203,0.0024845356,0.9754311,0.009500292,0.00034397535,0.0026807643,0.00035577093],"about_ca_topic_score_codex":0.000007328568,"about_ca_topic_score_gemma":0.000007580719,"teacher_disagreement_score":0.36642277,"about_ca_system_score_codex":0.00003294735,"about_ca_system_score_gemma":0.000025391668,"threshold_uncertainty_score":0.5002684},"labels":[],"label_agreement":null},{"id":"W4379524577","doi":"10.1109/icps57144.2023.10142080","title":"AC Electromagnetic Interference Study between Railways and Nearby Power Lines under Steady-State Operation","year":2023,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Saskatchewan; Manitoba Hydro","funders":"","keywords":"EMI; Electromagnetic interference; Ballast; Electrical engineering; Engineering; Line (geometry); Power (physics); Electric power transmission; Voltage; Electric power system; Sensitivity (control systems); Automotive engineering; Electronic engineering; Physics","score_opus":0.01644017735579986,"score_gpt":0.23429610391440128,"score_spread":0.21785592655860142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379524577","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":[0.99109125,0.000108360226,0.0027371317,0.00005824813,0.00019955104,0.00019643681,0.000002428837,0.00056266127,0.0050439476],"genre_scores_gemma":[0.99673647,0.00003192785,0.000049434573,0.000012613685,0.00005687271,0.000023159966,0.000007070501,0.000029207158,0.0030532698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991293,0.000029046932,0.00023750009,0.00020739324,0.00012667548,0.00027011408],"domain_scores_gemma":[0.9996749,0.000044075576,0.000013612757,0.00017265599,0.000027257911,0.00006746535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017845999,0.0001537273,0.0001763467,0.00011791745,0.0000728141,0.000111591624,0.000106262276,0.000043842272,0.00006492086],"category_scores_gemma":[0.0000089178875,0.00012751049,0.000019914456,0.00029478295,0.000018429751,0.00012846461,0.00004318374,0.00009825838,0.00013386413],"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":[0.00004889897,0.00039067087,0.07473383,0.0003652368,0.00074647873,0.00008344223,0.0318489,0.60777307,0.22180118,0.0045555253,0.013217088,0.04443568],"study_design_scores_gemma":[0.002474822,0.0032677376,0.61196357,0.00011404117,0.00008562612,0.000017720902,0.009842121,0.35800377,0.008719732,0.0005287811,0.0032449986,0.0017370532],"about_ca_topic_score_codex":0.000098764656,"about_ca_topic_score_gemma":0.00013868109,"teacher_disagreement_score":0.5372298,"about_ca_system_score_codex":0.000020450716,"about_ca_system_score_gemma":0.00000994694,"threshold_uncertainty_score":0.5199727},"labels":[],"label_agreement":null},{"id":"W4380324379","doi":"10.1680/jtran.22.00077","title":"Energy-optimal control of intelligent track inspection trains: design and experiment","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Institution of Civil Engineers - Transport","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trajectory; Optimal control; Energy consumption; Controller (irrigation); MATLAB; Train; Model predictive control; Control theory (sociology); Computer science; Track (disk drive); Energy (signal processing); Trajectory optimization; State of charge; Software; Simulation; Control engineering; Battery (electricity); Engineering; Control (management); Mathematical optimization; Artificial intelligence","score_opus":0.010498190350443052,"score_gpt":0.19052789304740636,"score_spread":0.1800297026969633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380324379","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":[0.7658034,0.00061712926,0.23047255,0.000032123604,0.00046677445,0.0002662494,0.000013984113,0.00028160552,0.00204618],"genre_scores_gemma":[0.9991861,0.00030233373,0.00038942468,0.0000028825016,0.000033314678,0.000033595144,0.000001985646,0.000020422362,0.000029958175],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988966,0.0000030754336,0.00050215406,0.00014251114,0.00027724277,0.00017839763],"domain_scores_gemma":[0.99964476,0.000017072754,0.000102931444,0.00007859869,0.000104723986,0.00005189186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029113804,0.00016790384,0.00030202686,0.00021032,0.000041942974,0.0000046294786,0.00016349436,0.00009402623,0.000004767454],"category_scores_gemma":[0.000012146086,0.00013915656,0.00012050721,0.00047297892,0.00015466167,0.00016430029,0.000005793443,0.00008231447,3.3422793e-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":[0.00003628855,0.0000450332,0.00019780592,0.00039180875,0.00008785311,4.7156212e-7,0.0018985965,0.83510137,0.14806004,0.013476628,0.00010343328,0.0006007018],"study_design_scores_gemma":[0.00086229783,0.00018481555,0.0026841403,0.00040475943,0.00008773565,0.000011245907,0.0008348549,0.34056258,0.65253186,0.0000969377,0.0014747911,0.00026400032],"about_ca_topic_score_codex":0.000040561852,"about_ca_topic_score_gemma":0.000005766271,"teacher_disagreement_score":0.50447184,"about_ca_system_score_codex":0.00004316183,"about_ca_system_score_gemma":0.000026089305,"threshold_uncertainty_score":0.567464},"labels":[],"label_agreement":null},{"id":"W4381786149","doi":"10.1109/tte.2023.3287891","title":"Analysis and Control of Cascaded Energy Storage System for Energy Efficiency and Power Quality Improvement in Electrified Railways","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Transportation Electrification","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Power (physics); Power control; Energy storage; Computer science; Power management; Automotive engineering; Reliability engineering; Electric power system; Grid; AC power; Efficient energy use; Control engineering; Engineering; Electrical engineering; Voltage","score_opus":0.008007791120912426,"score_gpt":0.21833947880763113,"score_spread":0.2103316876867187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381786149","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":[0.33973682,0.00013590402,0.6595691,0.000016333164,0.00009326618,0.00018643144,0.000084697655,0.00015248195,0.000025022568],"genre_scores_gemma":[0.9991978,0.00024556706,0.00003955052,0.0000094731895,0.000009036443,0.0003406175,0.00007191656,0.000027797638,0.000058243608],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983919,0.000055848297,0.0007120349,0.00034363684,0.00021569744,0.00028083095],"domain_scores_gemma":[0.9993473,0.0001678153,0.00012768566,0.00019543983,0.00009164969,0.00007005363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043825788,0.00019473181,0.00038780126,0.00092489755,0.000106039435,0.000018302264,0.00007243905,0.00015792779,0.0000030342694],"category_scores_gemma":[0.0000028487293,0.00020730266,0.00012036283,0.00179669,0.000033099306,0.000110390065,1.8818211e-8,0.00009301258,3.845234e-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":[0.00011109765,0.00007261182,0.000064405525,0.00015968221,0.00019181192,7.671792e-7,0.00056520605,0.35010803,0.63361675,0.0028716947,0.0000025876543,0.012235393],"study_design_scores_gemma":[0.0018321943,0.00026889794,0.0111625325,0.000028929704,0.00027539665,7.8559316e-7,0.00034656454,0.29658043,0.68909764,0.000036044265,0.00006264276,0.00030791928],"about_ca_topic_score_codex":0.000638782,"about_ca_topic_score_gemma":0.0010776632,"teacher_disagreement_score":0.6595295,"about_ca_system_score_codex":0.00010044169,"about_ca_system_score_gemma":0.000028629029,"threshold_uncertainty_score":0.84535575},"labels":[],"label_agreement":null},{"id":"W4385236367","doi":"10.1109/itec55900.2023.10187054","title":"Design Considerations for Electric Traction Power Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ontario Tech University","funders":"","keywords":"Electrification; Upgrade; Transit system; Transit (satellite); Electric power system; Sustainability; Transport engineering; Automotive engineering; Public transport; Engineering; Power (physics); Computer science; Electrical engineering; Electricity","score_opus":0.03369536784059517,"score_gpt":0.22759855494726997,"score_spread":0.1939031871066748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385236367","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":[0.027346248,0.00023826805,0.94516593,0.00007629608,0.002091456,0.00054152403,0.0000029806126,0.00203002,0.022507286],"genre_scores_gemma":[0.9972602,0.000012810501,0.00038844653,0.000007721643,0.000044327546,0.00015130345,0.0000026678435,0.00001970523,0.0021127944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995353,0.000011177264,0.0001400252,0.00008103972,0.00006224021,0.0001702067],"domain_scores_gemma":[0.9996559,0.00019064356,0.00001006745,0.00008365628,0.000030225174,0.000029486857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017057887,0.00006415849,0.0000802771,0.000114372604,0.00007857245,0.00005001527,0.000023771076,0.000047051668,0.000029461033],"category_scores_gemma":[0.000031389565,0.000057600224,0.00002726113,0.00024251721,0.000002387462,0.000071621194,0.0000014422611,0.000030580824,0.00012713883],"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":[7.752267e-7,0.000003880968,0.000002041927,0.00001752245,0.000012464719,0.0000010842168,0.00007222017,0.9139447,0.031174716,0.0071944045,0.047476906,0.00009928571],"study_design_scores_gemma":[0.00010505147,0.000032549495,0.00008185433,0.0000050344893,0.0000042235183,0.000011149968,0.0000946856,0.99029154,0.0027996672,0.00020153292,0.0062779067,0.000094836236],"about_ca_topic_score_codex":0.000019209061,"about_ca_topic_score_gemma":0.000003514988,"teacher_disagreement_score":0.96991396,"about_ca_system_score_codex":0.000028673334,"about_ca_system_score_gemma":0.000012854146,"threshold_uncertainty_score":0.2348869},"labels":[],"label_agreement":null},{"id":"W4385349667","doi":"10.1287/trsc.2022.0271","title":"An Iterated Local Search Metaheuristic for the Capacitated Demand-Driven Timetabling Problem","year":2023,"lang":"en","type":"article","venue":"Transportation Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Polytechnique Montréal; École de Technologie Supérieure; Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Iterated local search; Train; Mathematical optimization; Public transport; Computer science; Metaheuristic; Iterated function; Scheduling (production processes); Local search (optimization); Integer programming; Moment (physics); Operations research; Transport engineering; Engineering; Mathematics","score_opus":0.02547928558253623,"score_gpt":0.2717124748811242,"score_spread":0.24623318929858798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385349667","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":[0.50168765,0.000050640767,0.49722964,0.00004256685,0.00020250755,0.00026341464,0.000024278525,0.00037257661,0.00012671606],"genre_scores_gemma":[0.99829996,0.000014788587,0.0014178074,0.00001071479,0.000031500735,0.00007213667,0.000045961566,0.000017565662,0.00008956937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881124,0.000014746316,0.00024586212,0.00022621731,0.00035375063,0.0003481991],"domain_scores_gemma":[0.9994388,0.000097912205,0.000020902762,0.00018061003,0.00017243427,0.00008931631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009628002,0.000100001256,0.000110388246,0.00018936775,0.0003567867,0.000104977466,0.00031413368,0.000030262305,0.0000146479815],"category_scores_gemma":[0.000010827985,0.00007159429,0.000036272097,0.0019679782,0.00018964213,0.00036513445,0.0000014063395,0.00007328799,0.000031921925],"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":[0.0000032110524,0.0000055713926,0.0000941607,0.000031062827,0.0000075172043,0.0000018616211,0.001969254,0.95334715,0.04120037,0.0010742927,0.000036086007,0.0022294421],"study_design_scores_gemma":[0.0001535189,0.000030118728,0.013434026,0.000014599534,0.000015706282,0.0000011094689,0.00045465215,0.98056614,0.004721422,0.000052679738,0.0004496159,0.000106406354],"about_ca_topic_score_codex":0.00008456511,"about_ca_topic_score_gemma":0.00009176216,"teacher_disagreement_score":0.49661228,"about_ca_system_score_codex":0.000024168914,"about_ca_system_score_gemma":0.00005733088,"threshold_uncertainty_score":0.29195303},"labels":[],"label_agreement":null},{"id":"W4386315536","doi":"10.1541/ieejias.143.636","title":"Transformerless Power Supply for AC Rail Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEJ Transactions on Industry Applications","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Nexen (Canada)","funders":"","keywords":"Transformer; Electrical engineering; AC adapter; Converters; Engineering; Switched-mode power supply; Power (physics); AC power; Automotive engineering; Computer science; Topology (electrical circuits); Voltage; Physics","score_opus":0.014137516516789503,"score_gpt":0.2387440328852007,"score_spread":0.22460651636841122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386315536","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":[0.06853489,0.00008287764,0.91532433,0.0012915838,0.0005999092,0.0012576564,0.00070947147,0.00206095,0.01013832],"genre_scores_gemma":[0.9927876,0.00005484634,0.0003031072,0.000040823303,0.000102616854,0.0038415869,0.000046456495,0.00006324156,0.0027597095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989393,0.000009501643,0.00028765007,0.0002502915,0.00014997616,0.00036329372],"domain_scores_gemma":[0.99936944,0.00009137041,0.00002017515,0.00034596777,0.00004665865,0.00012639828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011480168,0.0001869369,0.00016198457,0.00021303106,0.0003513415,0.00004317483,0.00020967284,0.00036092073,0.00013889515],"category_scores_gemma":[0.0000013235535,0.0001962678,0.00013433429,0.0009697659,0.000059603048,0.000117627395,8.3903865e-7,0.0004013944,0.00025973486],"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":[0.000014650598,0.00018037194,0.00002417244,0.00011717587,0.000121195204,0.0000011759089,0.00071312586,0.8380344,0.010976346,0.0053715,0.008205079,0.1362408],"study_design_scores_gemma":[0.0010599641,0.00011347901,0.0013096258,0.000077919416,0.00007732786,0.000016076203,0.0019156238,0.04157001,0.027446523,0.0008311141,0.9248006,0.00078175485],"about_ca_topic_score_codex":0.000014645253,"about_ca_topic_score_gemma":0.000022836659,"teacher_disagreement_score":0.92425275,"about_ca_system_score_codex":0.000060644703,"about_ca_system_score_gemma":0.000033874225,"threshold_uncertainty_score":0.8003569},"labels":[],"label_agreement":null},{"id":"W4386796680","doi":"10.23977/jeis.2023.080307","title":"Design of Software for Calculating Tidal Currents in AT Power Supply Systems","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Traction power network; Software; Power (physics); Range (aeronautics); Traction (geology); Switched-mode power supply applications; Mode (computer interface); Automotive engineering; Computer science; Voltage; Electrical engineering; Engineering; Switched-mode power supply; Mechanical engineering","score_opus":0.011318977985110855,"score_gpt":0.23523234402292617,"score_spread":0.2239133660378153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386796680","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":[0.92442286,0.00053946907,0.07437555,0.0000104780165,0.00040059915,0.00011085994,0.000003027411,0.000013869282,0.00012332168],"genre_scores_gemma":[0.99939924,0.00018089425,0.00039208104,0.0000033660112,0.000010766866,0.0000027257233,9.544135e-7,0.0000028268912,0.000007114149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904335,0.0000060102275,0.00045632807,0.000032924698,0.00025869568,0.00020267548],"domain_scores_gemma":[0.99949217,0.00005025539,0.00016417919,0.00004637461,0.0002063048,0.00004074646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012248445,0.000050491795,0.000118513424,0.00035064702,0.000062444335,0.00004810381,0.00011457477,0.000024878607,0.0000010726243],"category_scores_gemma":[0.00013066537,0.000041031006,0.00001918429,0.0006631856,0.000026266527,0.0013680531,0.000014840669,0.000061717576,0.0000012280955],"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":[0.000006529195,0.0000025841516,0.0005706797,0.0000603956,0.000002545819,1.7894855e-7,0.00068660406,0.9919489,0.0035622132,0.0009634572,0.00016927825,0.002026632],"study_design_scores_gemma":[0.0003175681,0.00015191696,0.00288211,0.000093318384,0.0000020045652,0.000024025256,0.00018251475,0.9920472,0.0011370572,0.000028936836,0.0030667332,0.00006662465],"about_ca_topic_score_codex":0.0000027182994,"about_ca_topic_score_gemma":6.1852217e-7,"teacher_disagreement_score":0.07497644,"about_ca_system_score_codex":0.000111492445,"about_ca_system_score_gemma":0.0001112152,"threshold_uncertainty_score":0.1673196},"labels":[],"label_agreement":null},{"id":"W4387128507","doi":"10.1155/2023/5230483","title":"Joint Optimal Train Rescheduling and Passenger Flow Control for Speed Limit and High-Demand Scenarios of Urban Rail Transits","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Solver; Limit (mathematics); Computer science; Speed limit; Mathematical optimization; Operations research; Engineering; Simulation; Transport engineering; Mathematics","score_opus":0.009185786479860831,"score_gpt":0.20521533698201966,"score_spread":0.19602955050215884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387128507","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":[0.9309289,0.0007377777,0.06770756,0.00012201806,0.0002645801,0.00016998184,0.000032071213,0.000032078307,0.0000050613107],"genre_scores_gemma":[0.98788774,0.00032415058,0.011611089,0.0000065997037,0.00011452056,0.0000041945746,0.000011876225,0.000026780264,0.000013069558],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989636,0.000013407156,0.0005877795,0.00010612953,0.0001620941,0.00016698464],"domain_scores_gemma":[0.9995196,0.00008207739,0.0001489832,0.00005363165,0.00011140931,0.00008432499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033655993,0.00012792669,0.00036133,0.00016334561,0.000046473568,0.000013469735,0.00003846016,0.00007056108,0.000002172748],"category_scores_gemma":[0.000020954765,0.0001156478,0.00008636581,0.00014058886,0.0000255068,0.00024531147,4.3754125e-7,0.00010858631,1.4543848e-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":[0.0001453179,0.0000115016755,0.00012864928,0.0002512763,0.000058166774,0.000011415182,0.0025639345,0.8285452,0.16452311,0.00008129507,0.00001988747,0.0036602183],"study_design_scores_gemma":[0.016780693,0.0012574035,0.23257796,0.0010207888,0.00041352562,0.000046066645,0.0032013617,0.70519674,0.03761645,0.00033719197,0.0009200326,0.0006318116],"about_ca_topic_score_codex":0.0000032405692,"about_ca_topic_score_gemma":0.000020099726,"teacher_disagreement_score":0.23244931,"about_ca_system_score_codex":0.00001309518,"about_ca_system_score_gemma":0.000015756501,"threshold_uncertainty_score":0.47159806},"labels":[],"label_agreement":null},{"id":"W4390872192","doi":"10.1109/access.2024.3353805","title":"Adaptive DDL Algorithm to Elucidate the Protection Misoperation in Malaysian Rapid Rail DC Traction System","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Lakehead University","funders":"Universiti Putra Malaysia","keywords":"Overcurrent; Traction power network; Traction (geology); Electric power system; Train; Computer science; Electrification; Power-system protection; Transient (computer programming); Fault detection and isolation; Fault (geology); Voltage; Engineering; Automotive engineering; Electrical engineering; Power (physics); Electricity","score_opus":0.021235665278903196,"score_gpt":0.2408109192651837,"score_spread":0.2195752539862805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390872192","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":[0.3673721,0.00080257765,0.61504716,0.00052787736,0.0077144103,0.0013667422,0.000015015969,0.0010244359,0.0061296625],"genre_scores_gemma":[0.9986974,0.000017217368,0.00010795062,0.000018308567,0.00054755853,0.00038863017,0.0000033764713,0.000034912853,0.00018464036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990613,0.000056797384,0.00026458653,0.00023524687,0.00017294203,0.00020912618],"domain_scores_gemma":[0.9996971,0.000019946345,0.000019703035,0.00018666287,0.00003148503,0.000045130335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032193185,0.00014665803,0.00013215766,0.00019491333,0.00008688344,0.00033536632,0.00018545183,0.00008625425,0.000014262394],"category_scores_gemma":[0.0000052779596,0.000109921195,0.00004438222,0.0006498413,0.000008709341,0.0005730004,0.000011591927,0.00019796265,0.00010920624],"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":[0.000010827024,0.000010902379,0.00000646951,0.00019111896,0.000032264452,0.000023279801,0.001048512,0.7853065,0.01799569,0.00021920966,0.0007915062,0.19436371],"study_design_scores_gemma":[0.0001024812,0.000042479747,0.00043525838,0.00025642905,0.000009162225,0.000029642653,0.000369588,0.97386926,0.017892351,0.0000094114885,0.0068166163,0.00016728924],"about_ca_topic_score_codex":0.00079416396,"about_ca_topic_score_gemma":0.0002349258,"teacher_disagreement_score":0.6313253,"about_ca_system_score_codex":0.00023675073,"about_ca_system_score_gemma":0.000019255689,"threshold_uncertainty_score":0.44824564},"labels":[],"label_agreement":null},{"id":"W4390964454","doi":"10.1155/2024/5964428","title":"Driving Strategy Using an Improved Ant Colony System for Energy‐Efficient Train","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Ant colony optimization algorithms; ANT; Ant colony; Computer science; Energy (signal processing); Automotive engineering; Simulation; Transport engineering; Operations research; Engineering; Artificial intelligence; Mathematics; Computer network","score_opus":0.010454438258259997,"score_gpt":0.23781888260890613,"score_spread":0.22736444435064612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390964454","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":[0.64373565,0.00081849826,0.3538731,0.000004100009,0.0013620943,0.0000755284,0.00001369099,0.00008581474,0.000031538537],"genre_scores_gemma":[0.9949569,0.000022603681,0.0046353852,0.0000020939892,0.00030288385,0.000008441348,0.000013163349,0.00004490869,0.000013594764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988523,0.000013940448,0.00062292145,0.00013177417,0.00017089724,0.00020813837],"domain_scores_gemma":[0.999551,0.0000392344,0.00011627523,0.000079023725,0.00011577611,0.00009871812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002541571,0.00014666464,0.00023940811,0.00017631643,0.000060395407,0.00005982705,0.00008685846,0.0000700807,0.0000023161363],"category_scores_gemma":[0.0000039488896,0.00012988382,0.00013904266,0.00020088241,0.00001166839,0.0003596011,4.4496943e-7,0.00010208278,1.8229898e-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":[0.000016710681,0.000018359462,0.000004223797,0.00026407914,0.000031715423,0.000025246438,0.00064327137,0.7902133,0.20047551,0.0008274082,0.0000033142542,0.007476872],"study_design_scores_gemma":[0.00048112634,0.00028725713,0.0007365351,0.000543494,0.0000723817,0.000050681712,0.001884693,0.98555136,0.0093354955,0.000040581523,0.00083265215,0.00018377036],"about_ca_topic_score_codex":0.000014980088,"about_ca_topic_score_gemma":0.000072731484,"teacher_disagreement_score":0.3512213,"about_ca_system_score_codex":0.00016696699,"about_ca_system_score_gemma":0.000068735906,"threshold_uncertainty_score":0.5296508},"labels":[],"label_agreement":null},{"id":"W4390969736","doi":"10.1109/tits.2024.3350743","title":"Power Flow Control-Based Regenerative Braking Energy Utilization in AC Electrified Railways: Review and Future Trends","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":83,"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":"Innovation and Technology Fund; Universidade de Macau; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Regenerative brake; Engineering; Energy storage; Energy flow; Power (physics); Control (management); Automotive engineering; Control engineering; Computer science; Energy (signal processing)","score_opus":0.014672467899997885,"score_gpt":0.23555434636914277,"score_spread":0.2208818784691449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390969736","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":[0.0013330427,0.04579832,0.9485286,0.00017347408,0.0025348424,0.00032208144,0.00014134086,0.00046180104,0.000706487],"genre_scores_gemma":[0.9913185,0.0074610547,0.000046118752,0.000109539185,0.00011654836,0.0003168551,0.00007700136,0.00007039273,0.0004839933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980447,0.00012638353,0.0008160382,0.00042173066,0.00029204873,0.0002990878],"domain_scores_gemma":[0.99945873,0.000097038814,0.00005263656,0.00021121919,0.000078389625,0.00010196909],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003282821,0.00034471348,0.00045815393,0.0006235542,0.00009273594,0.000100948906,0.000095878844,0.00019916565,0.00014447127],"category_scores_gemma":[0.0000012719179,0.00032229384,0.00015399924,0.001120042,0.00002645187,0.00023295669,6.6881086e-8,0.00025481806,0.000010343079],"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":[0.0000364817,0.000056055684,0.0000050198228,0.0012296896,0.00012341725,0.00002064976,0.0008126729,0.9648793,0.0010483668,0.0025914516,0.0003568382,0.028840095],"study_design_scores_gemma":[0.0008435357,0.00023204432,0.000112884205,0.0034252454,0.0002599936,0.000021368436,0.0004371521,0.8703648,0.016088746,0.000009481874,0.10738425,0.0008205165],"about_ca_topic_score_codex":0.00010239145,"about_ca_topic_score_gemma":0.0004115148,"teacher_disagreement_score":0.98998547,"about_ca_system_score_codex":0.00014641114,"about_ca_system_score_gemma":0.000046073248,"threshold_uncertainty_score":0.99992293},"labels":[],"label_agreement":null},{"id":"W4391838441","doi":"10.1155/2024/5467767","title":"Estimating the Railway Network Capacity Utilization with Mixed Train Routes and Stopping Patterns: A Multiobjective Optimization Approach","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Operations research; Transport engineering; Mathematical optimization; Engineering; Mathematics","score_opus":0.013259655507630442,"score_gpt":0.2121753069482086,"score_spread":0.19891565144057816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391838441","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":[0.38478342,0.0006262679,0.61409044,0.000013660728,0.0003104094,0.00009344929,0.0000035311646,0.00004474804,0.000034080433],"genre_scores_gemma":[0.8975011,0.00007101285,0.10218525,0.000005262113,0.00018288962,0.000010388748,0.000016370312,0.00002384573,0.000003877838],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991917,0.000030022391,0.00035511327,0.00011084611,0.0001813746,0.00013095654],"domain_scores_gemma":[0.9996409,0.00006375801,0.00011322182,0.000056384415,0.00008952701,0.000036242705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027454708,0.00012525317,0.00015856206,0.000074936244,0.0000945892,0.00006719668,0.00005136668,0.00004374614,0.0000019469676],"category_scores_gemma":[0.000010980393,0.000083596715,0.00004178968,0.00026780766,0.000023918687,0.0005428271,7.17338e-7,0.0001683827,7.160344e-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":[0.000015488353,0.000007978633,0.00029994472,0.0001702004,0.000048837643,0.0000044150693,0.0054286015,0.9818841,0.00027897733,0.00016680192,0.0000034506038,0.011691205],"study_design_scores_gemma":[0.0003205814,0.000067473404,0.016506944,0.0004915725,0.00005386582,0.000033679207,0.0012924402,0.98092514,0.00011906652,0.00004773998,0.00003304864,0.00010841981],"about_ca_topic_score_codex":0.000009335141,"about_ca_topic_score_gemma":0.000043690554,"teacher_disagreement_score":0.51271766,"about_ca_system_score_codex":0.000044798213,"about_ca_system_score_gemma":0.000017277209,"threshold_uncertainty_score":0.3408975},"labels":[],"label_agreement":null},{"id":"W4393994424","doi":"10.3390/su16073042","title":"Technical Feasibility of a Hydrail Tram–Train in NA: Okanagan Valley Electric Regional Passenger Rail (OVER PR)","year":2024,"lang":"en","type":"article","venue":"Sustainability","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Passenger transport; Transport engineering; Engineering; Civil engineering; Automotive engineering","score_opus":0.01043615284347976,"score_gpt":0.2522766367549758,"score_spread":0.24184048391149604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393994424","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":[0.99146885,0.0017927896,0.0019479243,0.0005159562,0.00023966115,0.0006575034,0.0000070282176,0.0004998333,0.0028704484],"genre_scores_gemma":[0.9993995,0.000021898282,0.000106068765,0.000013497609,0.000079149504,0.000087364475,0.000005675832,0.000042210468,0.00024463996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9975819,0.00014367241,0.0007079597,0.00056913926,0.00038743953,0.0006098897],"domain_scores_gemma":[0.9988143,0.00024483746,0.000034898963,0.0006135134,0.00017054325,0.00012190101],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014423156,0.00027979678,0.00044276682,0.00030182127,0.000040494924,0.000041524556,0.0002743319,0.0002674739,0.00006284973],"category_scores_gemma":[0.0004900286,0.0002575699,0.00024172141,0.0014217827,0.00013957485,0.00018517546,0.00004537679,0.00050423277,0.000004664478],"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":[0.00085041154,0.00571021,0.17660935,0.041247837,0.000579858,0.0016419454,0.014533068,0.3218644,0.0896817,0.15509698,0.030199217,0.16198501],"study_design_scores_gemma":[0.0016301202,0.00046751671,0.70437574,0.00034028516,0.00007164851,0.00007720179,0.0011898467,0.23221435,0.0013144662,0.030131971,0.026675945,0.0015109165],"about_ca_topic_score_codex":0.0004262755,"about_ca_topic_score_gemma":0.000394453,"teacher_disagreement_score":0.5277664,"about_ca_system_score_codex":0.0018403601,"about_ca_system_score_gemma":0.00045123213,"threshold_uncertainty_score":0.99998766},"labels":[],"label_agreement":null},{"id":"W4394692522","doi":"10.1371/journal.pone.0301762","title":"Measuring high-speed train delay severity: Static and dynamic analysis","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Hubei University; Hubei University of Arts and Science; Hubei Provincial Department of Education","keywords":"Train; Computer science; Context (archaeology); Bridge (graph theory); Resilience (materials science); Duration (music); Real-time computing; Operations research; Engineering","score_opus":0.029188608049184335,"score_gpt":0.18978750404816738,"score_spread":0.16059889599898305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394692522","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":[0.9911902,0.0018788426,0.005469514,0.00006108989,0.00007826833,0.000066407454,0.0000179834,0.0004225534,0.0008151552],"genre_scores_gemma":[0.99834865,0.00012687188,0.00097609137,0.000007506657,0.000035641904,0.00000853177,0.000011412153,0.00003078732,0.00045448457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920166,0.000017461463,0.000180425,0.0001845357,0.0002161954,0.00019974627],"domain_scores_gemma":[0.9997148,0.000039811526,0.000009218228,0.00014874522,0.000016246044,0.00007116543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015568762,0.00012103394,0.00025178134,0.0002305583,0.000037992424,0.00008982692,0.00006390385,0.000047285226,0.000045110442],"category_scores_gemma":[0.000011692496,0.00011638008,0.000049666756,0.00055215496,0.000016098476,0.00009703456,0.000012958718,0.0001058288,0.000027804592],"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":[0.000015193954,0.0005753612,0.0017152297,0.0058331266,0.020305432,0.00036782748,0.007823318,0.59932345,0.33147302,0.0015996046,0.00019929196,0.030769102],"study_design_scores_gemma":[0.00007694997,0.000015136871,0.0020411375,0.00014767039,0.0005846408,0.0000026282362,0.000057753783,0.99546987,0.0012900541,0.00007871969,0.00006491911,0.0001705264],"about_ca_topic_score_codex":0.000111495465,"about_ca_topic_score_gemma":0.00015638435,"teacher_disagreement_score":0.3961464,"about_ca_system_score_codex":0.00006414101,"about_ca_system_score_gemma":0.000008478816,"threshold_uncertainty_score":0.4745842},"labels":[],"label_agreement":null},{"id":"W4394987665","doi":"10.1016/j.asoc.2024.111640","title":"Railway network delay evolution: A heterogeneous graph neural network approach","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Train; Computer science; Scalability; Robustness (evolution); Artificial neural network; Graph; Network architecture; Distributed computing; Artificial intelligence; Theoretical computer science; Computer network","score_opus":0.007382969826896788,"score_gpt":0.1888585971402182,"score_spread":0.1814756273133214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394987665","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":[0.08846603,0.014335769,0.86325336,0.000016729602,0.0037133421,0.00033239767,0.0000020425123,0.0033526744,0.02652768],"genre_scores_gemma":[0.9889765,0.000010061524,0.0071257246,0.000053819178,0.003609951,0.000031725704,0.000018318895,0.00011017754,0.00006375447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977949,0.000032149546,0.00048123454,0.00049945485,0.00025778913,0.0009344303],"domain_scores_gemma":[0.9993191,0.00013659975,0.0000392135,0.00034024438,0.000021130485,0.00014375754],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042519555,0.00036075735,0.00035071227,0.00008769196,0.0003387802,0.0002125037,0.00029281413,0.00016907424,0.000011835949],"category_scores_gemma":[0.000003444756,0.00035393858,0.00017353987,0.0009522752,0.000050641534,0.000063157066,0.000101262,0.00039119142,0.000069611815],"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":[0.000003191906,0.000006938752,0.00006248602,0.00012303097,0.0000846058,0.000024291072,0.00022197046,0.96815526,0.00007581474,0.010810317,0.003617051,0.016815057],"study_design_scores_gemma":[0.00012451524,0.000015874217,0.00009456749,0.000077828685,0.000024014345,0.00013874231,0.000033394335,0.9884191,0.0000091065185,0.0008130518,0.009852009,0.00039775117],"about_ca_topic_score_codex":0.00001758847,"about_ca_topic_score_gemma":0.00000521203,"teacher_disagreement_score":0.90051043,"about_ca_system_score_codex":0.00010055276,"about_ca_system_score_gemma":0.00002272587,"threshold_uncertainty_score":0.9998913},"labels":[],"label_agreement":null},{"id":"W4396558190","doi":"10.1155/2024/7311720","title":"Metro Train Stopping Scheme Decision Based on Multisource Data in Express‐Local Train Mode","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Education Department of Jilin Province; National Natural Science Foundation of China","keywords":"Scheme (mathematics); Mode (computer interface); Transport engineering; Computer science; Operations research; Engineering; Mathematics","score_opus":0.014412204071756144,"score_gpt":0.2678985691914852,"score_spread":0.25348636511972905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396558190","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":[0.4856835,0.000551485,0.51311237,0.00002937961,0.00043661435,0.000056320056,0.00003130375,0.00004499831,0.000054010685],"genre_scores_gemma":[0.9647421,0.00004609987,0.034999326,0.000015001567,0.00009959845,0.0000039691813,0.000050025887,0.00003659864,0.0000072743874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985303,0.00002278217,0.00066664146,0.00019678338,0.0003899364,0.00019351165],"domain_scores_gemma":[0.9994256,0.0001680698,0.00007166661,0.00022140298,0.00003496927,0.00007828445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042413996,0.00015711391,0.00025069175,0.0004031453,0.000026015661,0.000031358806,0.00023391799,0.00007951308,0.000013456305],"category_scores_gemma":[0.00003314138,0.00014099659,0.00008689834,0.0003637221,0.000015858182,0.00059659395,0.0000014873201,0.0003254852,0.000001942016],"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":[0.00006650986,0.000031251548,0.00004936628,0.00008733617,0.000012646341,0.0000958206,0.00073430396,0.8951136,0.021470526,0.00008220242,0.000023084198,0.08223337],"study_design_scores_gemma":[0.0010025282,0.000097028955,0.0062499647,0.0011260115,0.00001651657,0.0000048429088,0.0006182735,0.98598963,0.0011844973,0.00008114367,0.0034607972,0.00016875056],"about_ca_topic_score_codex":0.000013409204,"about_ca_topic_score_gemma":0.00019120173,"teacher_disagreement_score":0.4790586,"about_ca_system_score_codex":0.00011191666,"about_ca_system_score_gemma":0.00003946837,"threshold_uncertainty_score":0.57496744},"labels":[],"label_agreement":null},{"id":"W4399194677","doi":"10.1155/2024/9961840","title":"Propagation‐Based Train Rescheduling under Recoverable Delay Disturbances","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Natural Science Foundation of China; China Railway","keywords":"Computer science; Adaptability; Pruning; Mathematical optimization; Arrival time; Real-time computing; Operations research; Simulation; Control theory (sociology); Engineering; Mathematics; Transport engineering; Control (management); Artificial intelligence","score_opus":0.007677640475831697,"score_gpt":0.22076737395955146,"score_spread":0.21308973348371976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399194677","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":[0.6113196,0.0040022573,0.3822394,0.00012696328,0.0016753939,0.000071253926,0.0000075352714,0.00010468896,0.00045290508],"genre_scores_gemma":[0.9931284,0.00013529047,0.006417423,0.000013715929,0.00017321078,0.00000478877,0.000013237828,0.000023975574,0.00008994853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912965,0.000011155041,0.0004418819,0.00008644877,0.0002081947,0.00012267234],"domain_scores_gemma":[0.9997108,0.000044071046,0.000067867986,0.000057936162,0.00007141449,0.00004788282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019143436,0.000099551144,0.00014498431,0.00017558105,0.00003770006,0.00004149376,0.000065008164,0.000044685898,0.000019256759],"category_scores_gemma":[0.0000069843104,0.00008296431,0.00009753869,0.0003326412,0.000011801946,0.00050708547,2.1486916e-7,0.00015906719,0.0000027778015],"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":[0.000017217693,0.000010033989,0.000024517398,0.00015258613,0.000025549714,0.000027304444,0.00045397916,0.97558403,0.015049118,0.0010530592,0.000060652226,0.0075419256],"study_design_scores_gemma":[0.0035859246,0.00091363577,0.03368289,0.0050797393,0.0003516955,0.00011765857,0.0031704023,0.7985026,0.061968975,0.006071461,0.08511138,0.0014436058],"about_ca_topic_score_codex":0.0000026977077,"about_ca_topic_score_gemma":0.000028777993,"teacher_disagreement_score":0.38180882,"about_ca_system_score_codex":0.000068572466,"about_ca_system_score_gemma":0.000051415096,"threshold_uncertainty_score":0.33831865},"labels":[],"label_agreement":null},{"id":"W4399385035","doi":"10.21203/rs.3.rs-4449743/v1","title":"A train trajectory optimization method based on the safety reinforcement learning with a relaxed dynamic reward","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"SR Research (Canada)","funders":"","keywords":"Reinforcement learning; Trajectory; Computer science; Reinforcement; Artificial intelligence; Psychology; Social psychology; Physics","score_opus":0.024080258581178758,"score_gpt":0.3124727896332973,"score_spread":0.28839253105211854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399385035","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":[0.0067214994,0.0012964188,0.9223526,0.0014858147,0.0006481967,0.0027401594,0.0000441146,0.0012779328,0.063433275],"genre_scores_gemma":[0.99075115,0.0001988644,0.005231187,0.000015899805,0.00013457512,0.000738686,0.00016122237,0.00018593615,0.0025824523],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960192,0.0008773831,0.00044054197,0.000604818,0.0013735307,0.00068456883],"domain_scores_gemma":[0.99808526,0.0007223057,0.00005647905,0.0008046702,0.00019333299,0.00013796007],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004287656,0.00038490197,0.000372838,0.000590997,0.00031362215,0.00024999646,0.00045294577,0.00033558032,0.00021222558],"category_scores_gemma":[0.0002495137,0.00025480014,0.00018440052,0.00080500665,0.00007751611,0.000033446227,0.00020642333,0.003358629,0.000048204925],"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":[0.00007517166,0.000016317043,0.000004141145,0.0014147885,0.0000841594,0.000032600507,0.0010883869,0.9942018,0.00006612436,0.0006104307,0.00038370912,0.0020223935],"study_design_scores_gemma":[0.00023682446,0.0002900693,0.000048927297,0.0027010036,0.00002012698,0.0000033935582,0.0007592552,0.9928014,0.000049968476,0.000061800776,0.0027277428,0.0002994843],"about_ca_topic_score_codex":0.00016663084,"about_ca_topic_score_gemma":0.00006035396,"teacher_disagreement_score":0.98402965,"about_ca_system_score_codex":0.0009387773,"about_ca_system_score_gemma":0.00037973584,"threshold_uncertainty_score":0.9999904},"labels":[],"label_agreement":null},{"id":"W4399659905","doi":"10.1115/jrc2024-122074","title":"Managing Hi-Railer Set-on and Set-Off in a Train Control System Using Axle Counters","year":2024,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Axle; Computer science; Set (abstract data type); Control system; Control (management); Artificial intelligence; Engineering; Electrical engineering; Structural engineering; Programming language","score_opus":0.011083706680389523,"score_gpt":0.21369977380843236,"score_spread":0.20261606712804284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399659905","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":[0.9378123,0.00321196,0.02528642,0.00012194586,0.0014603365,0.00028176064,0.000018732791,0.00087153254,0.030934976],"genre_scores_gemma":[0.9993693,0.000021689088,0.00008598134,0.000043627275,0.000095055286,0.000010548057,0.000001953232,0.000043765656,0.00032805512],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903315,0.000026362353,0.0002591004,0.00023824525,0.00014099154,0.00030215993],"domain_scores_gemma":[0.99972224,0.000058993795,0.000011073429,0.00013594412,0.0000079065585,0.000063825435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002928657,0.00017861648,0.00022570784,0.00022359022,0.000038194416,0.00014116609,0.00007025924,0.00007245891,0.000020244355],"category_scores_gemma":[0.0000035121384,0.00015391069,0.000041523657,0.00021293579,0.000017444663,0.00012825023,0.000010099588,0.00012838609,0.000024240948],"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":[0.000011488328,0.000009165535,0.00021708703,0.0013822991,0.000097597906,0.0002512891,0.002861098,0.96963525,0.005405066,0.008584164,0.001139725,0.010405749],"study_design_scores_gemma":[0.0003348446,0.000017790338,0.00011510878,0.0006564213,0.000011339327,0.000043732587,0.0017994489,0.9924877,0.000058902184,0.000010511411,0.0042751534,0.00018903211],"about_ca_topic_score_codex":0.00028869824,"about_ca_topic_score_gemma":0.00007652743,"teacher_disagreement_score":0.061556987,"about_ca_system_score_codex":0.00019380377,"about_ca_system_score_gemma":0.000012182487,"threshold_uncertainty_score":0.6276296},"labels":[],"label_agreement":null},{"id":"W4401163128","doi":"10.1109/tits.2024.3430031","title":"A Multi-Source Dynamic Temporal Point Process Model for Train Delay Prediction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Process (computing); Point process; Scheduling (production processes); Feature (linguistics); Artificial neural network; Artificial intelligence; Machine learning; Engineering","score_opus":0.021402285228514534,"score_gpt":0.2567176209679431,"score_spread":0.23531533573942856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401163128","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":[0.03680548,0.00063027925,0.95567495,0.00002148525,0.0033759489,0.0010779052,0.0008392539,0.0015226301,0.00005207862],"genre_scores_gemma":[0.9953626,0.00008862616,0.00089158287,0.000009168919,0.00007157637,0.0012368942,0.00013368977,0.00013804833,0.0020678493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791086,0.000024640825,0.00089159637,0.0004663665,0.00033939717,0.00036713752],"domain_scores_gemma":[0.9994023,0.000064375046,0.000048201513,0.00022624007,0.00011420614,0.00014469385],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029578386,0.00035676672,0.00031448773,0.00040375593,0.00016282305,0.00012950314,0.00014975472,0.00022295416,0.000018628367],"category_scores_gemma":[0.0000012727887,0.0003451071,0.00029106758,0.0004187832,0.00003366417,0.00032584052,7.1451225e-8,0.00026165895,0.000035490964],"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":[0.000030036816,0.0000894371,0.0000036628494,0.0011110073,0.00013444274,0.000004474727,0.0054669483,0.9864228,0.0019462046,0.00007301786,0.000079565194,0.0046384134],"study_design_scores_gemma":[0.00029791673,0.00009803744,0.000008956835,0.00045734484,0.00009225489,0.000016544833,0.001189558,0.993417,0.0025297336,0.000015774782,0.0015532429,0.000323628],"about_ca_topic_score_codex":0.000091430695,"about_ca_topic_score_gemma":0.00041397795,"teacher_disagreement_score":0.95855707,"about_ca_system_score_codex":0.00021464124,"about_ca_system_score_gemma":0.00006577012,"threshold_uncertainty_score":0.9999001},"labels":[],"label_agreement":null},{"id":"W4401899647","doi":"10.4203/ccc.7.25.2","title":"Transform Traditional Trains to Linear Docking and Save Significant Time","year":2024,"lang":"en","type":"article","venue":"Civil-comp conferences","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Engineering Link (Canada)","funders":"","keywords":"Train; Computer science; Railway line; Transport engineering; Travel time; Engineering; Simulation; Geography","score_opus":0.023655129381703086,"score_gpt":0.21074237870598253,"score_spread":0.18708724932427945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401899647","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":[0.6445381,0.0032100766,0.03191951,0.0010553016,0.0018823294,0.0004789913,0.00023769436,0.0014313045,0.3152467],"genre_scores_gemma":[0.99900794,0.00008199072,0.0001721575,0.000025364645,0.00029253474,0.000027600488,0.000017740269,0.000022055812,0.00035262812],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990868,0.000013389038,0.00022901961,0.0002410525,0.00017476162,0.00025497866],"domain_scores_gemma":[0.9996557,0.00008968879,0.000007975618,0.00008880237,0.000019723173,0.00013810271],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014088949,0.00017456843,0.00020353746,0.00011116913,0.00007038099,0.000158087,0.00012279883,0.00006532562,0.00028051742],"category_scores_gemma":[0.000005675872,0.00014904558,0.000057526697,0.00015142071,0.00004826966,0.00011740726,0.000006762431,0.00012764566,0.00009198598],"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":[0.000032935754,0.00017660412,0.0001668115,0.0018059392,0.00066075084,0.00026834875,0.027835246,0.19418357,0.06589544,0.3403667,0.022461189,0.34614646],"study_design_scores_gemma":[0.00021965009,0.00018626882,0.00137827,0.00047505516,0.000033689055,0.000047061756,0.0004339972,0.7778156,0.0025927161,0.001738574,0.21444643,0.00063269323],"about_ca_topic_score_codex":0.00005942577,"about_ca_topic_score_gemma":0.000080841804,"teacher_disagreement_score":0.583632,"about_ca_system_score_codex":0.000025490515,"about_ca_system_score_gemma":0.00005037,"threshold_uncertainty_score":0.60779023},"labels":[],"label_agreement":null},{"id":"W4402118939","doi":"10.1155/2024/2206358","title":"Optimization of the Operation Plan of Airport Express Train with Consideration of Train Departure Time Window","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Science and Technology Program of Gansu Province; Gansu Education Department; National Natural Science Foundation of China","keywords":"Window (computing); Plan (archaeology); Transport engineering; Computer science; Operations research; Engineering; Geography","score_opus":0.004173690044975333,"score_gpt":0.1833223087522515,"score_spread":0.17914861870727616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402118939","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":[0.84925985,0.0004173079,0.14964704,0.00004697316,0.00029418824,0.0001596656,0.00003555103,0.000017378337,0.00012203579],"genre_scores_gemma":[0.9930008,0.000045313063,0.0068368684,0.0000029774524,0.000041776493,0.0000030257377,0.00002927932,0.000017383263,0.000022571738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99891675,0.000022840151,0.0006528223,0.00006886161,0.00027018902,0.000068549794],"domain_scores_gemma":[0.9994458,0.000034431694,0.00025458907,0.00008138116,0.00016001015,0.000023821503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017600167,0.00009604918,0.00022893675,0.00010300497,0.00002050012,0.000008590251,0.00006478815,0.000058879046,0.000017426357],"category_scores_gemma":[0.000009137603,0.00006568674,0.00008203845,0.0001916668,0.000034233628,0.00037704996,4.6204295e-7,0.00009747707,1.0057103e-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":[0.000044504915,0.000017917546,0.000119225035,0.00018280515,0.00004022693,0.0000033347424,0.0024290045,0.8326784,0.16355455,0.0002001269,0.000020890897,0.0007090299],"study_design_scores_gemma":[0.0028053275,0.0009799871,0.04546762,0.0031499658,0.00037405494,0.00010750264,0.002240063,0.43166524,0.51201934,0.0002209008,0.0005512809,0.00041869457],"about_ca_topic_score_codex":0.0000044501585,"about_ca_topic_score_gemma":0.000037656147,"teacher_disagreement_score":0.40101314,"about_ca_system_score_codex":0.00001979492,"about_ca_system_score_gemma":0.00006944912,"threshold_uncertainty_score":0.26786277},"labels":[],"label_agreement":null},{"id":"W4402811362","doi":"10.1109/tvt.2024.3462708","title":"Cloud-Edge-End Collaboration for Intelligent Train Regulation Optimization in TACS","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Cloud computing; Enhanced Data Rates for GSM Evolution; Computer science; Operating system; Telecommunications","score_opus":0.007637992144576715,"score_gpt":0.22310755580634503,"score_spread":0.2154695636617683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402811362","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":[0.048994213,0.000549303,0.9468219,0.00044109926,0.0016806212,0.00042008536,0.000021406575,0.0009194329,0.00015191149],"genre_scores_gemma":[0.99625206,0.00013141928,0.0029790744,0.000009154096,0.000057258854,0.00032726713,0.000014886295,0.000045962457,0.00018294406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990938,0.000017988717,0.0003156957,0.00025489018,0.00010486625,0.00021276371],"domain_scores_gemma":[0.99966365,0.000037239643,0.00001804713,0.00020100764,0.0000532092,0.000026874406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016068149,0.00015549453,0.00016153292,0.00077539776,0.00007296207,0.00004803533,0.00009490093,0.0003391326,0.000035153666],"category_scores_gemma":[0.000005874038,0.00016392872,0.00006490925,0.0012643066,0.00004086839,0.00013238341,4.5860827e-7,0.0002022137,0.000017588587],"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":[0.000008729096,0.000032732158,6.945082e-7,0.00007098978,0.00002460427,0.000003863571,0.00022237282,0.961991,0.0059419577,0.0022135123,0.000113737784,0.029375803],"study_design_scores_gemma":[0.00018484826,0.00010924026,0.0000021987323,0.00009059365,0.00001790936,0.000011435199,0.00026010364,0.9311345,0.05942278,0.00039609126,0.008209645,0.00016062401],"about_ca_topic_score_codex":0.0000065005997,"about_ca_topic_score_gemma":0.00013276136,"teacher_disagreement_score":0.9472578,"about_ca_system_score_codex":0.00022386033,"about_ca_system_score_gemma":0.000032700973,"threshold_uncertainty_score":0.66848195},"labels":[],"label_agreement":null},{"id":"W4403124918","doi":"10.1109/pesgm51994.2024.10689072","title":"Synergizing Smart Electric Railway Networks with Integrated Wind Generation: An Optimal Energy Management Approach Considering Stochastic and Probabilistic Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Probabilistic logic; Wind power; Computer science; Energy management; Stochastic process; Energy (signal processing); Reliability engineering; Engineering; Electrical engineering; Artificial intelligence; Mathematics","score_opus":0.008782190297902618,"score_gpt":0.18459072604536764,"score_spread":0.17580853574746502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403124918","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":[0.111647114,0.0018150489,0.8815118,0.000006639554,0.00017344422,0.00014379177,9.415192e-7,0.0005618182,0.0041393754],"genre_scores_gemma":[0.9925015,0.000047356694,0.006630497,0.000012616883,0.0001650629,0.000067363966,0.000062775594,0.000056021803,0.00045683054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985246,0.000041361807,0.0003247857,0.0005160956,0.0001969566,0.00039617447],"domain_scores_gemma":[0.99948835,0.00003840098,0.000021773034,0.0002688985,0.00004071445,0.000141844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021559704,0.0003079194,0.00032837733,0.0004978394,0.00012446445,0.00039398228,0.00010406563,0.000091970665,0.00003590016],"category_scores_gemma":[0.0000036934146,0.00023685962,0.00006002651,0.0017615417,0.000038321654,0.00022243978,0.000026245347,0.00015507624,0.0000013248421],"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":[0.000005828016,0.000019220852,0.000012037515,0.000079071244,0.0007581986,0.000029706473,0.000113165384,0.98698026,0.00022332794,0.0070936535,0.000085661006,0.0045998786],"study_design_scores_gemma":[0.00012991457,0.00007158632,0.00008152876,0.00004157543,0.00043770822,0.000050789306,0.0001578141,0.99830765,0.00003876526,0.0000062678773,0.00033939906,0.00033702835],"about_ca_topic_score_codex":0.00011649823,"about_ca_topic_score_gemma":0.00015880055,"teacher_disagreement_score":0.88085437,"about_ca_system_score_codex":0.00009566289,"about_ca_system_score_gemma":0.000022012518,"threshold_uncertainty_score":0.96588546},"labels":[],"label_agreement":null},{"id":"W4403126960","doi":"10.1109/pesgm51994.2024.10688685","title":"Machine Learning Based Modeling for Real-Time Inferencer-in-the-Loop Hardware Emulation of High-Speed Rail Microgrid","year":2024,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Emulation; Microgrid; Computer science; Hardware emulation; Loop (graph theory); Hardware-in-the-loop simulation; Embedded system; Computer hardware; Computer architecture; Field-programmable gate array; Control (management); Artificial intelligence","score_opus":0.01517220296329401,"score_gpt":0.22461530508619162,"score_spread":0.20944310212289763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403126960","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":[0.73584175,0.00086395664,0.2574982,0.000119145494,0.00039367378,0.0003377062,0.000023020551,0.00056130823,0.004361204],"genre_scores_gemma":[0.99771035,0.00003903206,0.0014655938,0.000006672278,0.000078878686,0.000017824526,0.00007557376,0.00003369914,0.0005723849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991479,0.00002692863,0.00032665787,0.00016409268,0.00013683592,0.00019758206],"domain_scores_gemma":[0.99964505,0.0001264431,0.000019538686,0.00014148565,0.000042863143,0.000024593553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034687042,0.00013419306,0.00019357717,0.00015795072,0.000043245705,0.000058192807,0.00011728182,0.00007101446,0.000081806604],"category_scores_gemma":[0.00002421151,0.000101723024,0.00008023279,0.00025898297,0.000008644902,0.00009567699,0.000008745682,0.000114714545,0.000014287443],"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":[0.0000056872423,0.000008736722,0.00006156839,0.00020483187,0.000011757216,0.0000014740979,0.00026157039,0.97992015,0.015862131,0.0006802529,0.00009584605,0.0028859896],"study_design_scores_gemma":[0.00023520408,0.000037318063,0.000050419192,0.00012519342,0.000011815406,0.0000013362809,0.00005167012,0.9966446,0.0014660711,0.00006281913,0.001189857,0.00012366577],"about_ca_topic_score_codex":0.0010455952,"about_ca_topic_score_gemma":0.0000736938,"teacher_disagreement_score":0.26186857,"about_ca_system_score_codex":0.000038470247,"about_ca_system_score_gemma":0.000023103486,"threshold_uncertainty_score":0.41481444},"labels":[],"label_agreement":null},{"id":"W4403428305","doi":"10.28924/2291-8639-22-2024-184","title":"A Comprehensive View of the Solvability and Stability of a Feedback Control Problem with a State-Dependent Delay Implicit Pantograph Equation","year":2024,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Pantograph; Mathematics; Stability (learning theory); State (computer science); Control (management); Control theory (sociology); Applied mathematics; Calculus (dental); Computer science; Algorithm; Artificial intelligence; Engineering; Machine learning; Medicine; Mechanical engineering; Orthodontics","score_opus":0.007940438078689181,"score_gpt":0.2278253379011054,"score_spread":0.2198848998224162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403428305","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":[0.71619487,0.0016113885,0.2818385,0.000073368035,0.000026249027,0.00012544623,0.000047574675,0.0000060067846,0.00007656439],"genre_scores_gemma":[0.9992477,0.00026850816,0.00042925123,0.000006636688,0.00002512294,0.000013585622,0.0000020182329,0.0000049889936,0.000002138378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990432,0.000027987691,0.0004981731,0.00008644894,0.0002768733,0.0000672764],"domain_scores_gemma":[0.99912643,0.00012409883,0.00017466923,0.00010497852,0.00042986366,0.000039973194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035506696,0.00007421904,0.00022480101,0.00017647725,0.000023793667,0.000032110132,0.000119291515,0.000022613422,0.000006357533],"category_scores_gemma":[0.0000043222303,0.00004512181,0.00012537192,0.00042816837,0.000095707415,0.00008930796,0.000016901735,0.000080902224,1.081402e-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":[0.00018608468,0.0005952753,0.31121632,0.0011079441,0.015804846,0.000003945621,0.004578982,0.46249667,0.07456859,0.016149545,0.00003222152,0.11325959],"study_design_scores_gemma":[0.0022051132,0.00038079632,0.5616829,0.0007206891,0.0035795127,0.00020601059,0.002804662,0.40195897,0.014743602,0.005230684,0.0059255916,0.00056151015],"about_ca_topic_score_codex":0.00012332089,"about_ca_topic_score_gemma":0.00012443008,"teacher_disagreement_score":0.28305286,"about_ca_system_score_codex":0.000028427105,"about_ca_system_score_gemma":0.000025542231,"threshold_uncertainty_score":0.1840014},"labels":[],"label_agreement":null},{"id":"W4404563807","doi":"10.1109/vppc63154.2024.10755503","title":"Enhanced EMR-Based Modelling for Electric Urban Buses Performance Studies","year":2024,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Université de Sherbrooke","funders":"Foundation for Science and Technology; Mitacs; Canada Research Chairs","keywords":"Computer science","score_opus":0.02654648167867065,"score_gpt":0.23851487802294985,"score_spread":0.2119683963442792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404563807","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":[0.43706524,0.009473762,0.54730517,0.000015077224,0.00091417646,0.00011687096,0.0000011915131,0.00075719634,0.004351305],"genre_scores_gemma":[0.995742,0.00033463884,0.0012645437,0.000016600758,0.00018317762,0.00010547083,0.0000019028688,0.000035106004,0.0023165543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993254,0.0000032523974,0.00017433928,0.00015733094,0.00008683906,0.00025282693],"domain_scores_gemma":[0.99974453,0.00007912686,0.0000070424926,0.000100851634,0.00003969945,0.000028755216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009934213,0.00012926424,0.000151091,0.00011573411,0.00006487258,0.000042920874,0.000059882284,0.000039258157,0.00000950112],"category_scores_gemma":[0.0000063365105,0.00010234882,0.000060398314,0.00027529508,0.000008265395,0.0001009005,0.0000043111145,0.000057211557,0.000024897594],"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":[0.0000024396859,0.000004435399,0.0000017583279,0.00039722322,0.000039328916,4.6537787e-7,0.00018348916,0.9830703,0.009412366,0.0005929904,0.0021707427,0.0041244556],"study_design_scores_gemma":[0.00007085594,0.000046269983,0.0000016204167,0.000097506214,0.000009942308,6.3136923e-7,0.000043840657,0.9038957,0.08604811,0.000015355969,0.00963309,0.00013709396],"about_ca_topic_score_codex":0.0000036665813,"about_ca_topic_score_gemma":0.0000024200028,"teacher_disagreement_score":0.5586768,"about_ca_system_score_codex":0.000055160566,"about_ca_system_score_gemma":0.000019705156,"threshold_uncertainty_score":0.41736636},"labels":[],"label_agreement":null},{"id":"W4406195408","doi":"10.1016/j.trpro.2024.12.193","title":"Network-wide mixed-rail traffic scheduler: challenges and implementation aspects","year":2025,"lang":"en","type":"article","venue":"Transportation research procedia","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Transport engineering; Computer network; Engineering","score_opus":0.032647638145474764,"score_gpt":0.31199490091428167,"score_spread":0.2793472627688069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406195408","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":[0.97764397,0.012633336,0.0010626797,0.00074197684,0.00032820806,0.0005396928,0.000009229259,0.00052767806,0.0065132533],"genre_scores_gemma":[0.9962366,0.0030135328,0.0002543253,0.000020680742,0.0000851316,0.00019092135,0.00003939965,0.000030221945,0.00012917077],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99860364,0.000038904007,0.00029417852,0.00026018757,0.00033943844,0.00046363915],"domain_scores_gemma":[0.9994925,0.00011484938,0.000019459936,0.00013264953,0.00014471659,0.0000958266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006225428,0.00013581547,0.00015765202,0.00022547663,0.00014695687,0.000043495198,0.00011433012,0.00009131234,0.000025453804],"category_scores_gemma":[0.000021044803,0.00013801812,0.000029227924,0.0004482264,0.0000593145,0.00017481216,0.0000039261977,0.00023117865,0.00001425931],"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":[0.00005572627,0.00012289984,0.0055307155,0.003862401,0.00022991338,0.000039844625,0.010984609,0.31463632,0.0015615225,0.31055355,0.021796236,0.33062625],"study_design_scores_gemma":[0.0019703018,0.00022091894,0.9229301,0.0004514948,0.000043014305,0.0000018211935,0.008001911,0.020225782,0.0021121113,0.0054705283,0.03796896,0.00060303917],"about_ca_topic_score_codex":0.000041798907,"about_ca_topic_score_gemma":0.0034282992,"teacher_disagreement_score":0.9173994,"about_ca_system_score_codex":0.00004543252,"about_ca_system_score_gemma":0.00007559914,"threshold_uncertainty_score":0.56282157},"labels":[],"label_agreement":null},{"id":"W4407149409","doi":"10.1139/cjce-2024-0366","title":"Novel approach to optimizing express–local metro train timetable considering multiple train stopping patterns","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transport engineering; Train; Engineering; Operations research; Geography","score_opus":0.012139985498277345,"score_gpt":0.17824090745892812,"score_spread":0.16610092196065077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407149409","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":[0.04434917,0.0011619165,0.947405,0.000041675234,0.0011849352,0.00013171835,0.00002261097,0.00008491029,0.0056181005],"genre_scores_gemma":[0.9802151,0.0000045083543,0.019372568,0.000052820156,0.0001786024,0.000011554697,0.0000024975054,0.00006724186,0.00009511226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818677,0.000016298536,0.0006549792,0.0002053174,0.00019656132,0.0007400543],"domain_scores_gemma":[0.9988021,0.000103520804,0.00006200084,0.00023591735,0.0000866719,0.00070977764],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005171703,0.00030767193,0.0005220694,0.0011128072,0.00010135638,0.000145125,0.00038246272,0.0001270104,0.000034278313],"category_scores_gemma":[0.00016928866,0.00033724698,0.00017315723,0.0005580169,0.000021684864,0.00025855697,0.000018857354,0.00042294626,0.0000024282913],"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":[0.0000017836761,0.000008336834,0.00014610855,0.00015840319,0.00011377239,0.000029153922,0.00065958494,0.97608405,0.021066904,0.0002647533,0.0005102866,0.0009568368],"study_design_scores_gemma":[0.0008257358,0.000033190423,0.00077887345,0.0008947363,0.000049544524,0.00015116732,0.0012178014,0.96996874,0.0039896066,0.000006372936,0.021535613,0.00054861093],"about_ca_topic_score_codex":0.0017567246,"about_ca_topic_score_gemma":0.017105354,"teacher_disagreement_score":0.93586594,"about_ca_system_score_codex":0.000425738,"about_ca_system_score_gemma":0.0002634538,"threshold_uncertainty_score":0.999908},"labels":[],"label_agreement":null},{"id":"W4408017109","doi":"10.1109/jiot.2025.3546016","title":"IoT-Enhanced Generative AI for Dynamic Train Control in Virtually Coupled Train Set Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Computer science; Set (abstract data type); Control system; Control engineering; Electrical engineering; Engineering","score_opus":0.006332780518829233,"score_gpt":0.24232885602322363,"score_spread":0.2359960755043944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408017109","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":[0.40381345,0.00055216975,0.5921347,0.000114287875,0.0025422636,0.00025840243,0.000015463593,0.000046129484,0.0005231257],"genre_scores_gemma":[0.9978152,0.000021858747,0.0004754521,0.00020022845,0.00012604312,0.00005176219,0.0000030160047,0.000033035965,0.0012733991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982315,0.0000772152,0.00092167646,0.00019032176,0.00021433366,0.0003650073],"domain_scores_gemma":[0.9992986,0.00015233156,0.00018511909,0.00013626817,0.00014970414,0.00007803056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009320599,0.00023886793,0.00056751317,0.0003528452,0.00004395251,0.00012796107,0.00037469593,0.00016126031,0.000013085338],"category_scores_gemma":[0.000070413036,0.00021516741,0.00018793644,0.00017709463,0.000042607426,0.0001850026,0.000008674643,0.00042440195,0.0000022248257],"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":[0.00010943819,0.00004146275,0.00001609613,0.00020644687,0.00025044865,0.000014263867,0.004611002,0.69048136,0.299226,0.0007232518,0.0025912486,0.0017289774],"study_design_scores_gemma":[0.0022488332,0.00016001402,0.000051720202,0.000774099,0.000025067066,0.000040132738,0.0005946322,0.9868932,0.008217189,0.00021216541,0.0005858022,0.00019717005],"about_ca_topic_score_codex":0.00013861575,"about_ca_topic_score_gemma":0.000094907686,"teacher_disagreement_score":0.59400177,"about_ca_system_score_codex":0.0002708543,"about_ca_system_score_gemma":0.000079873644,"threshold_uncertainty_score":0.8774272},"labels":[],"label_agreement":null},{"id":"W4408521086","doi":"10.1109/giis64151.2025.10921737","title":"Optimizing Resource Allocation and Scheduling towards FRMCS and GSM-R networks coexistence in Railway Systems","year":2025,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ministry of Economy","keywords":"GSM; Computer science; Scheduling (production processes); Resource allocation; Processor scheduling; Computer network; Cellular radio; Distributed computing; Resource (disambiguation); Base station; Engineering; Operations management","score_opus":0.008441028539158619,"score_gpt":0.2108301754804851,"score_spread":0.20238914694132648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408521086","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":[0.60962725,0.020408234,0.30042952,0.00018495235,0.00074873463,0.0003479005,9.4060135e-7,0.00041992316,0.067832515],"genre_scores_gemma":[0.996967,0.00033047414,0.0019357416,0.00003331457,0.000050289116,0.000022791457,0.000002372177,0.000014500778,0.0006434949],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915946,0.000028313616,0.0002861799,0.00021353955,0.00008329973,0.00022923575],"domain_scores_gemma":[0.999695,0.00005423539,0.000021806565,0.00015237492,0.000022896402,0.000053687967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035566004,0.00013763814,0.00020397335,0.00013938492,0.000067856105,0.000117623225,0.00008085486,0.00011542666,0.0000018055874],"category_scores_gemma":[0.000025307058,0.0001301571,0.000014943766,0.00029785236,0.000031977295,0.00011129048,0.000041543954,0.00013468447,7.1586345e-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":[0.0000023893488,0.000004057992,0.0005556507,0.0001376649,0.000010312661,0.0000016577952,0.00033097964,0.99016756,0.0002655955,0.004548889,0.0000817792,0.0038934888],"study_design_scores_gemma":[0.00023316439,0.000009470179,0.0013760638,0.00033651106,0.0000054857924,0.000006304518,0.0010501066,0.99387664,0.00007726855,0.000007692138,0.0028797905,0.00014151167],"about_ca_topic_score_codex":0.0003752232,"about_ca_topic_score_gemma":0.000067262874,"teacher_disagreement_score":0.38733977,"about_ca_system_score_codex":0.00005021041,"about_ca_system_score_gemma":0.000012366599,"threshold_uncertainty_score":0.5307653},"labels":[],"label_agreement":null},{"id":"W4408917637","doi":"10.18280/jesa.580220","title":"Addressing Future Power Demand Through Optimized Transmission Planning Using High-Power Conductors and Partial Dynamic Line Loading","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrical conductor; Dynamic demand; Power (physics); Electric power transmission; Transmission line; Line (geometry); Power transmission; Transmission (telecommunications); Computer science; Electrical engineering; Engineering; Physics; Mathematics","score_opus":0.022667410127476067,"score_gpt":0.2786505613163843,"score_spread":0.25598315118890824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408917637","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":[0.7853391,0.014891555,0.19621845,0.000069590664,0.0021066926,0.0001398723,0.0000051557267,0.0003322489,0.0008973191],"genre_scores_gemma":[0.9816977,0.0003614745,0.01743988,0.000041324824,0.0002107767,0.000003618821,0.0000030980102,0.00008020134,0.0001619632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781454,0.00016987893,0.0008379955,0.00030051116,0.0003181786,0.00055891677],"domain_scores_gemma":[0.99921435,0.000109423854,0.00019928919,0.00020518129,0.000090455396,0.00018127478],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044239196,0.00040640144,0.00062279345,0.00028137668,0.0006551805,0.00046299992,0.00021213762,0.0002138278,0.0000922985],"category_scores_gemma":[0.000047853246,0.0003361741,0.00014036954,0.00040766454,0.00009612215,0.00064863876,0.000046884954,0.0005057412,0.0000033471624],"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":[0.00006269041,0.00004766266,0.000295023,0.0006296237,0.00037236887,0.00032996756,0.0063811326,0.8958347,0.067955814,0.00056573906,0.000536496,0.026988817],"study_design_scores_gemma":[0.0020565405,0.00012338582,0.01207078,0.0050088647,0.00017456178,0.0011484107,0.001200831,0.9700808,0.0029793899,0.0003037255,0.004148693,0.00070403615],"about_ca_topic_score_codex":0.000020920887,"about_ca_topic_score_gemma":8.7196565e-7,"teacher_disagreement_score":0.19635855,"about_ca_system_score_codex":0.00022750316,"about_ca_system_score_gemma":0.00007329546,"threshold_uncertainty_score":0.99990904},"labels":[],"label_agreement":null},{"id":"W4409014175","doi":"10.1109/tmc.2025.3556143","title":"Edge Intelligence Enhanced Monte Carlo Tree Search for Virtually Coupled Train Set Optimal Control","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"Beijing Jiaotong University; Natural Science Foundation of Beijing Municipality; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Monte Carlo tree search; Monte Carlo method; Set (abstract data type); Tree (set theory); Algorithm; Mathematics; Statistics","score_opus":0.011992438618770775,"score_gpt":0.25474177258446307,"score_spread":0.2427493339656923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409014175","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":[0.25724608,0.00013543208,0.7400011,0.00001385965,0.0011585262,0.0005963122,0.000034053388,0.00033824518,0.00047644498],"genre_scores_gemma":[0.99761164,0.000021101045,0.0014981058,0.00003334424,0.000092252965,0.00023912133,0.0000022972702,0.00004401149,0.00045814508],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984263,0.000046441586,0.0004899744,0.00036623594,0.00017576978,0.0004952416],"domain_scores_gemma":[0.99901533,0.00045102093,0.00003325196,0.00029642673,0.00011121701,0.00009275327],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003708009,0.00026777585,0.0003657332,0.00023452104,0.0002994749,0.00007264957,0.00027845937,0.00013539239,0.000019537825],"category_scores_gemma":[0.0000055088203,0.00028198442,0.00021119977,0.000408536,0.000052080948,0.0000816378,0.0000015793046,0.00030762353,0.000012495531],"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":[0.000031265816,0.000052695643,8.13591e-7,0.00008601185,0.00008371492,0.0000013873943,0.0008744664,0.884359,0.018834777,0.00005240773,0.000040743376,0.09558273],"study_design_scores_gemma":[0.00065996085,0.00017374325,0.000011612544,0.0001376964,0.000030319005,0.0000024767237,0.0007622829,0.9436593,0.054039475,0.000003325332,0.00028461133,0.00023519216],"about_ca_topic_score_codex":0.00006364388,"about_ca_topic_score_gemma":0.000052833922,"teacher_disagreement_score":0.74036556,"about_ca_system_score_codex":0.00014801134,"about_ca_system_score_gemma":0.00006375635,"threshold_uncertainty_score":0.9999632},"labels":[],"label_agreement":null},{"id":"W4409791061","doi":"10.61091/jcmcc127a-342","title":"Optimal electric bus frequency setting combining LSTM prediction and two-layer planning","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Tianjin University; University of Hong Kong","keywords":"Layer (electronics); Computer science; Artificial intelligence; Materials science","score_opus":0.007541340329630994,"score_gpt":0.23138789986593727,"score_spread":0.22384655953630628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409791061","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":[0.96301466,0.0019929383,0.016239528,0.000032940086,0.014725734,0.00017962954,0.0000013628892,0.00013511594,0.00367807],"genre_scores_gemma":[0.9949818,0.00005620968,0.00394021,0.000011038633,0.0009579762,0.000002693986,0.0000011286236,0.000041986692,0.000006920813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99756837,0.00007470874,0.0012597892,0.00022112356,0.0004338862,0.0004421225],"domain_scores_gemma":[0.9984111,0.000506451,0.0004585471,0.000181685,0.00027908143,0.00016309573],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014323113,0.00034459037,0.0007342506,0.00042166232,0.0003739179,0.0002859851,0.00026010975,0.0001906871,0.0000020741218],"category_scores_gemma":[0.00025084132,0.00033141774,0.00011486483,0.0005186796,0.00004866809,0.00026598506,0.00011373188,0.000649716,6.4599226e-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":[0.00008315101,0.0004408235,0.003661465,0.0013935682,0.0006805566,0.00010044632,0.0057547404,0.06651214,0.025408225,0.8885535,0.00084552425,0.0065658716],"study_design_scores_gemma":[0.01076105,0.0011076676,0.0013526327,0.003374215,0.00037708424,0.00048607794,0.0018886613,0.7690947,0.003950049,0.20599316,0.0006601202,0.00095458206],"about_ca_topic_score_codex":0.000012022616,"about_ca_topic_score_gemma":1.4175232e-7,"teacher_disagreement_score":0.70258254,"about_ca_system_score_codex":0.00012557059,"about_ca_system_score_gemma":0.00009574585,"threshold_uncertainty_score":0.9999138},"labels":[],"label_agreement":null},{"id":"W4410294529","doi":"10.1109/tase.2025.3569178","title":"Network-Based Rail Running Band Anomaly Recognition via Recurrent Attention Graphs","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Natural Science Foundation of Hunan Province; China Scholarship Council; Natural Science Foundation of Hainan Province; National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Anomaly (physics); Artificial intelligence; Computer network; Pattern recognition (psychology); Physics","score_opus":0.009738982533731743,"score_gpt":0.20788074804742374,"score_spread":0.198141765513692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410294529","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":[0.2921683,0.00009035317,0.70513433,0.000038542996,0.0014937038,0.00011379956,0.0000024251372,0.00038624296,0.0005723289],"genre_scores_gemma":[0.9980324,0.000027101698,0.0017789591,0.00003193701,0.000026458021,0.00005701399,0.0000029151054,0.000013631245,0.000029619843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888515,0.000009076328,0.0002791616,0.00024758407,0.00027496697,0.00030403],"domain_scores_gemma":[0.99960536,0.000038397156,0.00002857394,0.00015132425,0.00009720819,0.00007911099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048289172,0.00016356567,0.00013892449,0.0006045571,0.00032808867,0.00013467464,0.00010384163,0.00006987964,0.000014695615],"category_scores_gemma":[0.0000072493694,0.00017242937,0.000047891266,0.0014940437,0.000056637342,0.0004433909,7.8225725e-7,0.00014695225,0.000013187944],"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":[0.000003423521,0.0000142933395,0.000019494006,0.000060239465,0.000008511214,5.0450603e-7,0.00004377515,0.8937362,0.023609433,0.00004968601,0.00003737664,0.08241705],"study_design_scores_gemma":[0.00025222113,0.000033818556,0.003160472,0.000253808,0.00001720606,0.0000036035667,0.000019027108,0.98531246,0.01042578,0.0000446216,0.00028942814,0.00018756278],"about_ca_topic_score_codex":0.000013899179,"about_ca_topic_score_gemma":0.0000077115255,"teacher_disagreement_score":0.7058641,"about_ca_system_score_codex":0.00012703767,"about_ca_system_score_gemma":0.000040539093,"threshold_uncertainty_score":0.7031465},"labels":[],"label_agreement":null},{"id":"W4410634313","doi":"10.1016/j.rser.2025.115869","title":"A review of research on traction load models and modeling methods for electrified railways","year":2025,"lang":"en","type":"review","venue":"Renewable and Sustainable Energy Reviews","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Northeast Electric Power University; Gansu Education Department; National Natural Science Foundation of China","keywords":"Traction (geology); Computer science; Engineering; Automotive engineering; Mechanical engineering","score_opus":0.12363451830265929,"score_gpt":0.42547476563427095,"score_spread":0.30184024733161163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410634313","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":[6.293209e-8,0.8550893,0.12938496,0.000009275191,0.00012607989,0.0016247676,0.000006266992,0.00006779797,0.013691465],"genre_scores_gemma":[0.0000012514945,0.9850142,0.0020271954,0.000043813332,0.00012855775,0.002097397,0.000048731126,0.000090406604,0.01054842],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954429,0.0008834496,0.0016766714,0.0007511405,0.00027153877,0.00097429485],"domain_scores_gemma":[0.9973971,0.00090461253,0.00026988945,0.0007359438,0.00052349194,0.00016895604],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008722104,0.00065590144,0.003835169,0.0007235277,0.00028760612,0.00007862489,0.00031915685,0.0004973736,0.0000055336677],"category_scores_gemma":[0.0006844199,0.0005146283,0.00055330235,0.0015645566,0.000050978655,0.00021249817,0.00009412511,0.00040842054,5.444537e-7],"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":[0.0000041143994,0.000014927816,1.0100727e-9,0.41449362,0.000053434276,0.0000012860505,0.0000106079415,0.014611764,0.0000015323195,0.0078468295,0.0030120646,0.5599498],"study_design_scores_gemma":[0.000120638826,0.00008574914,3.270314e-10,0.16950774,0.00031242435,0.000011907798,0.000041281903,0.063871294,0.000007110631,0.0015602814,0.7641545,0.00032706943],"about_ca_topic_score_codex":0.00083535677,"about_ca_topic_score_gemma":0.000020186759,"teacher_disagreement_score":0.76114243,"about_ca_system_score_codex":0.00040873542,"about_ca_system_score_gemma":0.0006459574,"threshold_uncertainty_score":0.9997305},"labels":[],"label_agreement":null},{"id":"W4410907363","doi":"10.21428/d82e957c.2ae359db","title":"TRIT-Net: Triplet-based Railway Instance Tracing Network Using Attraction Field Representation","year":2025,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Attraction; Tracing; Representation (politics); Field (mathematics); Net (polyhedron); Computer science; Mathematics; Programming language","score_opus":0.01685040888687487,"score_gpt":0.2579334522788474,"score_spread":0.24108304339197253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410907363","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":[0.23943141,0.00052920537,0.70610464,0.00010670081,0.001757452,0.00016163402,7.6992734e-7,0.00038542022,0.05152275],"genre_scores_gemma":[0.9952861,0.000019498193,0.0031916786,0.00014551537,0.00021657896,0.000015043415,0.0000050640238,0.00001696053,0.0011035275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990559,0.000031697018,0.0003372508,0.00019644423,0.00012602193,0.00025266124],"domain_scores_gemma":[0.99952745,0.0001412768,0.00003987587,0.00022270752,0.00003242316,0.00003627986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018768635,0.0001337294,0.00018202621,0.00011699348,0.00013093173,0.000058213755,0.00008477784,0.00011495533,0.00006223476],"category_scores_gemma":[0.000038682054,0.00013439717,0.00007412795,0.00062694866,0.000009740006,0.00018132567,0.0000065340896,0.00013779802,0.0000054305465],"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":[0.000015237993,0.000011705243,0.00057625765,0.00005148634,0.000016343096,0.0000022676406,0.00003449293,0.97537804,0.005224564,0.0006839274,0.0027775925,0.01522808],"study_design_scores_gemma":[0.00052167,0.000018818884,0.0009743454,0.00016511891,0.000016589993,0.0000019940348,0.00014488392,0.97280324,0.017665917,0.00008673592,0.007415981,0.00018471142],"about_ca_topic_score_codex":0.0003089055,"about_ca_topic_score_gemma":0.00015577831,"teacher_disagreement_score":0.7558547,"about_ca_system_score_codex":0.000094687064,"about_ca_system_score_gemma":0.00002857,"threshold_uncertainty_score":0.5480557},"labels":[],"label_agreement":null},{"id":"W4411584272","doi":"10.1109/ickecs65700.2025.11035454","title":"Benefits of using Active Tilting Technology and CAN Bus for Train Control in normal rakes","year":2025,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Control (management); Artificial intelligence","score_opus":0.006651920596315508,"score_gpt":0.20725313493527772,"score_spread":0.20060121433896222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411584272","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":[0.9807,0.00032355645,0.016109258,0.000055095537,0.00008320929,0.00013046949,0.00000958448,0.000050301853,0.0025384831],"genre_scores_gemma":[0.9991215,0.0000037719217,0.0007726408,0.000009198738,0.000010152189,0.000014659974,4.84543e-7,0.0000064517753,0.00006117372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995906,0.0000038038906,0.0001561858,0.00007920344,0.000024867337,0.00014535987],"domain_scores_gemma":[0.9998523,0.000044552427,0.000016331374,0.000054986293,0.00002075186,0.000011090652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000074466734,0.00006408581,0.00015874123,0.0002669683,0.000021892862,0.0000041939625,0.00004344622,0.00007265938,0.0000014579036],"category_scores_gemma":[0.000024238492,0.000060315426,0.000015614189,0.00024114929,0.000020105217,0.000030079556,0.000007420452,0.000042605654,3.5727652e-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":[0.0000204446,0.00001799929,0.0049524982,0.00019759126,0.00004898398,6.7825675e-7,0.0005281895,0.8182025,0.069892585,0.026349079,0.000011422953,0.07977802],"study_design_scores_gemma":[0.0018502524,0.000043126078,0.0073269564,0.000194977,0.000017715722,0.000004984833,0.0017119171,0.93649733,0.051662844,0.00024308171,0.00027155265,0.00017528559],"about_ca_topic_score_codex":0.00024181721,"about_ca_topic_score_gemma":0.00072082866,"teacher_disagreement_score":0.118294805,"about_ca_system_score_codex":0.000024854719,"about_ca_system_score_gemma":0.000013079964,"threshold_uncertainty_score":0.24595916},"labels":[],"label_agreement":null},{"id":"W4412130441","doi":"10.1109/ias62731.2025.11061501","title":"Corrosion Modeling and Assessment on Transmission Line Structures due to Nearby Stray Current from a DC-Electrified Railway","year":2025,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Manitoba Hydro; University of Saskatchewan","funders":"","keywords":"Stray voltage; Corrosion; Current (fluid); Transmission line; Electric power transmission; Line (geometry); Electrical engineering; Railway line; Engineering; Materials science; Engineering physics; Computer science; Civil engineering; Metallurgy","score_opus":0.013351318844404724,"score_gpt":0.2635064888171261,"score_spread":0.2501551699727214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412130441","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":[0.38838196,0.0009005662,0.6060106,0.00009692348,0.00049614947,0.00016992242,0.0000058943438,0.00017722121,0.003760778],"genre_scores_gemma":[0.99692607,0.00013463703,0.002574205,0.000056674737,0.000080064565,0.000017566048,0.000018397956,0.000020684496,0.00017167721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894345,0.000023942375,0.00029166794,0.00030361698,0.0001895461,0.00024775535],"domain_scores_gemma":[0.9995936,0.000042502845,0.000013882085,0.00019616263,0.000027578586,0.00012623763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000100999045,0.00021244126,0.00024957478,0.00015105949,0.00010037831,0.00007782575,0.000111351495,0.0000939151,0.000049130216],"category_scores_gemma":[0.0000067063434,0.00016617477,0.00004587353,0.00021642628,0.0000066496127,0.000053143667,0.00001627149,0.00023633009,0.000003527691],"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":[0.00002392515,0.00002527539,0.000009733942,0.000046523597,0.000009267434,0.0000018329595,0.0001261955,0.8001001,0.0562482,0.0021024453,0.0003925369,0.140914],"study_design_scores_gemma":[0.00039528002,0.00009186256,0.00037830672,0.00021894203,0.000013264899,7.954763e-7,0.000046583795,0.9762376,0.01617883,0.00051585084,0.0057231416,0.00019956335],"about_ca_topic_score_codex":0.0002308111,"about_ca_topic_score_gemma":0.000043242275,"teacher_disagreement_score":0.6085442,"about_ca_system_score_codex":0.00006549729,"about_ca_system_score_gemma":0.000036090878,"threshold_uncertainty_score":0.67764103},"labels":[],"label_agreement":null},{"id":"W4412383610","doi":"10.1155/atr/6689351","title":"Energy‐Efficient Train Operation Optimization Method for Urban Rail Intervals Based on Curve Splicing","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Natural Science Foundation of Hunan Province","keywords":"Energy (signal processing); Computer science; Automotive engineering; Transport engineering; Environmental science; Engineering; Mathematics; Statistics","score_opus":0.006499764600903915,"score_gpt":0.24974389853954918,"score_spread":0.24324413393864525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412383610","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":[0.03581871,0.00015864434,0.96251315,0.00010603763,0.00092705264,0.00014874761,0.000011119665,0.000043710035,0.0002728207],"genre_scores_gemma":[0.8913431,0.000019269375,0.1082989,0.00008835991,0.00009555788,0.000023530296,0.000045376048,0.000023532737,0.00006237102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884087,0.00003637851,0.0006649681,0.00012832336,0.00018375916,0.00014571364],"domain_scores_gemma":[0.99937874,0.000117668686,0.00017211429,0.00009740518,0.00018867658,0.00004540874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042542556,0.0001454789,0.0002516472,0.00030646444,0.00006720691,0.000028573637,0.00009169593,0.00007149297,0.000008821074],"category_scores_gemma":[0.00002596937,0.00013369886,0.00015051798,0.00025437286,0.000007966915,0.00017487397,5.027145e-7,0.000093734685,1.927967e-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":[0.00010048841,0.000045410212,0.000018889286,0.000081799015,0.00002470889,0.0000015861905,0.0004615431,0.9736351,0.010625002,0.001172904,0.00009598511,0.013736623],"study_design_scores_gemma":[0.001273219,0.00017780683,0.0007449686,0.00030568283,0.000043250322,8.8725864e-7,0.00019684975,0.9816567,0.014016991,0.000045201865,0.0014178833,0.00012058048],"about_ca_topic_score_codex":0.000006842519,"about_ca_topic_score_gemma":0.000021846441,"teacher_disagreement_score":0.8555244,"about_ca_system_score_codex":0.00011201982,"about_ca_system_score_gemma":0.00004447473,"threshold_uncertainty_score":0.5452081},"labels":[],"label_agreement":null},{"id":"W4412465510","doi":"10.1016/j.cie.2025.111372","title":"IRS-guided policy search in model based reinforcement learning for virtually coupled train sets control","year":2025,"lang":"en","type":"article","venue":"Computers & Industrial Engineering","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Reinforcement learning; Reinforcement; Control (management); Computer science; Artificial intelligence; Engineering; Operations research; Structural engineering","score_opus":0.024835418231712397,"score_gpt":0.24813511925901913,"score_spread":0.22329970102730673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412465510","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":[0.06686678,0.00006011433,0.93056285,0.00016320021,0.00090184156,0.0006454516,0.000004357925,0.0004117934,0.000383637],"genre_scores_gemma":[0.9976488,0.0000041575654,0.0016147493,0.0000751862,0.00030391515,0.00012571477,0.000023468256,0.000054263826,0.00014977514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982174,0.00002712541,0.000607786,0.0002861237,0.0002108415,0.0006507349],"domain_scores_gemma":[0.99927896,0.00028682745,0.000034545,0.00022635052,0.000051801308,0.000121532394],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005975455,0.00030892325,0.0004499808,0.00072729995,0.000076539414,0.00008619584,0.00029538278,0.00025543952,0.000003256643],"category_scores_gemma":[0.00018097127,0.00035194246,0.00013365546,0.0006432721,0.000016459528,0.00010758929,0.00003463273,0.00046905014,0.0000018107986],"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":[0.000028966264,0.000012569865,0.000025077823,0.00009116292,0.000053027266,0.0000035602814,0.00014814257,0.9890306,0.0036000414,0.0017735972,0.00036905144,0.004864175],"study_design_scores_gemma":[0.0046899966,0.00006232126,0.00003879767,0.00028261618,0.000011778467,0.0000010236246,0.000022686316,0.9926896,0.0005006566,0.0000070221313,0.0013956287,0.000297854],"about_ca_topic_score_codex":0.00012624073,"about_ca_topic_score_gemma":0.0000051031275,"teacher_disagreement_score":0.930782,"about_ca_system_score_codex":0.00052148174,"about_ca_system_score_gemma":0.00027401344,"threshold_uncertainty_score":0.99989325},"labels":[],"label_agreement":null},{"id":"W4413130002","doi":"10.54337/ojs.td.v32i.10640","title":"The Resignalling Challenge: Investigating the Possibilities and Limitations of a New CBTC Signalling System on the Copenhagen Metro","year":2025,"lang":"en","type":"article","venue":"Trafikdage på <<Aalborg=Ålborg>> Universitet/Trafikdage på Aalborg Universitet","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Bottleneck; Transport engineering; Software deployment; Headway; Computer science; Reliability (semiconductor); Engineering; Embedded system","score_opus":0.03184691234416075,"score_gpt":0.20254954936950667,"score_spread":0.17070263702534594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413130002","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":[0.7759655,0.013366691,0.00920473,0.00749243,0.0014354733,0.0026557278,0.0002574157,0.0012858995,0.18833613],"genre_scores_gemma":[0.9866566,0.0013944289,0.0004914244,0.00018386274,0.00018052275,0.000011952354,0.00003750352,0.00011398477,0.01092972],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9952919,0.0006421593,0.0010149339,0.000912097,0.0008722157,0.0012667101],"domain_scores_gemma":[0.9925238,0.0048570307,0.0003972237,0.0013316211,0.00041455522,0.00047579894],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001639212,0.0009212622,0.00086321845,0.0007961977,0.002934315,0.00043726445,0.0020199004,0.00040994337,0.00009611679],"category_scores_gemma":[0.0004155093,0.0006547747,0.0004161712,0.0023940026,0.00080103654,0.00067598507,0.00028565628,0.0011633289,0.00008579433],"study_design_candidate":"qualitative","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":[0.0004528861,0.00029458728,0.00064982614,0.0016320263,0.0035814221,0.0003550727,0.07347978,0.3297311,0.026269458,0.5110929,0.039731152,0.012729752],"study_design_scores_gemma":[0.0056595514,0.00093561184,0.0027840307,0.0044308845,0.0020402751,0.000052009433,0.59303,0.17109407,0.014561919,0.0011499341,0.20084602,0.0034157303],"about_ca_topic_score_codex":0.0024288867,"about_ca_topic_score_gemma":0.0038695203,"teacher_disagreement_score":0.5195502,"about_ca_system_score_codex":0.0007870672,"about_ca_system_score_gemma":0.00065946695,"threshold_uncertainty_score":0.99959034},"labels":[],"label_agreement":null},{"id":"W4413333888","doi":"10.1155/atr/6302741","title":"An Extended Space‐Time Network With Explicit Incompatibility Modelling for High‐Speed Railway Timetabling","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"China Academy of Railway Sciences; National Natural Science Foundation of China; China Railway","keywords":"Computer science; Space (punctuation); Transport engineering; Spacetime; Simulation; Engineering; Physics; Operating system","score_opus":0.006363286320643068,"score_gpt":0.22021903087346226,"score_spread":0.2138557445528192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413333888","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":[0.5065603,0.00022400681,0.492689,0.000015891303,0.0002671949,0.00012976608,0.0000049582122,0.00004620429,0.000062642845],"genre_scores_gemma":[0.9180218,0.00003444116,0.08166334,0.000010436654,0.00016043155,0.0000070556353,0.000027699409,0.00002710574,0.000047674606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875546,0.000019497213,0.0006386278,0.00015780766,0.00019685135,0.00023174888],"domain_scores_gemma":[0.9992159,0.00007403786,0.00020538285,0.0001752965,0.00025128192,0.00007808438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036716758,0.00017115215,0.00038545157,0.00014786956,0.000088923654,0.000028830731,0.00012875532,0.00006870982,0.000008547612],"category_scores_gemma":[0.0000056144668,0.00014477131,0.000091380476,0.00035208307,0.000013359373,0.00055632996,6.544016e-7,0.00015058542,5.8596925e-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":[0.0002462339,0.00004483015,0.0001240496,0.000086899716,0.000064492044,0.000003538634,0.0003107528,0.981346,0.013331986,0.001360198,0.000020407699,0.0030605772],"study_design_scores_gemma":[0.003209479,0.0005612058,0.017833766,0.0005944596,0.00019471874,0.0000060968678,0.00043364154,0.96407586,0.00797878,0.0037966059,0.0009091181,0.0004062902],"about_ca_topic_score_codex":0.000012786519,"about_ca_topic_score_gemma":0.000034948716,"teacher_disagreement_score":0.4114615,"about_ca_system_score_codex":0.00006175199,"about_ca_system_score_gemma":0.00004392852,"threshold_uncertainty_score":0.59036034},"labels":[],"label_agreement":null},{"id":"W4413955331","doi":"10.1155/atr/6619187","title":"Risk Mapping for Daily High‐Speed Railway Disturbances Based on Operation Loss","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government of Jiangsu Province; National Natural Science Foundation of China; Major Projects of Natural Sciences of University in Jiangsu Province of China; China Railway","keywords":"Transport engineering; Environmental science; Computer science; Engineering; Forensic engineering","score_opus":0.004859111819425225,"score_gpt":0.21432516616095032,"score_spread":0.2094660543415251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413955331","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":[0.6101555,0.00028253297,0.38763422,0.000110078225,0.0013882809,0.00015017102,0.000024947796,0.00003997358,0.00021425843],"genre_scores_gemma":[0.99281925,0.00009936839,0.006781008,0.000037369566,0.00012564778,0.000009816093,0.000033693395,0.000015668791,0.00007817434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9990271,0.000015359748,0.000547222,0.00010531682,0.0001720179,0.00013298434],"domain_scores_gemma":[0.9994586,0.00008967622,0.00018583397,0.00009152331,0.00013657022,0.000037772166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002199515,0.00012497121,0.00022090971,0.00019148234,0.00008798614,0.000026251146,0.000091985356,0.000055626715,0.000006169874],"category_scores_gemma":[0.000027131517,0.00010856295,0.00011737544,0.00020244934,0.000013699646,0.00027482974,3.2608057e-7,0.00012363533,8.55046e-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":[0.00010440646,0.000026188003,0.000782406,0.000102075326,0.00002963019,0.0000032440682,0.0002726077,0.98389214,0.006278181,0.000642592,0.00013098193,0.0077355695],"study_design_scores_gemma":[0.011206702,0.00092504895,0.55712,0.0016818423,0.0002477952,0.00000350035,0.001433682,0.34915617,0.036201347,0.001900535,0.039315853,0.00080747314],"about_ca_topic_score_codex":0.0000075193643,"about_ca_topic_score_gemma":0.000033592856,"teacher_disagreement_score":0.63473594,"about_ca_system_score_codex":0.00006860515,"about_ca_system_score_gemma":0.000032898864,"threshold_uncertainty_score":0.44270688},"labels":[],"label_agreement":null},{"id":"W4415151259","doi":"10.1155/atr/2721207","title":"Flexible Train Composition Mode–Based Rolling Stock Circulation Planning Problem for Regional Rapid Rail Transit","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; Beijing Municipal Natural Science Foundation","keywords":"Train; Stock (firearms); Adaptability; Urban rail transit; Mode (computer interface); Nonlinear programming; Linear programming; Dynamic programming","score_opus":0.015301295751042516,"score_gpt":0.2579782412938835,"score_spread":0.242676945542841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415151259","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":[0.25464115,0.00053266773,0.74400187,0.00010911143,0.00029961774,0.00021753451,0.000010924021,0.00006164818,0.00012548313],"genre_scores_gemma":[0.9720246,0.00002038017,0.0276694,0.000035181765,0.00009072427,0.00002491748,0.00009787067,0.000022183393,0.000014726932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884176,0.000015637548,0.0006508061,0.00011751608,0.00020653817,0.0001677693],"domain_scores_gemma":[0.9994648,0.00006542977,0.0001764667,0.00006718189,0.00017851558,0.00004758523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020748429,0.00014096254,0.00025081186,0.0002695288,0.00009979173,0.000021486636,0.000076359654,0.00008363989,0.0000036634797],"category_scores_gemma":[0.0000029656073,0.0001441206,0.00017035373,0.00024679245,0.000012702509,0.00037849988,2.0904737e-7,0.00013434833,1.6814808e-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":[0.00020591308,0.000030614035,0.000066151064,0.00024041104,0.000043050102,0.000002587709,0.0008537235,0.91481364,0.075517006,0.00071089965,0.00004649964,0.0074695116],"study_design_scores_gemma":[0.0071585127,0.00034793725,0.024171302,0.0019490924,0.00021880551,0.0000114997465,0.0006315188,0.9323045,0.025352022,0.0037517117,0.003622915,0.00048018398],"about_ca_topic_score_codex":0.000002431414,"about_ca_topic_score_gemma":0.000006014351,"teacher_disagreement_score":0.71738344,"about_ca_system_score_codex":0.00008889734,"about_ca_system_score_gemma":0.00006312991,"threshold_uncertainty_score":0.58770674},"labels":[],"label_agreement":null},{"id":"W4415168559","doi":"10.1049/icp.2025.1491","title":"Improving LV networks hosting capacity via the use of batteries – a Monte-Carlo analysis","year":2025,"lang":"en","type":"article","venue":"IET conference proceedings.","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Iron Ore Company (Canada)","funders":"","keywords":"Overvoltage; Limiting; Grid; Energy storage; Voltage; Power (physics); Transmission (telecommunications); Inverter; Reliability (semiconductor); Battery (electricity)","score_opus":0.024329137086652564,"score_gpt":0.19784047616351363,"score_spread":0.17351133907686106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415168559","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":[0.84671336,0.00017910312,0.15030622,0.000069956004,0.0003231882,0.00015282612,0.00000584814,0.00020718126,0.0020422793],"genre_scores_gemma":[0.99885833,0.00002841654,0.00075190497,0.00003841483,0.000056700865,0.00003680433,0.0000012439097,0.00001442387,0.00021375793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988492,0.000009136384,0.00040426935,0.00023956843,0.00016783262,0.0003300062],"domain_scores_gemma":[0.99929816,0.000066229455,0.00011777817,0.0001830123,0.00028785784,0.000046963218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023713644,0.00020792274,0.0003631109,0.00017652531,0.00014503597,0.00023667616,0.0003007546,0.0001138457,0.000016106416],"category_scores_gemma":[0.00012353598,0.00015739922,0.00013472511,0.0010762492,0.00010730623,0.00031079922,0.00007393826,0.00022890992,0.0000010286391],"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":[0.000027564072,0.000054795673,0.1029046,0.0008271749,0.0014667201,0.0000023853252,0.00648553,0.8149583,0.02688842,0.0062189535,0.0020806042,0.03808499],"study_design_scores_gemma":[0.000076687305,0.000015685739,0.008180171,0.00010410701,0.00018579922,0.0000013118928,0.0004382441,0.98874724,0.0012037144,0.000022528113,0.00085651607,0.00016802001],"about_ca_topic_score_codex":0.0010891436,"about_ca_topic_score_gemma":0.0001881395,"teacher_disagreement_score":0.17378895,"about_ca_system_score_codex":0.00004076655,"about_ca_system_score_gemma":0.000021980266,"threshold_uncertainty_score":0.6418554},"labels":[],"label_agreement":null},{"id":"W4415693395","doi":"10.1155/atr/5549207","title":"Pricing for Railway Group Tickets in Revenue Management Increasing Revenue and Attracting New Users","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Natural Science Foundation of China","keywords":"Revenue; Purchasing; Order (exchange); Ticket; Revenue management; Rationality; Preference; Total revenue","score_opus":0.006389199657713369,"score_gpt":0.23187099761006413,"score_spread":0.22548179795235077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415693395","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":[0.94740486,0.00074775104,0.05107711,0.000045555284,0.00044785737,0.00014183104,0.0000036837,0.000010464811,0.00012088829],"genre_scores_gemma":[0.9785192,0.00040240496,0.02089757,0.0000136085755,0.000060815328,0.0000045105244,0.0000034596671,0.0000130539165,0.00008536368],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992781,0.000012047616,0.00042109858,0.00007937174,0.00008585869,0.00012347999],"domain_scores_gemma":[0.9996828,0.00008841881,0.00012208478,0.00004989847,0.000021264323,0.0000355488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033367478,0.000081581,0.00016807958,0.00018858178,0.00003611421,0.000014869355,0.000048077825,0.000036213452,6.0664695e-7],"category_scores_gemma":[0.00002837407,0.00008517012,0.00004211952,0.0001880715,0.000005848695,0.0002720314,9.1370964e-7,0.00010270395,9.360909e-8],"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":[0.00014209712,0.000024634945,0.0047926744,0.0007815164,0.00006287425,0.000035554873,0.0022831592,0.8014803,0.019684985,0.0006473454,0.00013344664,0.16993141],"study_design_scores_gemma":[0.0059261895,0.00018377883,0.9354383,0.005947379,0.00020190352,0.000033369568,0.0031582348,0.0047939857,0.0027642192,0.0026573855,0.038434323,0.000460953],"about_ca_topic_score_codex":0.00003681961,"about_ca_topic_score_gemma":0.00027497322,"teacher_disagreement_score":0.9306456,"about_ca_system_score_codex":0.000065559674,"about_ca_system_score_gemma":0.000012390527,"threshold_uncertainty_score":0.34731367},"labels":[],"label_agreement":null},{"id":"W4415748289","doi":"10.1109/tsg.2025.3627870","title":"Optimal Scheduling of Railway Power Supply Systems Integrated With Microgrids: A CCAH-RL Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Engineering and Physical Sciences Research Council; China Scholarship Council; Strategic Innovation Fund","keywords":"Electrification; Electricity; Electric power system; Energy supply; Renewable energy; Scheduling (production processes); Traction power network; Energy management; Demand response","score_opus":0.005638797800268296,"score_gpt":0.18953420765105974,"score_spread":0.18389540985079145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415748289","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":[0.29459992,0.00047982612,0.69708127,0.000017302189,0.0022843264,0.0002934105,0.00007100764,0.00032079386,0.004852153],"genre_scores_gemma":[0.9944792,0.00004238352,0.004351876,0.000011939734,0.00005072861,0.00013026099,0.000011304412,0.00004987495,0.0008724637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985582,0.00004611853,0.00048381602,0.0003218219,0.00022536577,0.00036467292],"domain_scores_gemma":[0.99927706,0.00005503794,0.00005140021,0.00040752545,0.00012124356,0.00008772435],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020003896,0.00030996493,0.0004332618,0.00039670887,0.00012677959,0.00006433325,0.00022181019,0.0001681622,0.00002939677],"category_scores_gemma":[0.0000022784895,0.00025570765,0.00014043864,0.000840916,0.00008718042,0.00013536176,0.0000011424223,0.0003704438,0.000018369039],"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":[0.000063545274,0.00018482075,0.000038394624,0.00021369182,0.00022770296,0.000003264472,0.00027863128,0.98818725,0.009615235,0.00009881352,0.0004129237,0.0006756991],"study_design_scores_gemma":[0.0029762934,0.00059319014,0.0003587514,0.0016863238,0.00026220133,0.00012306639,0.004103582,0.84971255,0.11239502,0.0000020253635,0.026631711,0.0011552662],"about_ca_topic_score_codex":0.00031847093,"about_ca_topic_score_gemma":0.00003079363,"teacher_disagreement_score":0.6998793,"about_ca_system_score_codex":0.00010645373,"about_ca_system_score_gemma":0.00008408419,"threshold_uncertainty_score":0.9999895},"labels":[],"label_agreement":null},{"id":"W4416176502","doi":"10.1080/15397734.2025.2584324","title":"Study on real-time adaptive optimization of energy dissipation in train collision","year":2025,"lang":"en","type":"article","venue":"Mechanics Based Design of Structures and Machines","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; Natural Science Foundation of Hunan Province","keywords":"Dissipation; Collision; Energy (signal processing); Control theory (sociology); Acceleration; Vibration; Energy consumption","score_opus":0.009726722887228811,"score_gpt":0.2216048680914571,"score_spread":0.2118781452042283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416176502","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":[0.18740809,0.000100967656,0.811775,0.0000071398395,0.00012265836,0.0002302512,0.000014752711,0.000034487075,0.0003066124],"genre_scores_gemma":[0.99584514,0.00003655177,0.0040558367,0.000005118399,0.000007452636,0.0000137191,0.000008861624,0.000011684805,0.000015647045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993076,0.00007683909,0.00027430677,0.00013659826,0.00011901695,0.00008567909],"domain_scores_gemma":[0.9996781,0.000085292355,0.00006026159,0.000121512465,0.00003594776,0.000018905997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001729648,0.00012519209,0.00022975715,0.00024666855,0.000027067883,0.000007701408,0.00006715495,0.000066088716,0.0000065027316],"category_scores_gemma":[0.000020379228,0.00010353961,0.000023132648,0.0002648718,0.000007663055,0.000035183693,0.00000936627,0.000038616563,2.6599682e-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":[0.00008654267,0.000045007102,0.000030412322,0.00003165702,0.000013872947,0.0000010835623,0.00013160818,0.9721295,0.013128739,0.012061241,0.000018475383,0.0023218591],"study_design_scores_gemma":[0.00055487966,0.00032605644,0.00093370007,0.00007186905,0.000013747995,2.1721085e-7,0.00009844246,0.9876104,0.008434935,0.0018709619,0.0000021944795,0.0000826082],"about_ca_topic_score_codex":0.00022354293,"about_ca_topic_score_gemma":0.000027248905,"teacher_disagreement_score":0.80843705,"about_ca_system_score_codex":0.000021443642,"about_ca_system_score_gemma":0.0000202827,"threshold_uncertainty_score":0.4222223},"labels":[],"label_agreement":null},{"id":"W4416200207","doi":"10.1155/atr/9971176","title":"Collaborative Optimal Train Carriage Flexible Release Strategy and Passenger Flow Control Strategy for the Metro System","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Sichuan Province Science and Technology Support Program","keywords":"Scheme (mathematics); Equity (law); Linear programming; Nonlinear system; Control (management); Nonlinear programming; Flow (mathematics); Control variable","score_opus":0.005656265688096799,"score_gpt":0.23237989724902514,"score_spread":0.22672363156092834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416200207","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":[0.4796498,0.0068041794,0.5112793,0.00011051038,0.0009197317,0.00056169194,0.00015114817,0.000070194044,0.0004534147],"genre_scores_gemma":[0.9973608,0.00014973427,0.002235403,0.000010105904,0.000107061845,0.000038155682,0.0000082430015,0.000017515731,0.00007296142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990035,0.000028825574,0.0005412969,0.00010580565,0.0001496425,0.00017089302],"domain_scores_gemma":[0.99924344,0.00018936144,0.00015591024,0.00008678925,0.00026361542,0.000060870327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029179937,0.00014888025,0.00030266203,0.00011168377,0.00011455376,0.00004750762,0.0000860093,0.00006857057,0.0000027543936],"category_scores_gemma":[0.0000149095695,0.00010704091,0.000092455244,0.000298312,0.00002587866,0.00028260238,4.3340384e-7,0.00014219717,1.7130128e-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":[0.00018413184,0.000011928846,0.000024983226,0.00019590555,0.00015227663,0.000013531283,0.0004442554,0.98134464,0.005279955,0.0031586473,0.00007256603,0.009117183],"study_design_scores_gemma":[0.01483962,0.0014471067,0.04660697,0.0009858601,0.0010937845,0.00003705881,0.06116974,0.8525223,0.010306327,0.0003781342,0.009850823,0.000762283],"about_ca_topic_score_codex":0.000007614655,"about_ca_topic_score_gemma":0.00007403804,"teacher_disagreement_score":0.517711,"about_ca_system_score_codex":0.000067036985,"about_ca_system_score_gemma":0.000090403126,"threshold_uncertainty_score":0.4365002},"labels":[],"label_agreement":null},{"id":"W4416396695","doi":"10.1016/j.cor.2025.107330","title":"International travel time prediction for China–Europe Express trains via interpretable deep learning models","year":2025,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; China Railway","keywords":"Interpretability; Train; Deep learning; Mechanism (biology); Dual (grammatical number); Process (computing); Transfer of learning","score_opus":0.02463949121770068,"score_gpt":0.28290721556018783,"score_spread":0.2582677243424871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416396695","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":[0.013504781,0.00014455046,0.96069026,0.00016464166,0.00087086356,0.00037095402,0.000016193426,0.00019159338,0.024046184],"genre_scores_gemma":[0.98652464,0.00006983461,0.005683668,0.000014196314,0.00021419126,0.00022153447,0.000117108866,0.000031800253,0.0071230372],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998742,0.00009781985,0.0002873359,0.00027419752,0.00026343248,0.0003352431],"domain_scores_gemma":[0.9993308,0.00009209522,0.000007687084,0.00020859625,0.0002934371,0.00006734381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005265418,0.00012789853,0.00015601137,0.0004165083,0.0004213786,0.0002991909,0.00040710805,0.00007573495,0.000044848213],"category_scores_gemma":[0.000053013282,0.00013037713,0.000060528546,0.0004380244,0.000049207283,0.00037439755,0.00008984376,0.0003204208,0.00002510258],"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":[0.000008205614,0.000034892502,0.0000048464603,0.000028853743,0.00005216994,9.012545e-7,0.0010770959,0.96003854,0.01676951,0.0025942584,0.002291352,0.017099353],"study_design_scores_gemma":[0.00032067785,0.000054815926,0.00014172867,0.000077116936,0.000004146518,0.0000033596982,0.00007938993,0.9851497,0.00060347153,0.000071740054,0.013394518,0.00009937761],"about_ca_topic_score_codex":0.00006143457,"about_ca_topic_score_gemma":0.000013703336,"teacher_disagreement_score":0.97301984,"about_ca_system_score_codex":0.00013749019,"about_ca_system_score_gemma":0.000044700755,"threshold_uncertainty_score":0.53166246},"labels":[],"label_agreement":null},{"id":"W4416621729","doi":"10.48550/arxiv.2510.20597","title":"The Intermodal Railroad Blocking and Railcar Fleet-Management Planning Problem","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Solver; Heuristic; Container (type theory); Integer programming; Scheduling (production processes); Capacity planning; Blocking (statistics); Linear programming; Consolidation (business)","score_opus":0.018173374011277443,"score_gpt":0.24203762777526625,"score_spread":0.2238642537639888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416621729","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":[0.88890547,0.01481551,0.0073254267,0.0004933392,0.005988721,0.0014924003,0.000024362951,0.0005113501,0.080443434],"genre_scores_gemma":[0.9867522,0.002187325,0.0005871494,0.000068601614,0.0006041322,0.0003705096,0.00002208184,0.00010615222,0.009301828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950839,0.00017826185,0.0015143435,0.0013955019,0.00052001775,0.0013079223],"domain_scores_gemma":[0.9975055,0.00029338038,0.00037503635,0.001459864,0.000120285826,0.00024595417],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012844648,0.0010929086,0.00094333483,0.00034996757,0.0011562391,0.0005706678,0.0013045863,0.0005525398,0.00003004041],"category_scores_gemma":[0.000056624987,0.00091918354,0.0003525974,0.0004659157,0.00023937531,0.00017393318,0.002308514,0.0014868758,0.00005522589],"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":[0.00007696417,0.00009413686,0.052104943,0.003954569,0.002133008,0.00017628755,0.00728804,0.8503663,0.0003973221,0.0048918677,0.0014249553,0.07709164],"study_design_scores_gemma":[0.0033915811,0.00028171344,0.11882579,0.02199159,0.001145765,0.00010283566,0.0074778874,0.39408287,0.0010531546,0.0010345986,0.44591832,0.004693893],"about_ca_topic_score_codex":0.00035682172,"about_ca_topic_score_gemma":0.000050028684,"teacher_disagreement_score":0.4562834,"about_ca_system_score_codex":0.00030600638,"about_ca_system_score_gemma":0.000090935944,"threshold_uncertainty_score":0.9993259},"labels":[],"label_agreement":null},{"id":"W4416684127","doi":"10.18280/mmep.121023","title":"AI Ladder-Based Intelligent Enterprise Resource Planning for Predictive Railway Track Maintenance from UAV Imagery","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Track (disk drive); Resource (disambiguation); Enterprise resource planning; Operation planning; Expert system; Knowledge-based systems","score_opus":0.01594905746789905,"score_gpt":0.2232778284881583,"score_spread":0.20732877102025926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416684127","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":[0.011942991,0.0064106584,0.9775603,0.00035424906,0.00071795064,0.0010340193,0.00011169566,0.0005906854,0.0012774908],"genre_scores_gemma":[0.95087296,0.000173623,0.04689568,0.00013112456,0.00028051608,0.00050187344,0.000047155216,0.0002108072,0.00088627974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599755,0.000043380755,0.0014509358,0.0009696055,0.00035710697,0.0011814304],"domain_scores_gemma":[0.9976113,0.001147709,0.00013864486,0.0006321539,0.00012955489,0.00034064715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007982871,0.00087415345,0.0011268271,0.00039038595,0.0002565439,0.00039095964,0.00040909543,0.00048347117,0.00002574651],"category_scores_gemma":[0.00017931453,0.0008581572,0.00034893636,0.00040083105,0.00011383477,0.00020208722,0.000098970406,0.0007841774,0.000013137479],"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":[0.000084241234,0.00017025636,0.000010613126,0.0048451815,0.00029935894,0.000008899671,0.002590737,0.9855322,0.0005393558,0.0038691736,0.00069114193,0.001358812],"study_design_scores_gemma":[0.0009245803,0.00013651428,0.000004538989,0.010596169,0.00021181641,0.000004668873,0.00025658845,0.96927255,0.0011264224,0.006694701,0.010020341,0.0007510849],"about_ca_topic_score_codex":0.00004377072,"about_ca_topic_score_gemma":7.065799e-7,"teacher_disagreement_score":0.93893,"about_ca_system_score_codex":0.00020105229,"about_ca_system_score_gemma":0.00007000859,"threshold_uncertainty_score":0.9993869},"labels":[],"label_agreement":null},{"id":"W560685502","doi":"","title":"LONGER FREIGHT TRAINS: POSSIBILITIES AND LIMITATIONS","year":2000,"lang":"en","type":"article","venue":"Rail international","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":3,"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":"Train; Freight trains; Transport engineering; Rail freight transport; Engineering; Bogie; Automotive engineering; Overhead (engineering); Telecommunications; Electrical engineering; Geography","score_opus":0.013669949007322458,"score_gpt":0.1916079031702734,"score_spread":0.17793795416295094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W560685502","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":[0.6668494,0.00036704962,0.00053675944,0.00018393423,0.00044261635,0.000034169963,0.000014981371,0.00013600287,0.3314351],"genre_scores_gemma":[0.9848966,0.00015806631,0.00035849656,0.000046752593,0.00018578318,0.000013131567,0.000015118693,0.000011842881,0.014314225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995486,0.000005968697,0.00013499096,0.00009714751,0.00011466322,0.00009859677],"domain_scores_gemma":[0.9998132,0.000045160556,0.0000075035196,0.000072811534,0.000023931176,0.000037381295],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000047550515,0.00007161879,0.000060556973,0.000053213684,0.00003481637,0.000050103932,0.00008238771,0.000034217468,0.0012435202],"category_scores_gemma":[0.00001600654,0.00006741467,0.000027578031,0.00004381796,0.00003207838,0.000143874,0.000004864807,0.00004908586,0.00009025408],"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":[0.000023057919,0.00016384745,0.0017078324,0.00008251774,0.00037625758,0.000034694538,0.015955338,0.18201886,0.0036749123,0.09650159,0.031223748,0.6682373],"study_design_scores_gemma":[0.0008942735,0.000056018584,0.04519806,0.00008288184,0.000015318403,0.00009911511,0.0009634925,0.26597005,0.00055685604,0.003753287,0.68184566,0.00056496164],"about_ca_topic_score_codex":0.00002076186,"about_ca_topic_score_gemma":0.000049249473,"teacher_disagreement_score":0.6676724,"about_ca_system_score_codex":0.000025930422,"about_ca_system_score_gemma":0.0000061136634,"threshold_uncertainty_score":0.9996695},"labels":[],"label_agreement":null},{"id":"W566918620","doi":"","title":"How transatlantic technology has boosted the Docklands railway","year":2006,"lang":"en","type":"article","venue":"Traffic engineering & control","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Automatic control; Block (permutation group theory); Controller (irrigation); Engineering; Desk; Telecommunications; Control system; Automatic train control; Minicomputer; Track circuit; Computer science; Real-time computing; Simulation; Automotive engineering; Electrical engineering; Operating system; Control engineering","score_opus":0.003567395514860219,"score_gpt":0.14368787627599006,"score_spread":0.14012048076112985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W566918620","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":[0.8016683,0.005720447,0.18028893,0.003646835,0.0021465803,0.0006243095,0.000020209927,0.0048045567,0.0010798342],"genre_scores_gemma":[0.99874735,0.000016322716,0.00014118671,0.000014741225,0.00040301975,0.00009383601,0.000007919113,0.0000764672,0.00049918133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864197,0.000013933758,0.00030403843,0.0002386624,0.00020070652,0.0006006962],"domain_scores_gemma":[0.99937916,0.000089046895,0.000029815568,0.00040695316,0.000033077267,0.000061927785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016428546,0.0003283424,0.00036234598,0.00022659596,0.0001223427,0.00017203145,0.00035177267,0.00019713736,0.000009878309],"category_scores_gemma":[0.000018232593,0.00024419383,0.0001325907,0.0004919047,0.00006283703,0.00009064204,0.0000066188613,0.00031720946,0.000019584479],"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":[0.0000039298266,0.000017549319,0.00003928088,0.000048541442,0.00005704489,0.000018386503,0.000060761064,0.98276913,0.01190196,0.0011453915,0.0011576005,0.0027804074],"study_design_scores_gemma":[0.0014959661,0.000049932998,0.0009934632,0.000053100175,0.000056071156,0.000077117096,0.000043980603,0.9046589,0.00083819736,0.000009809828,0.091294944,0.00042852244],"about_ca_topic_score_codex":0.000021311065,"about_ca_topic_score_gemma":0.000043411128,"teacher_disagreement_score":0.19707902,"about_ca_system_score_codex":0.00005066788,"about_ca_system_score_gemma":0.000015790201,"threshold_uncertainty_score":0.9957935},"labels":[],"label_agreement":null},{"id":"W567315989","doi":"","title":"MONTREAL: LIGHT RAIL AND METRO PARTNERS: A NEW LIGHT RAIL SCHEME IS PROPOSED TO COMPLEMENT THE CANADIAN CITY'S EXPANDING METRO SYSTEM.","year":2001,"lang":"en","type":"article","venue":"Tramways & urban transit","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Light rail; Light rail transit; Transport engineering; Public transport; Scheme (mathematics); Rail transportation; Passenger transport; Complement (music); Telecommunications; Engineering; Business","score_opus":0.021267450039841138,"score_gpt":0.22925176442106798,"score_spread":0.20798431438122683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W567315989","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":[0.8512022,0.010519514,0.018489357,0.017291047,0.0020586674,0.003517148,0.00013361036,0.001895673,0.094892785],"genre_scores_gemma":[0.99719507,0.000032891166,0.00055030733,0.00027123408,0.0005023407,0.000067241366,0.000012602933,0.000095262876,0.0012730493],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99717915,0.00006150381,0.0006157129,0.0005365138,0.00049853354,0.0011086029],"domain_scores_gemma":[0.9982526,0.000043593816,0.000055814846,0.0005904473,0.00006208974,0.0009954788],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044508523,0.0005068779,0.0006054454,0.00036586061,0.00050232164,0.00032423076,0.0004326231,0.00016868413,0.00015634103],"category_scores_gemma":[0.000010667349,0.00039334173,0.00017593408,0.000817365,0.000038437633,0.00018554582,0.000017350389,0.00027226727,0.000059294074],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040528004,0.00038519088,0.022247145,0.0020992074,0.0032258364,0.0010485759,0.1308102,0.031171927,0.050300304,0.015542047,0.71301806,0.029746203],"study_design_scores_gemma":[0.003466924,0.0003880609,0.006871155,0.00061173073,0.00034793973,0.00009994311,0.0061413622,0.020462729,0.011851669,0.00003703293,0.9479728,0.0017486517],"about_ca_topic_score_codex":0.119614586,"about_ca_topic_score_gemma":0.39893258,"teacher_disagreement_score":0.27931798,"about_ca_system_score_codex":0.00051040616,"about_ca_system_score_gemma":0.00014064548,"threshold_uncertainty_score":0.9998518},"labels":[],"label_agreement":null},{"id":"W571859353","doi":"","title":"ECP BRAKING GETS RESULTS","year":2000,"lang":"en","type":"article","venue":"Railway age","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"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":"Automotive engineering; Brake; Air brake; Fuel efficiency; Power consumption; Engineering; Dynamic braking; Braking distance; Electronic brakeforce distribution; Engine braking; Power (physics); Transport engineering; Braking system","score_opus":0.008440694317661632,"score_gpt":0.1953017954199994,"score_spread":0.18686110110233778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W571859353","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":[0.40338346,0.00069413043,0.00025953294,0.000055287204,0.0005035894,0.00008012845,0.000012123128,0.0006976764,0.59431404],"genre_scores_gemma":[0.98520315,0.00012272343,0.00028233245,0.000046941987,0.00030457968,0.000009350232,0.000013620391,0.00004049715,0.013976822],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896246,0.000017878432,0.0002867035,0.00020648255,0.00016333297,0.0003631209],"domain_scores_gemma":[0.99952,0.000032098775,0.000017942471,0.00032795215,0.00001035501,0.00009167173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017186043,0.00016319216,0.0001739103,0.00006083673,0.00008430373,0.000055729073,0.00018063757,0.00009230057,0.0004009914],"category_scores_gemma":[0.00001411814,0.00015414857,0.00007036834,0.00014204392,0.000022501537,0.00011712884,0.000010812674,0.00012870046,0.0007741199],"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":[0.00004431189,0.00006636118,0.0000432774,0.0001226992,0.00007860866,0.0005999792,0.0033554279,0.58760816,0.010309896,0.001084224,0.03576332,0.36092374],"study_design_scores_gemma":[0.0005807313,0.000029627923,0.0020610832,0.00007261459,0.00000690638,0.000025265628,0.00003339481,0.02450883,0.0019305758,0.00007582651,0.9703161,0.00035905166],"about_ca_topic_score_codex":0.00008903607,"about_ca_topic_score_gemma":0.000068448535,"teacher_disagreement_score":0.9345528,"about_ca_system_score_codex":0.00003840011,"about_ca_system_score_gemma":0.000006872902,"threshold_uncertainty_score":0.99500036},"labels":[],"label_agreement":null},{"id":"W576321415","doi":"","title":"The ITS-Platform for Electromobility in Norway","year":2013,"lang":"en","type":"article","venue":"20th ITS World CongressITS Japan","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Incentive; Quarter (Canadian coin); Per capita; Business; Key (lock); Transport engineering; Telecommunications; Finance; Environmental economics; Engineering; Computer science; Computer security; Economics; Geography; Population","score_opus":0.01114972127962567,"score_gpt":0.21800741277717678,"score_spread":0.20685769149755112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W576321415","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":[0.9537864,0.0026025428,0.00010216395,0.00032441394,0.0012143817,0.0009971475,0.000009450964,0.00022950461,0.040734034],"genre_scores_gemma":[0.98399353,0.000052099807,0.00004881347,0.00004038883,0.00016797242,0.00047920633,0.000005101552,0.000040391707,0.015172515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984527,0.000019918485,0.00041192485,0.00025333694,0.00017789987,0.0006841904],"domain_scores_gemma":[0.99915767,0.0002077638,0.000054982673,0.00035912104,0.000108242355,0.0001122065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034029735,0.00022682322,0.00024716725,0.00011753153,0.00020233735,0.00015645623,0.00038120805,0.00007738208,0.00018635285],"category_scores_gemma":[0.0000651399,0.00016056185,0.0000810482,0.00042448286,0.00003352979,0.00030880806,0.000031618023,0.00021069693,0.0003013985],"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":[0.0001631603,0.0005061292,0.0148043195,0.0015303399,0.0005426418,0.000017532715,0.0045369966,0.09915229,0.120761275,0.10882141,0.15384984,0.49531406],"study_design_scores_gemma":[0.002261741,0.00020430134,0.09504271,0.00032633086,0.000031348653,0.000017110253,0.00043150183,0.5526076,0.0348009,0.0012032904,0.3116026,0.0014705467],"about_ca_topic_score_codex":0.00006139839,"about_ca_topic_score_gemma":0.0018711562,"teacher_disagreement_score":0.49384353,"about_ca_system_score_codex":0.000103823615,"about_ca_system_score_gemma":0.000029726378,"threshold_uncertainty_score":0.6547522},"labels":[],"label_agreement":null},{"id":"W578196681","doi":"","title":"RAIL AMS ON THE BORDER","year":2001,"lang":"en","type":"article","venue":"American shipper","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Aeronautics; Subtitle; Business; Engineering; Transport engineering; Computer science","score_opus":0.007964132081314355,"score_gpt":0.21828614099165022,"score_spread":0.21032200891033587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W578196681","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":[0.80956656,0.00010616082,0.00046698088,0.0008147845,0.00024273225,0.00005044289,8.922926e-7,0.00020234994,0.18854912],"genre_scores_gemma":[0.9964073,0.00006404341,0.000030366435,0.0007727266,0.00015986564,0.000021841914,9.984303e-7,0.000029167548,0.0025137265],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999368,0.00002149798,0.00010823861,0.00011560411,0.00013599587,0.0002506329],"domain_scores_gemma":[0.9995707,0.00006067148,0.000017605355,0.00028960008,0.00001151976,0.000049874277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091958216,0.00011255746,0.00011469564,0.000032809512,0.000065677326,0.000026543792,0.0001597746,0.000020023635,0.0003715267],"category_scores_gemma":[0.00001590127,0.00007089232,0.000047710106,0.00030726034,0.00009038535,0.000032136082,0.000010100919,0.000108248525,0.00044897528],"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":[0.000043889107,0.00018732689,0.02156639,0.000026812328,0.00023436082,0.00008321242,0.0038581693,0.24022226,0.007367153,0.027756995,0.15733464,0.5413188],"study_design_scores_gemma":[0.00012453797,0.00009935614,0.0149014285,0.000019469537,0.000006293005,0.000016893147,0.0010778772,0.011198542,0.0003123163,0.000042663007,0.9719045,0.0002960997],"about_ca_topic_score_codex":0.00019890827,"about_ca_topic_score_gemma":0.000034700683,"teacher_disagreement_score":0.8145699,"about_ca_system_score_codex":0.000024690453,"about_ca_system_score_gemma":0.000005798095,"threshold_uncertainty_score":0.57708186},"labels":[],"label_agreement":null},{"id":"W579917146","doi":"","title":"Remote Control Locomotive Operations: Results of Focus Groups with Remote Control Operators in the U.S. and Canada","year":2005,"lang":"en","type":"article","venue":"Research Results","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Focus group; Control (management); Focus (optics); Engineering; Business; Computer science; Marketing; Artificial intelligence","score_opus":0.0137352353268337,"score_gpt":0.2503837582180158,"score_spread":0.23664852289118207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W579917146","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":[0.97329396,0.0019752786,0.0021143747,0.005425809,0.00010240842,0.001562526,0.0003915264,0.00006027884,0.015073866],"genre_scores_gemma":[0.99915224,0.00019572877,0.0002445488,0.000045833716,0.00014279483,0.000013681852,0.000009771986,0.00002490034,0.00017049415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972245,0.0004426989,0.0005988324,0.00032669905,0.0008294836,0.0005777883],"domain_scores_gemma":[0.9983378,0.0006915065,0.000037783397,0.0005089476,0.00028717946,0.0001367899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027898964,0.00018662534,0.00031600543,0.00019093054,0.0001730951,0.00008709118,0.00035951383,0.00008832653,0.0000020606483],"category_scores_gemma":[0.0006472793,0.0001199233,0.000025484014,0.0005502219,0.00015064201,0.00015564219,0.00002409799,0.00048586217,0.000003261923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015770949,0.00007539161,0.0001706853,0.00008672664,0.00012052329,0.00016553863,0.0034844126,0.9490056,0.0018813006,0.00071028754,0.0059587806,0.036763676],"study_design_scores_gemma":[0.01689061,0.0006186576,0.0085526295,0.00044467993,0.000014758802,0.000052301857,0.0018930443,0.94540775,0.0019977654,0.00002840336,0.02368964,0.00040978135],"about_ca_topic_score_codex":0.58642334,"about_ca_topic_score_gemma":0.8687243,"teacher_disagreement_score":0.28230098,"about_ca_system_score_codex":0.00023248786,"about_ca_system_score_gemma":0.0003215994,"threshold_uncertainty_score":0.48903304},"labels":[],"label_agreement":null},{"id":"W596239611","doi":"","title":"MONTREAL'S BOX BACKUP","year":2004,"lang":"en","type":"article","venue":"Traffic world","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Backup; Telecommunications; Business; Advertising; Aeronautics; Engineering; Transport engineering; Computer science; Operating system","score_opus":0.005806310220105412,"score_gpt":0.17991825696542404,"score_spread":0.17411194674531863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W596239611","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":[0.78463155,0.0009110702,0.0011468066,0.00015573691,0.0010721297,0.00010332292,0.0000034067389,0.0012728669,0.21070312],"genre_scores_gemma":[0.9958743,0.000018778022,0.00049577124,0.000028569835,0.00021026202,0.000011045265,0.0000036587885,0.0000323475,0.0033252584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99929994,0.000004907979,0.0001702274,0.0001324963,0.00011668324,0.0002757174],"domain_scores_gemma":[0.9996733,0.0000118653,0.000011953458,0.0002067158,0.000009862553,0.00008632074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005944702,0.00013118154,0.00013852757,0.00009770582,0.00004388304,0.000027512315,0.00012443637,0.000035796907,0.00009565404],"category_scores_gemma":[0.0000029744792,0.00012155442,0.0000588102,0.00030004172,0.000021325706,0.00006416928,0.0000065539766,0.000095712,0.00040145483],"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":[0.0000012559383,0.000020681593,0.0000080733935,0.000016437214,0.0000112720145,0.000012771949,0.00019321054,0.98230076,0.00018583934,0.0016470614,0.003834545,0.011768096],"study_design_scores_gemma":[0.0067276275,0.00023939056,0.019607278,0.0006314538,0.00010105622,0.00013302448,0.0006048541,0.1709138,0.005698197,0.0010714069,0.79112965,0.0031422528],"about_ca_topic_score_codex":0.00012275415,"about_ca_topic_score_gemma":0.001725207,"teacher_disagreement_score":0.81138694,"about_ca_system_score_codex":0.00007017215,"about_ca_system_score_gemma":0.000012873788,"threshold_uncertainty_score":0.51600236},"labels":[],"label_agreement":null},{"id":"W597574531","doi":"","title":"ECP: HOW SOON?","year":2001,"lang":"en","type":"article","venue":"Railway age","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":2,"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":"Fence (mathematics); Revenue; Service (business); Engineering; Transport engineering; Aeronautics; Business; Automotive engineering; Finance; Marketing","score_opus":0.010290089535851374,"score_gpt":0.1868596856173534,"score_spread":0.17656959608150202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W597574531","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":[0.7129927,0.0010269094,0.007108482,0.00025222448,0.0013971885,0.00012205275,0.0000044259496,0.0010722717,0.27602372],"genre_scores_gemma":[0.98593473,0.0001047978,0.00022938597,0.00004086965,0.00033411302,0.000014198502,0.0000062138492,0.000038936298,0.013296755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992167,0.000012817721,0.00012796356,0.00015585785,0.00014806468,0.00033861722],"domain_scores_gemma":[0.99957466,0.000019269657,0.000015406242,0.00027964133,0.000013956326,0.000097049524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010533387,0.0001506999,0.0001620477,0.0000713704,0.000062915824,0.000070341,0.00016684136,0.00008266763,0.00017341992],"category_scores_gemma":[0.000012702388,0.00013811851,0.00007083663,0.00021966561,0.000022876982,0.00012185047,0.000016852855,0.000107863845,0.00028421142],"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":[0.000031914802,0.00028414643,0.0051178224,0.00046406136,0.00034642112,0.0030560386,0.004418129,0.344086,0.16122787,0.023244265,0.28143466,0.17628871],"study_design_scores_gemma":[0.0003344538,0.0000323448,0.003957999,0.00003184071,0.000008340002,0.000061505925,0.00012796946,0.016517432,0.0013415706,0.00013160844,0.9770743,0.000380606],"about_ca_topic_score_codex":0.00002673006,"about_ca_topic_score_gemma":0.000060511247,"teacher_disagreement_score":0.69563967,"about_ca_system_score_codex":0.000037159916,"about_ca_system_score_gemma":0.0000061623546,"threshold_uncertainty_score":0.56323093},"labels":[],"label_agreement":null},{"id":"W603490574","doi":"","title":"Passenger train in Canada : toward ghost stations?","year":2013,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"History; Geography; Aeronautics; Engineering","score_opus":0.005439158611774589,"score_gpt":0.1478991523470948,"score_spread":0.1424599937353202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W603490574","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":[0.93971497,0.00008985897,0.0014861821,0.00033794052,0.00032800756,0.00009324504,0.0000028525908,0.000077893616,0.057869073],"genre_scores_gemma":[0.9989154,0.0000058652745,0.00024128011,0.00006130235,0.00002122983,0.000031714353,0.000002422069,0.0000100215275,0.000710808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995359,0.0000065476124,0.00014044084,0.00006497072,0.0000806163,0.00017152484],"domain_scores_gemma":[0.9998333,0.000019755418,0.0000054232337,0.00007965441,0.000012537408,0.00004935398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000032628435,0.00006241682,0.00007472064,0.000036072783,0.000010514463,0.000016391217,0.000054528282,0.00001828335,0.00069377857],"category_scores_gemma":[0.000005134935,0.000054029646,0.000010029686,0.000115704424,0.0000033108488,0.00007645605,0.0000036863446,0.00004693974,0.00005594635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.613148e-7,0.000023198709,0.008700997,0.00009394998,0.000025787278,0.000021943502,0.0016809514,0.78756046,0.0046372768,0.0035545612,0.14681768,0.04688251],"study_design_scores_gemma":[0.00046586036,0.00001735147,0.27956882,0.00003432316,0.0000027275062,0.0000063212574,0.0055634165,0.64655524,0.0013813144,0.00015740814,0.0656625,0.00058471266],"about_ca_topic_score_codex":0.9608416,"about_ca_topic_score_gemma":0.9661101,"teacher_disagreement_score":0.27086782,"about_ca_system_score_codex":0.00016621232,"about_ca_system_score_gemma":0.000089942034,"threshold_uncertainty_score":0.7596389},"labels":[],"label_agreement":null},{"id":"W613863513","doi":"","title":"Switzerland: North-South Axis Traffic Management","year":2007,"lang":"en","type":"article","venue":"23RD PIARC WORLD ROAD CONGRESS PARIS, 17-21 SEPTEMBER 2007","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transport engineering; Limiting; Order (exchange); Speed limit; Quarter (Canadian coin); Business; Engineering; Geography; Finance","score_opus":0.007713007905097351,"score_gpt":0.21421073945718885,"score_spread":0.2064977315520915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W613863513","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":[0.37155226,0.00569152,0.004231566,0.000054234566,0.0066040624,0.00087137445,0.000042821095,0.0016151653,0.609337],"genre_scores_gemma":[0.8948969,0.00017663672,0.0006476859,0.0001438594,0.00074686884,0.000069423666,0.00007228288,0.00016934244,0.10307697],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99570054,0.000057833437,0.0011349123,0.0007757494,0.0007981384,0.0015328451],"domain_scores_gemma":[0.99813014,0.00008919445,0.00018027145,0.0009874286,0.00010851355,0.0005044668],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007867394,0.0007380091,0.00070830033,0.00070139545,0.00026730812,0.00017754232,0.000636511,0.00022842971,0.0014846192],"category_scores_gemma":[0.000008038753,0.0007099012,0.00030819542,0.0011693931,0.00012838993,0.0002452441,0.00012399844,0.0005704947,0.0014723529],"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":[0.00013291911,0.00031911914,0.035242736,0.00050443056,0.0009101175,0.000916784,0.0014745077,0.4076282,0.000057623496,0.0007260122,0.45735437,0.094733156],"study_design_scores_gemma":[0.0013938587,0.00005276448,0.050884314,0.00022064765,0.00010285797,0.000032001324,0.00031949262,0.01897683,0.00024654312,0.000009666961,0.92648727,0.0012737542],"about_ca_topic_score_codex":0.0001267985,"about_ca_topic_score_gemma":0.00358295,"teacher_disagreement_score":0.52334464,"about_ca_system_score_codex":0.00021518953,"about_ca_system_score_gemma":0.00002495971,"threshold_uncertainty_score":0.9995352},"labels":[],"label_agreement":null},{"id":"W621587556","doi":"","title":"COMMUNICATIONS BASED TRAIN CONTROL MAJOR CONTRIBUTOR TO ENERGY EFFICIENCY","year":2011,"lang":"en","type":"article","venue":"CONGRESS - DUBAI 2011","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Public transport; Control (management); Energy (signal processing); Efficient energy use; Transport engineering; Presentation (obstetrics); Power (physics); Energy conservation; Mass transportation; Computer science; Telecommunications; Engineering; Environmental economics; Electrical engineering; Economics","score_opus":0.018897206153014615,"score_gpt":0.20742339847694202,"score_spread":0.18852619232392742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W621587556","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":[0.13496585,0.01071334,0.62145805,0.0011514928,0.009640625,0.0019553278,0.00096693507,0.00369287,0.21545552],"genre_scores_gemma":[0.9973764,0.000020517764,0.00086168054,0.00031304793,0.00009349379,0.00022265848,0.000023417986,0.000060241586,0.0010285367],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984303,0.0000916639,0.00044788455,0.0002790646,0.00020134675,0.0005497026],"domain_scores_gemma":[0.99813336,0.000123685,0.000064540814,0.0012227724,0.00014537366,0.00031027134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028834934,0.00028103217,0.00037042968,0.00019180603,0.0001767987,0.000045941768,0.000937906,0.0001474549,0.00071688584],"category_scores_gemma":[0.000053937147,0.0002662654,0.00012455179,0.00021778805,0.00012353198,0.00011115742,0.00005685658,0.00013970837,0.00037866805],"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":[0.00078929646,0.004129961,0.010139685,0.0006879825,0.0016804886,0.00041605087,0.012359123,0.102287054,0.093668185,0.39411604,0.2660979,0.11362821],"study_design_scores_gemma":[0.005895963,0.0005063309,0.006668354,0.0002461017,0.00017641895,0.000027301445,0.00026307738,0.4911155,0.020053834,0.00021303937,0.4728556,0.0019784695],"about_ca_topic_score_codex":0.0006322419,"about_ca_topic_score_gemma":0.0002523745,"teacher_disagreement_score":0.86241055,"about_ca_system_score_codex":0.00006572577,"about_ca_system_score_gemma":0.000050125596,"threshold_uncertainty_score":0.99997896},"labels":[],"label_agreement":null},{"id":"W625169584","doi":"","title":"DELIVERING COMMUTER TRAIN: WHERE SMART TRANSPORT AND ENERGY PLANNING MEET","year":2011,"lang":"en","type":"article","venue":"CONGRESS - DUBAI 2011","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transport engineering; Computer science; Energy (signal processing); Engineering; Physics","score_opus":0.021242811311582767,"score_gpt":0.1881039681645759,"score_spread":0.16686115685299313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W625169584","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":[0.8825767,0.0073731067,0.0063657328,0.000017990847,0.0017365719,0.000077350975,0.000023442231,0.0005321899,0.10129693],"genre_scores_gemma":[0.9985749,0.00019070372,0.00035402444,0.000033512457,0.0000882769,0.000024545778,0.000007494408,0.0000553642,0.0006711531],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99899733,0.000020213345,0.00027494214,0.0002244322,0.000121466874,0.0003615972],"domain_scores_gemma":[0.9994759,0.000019489748,0.00003553626,0.0002836001,0.000031109037,0.00015436116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011138866,0.00023838766,0.0002659892,0.000088450535,0.00010725154,0.000023777508,0.00020542706,0.00013166617,0.00032274],"category_scores_gemma":[0.0000017875304,0.00022834296,0.000060470284,0.000056896235,0.00008128006,0.00020389065,0.000025385049,0.00010685105,0.000017510454],"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":[0.000502527,0.0007494448,0.45445475,0.003529729,0.0026766304,0.0042191637,0.09815201,0.06605872,0.022838647,0.073588476,0.0757093,0.1975206],"study_design_scores_gemma":[0.0047348565,0.0005989793,0.24345268,0.0020189667,0.00032524133,0.0006326965,0.0018492928,0.1447032,0.014244954,0.0004905757,0.5821733,0.0047752718],"about_ca_topic_score_codex":0.001125078,"about_ca_topic_score_gemma":0.00047474916,"teacher_disagreement_score":0.506464,"about_ca_system_score_codex":0.000017189244,"about_ca_system_score_gemma":0.000009362136,"threshold_uncertainty_score":0.9311556},"labels":[],"label_agreement":null},{"id":"W656881545","doi":"","title":"It's not just about running longer trains","year":2011,"lang":"en","type":"article","venue":"Railway gazette international","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Transport engineering; Key (lock); Terrain; Automotive engineering; Service (business); Engineering; Telecommunications; Computer science; Business; Computer security","score_opus":0.03443993444053773,"score_gpt":0.23669866162158654,"score_spread":0.20225872718104881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W656881545","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":[0.347146,0.00016266294,0.014352173,0.0006116568,0.0064169806,0.00014102667,0.000040470346,0.0006796109,0.6304494],"genre_scores_gemma":[0.99312985,0.000036586673,0.0011265421,0.0005708693,0.00072543585,0.0000309003,0.00002355906,0.000056331697,0.0042999377],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986195,0.000012112619,0.0003907612,0.0002557093,0.00037020928,0.00035170882],"domain_scores_gemma":[0.9994932,0.000026688782,0.000053132917,0.00023546693,0.0000799145,0.00011160016],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00020104843,0.00022288779,0.00018211953,0.00018775027,0.00006980373,0.00006385317,0.00044218518,0.000113952665,0.0021525235],"category_scores_gemma":[0.000025350242,0.00022054238,0.00012746618,0.00012976621,0.000050979204,0.00026307753,0.000039215716,0.00020549598,0.0004946863],"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":[0.00024165725,0.00090740336,0.014678207,0.0004272141,0.002445693,0.00089343754,0.08167065,0.18091632,0.11298339,0.09886945,0.39069194,0.11527464],"study_design_scores_gemma":[0.0016998635,0.00008898302,0.061950948,0.00028847856,0.000050180286,0.00018238593,0.00092762825,0.1395304,0.02387623,0.00016939794,0.769805,0.0014304786],"about_ca_topic_score_codex":0.000055126402,"about_ca_topic_score_gemma":0.00004184536,"teacher_disagreement_score":0.6459838,"about_ca_system_score_codex":0.00009759795,"about_ca_system_score_gemma":0.000018993775,"threshold_uncertainty_score":0.9987596},"labels":[],"label_agreement":null},{"id":"W657692336","doi":"","title":"MIGRATION PATH TO TOTAL TRAIN CONTROL","year":2001,"lang":"en","type":"article","venue":"Rail Transit ConferenceAmerican Public Transportation Association","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Modular design; Path (computing); Control system; Control (management); Computer science; Enforcement; Simple (philosophy); Engineering; Telecommunications; Electrical engineering; Artificial intelligence; Computer network; Operating system; Political science","score_opus":0.0067379801587857165,"score_gpt":0.1837404054204576,"score_spread":0.1770024252616719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W657692336","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":[0.6715161,0.00003441899,0.31977612,0.0027506305,0.00043259337,0.0003622495,0.0001411531,0.0006393696,0.0043473267],"genre_scores_gemma":[0.998312,0.000058495265,0.00031986274,0.0002706424,0.00013493776,0.00012704254,0.00028146667,0.000042812077,0.0004527469],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99794436,0.00007478857,0.0005938613,0.00032094124,0.00054352335,0.0005225063],"domain_scores_gemma":[0.99908876,0.0000725538,0.00013472266,0.00017722136,0.00026316845,0.00026358137],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003592362,0.00027226002,0.0003732018,0.0002408829,0.00011854008,0.00014149913,0.00014950272,0.00016529014,0.0002699809],"category_scores_gemma":[0.00004271826,0.00029631853,0.00014500729,0.00088772725,0.000021731528,0.0004394502,6.252118e-7,0.0001752379,0.00006501095],"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":[0.00019350287,0.00067612214,0.23611248,0.0002449735,0.0010607734,0.000055466673,0.032220352,0.43919528,0.06392794,0.034593236,0.00818295,0.18353692],"study_design_scores_gemma":[0.0033037278,0.00035423937,0.8279234,0.000074015676,0.0001436382,0.0000091588845,0.0031813867,0.08074314,0.00042505702,0.00015289478,0.08229951,0.0013898299],"about_ca_topic_score_codex":0.00039326394,"about_ca_topic_score_gemma":0.0018689714,"teacher_disagreement_score":0.59181094,"about_ca_system_score_codex":0.0003092408,"about_ca_system_score_gemma":0.00010048731,"threshold_uncertainty_score":0.9999489},"labels":[],"label_agreement":null},{"id":"W6885990636","doi":"10.14288/1.0362183","title":"CP Rail Eastern Region--Sudbury and Schreiber Divisions timetable","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Work (physics); Payment","score_opus":0.02193066969864584,"score_gpt":0.23762642662352684,"score_spread":0.215695756924881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6885990636","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":[0.008405786,0.00029439531,0.003338874,0.00015095888,0.00091713073,0.00026775518,0.000008911389,0.00012216855,0.986494],"genre_scores_gemma":[0.46069178,0.000086794666,0.00038836326,0.00001465924,0.00009721912,0.00007796946,0.0000017675276,0.000026287078,0.53861517],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934727,0.0000148991685,0.00015045905,0.0001896017,0.000086748754,0.00021101598],"domain_scores_gemma":[0.9992515,0.000025928171,0.00003918132,0.00054033723,0.00003672599,0.00010636384],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00011640995,0.00011816736,0.00017121539,0.000048695932,0.0039013133,0.002360595,0.00038016617,0.00006667446,0.00022511977],"category_scores_gemma":[0.00004096936,0.00011118211,0.000037636528,0.00017562677,0.000052937226,0.00046650576,0.00019827245,0.00010981774,0.000050869556],"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":[0.0000094375455,0.00007755366,0.0011416764,0.000033446304,0.00014125025,0.000021607586,0.000264149,0.016665012,0.00034384147,0.00079417013,0.971149,0.009358854],"study_design_scores_gemma":[0.0007975615,0.000050783732,0.0037171685,0.00011064953,0.000038618167,0.000087203916,0.00014554929,0.06343043,0.00021004911,0.00047739726,0.9305301,0.00040452756],"about_ca_topic_score_codex":0.006632565,"about_ca_topic_score_gemma":0.001194089,"teacher_disagreement_score":0.452286,"about_ca_system_score_codex":0.000029008947,"about_ca_system_score_gemma":0.000028140787,"threshold_uncertainty_score":0.99998236},"labels":[],"label_agreement":null},{"id":"W6886007491","doi":"10.14288/1.0356996","title":"Canadian Pacific Railway timetables","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Pacific Rim; Pacific ocean; Work (physics)","score_opus":0.012833663997764706,"score_gpt":0.21682728117479166,"score_spread":0.20399361717702696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6886007491","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":[0.0006403007,0.00006269814,0.000209884,0.00013026196,0.0010590334,0.00015962022,0.000028275905,0.00006980447,0.99764013],"genre_scores_gemma":[0.48025057,0.000019781379,0.0002050921,0.000007063546,0.00007441227,0.00006118112,0.0000031582085,0.000017811313,0.51936096],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994609,0.000009099835,0.000106764586,0.00012171433,0.000056514673,0.00024505268],"domain_scores_gemma":[0.9993011,0.000009761585,0.000020080135,0.00047688367,0.000028821423,0.00016335498],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000110615845,0.00008716899,0.00011979979,0.000062604784,0.004959252,0.0028223826,0.0005094734,0.00005274158,0.0010104778],"category_scores_gemma":[0.000025299785,0.000087088374,0.00003280539,0.00018535633,0.000021841759,0.0002748921,0.000039974922,0.00007107017,0.000112562615],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.6947387e-7,0.000008807646,0.000116681964,0.0000050252443,0.00003645197,0.0000062016343,0.000071502895,0.011773871,0.000099886434,0.0003816872,0.9859723,0.0015267137],"study_design_scores_gemma":[0.00015265538,0.000010809468,0.00056644605,0.000014615366,0.000007316295,0.000011895303,0.000111406,0.011064939,0.00019028923,0.0001598377,0.98755044,0.00015937637],"about_ca_topic_score_codex":0.6947459,"about_ca_topic_score_gemma":0.7511031,"teacher_disagreement_score":0.47961026,"about_ca_system_score_codex":0.00014348268,"about_ca_system_score_gemma":0.0001290082,"threshold_uncertainty_score":0.9999027},"labels":[],"label_agreement":null},{"id":"W6886017123","doi":"10.14288/1.0355276","title":"Sessional papers. Return ... on all subjects affecting the Canadian Pacific Railway","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Work (physics); Ethnic group; Metropolitan area; Pacific Area","score_opus":0.019924942074277762,"score_gpt":0.24142803674403424,"score_spread":0.22150309466975648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6886017123","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":[0.0068392246,0.00002696198,0.000010109816,0.0006723758,0.0017051746,0.00031398953,0.000016097601,0.000070849695,0.99034524],"genre_scores_gemma":[0.85241836,0.000009944487,0.00003934397,0.000054671335,0.00017862291,0.00007817812,0.000004096261,0.000025894004,0.14719091],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991516,0.00004052408,0.00013217167,0.00019208553,0.00014655275,0.0003370575],"domain_scores_gemma":[0.9990975,0.00008546571,0.000044393713,0.00057810446,0.000032314347,0.00016217894],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003129426,0.00013950933,0.00013295487,0.000057429304,0.0100704525,0.0027972735,0.00061903335,0.000091661415,0.00039468505],"category_scores_gemma":[0.000102610255,0.00010182848,0.000058533413,0.0002140813,0.000046614015,0.000160718,0.000056777433,0.0002521863,0.000040104962],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011499448,0.00003817162,0.00045113836,0.00001681222,0.00013857314,0.000031939893,0.0011172087,0.03274385,0.0010906492,0.00088595966,0.96088505,0.0025891433],"study_design_scores_gemma":[0.00038184132,0.000063263746,0.004146637,0.00010168448,0.000018253748,0.000045901365,0.00066668884,0.010124239,0.0006600476,0.0001260039,0.9833304,0.00033498876],"about_ca_topic_score_codex":0.36133492,"about_ca_topic_score_gemma":0.8245167,"teacher_disagreement_score":0.8455791,"about_ca_system_score_codex":0.00026044072,"about_ca_system_score_gemma":0.0001782923,"threshold_uncertainty_score":0.9982379},"labels":[],"label_agreement":null},{"id":"W6886021223","doi":"10.14288/1.0362217","title":"Canadian Pacific Railway Eastern Division--Montreal terminals timetable","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Work (physics); Port (circuit theory); Fishing","score_opus":0.012776886268652253,"score_gpt":0.2276786040839664,"score_spread":0.21490171781531414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6886021223","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":[0.0036939436,0.00006942013,0.0003458399,0.000115678464,0.0011135709,0.00026722942,0.000051022664,0.00007680647,0.9942665],"genre_scores_gemma":[0.55039984,0.00001088477,0.00008899315,0.000006526695,0.000067894536,0.00006483174,0.0000044250987,0.000020834561,0.44933578],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991095,0.000018257959,0.00018600293,0.00020510958,0.00010511086,0.00037600796],"domain_scores_gemma":[0.9989582,0.000018927833,0.000041926593,0.00066903204,0.00004439878,0.00026756315],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00016965206,0.00014507439,0.00020510599,0.00009634934,0.0046814303,0.00347074,0.00068695506,0.000077754106,0.00084156595],"category_scores_gemma":[0.00003186242,0.00014024456,0.000057412522,0.00021343077,0.000028462866,0.00041796803,0.0000847624,0.00010388249,0.00019598335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004964785,0.000051845334,0.0017470084,0.000020703072,0.00011448989,0.000049255563,0.00035399804,0.022254791,0.00015881588,0.000092751485,0.95617557,0.01897583],"study_design_scores_gemma":[0.00057859486,0.00005751246,0.009184655,0.00008250033,0.00002813671,0.000037431768,0.00028004788,0.07449439,0.00020523173,0.00024279788,0.91431767,0.0004910369],"about_ca_topic_score_codex":0.80362415,"about_ca_topic_score_gemma":0.77756435,"teacher_disagreement_score":0.5467059,"about_ca_system_score_codex":0.0001643663,"about_ca_system_score_gemma":0.00014396675,"threshold_uncertainty_score":0.9975638},"labels":[],"label_agreement":null},{"id":"W6886036946","doi":"10.14288/1.0056441","title":"Profile of main line from Quebec &amp; Montreal to Vancouver City","year":2015,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Line (geometry); Government (linguistics); Metropolitan area; Population","score_opus":0.02438898920952343,"score_gpt":0.2370710168934868,"score_spread":0.21268202768396338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6886036946","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":[0.06195993,0.000053604173,0.022050576,0.000040888262,0.0018382934,0.00056982343,0.00027264233,0.00014240695,0.9130718],"genre_scores_gemma":[0.33806345,0.000002595365,0.005708534,0.000018961264,0.0001748253,0.00016055358,0.000021862752,0.00002632755,0.6558229],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99940795,0.000019104393,0.00019199472,0.00013085337,0.00011030826,0.00013976631],"domain_scores_gemma":[0.9995218,0.000024655297,0.000021923144,0.00023344782,0.00006775336,0.00013042966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010998155,0.00008706027,0.00017269498,0.00004715582,0.00016852835,0.0001293117,0.00019311873,0.000051047118,0.00045245554],"category_scores_gemma":[0.000040355746,0.00008359484,0.00003107437,0.0006302674,0.000010408158,0.00010231112,0.00006250062,0.000059416587,0.00003291937],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011596422,0.00004105869,0.000028554721,0.0000030366514,0.000021894966,7.902333e-7,0.00039815507,0.12821984,0.00032129732,0.000011351836,0.8704628,0.00047966698],"study_design_scores_gemma":[0.001265478,0.00014130915,0.0011430061,0.00007302198,0.000032007356,0.000003528235,0.0009286533,0.036564406,0.0031180412,0.00056314,0.9557258,0.00044161003],"about_ca_topic_score_codex":0.56912684,"about_ca_topic_score_gemma":0.8701171,"teacher_disagreement_score":0.30099028,"about_ca_system_score_codex":0.00014534288,"about_ca_system_score_gemma":0.000103363316,"threshold_uncertainty_score":0.4954071},"labels":[],"label_agreement":null},{"id":"W6886063477","doi":"10.14288/1.0362201","title":"Canadian Pacific Railway Eastern Division--Montreal terminals timetable","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Work (physics); Port (circuit theory); Fishing","score_opus":0.012776886268652253,"score_gpt":0.2276786040839664,"score_spread":0.21490171781531414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6886063477","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":[0.0036939436,0.00006942013,0.0003458399,0.000115678464,0.0011135709,0.00026722942,0.000051022664,0.00007680647,0.9942665],"genre_scores_gemma":[0.55039984,0.00001088477,0.00008899315,0.000006526695,0.000067894536,0.00006483174,0.0000044250987,0.000020834561,0.44933578],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991095,0.000018257959,0.00018600293,0.00020510958,0.00010511086,0.00037600796],"domain_scores_gemma":[0.9989582,0.000018927833,0.000041926593,0.00066903204,0.00004439878,0.00026756315],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00016965206,0.00014507439,0.00020510599,0.00009634934,0.0046814303,0.00347074,0.00068695506,0.000077754106,0.00084156595],"category_scores_gemma":[0.00003186242,0.00014024456,0.000057412522,0.00021343077,0.000028462866,0.00041796803,0.0000847624,0.00010388249,0.00019598335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004964785,0.000051845334,0.0017470084,0.000020703072,0.00011448989,0.000049255563,0.00035399804,0.022254791,0.00015881588,0.000092751485,0.95617557,0.01897583],"study_design_scores_gemma":[0.00057859486,0.00005751246,0.009184655,0.00008250033,0.00002813671,0.000037431768,0.00028004788,0.07449439,0.00020523173,0.00024279788,0.91431767,0.0004910369],"about_ca_topic_score_codex":0.80362415,"about_ca_topic_score_gemma":0.77756435,"teacher_disagreement_score":0.5467059,"about_ca_system_score_codex":0.0001643663,"about_ca_system_score_gemma":0.00014396675,"threshold_uncertainty_score":0.9975638},"labels":[],"label_agreement":null},{"id":"W6886084236","doi":"10.14288/1.0372598","title":"Canadian Pacific Railway ticket envelope","year":2018,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Ticket; Envelope (radar); Transit (satellite)","score_opus":0.010762579880040741,"score_gpt":0.2085608416407053,"score_spread":0.19779826176066456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6886084236","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":[0.0012136227,0.000028334418,0.00059782015,0.00011483208,0.0013914936,0.00018936163,0.000022794575,0.000117003015,0.9963247],"genre_scores_gemma":[0.5785726,0.000011599389,0.000427461,0.000033011736,0.00021634041,0.000071539165,0.0000056338813,0.00002591982,0.4206359],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993293,0.000015477988,0.00014174351,0.00014623613,0.00006836645,0.000298829],"domain_scores_gemma":[0.9994816,0.000013857748,0.000010644208,0.00023612552,0.000059039536,0.00019870869],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000114457165,0.00010048595,0.00011278105,0.00010255938,0.0015988741,0.0007118961,0.000271027,0.0000633545,0.0024813656],"category_scores_gemma":[0.000015785281,0.000101635385,0.000026577654,0.0010001403,0.00003330484,0.00013446223,0.000026880658,0.00007398297,0.00038727623],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012794378,0.000008154784,0.00003176415,0.0000034529453,0.000023553372,0.000003236257,0.0002625366,0.0014103826,0.00012215994,0.00026310526,0.99705637,0.00081403024],"study_design_scores_gemma":[0.00012294989,0.000035275014,0.00018500985,0.000011914809,0.000004817228,0.000018497456,0.00023290783,0.005081977,0.00035257693,0.00009648717,0.9936961,0.00016150603],"about_ca_topic_score_codex":0.38479906,"about_ca_topic_score_gemma":0.70710516,"teacher_disagreement_score":0.57735896,"about_ca_system_score_codex":0.0002445261,"about_ca_system_score_gemma":0.00020499023,"threshold_uncertainty_score":0.9997009},"labels":[],"label_agreement":null},{"id":"W6888924845","doi":"10.25318/3610004101-eng","title":"Financial flows, persons and unincorporated business, quarterly, 1961 - 2012","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Capital (architecture); Table (database); Consumption (sociology); Financial capital; Revenue; Economic capital","score_opus":0.004364814760172567,"score_gpt":0.19406574942972615,"score_spread":0.1897009346695536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888924845","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00028750754,0.0004273253,0.0029720122,0.000013158113,0.0020944113,0.00016328601,0.9939595,0.000031703727,0.000051093844],"genre_scores_gemma":[0.002535952,0.00005496876,0.00020071246,0.000010226178,0.00015891848,0.000020595027,0.99622494,0.000036128855,0.0007575693],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988316,0.000020089821,0.00029918118,0.00024060624,0.00033576664,0.00027273817],"domain_scores_gemma":[0.99930024,0.00008362838,0.00011113691,0.00026528252,0.0001523622,0.00008732652],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007169865,0.00030271767,0.00030450162,0.00011903705,0.000093506154,0.00006726158,0.00014452533,0.00020402463,0.00009476999],"category_scores_gemma":[0.00006741523,0.00032494098,0.000012078593,0.000266742,0.000025504469,0.00008870256,0.000011912968,0.00020433584,0.000009728474],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001986154,0.000007868854,0.0000028472239,0.0004699392,0.000015417343,0.000035857513,0.000025116358,0.0015951506,0.000026736525,0.00008305951,0.9956004,0.0021356095],"study_design_scores_gemma":[0.00011690033,0.000032429645,0.002836628,0.00018396303,0.00006302023,0.000011737977,0.000057569887,0.010238169,0.0000040253612,0.00002247827,0.98594046,0.0004926361],"about_ca_topic_score_codex":0.09353071,"about_ca_topic_score_gemma":0.69981486,"teacher_disagreement_score":0.60628414,"about_ca_system_score_codex":0.0002127052,"about_ca_system_score_gemma":0.00043429265,"threshold_uncertainty_score":0.99992025},"labels":[],"label_agreement":null},{"id":"W6889062326","doi":"10.25384/sage.25575001","title":"sj-docx-1-cjo-10.1177_00084174241245622 - Supplemental material for Practicing During the COVID-19 Pandemic: Experiences of Canadian Hospital-Based Occupational Therapists","year":2024,"lang":"en","type":"article","venue":"Sage Journals Data","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Occupational therapy; Qualitative research; MEDLINE; Occupational safety and health","score_opus":0.053571011133814045,"score_gpt":0.3216080521866077,"score_spread":0.2680370410527937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6889062326","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":[0.95972806,0.009690785,0.006554923,0.001608322,0.0034041004,0.0006814513,0.017792901,0.00020311259,0.0003363351],"genre_scores_gemma":[0.997604,0.0004204277,0.000343741,0.000083083054,0.0005560395,0.00008405748,0.00074649777,0.000037987895,0.00012417085],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848175,0.000056797224,0.0004893161,0.0002638287,0.00031840545,0.00038991382],"domain_scores_gemma":[0.9989593,0.0002206703,0.00009757229,0.0004693301,0.00003662561,0.00021650293],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00093498314,0.00018377451,0.00019381856,0.0003087885,0.00031823062,0.00031562496,0.00067391933,0.00006810627,0.010185818],"category_scores_gemma":[0.0001109814,0.00013378228,0.00007107293,0.00034091697,0.0000617358,0.0005206224,0.0000565803,0.00014339469,0.000007148749],"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":[0.00054354186,0.00033963556,0.028639145,0.0046560504,0.0021655757,0.0006351462,0.10816493,0.17582144,0.11942687,0.00027724533,0.5280739,0.031256504],"study_design_scores_gemma":[0.0018348715,0.00024168509,0.0033637385,0.001049597,0.0001234066,0.0003998178,0.022724522,0.12284916,0.008365091,0.00007191333,0.83795035,0.0010258714],"about_ca_topic_score_codex":0.009969122,"about_ca_topic_score_gemma":0.009054299,"teacher_disagreement_score":0.3098764,"about_ca_system_score_codex":0.0002271638,"about_ca_system_score_gemma":0.0004226398,"threshold_uncertainty_score":0.9966236},"labels":[],"label_agreement":null},{"id":"W6890029469","doi":"10.3389/fpls.2020.583738.s015","title":"Table_2_Identification of New Leaf Rust Resistance Loci in Wheat and Wild Relatives by Array-Based SNP Genotyping and Association Genetics.XLSX","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Rust (programming language); Single-nucleotide polymorphism; Quantitative trait locus; Germplasm; Wheat leaf rust; Plant disease resistance; Association mapping; Gene; Candidate gene; Genome-wide association study","score_opus":0.01841678460020454,"score_gpt":0.21396979271718616,"score_spread":0.19555300811698162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890029469","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014002276,0.009469557,0.00002492581,0.000048524074,0.000066276276,0.00018203758,0.9899454,0.000035912395,0.00008731229],"genre_scores_gemma":[0.0016126806,0.00031374238,0.000095029565,0.000014931088,0.00008807787,0.0000388979,0.9974846,0.00002819464,0.0003238495],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99890494,0.000036650174,0.00036796276,0.0002985934,0.00020610468,0.00018576976],"domain_scores_gemma":[0.9993682,0.000086041255,0.00021080079,0.00021361273,0.000045126133,0.00007623673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073767915,0.00020691025,0.00031291373,0.00010317273,0.000045777124,0.00007566498,0.00013361986,0.00030943385,0.0008477303],"category_scores_gemma":[0.00033157718,0.00024252202,0.000029557206,0.00023143401,0.000005968568,0.0000971483,0.000023501218,0.00024919148,0.000047120782],"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":[0.0000036707847,0.000005479808,0.00002328766,0.0012982005,0.000018183047,0.0000011624611,0.00008688503,0.0033789468,0.00056860765,4.6332994e-7,0.9944514,0.00016368141],"study_design_scores_gemma":[0.00022820824,0.000011158802,0.0010421611,0.0019164028,0.000016814447,2.4800875e-7,0.000023947956,0.0012378945,0.0005731895,0.0000037215946,0.99470854,0.0002377181],"about_ca_topic_score_codex":0.00012288509,"about_ca_topic_score_gemma":0.00046448057,"teacher_disagreement_score":0.009155815,"about_ca_system_score_codex":0.0001447294,"about_ca_system_score_gemma":0.000059642676,"threshold_uncertainty_score":0.9889761},"labels":[],"label_agreement":null},{"id":"W6890257979","doi":"10.34943/10684d03-7a29-43f2-9d14-25286141401e","title":"Endeavour North Conductivity Temperature Depth Deployed 2018-06-27","year":2019,"lang":"en","type":"dataset","venue":"Ocean Networks Canada Society","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Temperature measurement; Conductivity; Global Positioning System; Hydrothermal vent; Pressure sensor; Turbidity; Software deployment","score_opus":0.00765544849727476,"score_gpt":0.17654250396701304,"score_spread":0.16888705546973828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6890257979","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056283134,0.0030038534,0.00009282074,0.000036692963,0.009046259,0.00041985395,0.98143315,0.000244121,0.00009491791],"genre_scores_gemma":[0.0412159,0.0013122287,0.000041688334,0.00044953745,0.0020748372,0.00001057324,0.9533847,0.00017050112,0.0013400555],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969615,0.00006861806,0.0005343915,0.0006893144,0.00067850295,0.0010677056],"domain_scores_gemma":[0.99811727,0.00010496967,0.00017318995,0.0011602954,0.00008336801,0.0003608919],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020995692,0.00084912404,0.0008854804,0.00003706834,0.00023758035,0.00013397334,0.0007464932,0.0009635121,0.00003180957],"category_scores_gemma":[0.000013107058,0.00081646827,0.00038818212,0.00045999407,0.00006904664,0.0001359849,0.00009638048,0.0017980518,0.000007413509],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000013641826,0.000011154628,0.00014950633,0.00020116252,0.00021136148,0.00002060329,0.000014300615,0.3313561,0.0000018979572,0.0000011150198,0.6679997,0.000031745494],"study_design_scores_gemma":[0.00026097437,0.0000180998,0.00036024183,0.00010230769,0.00009157057,0.000020885887,0.00007449882,0.017763838,0.0000031190025,4.5695575e-7,0.9804114,0.0008925703],"about_ca_topic_score_codex":0.1977462,"about_ca_topic_score_gemma":0.7872883,"teacher_disagreement_score":0.58954215,"about_ca_system_score_codex":0.0008301303,"about_ca_system_score_gemma":0.0008439583,"threshold_uncertainty_score":0.99942863},"labels":[],"label_agreement":null},{"id":"W6892753208","doi":"10.5281/zenodo.12111632","title":"Iso 15622 pdf","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Subpoena; Filter (signal processing); Quality (philosophy); Nucleofection; Work (physics); Limiting","score_opus":0.015250964435115295,"score_gpt":0.20552556347346196,"score_spread":0.19027459903834668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892753208","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":[0.000018478711,0.0016123919,0.0015881617,0.00005008226,0.0007206136,0.00019747164,0.00016298797,0.00425946,0.99139035],"genre_scores_gemma":[0.0046045324,0.0003713085,0.00009107823,0.000023831697,0.00076799677,4.67824e-8,0.0008048007,0.018045254,0.97529113],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998751,0.00006350819,0.00020430876,0.00035445063,0.00027961965,0.00034710925],"domain_scores_gemma":[0.9992947,0.000004057349,0.00003913438,0.0004632493,0.0000654681,0.0001333804],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002145523,0.00022526193,0.00019882573,0.0004085313,0.000285259,0.0005736277,0.0007090018,0.00017372874,0.12936042],"category_scores_gemma":[0.000050239345,0.00023217955,0.00007100405,0.00042931875,0.00006505887,0.000051816514,0.00035716512,0.00032528298,0.28636545],"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":[0.0000013077658,0.000014032696,2.2998167e-8,0.00032702432,0.000063387444,0.000019501675,0.00017098228,0.00033532755,0.00021935253,0.0014584557,0.98236126,0.015029369],"study_design_scores_gemma":[0.00009771167,0.000038837225,0.0000017660334,0.00018480011,0.000015042201,0.00005389482,0.00005735951,0.0011157447,0.000026163902,0.000038897313,0.99812096,0.00024881616],"about_ca_topic_score_codex":0.00003151869,"about_ca_topic_score_gemma":9.4313623e-7,"teacher_disagreement_score":0.15700503,"about_ca_system_score_codex":0.00010933109,"about_ca_system_score_gemma":0.0000014637591,"threshold_uncertainty_score":0.9468007},"labels":[],"label_agreement":null},{"id":"W6893002385","doi":"10.5281/zenodo.12540970","title":"Zero-emission trains on non-electrified Czech railways","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrification; Train; Catenary; Battery (electricity); Flexibility (engineering); Software deployment; Energy supply","score_opus":0.019887986872535877,"score_gpt":0.21847396415221462,"score_spread":0.19858597727967875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893002385","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":[0.0737674,0.0006782449,0.041254226,0.0004043253,0.0009687208,0.0005095938,0.00011750608,0.0069571063,0.8753429],"genre_scores_gemma":[0.9946027,0.00010602484,0.00005328678,0.000033000342,0.00021134547,6.093332e-8,0.0002724413,0.0013669723,0.003354186],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986761,0.000069423106,0.00022239947,0.000336187,0.0003072843,0.00038855747],"domain_scores_gemma":[0.9993739,0.000021149777,0.000017641294,0.0003258178,0.00009417688,0.00016732291],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004137758,0.00016830153,0.000136003,0.00029248648,0.0007908826,0.0006631783,0.00051107915,0.00009120831,0.0022762408],"category_scores_gemma":[0.00009700281,0.00016078862,0.00006563457,0.0006983401,0.00004165189,0.00015717777,0.00011863436,0.00032111316,0.006994597],"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":[0.000022244818,0.000064212436,2.257878e-7,0.0002438197,0.00005729797,0.000050937662,0.0015644671,0.0104730595,0.09541048,0.012178645,0.62901604,0.25091857],"study_design_scores_gemma":[0.00018688229,0.00016532444,0.000037593323,0.000111575195,0.0000066126,0.000053777152,0.000069232294,0.04233804,0.0062793973,0.00008322505,0.9504755,0.00019282087],"about_ca_topic_score_codex":0.000006080002,"about_ca_topic_score_gemma":6.465648e-8,"teacher_disagreement_score":0.92083526,"about_ca_system_score_codex":0.00016278184,"about_ca_system_score_gemma":0.0000033337635,"threshold_uncertainty_score":0.9986358},"labels":[],"label_agreement":null},{"id":"W6903815524","doi":"10.13140/rg.2.2.17943.57767","title":"Enhancing Canadian Rail Safety - A business case for analytics-based railway safety management in Canada","year":2017,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Case analysis; Business case; Risk management; Case management; Government (linguistics)","score_opus":0.009912253286420685,"score_gpt":0.20442898309175986,"score_spread":0.19451672980533918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6903815524","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":[0.40778065,0.00038095433,0.20800646,0.0029146764,0.0064158887,0.0028182198,0.00049275084,0.00038319535,0.3708072],"genre_scores_gemma":[0.99719507,0.000013069606,0.0012132676,0.00011465562,0.00006855328,0.000047754955,0.000019255996,0.00003958163,0.0012887985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998499,0.000011148039,0.0004379792,0.00026210735,0.0001509038,0.00063890306],"domain_scores_gemma":[0.9989214,0.000049539918,0.000054345663,0.0006403347,0.0000676112,0.0002667606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003296892,0.00022189613,0.00028918934,0.00019769742,0.00044296615,0.00010093955,0.00031540802,0.00006404564,0.00010627201],"category_scores_gemma":[0.000043360382,0.00021841995,0.00005033641,0.00018350592,0.000019552273,0.00012108775,0.000023984929,0.00008414581,0.0000052441496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002186585,0.000010482886,0.001355859,0.0003668406,0.00007187461,0.0020178522,0.000069009235,0.97959155,0.00004769408,0.0013882739,0.0020652576,0.0129934205],"study_design_scores_gemma":[0.002323708,0.000012514843,0.030915502,0.00020999744,0.00004439635,0.00008692374,0.0007234179,0.8092105,0.0005049736,0.000018642422,0.15507598,0.0008734445],"about_ca_topic_score_codex":0.99809813,"about_ca_topic_score_gemma":0.9999711,"teacher_disagreement_score":0.5894144,"about_ca_system_score_codex":0.0018885201,"about_ca_system_score_gemma":0.0009035866,"threshold_uncertainty_score":0.89069074},"labels":[],"label_agreement":null},{"id":"W6904615033","doi":"10.14288/1.0357590","title":"Canadian Pacific Airlines system timetable effective 29 Apr. to 30 Jun. 1962","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Aviation; Pacific Area; Tourism","score_opus":0.007877357756989615,"score_gpt":0.21334343583918483,"score_spread":0.20546607808219522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6904615033","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":[0.0017484849,0.00007261945,0.0013898286,0.00011751138,0.0024532203,0.0009958505,0.00007031506,0.00017683646,0.99297535],"genre_scores_gemma":[0.6835135,0.000003194478,0.000272673,0.000010533107,0.00019363199,0.00051495456,0.0000038604603,0.000036117333,0.3154515],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99902046,0.000030764473,0.00019228877,0.00025426794,0.000104171246,0.00039802218],"domain_scores_gemma":[0.9988591,0.00002956768,0.000036987436,0.0006551193,0.00008617697,0.00033305614],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0002280705,0.0001719318,0.00026874707,0.00014155585,0.0045632944,0.0022076871,0.0005776541,0.00008811255,0.0002083136],"category_scores_gemma":[0.00005269839,0.00016961411,0.000058430498,0.0004452495,0.000017996314,0.00025175483,0.00007612711,0.00011235336,0.00040118681],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006536286,0.00002519705,0.00021878566,0.000074728,0.0001434316,0.00002333718,0.00040138888,0.18062961,0.00022921267,0.00045244,0.81552756,0.0022677912],"study_design_scores_gemma":[0.00043472595,0.000086451066,0.0012643713,0.00017907002,0.000040719588,0.0000411567,0.00074334716,0.057621352,0.00060584466,0.000024334795,0.9384699,0.000488762],"about_ca_topic_score_codex":0.7144241,"about_ca_topic_score_gemma":0.5679573,"teacher_disagreement_score":0.6817651,"about_ca_system_score_codex":0.00079054676,"about_ca_system_score_gemma":0.00023249678,"threshold_uncertainty_score":0.9988281},"labels":[],"label_agreement":null},{"id":"W6904770570","doi":"10.14288/1.0446344","title":"[Time roll of operating department]","year":2024,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Work (physics); Process (computing); Welding","score_opus":0.008172867012133378,"score_gpt":0.22183235488708017,"score_spread":0.2136594878749468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6904770570","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":[0.007005632,0.00044291432,0.0011205,0.00001884963,0.00064045907,0.00019303818,0.00002362061,0.00018774185,0.99036723],"genre_scores_gemma":[0.67380154,0.00002331541,0.0008285936,0.000006146928,0.000089748006,0.00011280555,0.000007036306,0.000028867578,0.32510194],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995957,0.000010052562,0.0001471918,0.00009166835,0.000055718094,0.000099641846],"domain_scores_gemma":[0.99982095,0.000026438698,0.000005987214,0.00010196229,0.000016553986,0.000028113067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087427514,0.000060429316,0.000100138925,0.000042619075,0.0003008633,0.00045761876,0.00011636081,0.00002735239,0.00063316757],"category_scores_gemma":[0.0000073020456,0.000055878056,0.000033619766,0.0006591561,0.000010666788,0.00012928063,0.000030066987,0.000048288308,0.000049036076],"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":[0.0000011464041,0.000026631966,0.000003401741,0.00005343669,0.000094636016,0.000006056311,0.0002687202,0.4255473,0.0055523273,0.0004239177,0.5670035,0.0010189184],"study_design_scores_gemma":[0.00014024404,0.00005505346,0.00001547625,0.0001063079,0.000018483008,0.000023829696,0.00010735601,0.6576402,0.0044300472,0.000097274526,0.3372151,0.00015063284],"about_ca_topic_score_codex":0.00067830126,"about_ca_topic_score_gemma":0.00016946868,"teacher_disagreement_score":0.6667959,"about_ca_system_score_codex":0.000040530656,"about_ca_system_score_gemma":0.00003561401,"threshold_uncertainty_score":0.6932741},"labels":[],"label_agreement":null},{"id":"W6920330302","doi":"10.60692/de3en-a6d37","title":"Adaptive DDL Algorithm to Elucidate the Protection Misoperation in Malaysian Rapid Rail DC Traction System","year":2024,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Overcurrent; Traction (geology); Electrification; Transient (computer programming); Fault (geology); Voltage; Fault detection and isolation; Overvoltage; Traction substation","score_opus":0.018228735403194203,"score_gpt":0.17901550797573124,"score_spread":0.16078677257253704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920330302","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":[0.21051672,0.00005500701,0.7724432,0.00014162273,0.004076033,0.0018731201,0.000072869334,0.0017892868,0.00903219],"genre_scores_gemma":[0.99869376,4.1033263e-7,0.00021537954,0.000018885145,0.0002813364,0.0006209394,0.000014842361,0.000028264309,0.00012617407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998376,0.00008932842,0.0007411406,0.00018923325,0.00031599635,0.00028828572],"domain_scores_gemma":[0.9994752,0.000006645589,0.00007216166,0.00028709136,0.00008309375,0.000075824384],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006725634,0.00023898372,0.0002194278,0.0004999462,0.00014871129,0.00046516862,0.00013811985,0.00014763212,0.00000905824],"category_scores_gemma":[0.0000055937876,0.00017261055,0.00007638689,0.00074199645,0.000010314903,0.0010834244,0.000016782818,0.00020136293,0.0010919033],"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":[0.00005805966,0.0000031201976,0.00017325894,0.0026121356,0.0001345223,0.000021167747,0.14336948,0.77766037,0.00026371615,0.0012756296,0.00044639723,0.07398213],"study_design_scores_gemma":[0.00024226971,0.00005411392,0.0012283844,0.0007115692,0.000014317406,0.00011326815,0.02087472,0.9724982,0.0011614517,2.2488497e-7,0.0028720456,0.00022946527],"about_ca_topic_score_codex":0.000095300384,"about_ca_topic_score_gemma":0.0000037410164,"teacher_disagreement_score":0.7881771,"about_ca_system_score_codex":0.0005880616,"about_ca_system_score_gemma":0.000026499554,"threshold_uncertainty_score":0.9996859},"labels":[],"label_agreement":null},{"id":"W6923515547","doi":"10.14288/1.0362203","title":"CP Rail Prairie Region--Lakehead, Winnipeg and Brandon Divisions timetable","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Work (physics)","score_opus":0.020673766645231146,"score_gpt":0.24019247223651682,"score_spread":0.21951870559128567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6923515547","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":[0.0064047226,0.00036624755,0.0073059765,0.00057293044,0.0011158959,0.0005241465,0.00001871236,0.00020127228,0.9834901],"genre_scores_gemma":[0.58453053,0.000043449472,0.00024401769,0.000014610669,0.000099774166,0.00011669743,0.0000035633748,0.00003013006,0.4149172],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992314,0.000022423561,0.00018597489,0.00021852551,0.000099259,0.00024243248],"domain_scores_gemma":[0.9991652,0.000049653867,0.00005079509,0.000570675,0.00004447748,0.00011924414],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00016417485,0.00013896519,0.00022260145,0.00005581851,0.004952436,0.0022479303,0.0003918692,0.00007659142,0.00013706944],"category_scores_gemma":[0.00008871336,0.00013026826,0.000046020574,0.00024197748,0.00006894949,0.00052537944,0.00015614042,0.00012179941,0.00002468607],"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":[0.000013255457,0.000058229332,0.0004803764,0.0000239767,0.000082238854,0.000011081751,0.00017885363,0.00968848,0.00029603994,0.00045390462,0.98443073,0.004282809],"study_design_scores_gemma":[0.0013732273,0.00007000241,0.003848996,0.000081015896,0.000040129715,0.00008822932,0.00009024998,0.024650242,0.0002826739,0.000422484,0.9686901,0.00036265125],"about_ca_topic_score_codex":0.0058119004,"about_ca_topic_score_gemma":0.00570657,"teacher_disagreement_score":0.57812583,"about_ca_system_score_codex":0.00003550287,"about_ca_system_score_gemma":0.000037305348,"threshold_uncertainty_score":0.9987878},"labels":[],"label_agreement":null},{"id":"W6923825325","doi":"10.14288/1.0356776","title":"Pamphlet listing train rates to points in western Canada","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Listing (finance); Work (physics); Point (geometry)","score_opus":0.01938305998567687,"score_gpt":0.2527250968715464,"score_spread":0.2333420368858695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6923825325","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":[0.17080688,0.000030125755,0.0007585065,0.00069066155,0.0017497306,0.0005295745,0.000056077966,0.000072410345,0.82530606],"genre_scores_gemma":[0.88528734,0.0000027687718,0.0002741274,0.00005663647,0.000069882306,0.00010325772,0.000002319059,0.00001878353,0.11418491],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994417,0.000014345118,0.00015150548,0.00012582753,0.00007050477,0.00019614879],"domain_scores_gemma":[0.9995935,0.000026511538,0.00002201258,0.0002623283,0.000019038627,0.000076636614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014244062,0.00008085109,0.00012890875,0.000036623467,0.0011762069,0.0010088941,0.00037263383,0.000027431028,0.00007945327],"category_scores_gemma":[0.000084555475,0.000084784624,0.000013178832,0.00021583652,0.0000076582755,0.0001350269,0.00006678866,0.00006972203,0.0000062452523],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010928744,0.000052395004,0.0071622347,0.000043994765,0.0000442349,0.00006960296,0.0011705009,0.26371485,0.0008151726,0.000103200306,0.7227624,0.004050491],"study_design_scores_gemma":[0.0011558618,0.00006376789,0.05052731,0.00034104555,0.0000094267025,0.000036142214,0.00088924496,0.043931663,0.0010559458,0.00012166067,0.9011116,0.00075633626],"about_ca_topic_score_codex":0.93428814,"about_ca_topic_score_gemma":0.9938712,"teacher_disagreement_score":0.71448046,"about_ca_system_score_codex":0.00017789482,"about_ca_system_score_gemma":0.0001354563,"threshold_uncertainty_score":0.9728789},"labels":[],"label_agreement":null},{"id":"W6925250892","doi":"10.17632/2xzwppwxrw.3","title":"Double-Double Laminate Finder","year":2024,"lang":"en","type":"dataset","venue":"Data Archiving and Networked Services (DANS)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"","keywords":"Flexural strength; Stiffness; Composite number; Homogenization (climate); Flexural rigidity; Lamination; Stacking","score_opus":0.01513614358457434,"score_gpt":0.2318872149703146,"score_spread":0.21675107138574023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925250892","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036656766,0.0057096845,0.00011046816,0.000022592765,0.0027520575,0.00023411638,0.9833119,0.00050273817,0.0036907524],"genre_scores_gemma":[0.0040806304,0.0034010033,0.00007937734,0.000048756716,0.0013154082,0.00004224362,0.99017113,0.000118157164,0.0007433053],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974118,0.000046034897,0.0005295613,0.00093908474,0.0003341377,0.0007394095],"domain_scores_gemma":[0.99750996,0.0001024268,0.00008632246,0.0020734174,0.00001600755,0.0002118618],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046176012,0.0006590535,0.0005967981,0.00021266304,0.00021539618,0.00045812162,0.0020613822,0.0002991162,0.000053441894],"category_scores_gemma":[0.0000012576221,0.00057021726,0.000077775876,0.000319767,0.00006996622,0.00023593532,0.001170221,0.00080009043,0.0005681881],"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":[0.00002824178,0.000019108891,0.000015605645,0.0032802068,0.00019732391,0.00007838612,0.00033866314,0.023866592,0.0000138610085,0.00002066556,0.97072345,0.00141787],"study_design_scores_gemma":[0.0003515344,0.000018790019,0.00006477703,0.0011807359,0.00015310018,0.000042276864,0.00010192702,0.16686864,0.0000037709933,0.000026882524,0.8306298,0.00055770855],"about_ca_topic_score_codex":0.0027790535,"about_ca_topic_score_gemma":0.0086128265,"teacher_disagreement_score":0.14300205,"about_ca_system_score_codex":0.000034936133,"about_ca_system_score_gemma":0.000030225247,"threshold_uncertainty_score":0.9996749},"labels":[],"label_agreement":null},{"id":"W6926282261","doi":"10.2307/j.ctt1n2tv7r","title":"","year":2023,"lang":"en","type":"other","venue":"Directory of Open access Books (OAPEN Foundation)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Interpretation (philosophy); Afterlife; Diversity (politics); Cult; Literary criticism; Meaning (existential)","score_opus":0.061757245090055736,"score_gpt":0.3578733812302733,"score_spread":0.29611613614021753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926282261","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":[0.0015552161,0.00038739588,0.0007583208,0.000029617828,0.0046058353,0.001778969,0.000013753254,0.0040598954,0.986811],"genre_scores_gemma":[0.044167813,0.0011513139,0.00042014333,0.00006168053,0.00079060247,0.0010644926,0.00034531602,0.005798563,0.9462001],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960627,0.00012625835,0.0013232814,0.0009517154,0.0008033138,0.00073275977],"domain_scores_gemma":[0.99717987,0.00021146606,0.0007360203,0.0014819955,0.00013474455,0.00025591967],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00070428714,0.0009331812,0.0016257202,0.0012671425,0.00019684077,0.002284015,0.0063179927,0.00030991406,0.0064860764],"category_scores_gemma":[0.000111538975,0.0009845039,0.0002738309,0.0009142471,0.00021704026,0.0027465888,0.0013916269,0.00037480268,0.001127573],"study_design_candidate":"not_applicable","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":[0.00009431327,0.00032547553,0.006783435,0.0036969818,0.0031949747,0.00011434702,0.0006443944,0.04939906,0.001618692,0.039886437,0.3690574,0.5251845],"study_design_scores_gemma":[0.0007624227,0.000027576796,0.006014801,0.0013478334,0.00007433525,0.0000039202273,0.000049893762,0.0014933464,0.00060790224,0.00015169872,0.98824674,0.0012195415],"about_ca_topic_score_codex":0.016646968,"about_ca_topic_score_gemma":0.019058965,"teacher_disagreement_score":0.6191893,"about_ca_system_score_codex":0.00027153202,"about_ca_system_score_gemma":0.0002055722,"threshold_uncertainty_score":0.9996502},"labels":[],"label_agreement":null},{"id":"W6926408134","doi":"10.25316/ir-13908","title":"Nanaimo Free Press [Saturday, January 28, 1893]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"","score_opus":0.00598330271210095,"score_gpt":0.16887612672436406,"score_spread":0.1628928240122631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926408134","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":[0.0000052606197,0.0018319795,0.0015031788,0.000020118647,0.0052201254,0.0003425779,0.00028452955,0.00089704833,0.98989516],"genre_scores_gemma":[0.0008288968,0.001028111,0.00017325513,0.000014631437,0.00050836016,0.0000016002488,0.0000022294967,0.00043615713,0.9970068],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839735,0.00004443755,0.00015173851,0.0005152696,0.00034019872,0.000551003],"domain_scores_gemma":[0.99856645,0.00003437702,0.00012017592,0.0010845312,0.000035700763,0.00015877507],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005290655,0.00055242534,0.0005849431,0.00062586274,0.00007378176,0.00004518567,0.00084357016,0.00064187445,0.0002841864],"category_scores_gemma":[0.000008772496,0.0005681407,0.00020634591,0.00040981593,0.00006277197,0.00014833285,0.0001830873,0.0004180479,0.00028210934],"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":[0.000009315306,0.000022398699,9.862462e-7,0.00027545547,0.0001798064,0.00012355048,0.00012527766,0.0075679296,0.000020503932,0.0006097316,0.9907812,0.0002838796],"study_design_scores_gemma":[0.0008933166,0.000038773724,0.000001949692,0.00031548954,0.00008095813,0.0000033730762,0.00021532865,0.0012313148,0.000029940891,0.000006921035,0.9964724,0.00071023667],"about_ca_topic_score_codex":0.0016079597,"about_ca_topic_score_gemma":0.0037793927,"teacher_disagreement_score":0.007111578,"about_ca_system_score_codex":0.00018280372,"about_ca_system_score_gemma":0.000064065,"threshold_uncertainty_score":0.999677},"labels":[],"label_agreement":null},{"id":"W6928621838","doi":"10.3929/ethz-b-000722690","title":"A Comprehensive Stochastic Programming Model for Transfer Synchronization in Transit Networks","year":2024,"lang":"en","type":"article","venue":"Repository for Publications and Research Data (ETH Zurich)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Dwell time; Synchronization (alternating current); Transfer (computing); Stochastic modelling; Stochastic programming; Sample (material); Transit (satellite); Reduction (mathematics)","score_opus":0.09711776559992463,"score_gpt":0.34299122925155673,"score_spread":0.2458734636516321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6928621838","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":[0.0029528807,0.0065896804,0.98831326,0.00023575143,0.00017916554,0.0012122855,0.00016652788,0.00020509299,0.00014536889],"genre_scores_gemma":[0.99441445,0.00017561692,0.0027958003,0.0000032984285,0.00018783259,0.00107092,0.00078983535,0.000050334253,0.00051190844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986431,0.00004115619,0.00029593558,0.0004219413,0.0001908146,0.0004070646],"domain_scores_gemma":[0.998855,0.00034509032,0.0000075155435,0.0004628517,0.00022554044,0.00010402487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070292526,0.000118256896,0.0001454412,0.00035978333,0.00030259817,0.00045107058,0.0003190629,0.00011467137,9.960036e-7],"category_scores_gemma":[0.00004730829,0.00010706073,0.00003673501,0.0007004546,0.00007393901,0.000527999,0.00003766807,0.00021452691,5.450388e-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":[0.000010299558,0.000050010945,0.0000068233576,0.0008230758,0.000049328675,9.686353e-7,0.00055306475,0.9621586,0.00058631547,0.007450313,0.004107682,0.02420348],"study_design_scores_gemma":[0.00019004512,0.00004112952,0.000040631403,0.00010355446,0.000013843242,0.000010771672,0.000102674756,0.95635253,0.000015394202,0.00008560326,0.042925656,0.000118145734],"about_ca_topic_score_codex":0.000043233154,"about_ca_topic_score_gemma":0.000047641908,"teacher_disagreement_score":0.9914616,"about_ca_system_score_codex":0.0000821531,"about_ca_system_score_gemma":0.00009781953,"threshold_uncertainty_score":0.436581},"labels":[],"label_agreement":null},{"id":"W6930278092","doi":"10.5281/zenodo.11440605","title":"**$^- [[[[WhatsApp ((( (+27) 736616875))) *____**)) TOP QUALITY COUNTERFEIT MONEY FOR SALE Nauru Peqin Zimbabwe\\\\EUROPE USA 3","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Counterfeit; Quality (philosophy); Counterfeit Drugs; Electronic money","score_opus":0.028793730907338683,"score_gpt":0.25184604288542173,"score_spread":0.22305231197808303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930278092","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":[0.0006185126,0.0021408245,0.00597382,0.00013648985,0.0017165715,0.0009354219,0.0016795368,0.0041508554,0.98264796],"genre_scores_gemma":[0.025226522,0.00092368,0.00047019817,0.00009761299,0.0019037885,0.0000011528299,0.0032881475,0.026037186,0.9420517],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969964,0.00020894306,0.0006172528,0.0008430506,0.000582208,0.0007521506],"domain_scores_gemma":[0.9982412,0.000031072497,0.00016097583,0.0009512173,0.00032807566,0.0002874893],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008684728,0.00051849335,0.0005282238,0.0005424401,0.00052847393,0.0011772692,0.0013434081,0.00033374486,0.02007108],"category_scores_gemma":[0.00026849456,0.0005386205,0.00018571684,0.00072658475,0.00015042612,0.00015684713,0.0006025565,0.0005213528,0.03890705],"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":[0.00002234998,0.00006153769,7.779256e-7,0.0012780236,0.00016230613,0.000019120422,0.0004247939,0.0010204649,0.0010371587,0.0029272032,0.9844355,0.00861074],"study_design_scores_gemma":[0.00042993392,0.0001348501,0.000017475186,0.00035505547,0.000045238532,0.00005814795,0.00014372209,0.0020664688,0.00011743569,0.000035603003,0.99600255,0.0005934935],"about_ca_topic_score_codex":0.00013515179,"about_ca_topic_score_gemma":0.000011284526,"teacher_disagreement_score":0.040596254,"about_ca_system_score_codex":0.00023092476,"about_ca_system_score_gemma":0.000009510043,"threshold_uncertainty_score":0.99985963},"labels":[],"label_agreement":null},{"id":"W6930635049","doi":"10.5281/zenodo.14171706","title":"How much effective VitaSlim Keto Gummies for Weight reduction? Huge Discounts USA","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Limiting; Population; Work (physics); Filter (signal processing)","score_opus":0.01262234810647965,"score_gpt":0.21731563226860098,"score_spread":0.20469328416212135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930635049","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":[0.00031640937,0.0018797921,0.0070763114,0.00027518152,0.0020362847,0.001335379,0.0010845786,0.0025124317,0.9834836],"genre_scores_gemma":[0.06148352,0.00042931104,0.00026872166,0.000012710176,0.0021610754,0.0000018969859,0.0016898455,0.015487691,0.91846526],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99830765,0.00010277435,0.00021860961,0.0005632729,0.00034217336,0.0004655499],"domain_scores_gemma":[0.9990894,0.000014523464,0.00007698692,0.0005092658,0.000168558,0.00014122663],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003088218,0.00034394782,0.0003126671,0.0005003129,0.0005500522,0.0011037763,0.0006654978,0.00021667934,0.0076873754],"category_scores_gemma":[0.00011859054,0.0003313286,0.0001238927,0.0004636787,0.000120176526,0.00016283193,0.00029790672,0.00031074768,0.012985423],"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":[0.000009659651,0.000030593634,1.0148683e-7,0.00075003615,0.00018110825,0.000006833907,0.00040071303,0.000132467,0.0012432944,0.0028725343,0.98033226,0.014040407],"study_design_scores_gemma":[0.00024714964,0.00012354796,0.0000053904946,0.00026907682,0.000044841392,0.00005338311,0.00028445583,0.0006065642,0.00036011412,0.00013114377,0.9975081,0.0003662021],"about_ca_topic_score_codex":0.000022325981,"about_ca_topic_score_gemma":0.0000022094357,"teacher_disagreement_score":0.0650184,"about_ca_system_score_codex":0.00023826983,"about_ca_system_score_gemma":0.0000022028541,"threshold_uncertainty_score":0.9999332},"labels":[],"label_agreement":null},{"id":"W6949177014","doi":"10.5281/zenodo.12540971","title":"Zero-emission trains on non-electrified Czech railways","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrification; Train; Catenary; Battery (electricity); Flexibility (engineering); Software deployment; Energy supply","score_opus":0.019887986872535877,"score_gpt":0.21847396415221462,"score_spread":0.19858597727967875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949177014","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":[0.0737674,0.0006782449,0.041254226,0.0004043253,0.0009687208,0.0005095938,0.00011750608,0.0069571063,0.8753429],"genre_scores_gemma":[0.9946027,0.00010602484,0.00005328678,0.000033000342,0.00021134547,6.093332e-8,0.0002724413,0.0013669723,0.003354186],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986761,0.000069423106,0.00022239947,0.000336187,0.0003072843,0.00038855747],"domain_scores_gemma":[0.9993739,0.000021149777,0.000017641294,0.0003258178,0.00009417688,0.00016732291],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004137758,0.00016830153,0.000136003,0.00029248648,0.0007908826,0.0006631783,0.00051107915,0.00009120831,0.0022762408],"category_scores_gemma":[0.00009700281,0.00016078862,0.00006563457,0.0006983401,0.00004165189,0.00015717777,0.00011863436,0.00032111316,0.006994597],"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":[0.000022244818,0.000064212436,2.257878e-7,0.0002438197,0.00005729797,0.000050937662,0.0015644671,0.0104730595,0.09541048,0.012178645,0.62901604,0.25091857],"study_design_scores_gemma":[0.00018688229,0.00016532444,0.000037593323,0.000111575195,0.0000066126,0.000053777152,0.000069232294,0.04233804,0.0062793973,0.00008322505,0.9504755,0.00019282087],"about_ca_topic_score_codex":0.000006080002,"about_ca_topic_score_gemma":6.465648e-8,"teacher_disagreement_score":0.92083526,"about_ca_system_score_codex":0.00016278184,"about_ca_system_score_gemma":0.0000033337635,"threshold_uncertainty_score":0.9986358},"labels":[],"label_agreement":null},{"id":"W6961100899","doi":"10.14288/1.0362198","title":"Canadian Pacific Railway Eastern Division--Montreal terminals timetable","year":2017,"lang":"en","type":"article","venue":"Open Collections","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Government (linguistics); Work (physics); Port (circuit theory); Fishing","score_opus":0.012776886268652253,"score_gpt":0.2276786040839664,"score_spread":0.21490171781531414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6961100899","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":[0.0036939436,0.00006942013,0.0003458399,0.000115678464,0.0011135709,0.00026722942,0.000051022664,0.00007680647,0.9942665],"genre_scores_gemma":[0.55039984,0.00001088477,0.00008899315,0.000006526695,0.000067894536,0.00006483174,0.0000044250987,0.000020834561,0.44933578],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991095,0.000018257959,0.00018600293,0.00020510958,0.00010511086,0.00037600796],"domain_scores_gemma":[0.9989582,0.000018927833,0.000041926593,0.00066903204,0.00004439878,0.00026756315],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00016965206,0.00014507439,0.00020510599,0.00009634934,0.0046814303,0.00347074,0.00068695506,0.000077754106,0.00084156595],"category_scores_gemma":[0.00003186242,0.00014024456,0.000057412522,0.00021343077,0.000028462866,0.00041796803,0.0000847624,0.00010388249,0.00019598335],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004964785,0.000051845334,0.0017470084,0.000020703072,0.00011448989,0.000049255563,0.00035399804,0.022254791,0.00015881588,0.000092751485,0.95617557,0.01897583],"study_design_scores_gemma":[0.00057859486,0.00005751246,0.009184655,0.00008250033,0.00002813671,0.000037431768,0.00028004788,0.07449439,0.00020523173,0.00024279788,0.91431767,0.0004910369],"about_ca_topic_score_codex":0.80362415,"about_ca_topic_score_gemma":0.77756435,"teacher_disagreement_score":0.5467059,"about_ca_system_score_codex":0.0001643663,"about_ca_system_score_gemma":0.00014396675,"threshold_uncertainty_score":0.9975638},"labels":[],"label_agreement":null},{"id":"W6964514791","doi":"10.25316/ir-12719","title":"Nanaimo Free Press [Thursday, June 22, 1893]","year":2019,"lang":"en","type":"other","venue":"VIURRSpace (Vancouver Island University)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"","score_opus":0.005967656859823451,"score_gpt":0.16975222177960048,"score_spread":0.16378456491977703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6964514791","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":[0.0000037728348,0.0014189739,0.0019785617,0.00002281988,0.007385684,0.00031215945,0.00025280958,0.00092437654,0.9877008],"genre_scores_gemma":[0.00065825396,0.00086258014,0.00014906435,0.000011251846,0.0004695018,0.0000015586892,0.000001808952,0.0004895448,0.9973564],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984027,0.000041413896,0.00015358915,0.0005056944,0.0003429617,0.0005536464],"domain_scores_gemma":[0.9985731,0.000031672513,0.0001235879,0.0010743102,0.00003809462,0.0001592335],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000055008604,0.0005399284,0.0005962867,0.00062589714,0.0000718475,0.0000453619,0.00083177653,0.00058867526,0.0005933389],"category_scores_gemma":[0.0000092816035,0.0005602087,0.00019764906,0.00043976615,0.00006599165,0.00013697815,0.00016900197,0.00036110097,0.00031351112],"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":[0.0000081919015,0.000026040037,7.4948616e-7,0.00026638148,0.0001765742,0.00009833873,0.000098238714,0.007836193,0.000020611447,0.00082479476,0.9903904,0.00025346666],"study_design_scores_gemma":[0.0010317677,0.000032470245,9.49491e-7,0.00030705915,0.000086305256,0.0000029560672,0.00019199785,0.0012567725,0.000031585758,0.000006699149,0.996356,0.00069542666],"about_ca_topic_score_codex":0.0014597583,"about_ca_topic_score_gemma":0.0053061387,"teacher_disagreement_score":0.009655594,"about_ca_system_score_codex":0.00018018499,"about_ca_system_score_gemma":0.00006017073,"threshold_uncertainty_score":0.99968493},"labels":[],"label_agreement":null},{"id":"W6976612259","doi":"10.6068/dp14ba8d6b3f457","title":"Trend 2006 - 2013. Statistics Canada. CANSIM: Ethnic Diversity and Immigration - Labor Market and Income | Country: Canada | Table: Labour force survey estimates (LFS), by immigrant status, educational attainment, sex and age group | Variable: 15 years and over, Labour force, High school graduate, Both sexes, Immigrants, landed more than 10 years earlier | Units: , 2006-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-094.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Immigration; Unemployment; Census; Official statistics; Socioeconomic status; Descriptive statistics; Population; Ethnic group; Diversity (politics); Government (linguistics)","score_opus":0.012451439508335438,"score_gpt":0.2191970162314547,"score_spread":0.20674557672311927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976612259","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012007862,0.011356547,0.000010501803,0.0000028674035,0.0003683377,0.00044142816,0.9864782,0.00006026398,0.000081048296],"genre_scores_gemma":[0.0011390205,0.008141379,0.00013345192,0.00007253992,0.00009700689,0.000015048023,0.9803614,0.00016122252,0.009878931],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.996747,0.00024285252,0.0005681968,0.0009043373,0.00078790006,0.0007496998],"domain_scores_gemma":[0.9975707,0.00045605924,0.00030409108,0.0009586373,0.000047147754,0.0006633539],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067933404,0.000665263,0.000822229,0.00012307022,0.0002396489,0.00028718874,0.0005687056,0.0003674313,0.00060914294],"category_scores_gemma":[0.00005824002,0.00067156524,3.253762e-7,0.00030829085,0.00019141787,0.0003648813,0.0005773419,0.0004966163,0.0000023813107],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006468001,0.000029387134,0.003011852,0.0005689483,0.0002644959,0.000092307164,0.000016493605,0.000079315934,0.0000053794934,0.00009214948,0.995702,0.0000729692],"study_design_scores_gemma":[0.0010671157,0.00005503945,0.019864043,0.00005953085,0.00016149216,0.000046815967,0.0002529406,0.0046730097,1.421514e-8,8.355311e-7,0.97305447,0.00076470186],"about_ca_topic_score_codex":0.999825,"about_ca_topic_score_gemma":0.99909174,"teacher_disagreement_score":0.02264756,"about_ca_system_score_codex":0.00018125858,"about_ca_system_score_gemma":0.0014332301,"threshold_uncertainty_score":0.9995735},"labels":[],"label_agreement":null},{"id":"W6977169496","doi":"10.60692/svg2k-dzq05","title":"Adaptive DDL Algorithm to Elucidate the Protection Misoperation in Malaysian Rapid Rail DC Traction System","year":2024,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Overcurrent; Traction (geology); Electrification; Transient (computer programming); Fault (geology); Voltage; Fault detection and isolation; Overvoltage; Traction substation","score_opus":0.018228735403194203,"score_gpt":0.17901550797573124,"score_spread":0.16078677257253704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977169496","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":[0.21051672,0.00005500701,0.7724432,0.00014162273,0.004076033,0.0018731201,0.000072869334,0.0017892868,0.00903219],"genre_scores_gemma":[0.99869376,4.1033263e-7,0.00021537954,0.000018885145,0.0002813364,0.0006209394,0.000014842361,0.000028264309,0.00012617407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998376,0.00008932842,0.0007411406,0.00018923325,0.00031599635,0.00028828572],"domain_scores_gemma":[0.9994752,0.000006645589,0.00007216166,0.00028709136,0.00008309375,0.000075824384],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006725634,0.00023898372,0.0002194278,0.0004999462,0.00014871129,0.00046516862,0.00013811985,0.00014763212,0.00000905824],"category_scores_gemma":[0.0000055937876,0.00017261055,0.00007638689,0.00074199645,0.000010314903,0.0010834244,0.000016782818,0.00020136293,0.0010919033],"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":[0.00005805966,0.0000031201976,0.00017325894,0.0026121356,0.0001345223,0.000021167747,0.14336948,0.77766037,0.00026371615,0.0012756296,0.00044639723,0.07398213],"study_design_scores_gemma":[0.00024226971,0.00005411392,0.0012283844,0.0007115692,0.000014317406,0.00011326815,0.02087472,0.9724982,0.0011614517,2.2488497e-7,0.0028720456,0.00022946527],"about_ca_topic_score_codex":0.000095300384,"about_ca_topic_score_gemma":0.0000037410164,"teacher_disagreement_score":0.7881771,"about_ca_system_score_codex":0.0005880616,"about_ca_system_score_gemma":0.000026499554,"threshold_uncertainty_score":0.9996859},"labels":[],"label_agreement":null},{"id":"W6981942465","doi":"","title":"Gaga pulls out of Rock in Rio, posts photo of IV in her arm","year":2017,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Thursday; Maroon; Medical care; Amateur; Patient care","score_opus":0.010572974977601216,"score_gpt":0.22444601545399107,"score_spread":0.21387304047638986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6981942465","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":[0.020100083,0.00090157764,0.0022511815,0.000006923127,0.001467567,0.0006127829,0.00040309696,0.00013456249,0.9741222],"genre_scores_gemma":[0.49500406,0.00022041023,0.00027511065,0.000010487229,0.00016221814,0.000050558,0.0000906245,0.00036863258,0.5038179],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969303,0.00016160328,0.0011196843,0.00068444863,0.00039078543,0.00071318186],"domain_scores_gemma":[0.99812883,0.00016309784,0.0005410353,0.0009826295,0.000023715731,0.00016069163],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00024003223,0.0007326318,0.0013973439,0.0013856174,0.000007906098,0.000038553204,0.0015065884,0.0002791747,0.0021016866],"category_scores_gemma":[0.0000686847,0.00071871886,0.00037374967,0.000100751764,0.00020596183,0.00007774319,0.00039509826,0.00075451704,0.00025490063],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021318035,0.00044771636,0.0036496667,0.0016044067,0.00044593433,0.00026681664,0.009463943,0.00861686,0.0058687637,0.0007735372,0.96765727,0.0009919133],"study_design_scores_gemma":[0.0024367496,0.0005716115,0.0051967157,0.016957682,0.00005608189,0.00003368505,0.00018618393,0.06079074,0.004125707,0.00047313143,0.907596,0.0015757439],"about_ca_topic_score_codex":0.04543721,"about_ca_topic_score_gemma":0.13029937,"teacher_disagreement_score":0.47490397,"about_ca_system_score_codex":0.00007952818,"about_ca_system_score_gemma":0.000058548056,"threshold_uncertainty_score":0.9995264},"labels":[],"label_agreement":null},{"id":"W6982199296","doi":"","title":"High frequency train | A design that will go through Quebec | Wire News","year":2023,"lang":"en","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Key (lock); Work (physics); Noise (video); Mode (computer interface); Power (physics)","score_opus":0.02231355416533228,"score_gpt":0.20944924322770564,"score_spread":0.18713568906237335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6982199296","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":[0.000023144788,0.0020494065,0.10486227,0.00008330406,0.0031548478,0.00041647133,0.000045058405,0.007134681,0.8822308],"genre_scores_gemma":[0.0023542359,0.0007857295,0.0070569133,0.0000654073,0.00089875504,0.00011402323,0.000027155773,0.0023356623,0.9863621],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99833566,0.000046024386,0.00032479982,0.00044127705,0.0003010009,0.0005512318],"domain_scores_gemma":[0.9991624,0.00005738505,0.00007387127,0.0005948724,0.000011148332,0.00010036242],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000111142166,0.0005524429,0.0005950073,0.00018263265,0.00004242774,0.00007312276,0.00037529969,0.0005955326,0.0031023074],"category_scores_gemma":[0.000012927769,0.0004626009,0.0001582517,0.0003438107,0.00004779674,0.00015639963,0.000022878534,0.00024161756,0.0010869954],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.4803196e-7,0.000013254505,0.000008443482,0.00014150752,0.00014161361,0.000052604966,0.0002645379,0.012148783,0.00009105621,0.008899113,0.9731144,0.00512386],"study_design_scores_gemma":[0.0003311982,0.000032779808,0.000027664042,0.0005196678,0.000038308543,0.000010244402,0.00020301538,0.0012792886,0.000116188254,0.00042062666,0.99601597,0.0010050685],"about_ca_topic_score_codex":0.14811447,"about_ca_topic_score_gemma":0.14322706,"teacher_disagreement_score":0.104131304,"about_ca_system_score_codex":0.00016003262,"about_ca_system_score_gemma":0.00006278712,"threshold_uncertainty_score":0.99978256},"labels":[],"label_agreement":null},{"id":"W6987797247","doi":"","title":"Transfer Time Optimization in Transit Scheduling","year":2023,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Headway; Dwell time; Transfer (computing); Scheduling (production processes); Public transport; Transfer function; Heuristic; Job shop scheduling; Linear programming; Node (physics)","score_opus":0.00911381847693486,"score_gpt":0.24422417398253704,"score_spread":0.2351103555056022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6987797247","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":[0.8748141,0.001686485,0.022493849,0.00007637022,0.0026436655,0.00044684028,0.0000062339514,0.0017013353,0.0961311],"genre_scores_gemma":[0.9080994,0.0006194748,0.0014193953,0.00001011005,0.0003107493,0.000112862166,0.0012920006,0.00040817316,0.0877278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917597,0.000013654069,0.00023499342,0.00018931487,0.00015297823,0.00023311995],"domain_scores_gemma":[0.9997724,0.00001790121,0.000011294662,0.00013623261,0.000021162154,0.00004098175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008490118,0.0002051327,0.0002570984,0.00023980829,0.000026509872,0.00002844233,0.00010352993,0.00028503855,0.00014763889],"category_scores_gemma":[0.0000070199476,0.00022861784,0.00006823481,0.00040777013,0.0000041855865,0.00006977615,0.0000012071104,0.00021361376,0.0002100277],"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":[0.0000055707924,0.0000057944035,0.0000011224422,0.00020370062,0.000013907765,0.000010238554,0.0044815433,0.9908017,0.003467521,0.000033833094,0.00009109672,0.000883972],"study_design_scores_gemma":[0.00024726518,0.000013496075,0.00012456204,0.00042079302,0.000020385325,0.0000015947572,0.002091694,0.9941402,0.0019246485,0.0000049995606,0.00063381984,0.00037650453],"about_ca_topic_score_codex":0.00014534417,"about_ca_topic_score_gemma":0.00039554955,"teacher_disagreement_score":0.033285312,"about_ca_system_score_codex":0.000057279056,"about_ca_system_score_gemma":0.00002403819,"threshold_uncertainty_score":0.9322764},"labels":[],"label_agreement":null},{"id":"W6990939073","doi":"","title":"ENGIE Transport Canada places its expertise in rail electrification at the service of the Réseau express métropolitain (REM)","year":2019,"lang":"fr","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrification; Service (business); Government (linguistics); Work (physics); Public transport","score_opus":0.006427497387258357,"score_gpt":0.1773197621230373,"score_spread":0.17089226473577893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990939073","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":[0.33949146,0.028403832,0.0007824104,0.004532299,0.0036983064,0.0019809892,0.00014699451,0.0000980342,0.6208657],"genre_scores_gemma":[0.6001252,0.00022162352,0.000022285823,0.00008567289,0.00013609743,0.00007154386,0.000013296355,0.00013232086,0.39919195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976418,0.00016273581,0.0006791444,0.00040313616,0.0005167313,0.00059647724],"domain_scores_gemma":[0.9985759,0.00013318447,0.00020032775,0.0009248414,0.00008333152,0.0000823777],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027813573,0.00045216602,0.00050181325,0.000092722905,0.00009246009,0.000014815625,0.0008760868,0.0003463216,0.002444563],"category_scores_gemma":[0.00002554878,0.00028015368,0.00012929534,0.00053089665,0.000047597663,0.00006175811,0.000025266083,0.0003232056,0.00006383521],"study_design_candidate":"not_applicable","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":[0.00005357373,0.00012671198,0.0025268572,0.002347991,0.0002546717,0.000011278659,0.0074939025,0.8223753,0.092051834,0.018894807,0.05028943,0.003573671],"study_design_scores_gemma":[0.0008640871,0.000031414438,0.0069112643,0.0009553382,0.000067049965,0.000013173345,0.0021022498,0.06131663,0.07277353,0.0000083496225,0.85409516,0.00086172915],"about_ca_topic_score_codex":0.87968946,"about_ca_topic_score_gemma":0.9612339,"teacher_disagreement_score":0.80380577,"about_ca_system_score_codex":0.0013526485,"about_ca_system_score_gemma":0.0007596237,"threshold_uncertainty_score":0.9999651},"labels":[],"label_agreement":null},{"id":"W6991339948","doi":"","title":"Fuel consumption for double-stack intermodal trains","year":2014,"lang":"en","type":"dissertation","venue":"UPCommons institutional repository (Universitat Politècnica de Catalunya)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Train; Scope (computer science); Track (disk drive); Fuel efficiency; Section (typography); Freight trains","score_opus":0.014982176800321732,"score_gpt":0.23284545598963255,"score_spread":0.2178632791893108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991339948","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":[0.5899211,0.0019131093,0.01135188,0.000086561464,0.0077087637,0.0010076736,0.00052901334,0.0008986783,0.3865832],"genre_scores_gemma":[0.98454374,0.000060042184,0.0005794733,0.00002883676,0.00058588904,0.00010334763,0.0034042622,0.00008831465,0.010606118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99788,0.000044621185,0.0005257358,0.0005354516,0.00038131935,0.000632912],"domain_scores_gemma":[0.99857295,0.00011994248,0.00019291337,0.0005870515,0.00020506512,0.00032206823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002137694,0.00051837706,0.0005373062,0.00048000208,0.0007059031,0.00012580639,0.0006596687,0.00064876606,0.000033992026],"category_scores_gemma":[0.000027174308,0.00062580936,0.0004472563,0.00018679224,0.00022090554,0.00037211578,0.00004119141,0.0004880974,0.000066396664],"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":[0.0014121685,0.00045461458,0.0007210609,0.0069547845,0.001817872,0.0003700789,0.006976494,0.13827668,0.01542952,0.7840087,0.04046732,0.0031107448],"study_design_scores_gemma":[0.01025118,0.00058138877,0.016393881,0.0026705272,0.0016156313,0.0013154863,0.006099155,0.09505372,0.0075042336,0.0020384449,0.85112125,0.005355097],"about_ca_topic_score_codex":0.00021842319,"about_ca_topic_score_gemma":0.0005166137,"teacher_disagreement_score":0.8106539,"about_ca_system_score_codex":0.0013033585,"about_ca_system_score_gemma":0.0005016394,"threshold_uncertainty_score":0.9996193},"labels":[],"label_agreement":null},{"id":"W6991737538","doi":"","title":"Influence of Locomotive Speed and Throttle Profiles in Noise Modelling","year":2022,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Throttle; Noise (video); Finite element method; Noise control","score_opus":0.007163507034679095,"score_gpt":0.1723822393409173,"score_spread":0.16521873230623818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991737538","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":[0.99681425,0.00023304201,0.0014921962,0.000009859206,0.00007794705,0.00009505269,0.00008555213,0.000017028417,0.0011750916],"genre_scores_gemma":[0.99965763,0.000018926485,0.00021370628,0.000014844729,0.000015816608,0.0000074473346,0.000004154693,0.000014269486,0.00005318985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951494,0.000009402158,0.00013452764,0.000082171704,0.00007595611,0.00018302188],"domain_scores_gemma":[0.99975723,0.00001670964,0.000015445468,0.000092646624,0.000019093239,0.00009887107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007590412,0.00006624449,0.00010283813,0.00014518108,0.00004987603,0.0000065457084,0.00008290011,0.000026113707,0.000015723816],"category_scores_gemma":[0.00001004707,0.00007764635,0.000009696435,0.00019108457,0.000027337735,0.0000330618,0.000015265636,0.00010912897,0.0000011564752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.3001384e-7,0.0000033256665,0.0016493241,0.00004336984,0.0000030923427,0.000013626292,0.0006185153,0.99260294,0.0047027525,0.00021581959,0.00007302646,0.00007366439],"study_design_scores_gemma":[0.00008653,0.000016246218,0.008369928,0.000022593611,0.0000034709856,0.0000047192516,0.00052125007,0.9904117,0.0001878513,0.00005643682,0.00022026559,0.00009898682],"about_ca_topic_score_codex":0.027564798,"about_ca_topic_score_gemma":0.003787438,"teacher_disagreement_score":0.02377736,"about_ca_system_score_codex":0.00014803589,"about_ca_system_score_gemma":0.00009027591,"threshold_uncertainty_score":0.97891074},"labels":[],"label_agreement":null},{"id":"W6995940781","doi":"","title":"Raideliikenteen aiheuttama staattinen kuormavaikutus siltarakenteisiin","year":2019,"lang":"fi","type":"other","venue":"Tampere University Institutional Repository (Tampere University)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Quarter (Canadian coin); Population; Poverty","score_opus":0.006297760802456591,"score_gpt":0.15580123413227107,"score_spread":0.14950347332981448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6995940781","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":[0.05280515,0.0010258869,0.0032692747,0.00014460106,0.0058289664,0.0011237023,0.0007683553,0.0009587262,0.93407536],"genre_scores_gemma":[0.18527637,0.0009257563,0.00049859897,0.000052431973,0.0009840404,6.478015e-7,0.00037284897,0.00027260458,0.8116167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99403113,0.00042009665,0.0008129445,0.0018856794,0.0013414013,0.0015087542],"domain_scores_gemma":[0.99605834,0.00020262861,0.0006488052,0.0015852583,0.0006244551,0.00088050513],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0002849686,0.0016210009,0.0015177219,0.0023189634,0.0018338793,0.00020057418,0.0023690176,0.0017820967,0.0022535159],"category_scores_gemma":[0.00006313926,0.0020470214,0.0010733783,0.0016734481,0.0009779956,0.0011685275,0.0007847238,0.001600155,0.0039251028],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017478679,0.002254469,0.01055667,0.004512747,0.007264885,0.025774958,0.0037318943,0.48606214,0.012265056,0.27244464,0.16769536,0.005689325],"study_design_scores_gemma":[0.0031011235,0.00021016244,0.0008761001,0.0013157198,0.00053801097,0.00034879724,0.0037731593,0.0045553953,0.0002056241,0.0000045099073,0.9829698,0.0021015625],"about_ca_topic_score_codex":0.0052428916,"about_ca_topic_score_gemma":0.00049057225,"teacher_disagreement_score":0.8152745,"about_ca_system_score_codex":0.00441815,"about_ca_system_score_gemma":0.0018679046,"threshold_uncertainty_score":0.99965376},"labels":[],"label_agreement":null},{"id":"W6996428648","doi":"","title":"Scheduled service network design for integrated planning of rail freight\\ntransportation","year":2011,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Rail transportation; Rail network; Rail traffic; Concurrence; Integrated business planning","score_opus":0.0069787086373676215,"score_gpt":0.1385502000187727,"score_spread":0.13157149138140506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996428648","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":[0.011955135,0.007167781,0.14561711,0.00056382373,0.0022662433,0.001142043,0.0008518056,0.000054625554,0.83038145],"genre_scores_gemma":[0.7343938,0.00047184134,0.09612926,0.0004565893,0.00063584885,0.000098352146,0.00016918154,0.0004745054,0.16717063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978604,0.000065897686,0.0006054956,0.00029053263,0.00072887365,0.00044883735],"domain_scores_gemma":[0.9989133,0.00029335837,0.00035107942,0.00021669018,0.000002163985,0.00022340605],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00002534799,0.0003953995,0.0006007856,0.000040860526,0.00010530693,0.000013129914,0.0002753706,0.00012801432,0.0004325282],"category_scores_gemma":[0.0000027249241,0.0003928403,0.000055526354,0.00015346122,0.00005246213,0.00016402965,0.000023339915,0.0001717524,6.9719848e-9],"study_design_candidate":"simulation_or_modeling","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":[0.00089243235,0.0000531679,0.004577185,0.004865244,0.0009075731,0.00006365229,0.00089946506,0.67903495,0.0114566805,0.28200737,0.005972755,0.0092695495],"study_design_scores_gemma":[0.0025434524,0.0005041362,0.024880333,0.009431093,0.00048629183,0.000012722269,0.004568186,0.37976512,0.09609785,0.0029178036,0.4766132,0.0021798122],"about_ca_topic_score_codex":0.019826384,"about_ca_topic_score_gemma":0.067929,"teacher_disagreement_score":0.7224387,"about_ca_system_score_codex":0.000013140177,"about_ca_system_score_gemma":0.0014606926,"threshold_uncertainty_score":0.99985236},"labels":[],"label_agreement":null},{"id":"W6996585158","doi":"","title":"Sky Train: linea metropolitana di Vancouver","year":2009,"lang":"it","type":"book-chapter","venue":"Florence Research (University of Florence)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Nucleofection; Gestational period; TSG101; Liquation; Dysgeusia; Diafiltration; Emperipolesis; Triacetin; Durvalumab","score_opus":0.046132948077424636,"score_gpt":0.2571376246988868,"score_spread":0.21100467662146216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996585158","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":[0.0038319023,0.0023901577,0.01057103,0.00040111074,0.0024459206,0.0010339156,0.0004463759,0.00032963077,0.97854996],"genre_scores_gemma":[0.6629105,0.010620536,0.0036407178,0.000017476796,0.0014639808,0.0000025038316,0.00016068142,0.00019900853,0.32098457],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9922168,0.0002395277,0.00089263334,0.001449473,0.003079108,0.0021224103],"domain_scores_gemma":[0.99519295,0.00042857256,0.00036660105,0.0016636257,0.0013412798,0.0010069833],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002221398,0.0009884859,0.0016274318,0.002129376,0.00097616715,0.000113096066,0.0028275829,0.0012162442,0.005889405],"category_scores_gemma":[0.00020369007,0.0011739335,0.00081920635,0.0011395449,0.0021481318,0.00069656555,0.0005301544,0.0020352243,0.0008971843],"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":[0.00046731863,0.0005698058,0.00022494447,0.002804085,0.0016876244,0.002697297,0.011608676,0.035764664,0.002413242,0.37554708,0.07289143,0.49332383],"study_design_scores_gemma":[0.0023542438,0.002593606,0.0024489237,0.00428626,0.00033441326,0.000037921327,0.013008754,0.08735963,0.00034272755,0.006989729,0.8768114,0.0034324268],"about_ca_topic_score_codex":0.004305148,"about_ca_topic_score_gemma":0.0019662248,"teacher_disagreement_score":0.8039199,"about_ca_system_score_codex":0.0011135862,"about_ca_system_score_gemma":0.00066086644,"threshold_uncertainty_score":0.99988073},"labels":[],"label_agreement":null},{"id":"W7000414336","doi":"","title":"ExcÃ¨s et Ã©clatement dans l'oeuvre de Marie Laberge : une certaine hystÃ©rie textuelle","year":2001,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Identification (biology); Feature (linguistics); MEDLINE; Articulation (sociology)","score_opus":0.0024623569169557533,"score_gpt":0.1370753874392719,"score_spread":0.13461303052231613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7000414336","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":[0.009802241,0.0034502519,0.0022677337,0.0042290897,0.0009811717,0.00030894243,0.00047383006,0.000045205714,0.97844154],"genre_scores_gemma":[0.34484598,0.0016276365,0.0011635349,0.0005599245,0.00025332024,0.000025099374,0.00004221952,0.00023034688,0.651252],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99619025,0.00013260425,0.0006749139,0.0005043909,0.0016054905,0.00089233567],"domain_scores_gemma":[0.99840903,0.0002532859,0.00026099815,0.0004784638,8.857544e-7,0.00059733697],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000031826643,0.00066152686,0.00069824327,0.00006009981,0.00021788581,0.00004849471,0.0004508957,0.00015832648,0.0025713516],"category_scores_gemma":[0.0000051078537,0.00067746686,0.00008774696,0.00022328229,0.00012594076,0.00021499861,0.00019282832,0.00037350427,7.099938e-8],"study_design_candidate":"not_applicable","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":[0.0002109482,0.00023610701,0.027298082,0.0034391012,0.0012133446,0.0017275017,0.0013305913,0.12547445,0.023515265,0.72487724,0.038933307,0.05174404],"study_design_scores_gemma":[0.00058676396,0.000100275254,0.008735326,0.0008265036,0.00007502651,0.000026370828,0.0021818809,0.021446964,0.0050387355,0.0004918762,0.9596748,0.00081549364],"about_ca_topic_score_codex":0.1678908,"about_ca_topic_score_gemma":0.47075272,"teacher_disagreement_score":0.9207415,"about_ca_system_score_codex":0.00008414656,"about_ca_system_score_gemma":0.0020096106,"threshold_uncertainty_score":0.9995676},"labels":[],"label_agreement":null},{"id":"W7001313491","doi":"","title":"Inventing the Loyalists the Ontario Loyalist tradition and the creation of usable pasts","year":2023,"lang":"en","type":"article","venue":"Project Muse (Johns Hopkins University)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"USable; Narrative; Indigenous; Craft","score_opus":0.015203136454441714,"score_gpt":0.1880908616286676,"score_spread":0.17288772517422588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7001313491","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":[0.72822404,0.0000049133273,0.00038927217,0.00021549097,0.00022953241,0.0003393638,0.000007826145,0.00015293706,0.27043664],"genre_scores_gemma":[0.9972059,0.0024847877,0.000016181191,0.000016245962,0.00005607023,0.000004403458,0.000011201169,0.0000139868025,0.00019121947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928176,0.00009924609,0.0001437318,0.0001325873,0.00016122844,0.00018145576],"domain_scores_gemma":[0.9994735,0.00015389222,0.000065138054,0.00024945763,0.000037508922,0.000020472566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040858946,0.00011577247,0.00013371962,0.00087007316,0.00029278168,0.000053712392,0.00024905082,0.00004865819,0.000008759839],"category_scores_gemma":[0.00002353289,0.00006655722,0.000074333046,0.0028211484,0.00017013817,0.00014821603,0.000055249384,0.00014200521,0.000005303938],"study_design_candidate":"not_applicable","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":[0.00046933373,0.00023140333,0.012066915,0.0009866481,0.0011032429,0.00014394298,0.33848122,0.34015056,0.0004187656,0.24213792,0.008555142,0.05525492],"study_design_scores_gemma":[0.00086083496,0.000028169812,0.004322792,0.00007008816,0.000075993106,0.000009133383,0.0024300956,0.04441805,0.00027366253,0.000010861018,0.9473513,0.00014903833],"about_ca_topic_score_codex":0.3097636,"about_ca_topic_score_gemma":0.1715225,"teacher_disagreement_score":0.93879616,"about_ca_system_score_codex":0.00010536627,"about_ca_system_score_gemma":0.000063575804,"threshold_uncertainty_score":0.8435951},"labels":[],"label_agreement":null},{"id":"W7002566375","doi":"","title":"Gold house","year":2001,"lang":"en","type":"other","venue":"Arca (British Columbia Electronic Library Network)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Work (physics); Frith; Reduction (mathematics); Period (music)","score_opus":0.00262155901646439,"score_gpt":0.14507066874453856,"score_spread":0.14244910972807417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7002566375","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":[0.000061868835,0.04772595,0.000028585346,0.00001763453,0.001665395,0.00045704827,0.000042446158,0.0077512534,0.94224983],"genre_scores_gemma":[0.07057346,0.024057474,0.00022371316,0.00019162055,0.004749032,0.00015688613,0.00018243228,0.0033008205,0.89656454],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964922,0.00007162823,0.0004925487,0.0006686735,0.00032078754,0.0019541522],"domain_scores_gemma":[0.99885637,0.00003745335,0.00011967614,0.0007248457,0.000008272779,0.00025338013],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000093417024,0.00043759917,0.000735478,0.0000939747,0.00010152499,0.00092761626,0.00074568647,0.00067007664,0.26963186],"category_scores_gemma":[0.0000038593575,0.00083111133,0.00024333144,0.0007996857,0.00007456244,0.00031025006,0.00011124743,0.00081850874,0.0003575001],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011776044,0.000040036204,0.00026841913,0.00010233262,0.0001842413,0.00016766874,0.000004977002,0.00532855,2.283586e-7,0.0038067377,0.9701064,0.019989228],"study_design_scores_gemma":[0.00037427086,0.00009448439,0.000015272733,0.0007005545,0.000036769645,0.00020500655,0.0000039401807,0.0022880991,7.0884704e-7,0.008471228,0.98690265,0.00090703776],"about_ca_topic_score_codex":0.012795824,"about_ca_topic_score_gemma":0.115137324,"teacher_disagreement_score":0.26927435,"about_ca_system_score_codex":0.00011263337,"about_ca_system_score_gemma":0.00016948848,"threshold_uncertainty_score":0.99941397},"labels":[],"label_agreement":null},{"id":"W7008785669","doi":"","title":"Colloque - René Chartier, 1572 - 1664","year":2010,"lang":"fr","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Period (music); Quarter (Canadian coin); Table (database)","score_opus":0.008282201630162363,"score_gpt":0.20358002057395988,"score_spread":0.19529781894379752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008785669","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":[0.0019949824,0.0034903162,0.0067457985,0.010957303,0.035687722,0.0017176173,0.0013398286,0.0008364107,0.93723],"genre_scores_gemma":[0.045266844,0.0015141981,0.004571577,0.005518626,0.012069176,0.0015222711,0.0034930578,0.0011661757,0.92487806],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942949,0.00026113086,0.0015348232,0.0013995238,0.0010956071,0.0014140157],"domain_scores_gemma":[0.99647105,0.00018267646,0.0006466183,0.0015365101,0.000368309,0.0007948678],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.00077965786,0.0014105042,0.0014098956,0.0006764803,0.0007052007,0.00065649976,0.0011736266,0.0021228453,0.17639862],"category_scores_gemma":[0.00018560162,0.0015390621,0.00053559087,0.0008562641,0.00039414002,0.0055077244,0.0002336651,0.0015990735,0.029303135],"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":[0.000058158214,0.0006501187,0.000071571194,0.0011231093,0.0005309493,0.0002905688,0.00040954427,0.01170306,0.0062474096,0.44955125,0.5045453,0.024819003],"study_design_scores_gemma":[0.0013177382,0.00022398151,0.0021202718,0.0012899529,0.00018511548,0.00016792801,0.00012393178,0.0022625823,0.0027648734,0.00063162524,0.98712873,0.0017832487],"about_ca_topic_score_codex":0.0011476398,"about_ca_topic_score_gemma":0.011211694,"teacher_disagreement_score":0.4825835,"about_ca_system_score_codex":0.00059522205,"about_ca_system_score_gemma":0.0003652912,"threshold_uncertainty_score":0.9998645},"labels":[],"label_agreement":null},{"id":"W7009775826","doi":"","title":"Electric Shuttle Buses Coming to Billy Bishop Toronto City Airport (YTZ) in 2023","year":2022,"lang":"en","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Work (physics)","score_opus":0.008408910782565055,"score_gpt":0.20827921628476526,"score_spread":0.1998703055022002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009775826","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":[0.002151924,0.0028230515,0.0005146933,0.000019515039,0.0013211033,0.00028225343,0.000017579072,0.0007555299,0.99211437],"genre_scores_gemma":[0.029923093,0.00034939143,0.00038721898,0.000116439725,0.00042346335,0.00016958588,0.000032203614,0.0006092049,0.9679894],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99855417,0.000021703947,0.00033185797,0.00035725933,0.00028364806,0.0004513467],"domain_scores_gemma":[0.99938804,0.0000272492,0.000043449316,0.000420948,0.000010133629,0.00011019844],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019018147,0.00031617034,0.0004378206,0.00037240662,0.000036954087,0.000033156106,0.00034131928,0.00019896017,0.028362058],"category_scores_gemma":[0.000021166701,0.00030257078,0.00008018141,0.0005647628,0.0000064248484,0.000047583973,0.000078946476,0.00019661673,0.00014394528],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000020523773,0.000032106378,0.0007534281,0.00008556197,0.000041937732,0.000028868413,0.000099317724,0.01486603,0.0002576952,0.00023120859,0.9810013,0.0026004834],"study_design_scores_gemma":[0.00013131334,0.000033480428,0.0006649307,0.000055119697,0.00000663035,0.000005128456,0.00011783671,0.00201325,0.000034541445,0.0000033301567,0.99647295,0.0004614855],"about_ca_topic_score_codex":0.029869748,"about_ca_topic_score_gemma":0.06621036,"teacher_disagreement_score":0.03634061,"about_ca_system_score_codex":0.0005204962,"about_ca_system_score_gemma":0.000044378612,"threshold_uncertainty_score":0.99994266},"labels":[],"label_agreement":null},{"id":"W7011118853","doi":"","title":"Les investissements Ã©trangers directs en Chine : vers un Ã©quilibre entre la protection des investisseurs et la protection du marchÃ© chinois","year":2008,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"China; Capital flows; Capital (architecture)","score_opus":0.003916855017051444,"score_gpt":0.13965978159098152,"score_spread":0.13574292657393008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7011118853","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":[0.22261028,0.0017600694,0.0009510032,0.0017232399,0.0007208692,0.0009605326,0.00026736202,0.00011299082,0.77089363],"genre_scores_gemma":[0.90945,0.0024521209,0.0020488158,0.000114424336,0.00023106702,0.000110634996,0.000030494504,0.00026795146,0.0852945],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99655664,0.00051993213,0.00049287075,0.000511298,0.001388397,0.00053087674],"domain_scores_gemma":[0.9988139,0.00020760731,0.00026127105,0.0002637301,0.0000014620379,0.00045197262],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000047502715,0.00063532783,0.00053440296,0.00011658424,0.00032430506,0.0000629042,0.00030224634,0.00020681207,0.00024585842],"category_scores_gemma":[0.000022045346,0.0006496605,0.00007982582,0.00023065771,0.00029381804,0.00042020797,0.00011414909,0.0005452577,3.6473526e-8],"study_design_candidate":"not_applicable","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":[0.0008215398,0.00041668944,0.062320556,0.0105582,0.0022045777,0.0014815843,0.0052563963,0.06308842,0.10487682,0.11254784,0.004252888,0.6321745],"study_design_scores_gemma":[0.0016288088,0.00034996157,0.30153257,0.0036469747,0.0001390867,0.00031614,0.0013611901,0.01930133,0.034308743,0.0006373626,0.63507855,0.0016992815],"about_ca_topic_score_codex":0.07176418,"about_ca_topic_score_gemma":0.070722654,"teacher_disagreement_score":0.6868397,"about_ca_system_score_codex":0.000082080864,"about_ca_system_score_gemma":0.0011156843,"threshold_uncertainty_score":0.99959546},"labels":[],"label_agreement":null},{"id":"W7011394283","doi":"","title":"Lower-Extremity Soft-Tissue Sarcoma Functional Outcome and Quality of Life after Treatment","year":2022,"lang":"en","type":"other","venue":"Työväentutkimus Vuosikirja","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Quality of life (healthcare); Outcome (game theory); Population; Sarcoma; Soft tissue sarcoma; Functional impairment; MEDLINE","score_opus":0.026670514911723012,"score_gpt":0.2442783765672432,"score_spread":0.21760786165552018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7011394283","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":[0.11818812,0.041584194,0.0021390151,0.00011064148,0.006928033,0.0014112958,0.0015566141,0.0019093215,0.82617277],"genre_scores_gemma":[0.641347,0.00058861304,0.0002164531,0.00009264722,0.00073314016,0.0002994984,0.00020191279,0.0007370282,0.35578373],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977114,0.00009584781,0.0007724703,0.00050253957,0.0004990431,0.00041869355],"domain_scores_gemma":[0.99879485,0.00008306523,0.00021341702,0.00066523557,0.000022968914,0.00022045839],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022490893,0.0005664316,0.0009602672,0.00028994167,0.000071746945,0.000028922523,0.00018274612,0.0003146324,0.015622218],"category_scores_gemma":[0.000028192837,0.0005221271,0.00021896824,0.00020484239,0.00010648975,0.000050056664,0.00010472405,0.00023874722,0.00008179456],"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":[0.0006663867,0.0022467354,0.34711924,0.0064483415,0.003115609,0.00019231281,0.0011416689,0.019963033,0.0013376462,0.004159466,0.56153136,0.052078225],"study_design_scores_gemma":[0.0009700242,0.00012679442,0.020245371,0.00005039897,0.00007439932,0.000006933793,0.00005242485,0.0002721139,0.000016250508,0.000011372019,0.9775475,0.00062637305],"about_ca_topic_score_codex":0.0015144346,"about_ca_topic_score_gemma":0.0007286271,"teacher_disagreement_score":0.52315885,"about_ca_system_score_codex":0.00025799096,"about_ca_system_score_gemma":0.00005520448,"threshold_uncertainty_score":0.999723},"labels":[],"label_agreement":null},{"id":"W7011690963","doi":"","title":"mtÂ¦1 melatonin receptor localization in the human and guinea pig retina","year":2001,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Liquation; Triacetin; Durvalumab; Fusible alloy","score_opus":0.0023711696326827814,"score_gpt":0.13065238789481085,"score_spread":0.12828121826212807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7011690963","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":[0.0031783872,0.0018253882,0.00012635767,0.0003366365,0.00019633002,0.00014660851,0.00006990395,0.000026144191,0.99409425],"genre_scores_gemma":[0.6206128,0.0018773222,0.00027576005,0.0005122598,0.00029761033,0.000025101168,0.000050803334,0.00028027134,0.3760681],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99868655,0.000044223973,0.00022313803,0.00016745928,0.00067579583,0.00020283465],"domain_scores_gemma":[0.9996252,0.000045300512,0.0000745337,0.00017464021,1.5239597e-7,0.00008018591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000013551685,0.00019669584,0.00022454876,0.000038060534,0.00006362637,0.000020105335,0.0001693623,0.00005720667,0.00012781203],"category_scores_gemma":[0.0000011294836,0.0001602756,0.000013437373,0.00009004842,0.000045793622,0.00006269579,0.000033203654,0.00012600273,3.4203531e-9],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000489517,0.00005967941,0.02765253,0.0020346073,0.00023210145,0.00024621177,0.00082334067,0.01182511,0.0048756767,0.16559993,0.7707462,0.015855683],"study_design_scores_gemma":[0.00018758906,0.000023093588,0.0015973055,0.0003569449,0.000009328903,0.0000059196964,0.00091987813,0.004769149,0.0009449922,0.00006852789,0.9908581,0.00025921644],"about_ca_topic_score_codex":0.014097481,"about_ca_topic_score_gemma":0.09801003,"teacher_disagreement_score":0.61802614,"about_ca_system_score_codex":0.000006344748,"about_ca_system_score_gemma":0.00012458602,"threshold_uncertainty_score":0.9924677},"labels":[],"label_agreement":null},{"id":"W7011785317","doi":"","title":"No-Vacation Nation, Revised","year":2019,"lang":"en","type":"report","venue":"Issue Lab (Candid)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":5,"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":"Nucleofection; Hyporeflexia; TSG101; Gestational period; Pretext; Articular cartilage damage; Liquation","score_opus":0.017214419755382293,"score_gpt":0.24158196320028347,"score_spread":0.22436754344490117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7011785317","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":[0.0011890442,0.009308987,0.00059839257,0.00007392611,0.011163408,0.00047954274,0.00006268032,0.0006303508,0.97649366],"genre_scores_gemma":[0.20475562,0.009727108,0.00029800605,0.00007870704,0.010475765,0.00018135327,0.0011326532,0.00043250644,0.7729183],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979787,0.000028921675,0.0005754654,0.00036142758,0.00071820203,0.00033728674],"domain_scores_gemma":[0.99858516,0.000038434722,0.00017415691,0.00063838693,0.00047478476,0.000089064146],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00045130478,0.00035695598,0.0005412145,0.00020675434,0.00006220365,0.000084467334,0.00027730362,0.00047997723,0.0007575244],"category_scores_gemma":[0.00014561853,0.00034542382,0.00012771023,0.00028052824,0.000016977936,0.00010992892,0.000028111022,0.0003079222,0.0027880236],"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":[0.000003859464,0.000023235276,0.00024233756,0.0021649976,0.00013334176,0.000012028179,0.00019983748,0.018280221,0.00040233636,0.00019154589,0.9674157,0.010930576],"study_design_scores_gemma":[0.00018551669,0.000023527915,0.00032777872,0.00045784962,0.00003684463,0.000014600465,0.000009615775,0.0052875453,0.00011876677,0.000009315244,0.9931233,0.00040532535],"about_ca_topic_score_codex":0.0011744592,"about_ca_topic_score_gemma":0.00011211488,"teacher_disagreement_score":0.20357539,"about_ca_system_score_codex":0.0004369466,"about_ca_system_score_gemma":0.0002529449,"threshold_uncertainty_score":0.9998998},"labels":[],"label_agreement":null},{"id":"W7025341281","doi":"","title":"UPDATE 1-Alberta Power Grid Operator has lifted earlier consumption warnings.","year":2024,"lang":"en","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Power consumption; Operator (biology); Consumption (sociology); Grid; Power (physics); Power grid","score_opus":0.00875969956452222,"score_gpt":0.19738029788655706,"score_spread":0.18862059832203484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025341281","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":[0.00075144216,0.0056348327,0.0003166427,0.0000746536,0.0061865393,0.00019620979,0.000042775573,0.0017272647,0.98506963],"genre_scores_gemma":[0.012584077,0.00032107582,0.00018668665,0.00007552607,0.00064946734,0.00003649813,0.00007764446,0.001034323,0.9850347],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99878013,0.0000169166,0.00028532965,0.0003812815,0.00019304903,0.00034330884],"domain_scores_gemma":[0.9994264,0.000009861299,0.000035968842,0.0003905153,0.000016278322,0.0001209758],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007951604,0.00040294087,0.0003670228,0.00025726794,0.000031458043,0.00020449547,0.00019338235,0.00043761256,0.025045432],"category_scores_gemma":[0.000010227521,0.0003287872,0.00010964424,0.00015489993,0.00004614535,0.000049756276,0.000042756343,0.00030213257,0.027262384],"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":[9.1042364e-7,0.000007792645,0.000019497715,0.00017948446,0.00013601335,0.000014315328,0.00015684307,0.0004771383,0.00009972987,0.00063151703,0.99811274,0.00016399904],"study_design_scores_gemma":[0.00011950256,0.00001281769,0.000015595188,0.0003321262,0.000029340212,0.00001077835,0.000012332714,0.0030235066,0.000061187944,0.0000029093221,0.9959393,0.0004406027],"about_ca_topic_score_codex":0.0009947245,"about_ca_topic_score_gemma":0.00063547184,"teacher_disagreement_score":0.011832635,"about_ca_system_score_codex":0.000049979593,"about_ca_system_score_gemma":0.000023418934,"threshold_uncertainty_score":0.99991643},"labels":[],"label_agreement":null},{"id":"W7026819889","doi":"","title":"Battery-electric buses hit the roads in Metro Vancouver","year":2019,"lang":"en","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Metropolitan area; Work (physics); Intersection (aeronautics); Government (linguistics); Public transport","score_opus":0.003998560055439452,"score_gpt":0.16870866815098154,"score_spread":0.1647101080955421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026819889","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":[0.0003948541,0.0025821065,0.0012089598,0.000014945632,0.0024873882,0.00018726486,0.0000025932275,0.00039072978,0.99273115],"genre_scores_gemma":[0.1007642,0.00015274805,0.000053279033,0.00006543027,0.00037440672,0.000029460323,0.0000021805631,0.00039962455,0.89815867],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99909717,0.000021962855,0.00019811477,0.00019447858,0.00017519966,0.0003130462],"domain_scores_gemma":[0.99946904,0.000039967068,0.000036001078,0.00042132637,0.000006441973,0.000027193519],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00010436379,0.00024836307,0.00030487098,0.00034493802,0.000012427707,0.000027663113,0.00029759415,0.00022328293,0.001707005],"category_scores_gemma":[0.0000070212172,0.00015542044,0.0000712048,0.000409339,0.000008748587,0.000025724898,0.000020110281,0.00020899563,0.0006611613],"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":[3.9364707e-7,0.0000074923405,0.0002450237,0.000058380258,0.000034753393,0.0000027082585,0.000022507053,0.01474825,0.000030958858,0.00014224667,0.98321605,0.0014912304],"study_design_scores_gemma":[0.00013197269,0.000010966547,0.0001620019,0.00006752993,0.00000852358,4.5603008e-7,0.000050092203,0.0074409186,0.00005260764,0.0000049838463,0.9918107,0.00025925465],"about_ca_topic_score_codex":0.0018423178,"about_ca_topic_score_gemma":0.016486008,"teacher_disagreement_score":0.10036935,"about_ca_system_score_codex":0.0000753129,"about_ca_system_score_gemma":0.000018186522,"threshold_uncertainty_score":0.9992056},"labels":[],"label_agreement":null},{"id":"W7028414930","doi":"","title":"An electric passenger light rail for Okanagan Valley, Canada","year":2024,"lang":"en","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Light rail; Rail transportation; Passenger transport; Light rail transit","score_opus":0.0037749091621407647,"score_gpt":0.18533795370421066,"score_spread":0.1815630445420699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028414930","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":[0.00002013172,0.005437139,0.0021392042,0.00007332193,0.0027378832,0.00024223838,0.000026599213,0.0010814808,0.98824203],"genre_scores_gemma":[0.012140797,0.000093710034,0.000283917,0.00004836946,0.0012117323,0.000084433486,0.000029325218,0.0010730962,0.98503464],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989235,0.000007340701,0.00019902067,0.0003067101,0.00017633865,0.00038711773],"domain_scores_gemma":[0.99946636,0.000010443163,0.000023291834,0.00036185537,0.000014517818,0.00012351213],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006253932,0.0003083274,0.00030502505,0.0002086572,0.000019503446,0.000055987224,0.00022077851,0.0002387768,0.00091981876],"category_scores_gemma":[0.0000028275017,0.00025685562,0.000077801174,0.00021758905,0.0000032822707,0.00002012202,0.0000077970535,0.00013497735,0.00010766958],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.1107717e-7,0.000005402084,3.9510894e-7,0.00037831717,0.00012042066,0.000011472,0.0000144727655,0.001140879,0.0002796981,0.0012495253,0.9956245,0.0011745474],"study_design_scores_gemma":[0.000069949914,0.000022850203,0.0000014308821,0.00007469351,0.000031965687,0.000003872506,0.000016637048,0.0142267365,0.00017310935,0.000018718838,0.9849913,0.0003687464],"about_ca_topic_score_codex":0.27892083,"about_ca_topic_score_gemma":0.7781585,"teacher_disagreement_score":0.4992377,"about_ca_system_score_codex":0.00013979847,"about_ca_system_score_gemma":0.00013994794,"threshold_uncertainty_score":0.9999935},"labels":[],"label_agreement":null},{"id":"W7028826168","doi":"","title":"Montreal18","year":2018,"lang":"en","type":"article","venue":"Data Archiving and Networked Services (DANS)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Process (computing); Identification (biology); Product (mathematics)","score_opus":0.008560012381800507,"score_gpt":0.19937696614372266,"score_spread":0.19081695376192215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028826168","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":[0.9487954,0.0011128251,0.007204397,0.000023678114,0.00072092126,0.000080635444,0.000107047665,0.00047609714,0.04147902],"genre_scores_gemma":[0.9976375,0.0002728864,0.00066632265,0.00005167004,0.00091535674,0.0000046514406,0.00022883934,0.000033468692,0.00018926966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990079,0.00002991138,0.0001935677,0.00030489604,0.000113400485,0.00035035593],"domain_scores_gemma":[0.9990306,0.000053249416,0.000026261634,0.00076098356,0.000011686683,0.000117236275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020726096,0.00017101939,0.00016745686,0.000048305916,0.00018860423,0.00008284031,0.000661962,0.000044953307,0.000014433882],"category_scores_gemma":[0.0000021726416,0.00014482554,0.000017808707,0.00015271088,0.0000661388,0.00021756378,0.00024437005,0.000097612996,0.00003940215],"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":[0.00014706881,0.00020272368,0.056152176,0.002422999,0.00077247614,0.0001126212,0.04150527,0.19659826,0.016800327,0.0028694922,0.044179264,0.6382373],"study_design_scores_gemma":[0.00013338306,0.000029050787,0.01658887,0.0001414514,0.00001547318,0.000016420958,0.00026305256,0.934498,0.000049474056,0.00009363214,0.047963895,0.00020726935],"about_ca_topic_score_codex":0.00088312227,"about_ca_topic_score_gemma":0.0031336795,"teacher_disagreement_score":0.7378998,"about_ca_system_score_codex":0.000008195179,"about_ca_system_score_gemma":0.0000049604723,"threshold_uncertainty_score":0.59058136},"labels":[],"label_agreement":null},{"id":"W7029120387","doi":"","title":"Index, Canadian County, 11 of 15","year":2018,"lang":"en","type":"other","venue":"SHAREOK (University of Oklahoma)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"","score_opus":0.005535891941693237,"score_gpt":0.14809668204924106,"score_spread":0.14256079010754782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7029120387","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":[0.003468882,0.0004269979,0.00024454508,0.00001461507,0.0005483302,0.000111768306,0.0006994809,0.00014944043,0.99433595],"genre_scores_gemma":[0.33227146,0.00012447077,0.0003035126,0.000007547689,0.0002279514,3.004631e-7,0.00013978964,0.00027449516,0.6666505],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992609,0.000009853071,0.00010524697,0.0001785319,0.00019713395,0.00024836013],"domain_scores_gemma":[0.99931675,0.0000070377096,0.00010086064,0.00036305943,0.00005540002,0.00015691428],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000059013433,0.0001809063,0.00036142766,0.0004922661,0.00004534375,0.000005975669,0.00044515773,0.00032605958,0.010903669],"category_scores_gemma":[0.000004194536,0.00023702778,0.00010430707,0.00022035267,0.00012515161,0.000046834655,0.00004138683,0.00008033374,0.00018007007],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018255465,0.000010037485,0.0010288002,0.00033281368,0.00009459191,0.000026008282,0.00021040173,0.0002961351,0.000025082976,0.00036217677,0.99703765,0.0005745031],"study_design_scores_gemma":[0.00021411543,0.000023353736,0.0019889667,0.0003565467,0.000027375827,0.000003092607,0.00033320443,0.0014157661,0.0000109091425,0.000008308225,0.995373,0.00024532495],"about_ca_topic_score_codex":0.3209676,"about_ca_topic_score_gemma":0.6706604,"teacher_disagreement_score":0.34969273,"about_ca_system_score_codex":0.00011226905,"about_ca_system_score_gemma":0.00014763902,"threshold_uncertainty_score":0.9900005},"labels":[],"label_agreement":null},{"id":"W7029561815","doi":"","title":"Laboratory Electrical Model of the Louis-Hippolyte&#13;\\nLafontaine Tunnel","year":2019,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Electrical network; Electric power system; Emulation; Electric power; Generator (circuit theory); Electrical load; Electrical equipment; Work (physics)","score_opus":0.013743742533103917,"score_gpt":0.22578358977978943,"score_spread":0.2120398472466855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7029561815","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":[0.8118203,0.000610845,0.00007645464,0.000025412217,0.0015141687,0.00042929582,0.000029572699,0.00014232869,0.18535158],"genre_scores_gemma":[0.9182891,0.00015631135,0.0000062249032,0.0000029023615,0.00020740554,0.0000042226593,0.000027875178,0.000091893126,0.08121402],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968442,0.0002980431,0.00041519306,0.00054338196,0.0010548596,0.0008443417],"domain_scores_gemma":[0.99806917,0.00014468521,0.00016001375,0.0010363513,0.0003694391,0.00022033427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042807643,0.00038651188,0.00057038735,0.00091757404,0.00036827204,0.00007968356,0.0013038672,0.0005620037,0.000020761503],"category_scores_gemma":[0.000056896155,0.00035567948,0.00032747618,0.001926803,0.0001669153,0.0001899135,0.00011343275,0.0015154269,0.000032245112],"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":[0.0010411586,0.00053277786,0.025413549,0.003318652,0.001381844,0.0005331682,0.0037840344,0.3712648,0.54982996,0.026167495,0.015524194,0.0012083724],"study_design_scores_gemma":[0.003325931,0.0011675378,0.071677476,0.0013913892,0.0004502437,0.000040682247,0.0071826996,0.5428523,0.32906717,0.00067916996,0.038784545,0.0033808874],"about_ca_topic_score_codex":0.004975139,"about_ca_topic_score_gemma":0.005280783,"teacher_disagreement_score":0.2207628,"about_ca_system_score_codex":0.00086493784,"about_ca_system_score_gemma":0.0013306508,"threshold_uncertainty_score":0.9998895},"labels":[],"label_agreement":null},{"id":"W7043408970","doi":"","title":"State OKs Canada-to-Ludlow power line: Times Argus Online","year":2016,"lang":"en","type":"other","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"State (computer science); Power (physics); Argus; Term (time); Feature (linguistics)","score_opus":0.003497910628944896,"score_gpt":0.18735926876955458,"score_spread":0.1838613581406097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7043408970","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":[0.0001672816,0.0008090598,0.002707007,0.00011048774,0.0023354108,0.00015687165,0.00039631964,0.0007126927,0.99260485],"genre_scores_gemma":[0.004412609,0.00008831592,0.00045557044,0.00015545916,0.00049746496,0.000011302904,0.000033453554,0.0006024136,0.9937434],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987653,0.000009253791,0.00026112,0.00026577586,0.00025938087,0.0004391933],"domain_scores_gemma":[0.9992829,0.000017540113,0.000039601644,0.00042266055,0.000023820983,0.0002135075],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004349978,0.00035893335,0.00036780004,0.00017570412,0.000018337229,0.000021507356,0.00025698825,0.00016752964,0.011298439],"category_scores_gemma":[0.000008371034,0.00025446012,0.00005623509,0.00009786014,0.000012126548,0.000016210372,0.00003091865,0.00012298957,0.0007012806],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.645638e-7,0.000008142326,0.000004447482,0.00004034288,0.0000630533,0.000020774947,0.000011674957,0.0047894022,0.00006544478,0.000095788164,0.99271685,0.0021831081],"study_design_scores_gemma":[0.00011940597,0.000020259984,0.000020992795,0.0001745978,0.0000054838097,0.000003270754,0.000008578356,0.00063147343,0.00007373077,0.000004044907,0.9985241,0.00041405277],"about_ca_topic_score_codex":0.3005535,"about_ca_topic_score_gemma":0.7664654,"teacher_disagreement_score":0.46591192,"about_ca_system_score_codex":0.00013589568,"about_ca_system_score_gemma":0.00016152143,"threshold_uncertainty_score":0.99999076},"labels":[],"label_agreement":null},{"id":"W7066452807","doi":"","title":"Hospitalizations in immigrants and non-immigrants with chronic hepatitis C infection in Québec","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Chronic hepatitis; Immigration; Hospital admission; Hepatitis C virus; Hepatitis C","score_opus":0.0037874342810774856,"score_gpt":0.18731494769683069,"score_spread":0.1835275134157532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066452807","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":[0.9820421,0.0010415618,0.0000024324422,0.000004175687,0.0008050994,0.00055444636,0.000186159,0.00021600511,0.015148063],"genre_scores_gemma":[0.9950996,0.0026519618,0.000026446789,0.000011112876,0.00004206776,0.0003103181,0.00020643919,0.00020151689,0.0014505488],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9974309,0.00008210828,0.00077664014,0.0006871589,0.00037671078,0.0006464594],"domain_scores_gemma":[0.9990608,0.0000702812,0.0001828154,0.0004146787,0.00010774553,0.00016366116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002881176,0.00064288185,0.00067140587,0.00082410156,0.00029820955,0.00006166534,0.00021579185,0.0005837586,0.000046789457],"category_scores_gemma":[0.000080564285,0.0005885082,0.00009914905,0.0008266192,0.0000413318,0.0006886242,0.00002632109,0.00064389297,0.000060291364],"study_design_candidate":"observational","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":[0.0003486909,0.0008448923,0.39055848,0.006747303,0.0007943158,0.00063500635,0.00056123076,0.052867565,0.06805542,0.012327713,0.000030315225,0.46622905],"study_design_scores_gemma":[0.00305738,0.0004062091,0.9669589,0.005967739,0.000093811905,0.000041461786,0.00010179744,0.0020965408,0.011834722,0.00054850354,0.0066978233,0.0021951073],"about_ca_topic_score_codex":0.2530249,"about_ca_topic_score_gemma":0.9057735,"teacher_disagreement_score":0.65274864,"about_ca_system_score_codex":0.0013200454,"about_ca_system_score_gemma":0.000092833216,"threshold_uncertainty_score":0.9996566},"labels":[],"label_agreement":null},{"id":"W7072071208","doi":"","title":"The World Latest Progress of Heavy Railway Transportation Technology","year":2010,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Railway line; Heavy industry; Heavy load; Rail transportation; Heavy equipment; Technology development; China; Point (geometry)","score_opus":0.06787135773940142,"score_gpt":0.44994606519495406,"score_spread":0.3820747074555526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7072071208","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":[0.98177195,0.011161611,0.00015296155,0.00023898845,0.0014583927,0.00032851464,0.000020058944,0.00012641003,0.0047411253],"genre_scores_gemma":[0.99784416,0.0013680722,0.00019447845,0.000009841974,0.00011109713,0.000059662478,0.0000053621884,0.000051593328,0.00035570702],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981462,0.000039469902,0.0008562298,0.00021810143,0.00038428555,0.00035573],"domain_scores_gemma":[0.99864584,0.00015736806,0.0003856437,0.00048384414,0.00021371542,0.00011358112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078175345,0.00023573154,0.0004922433,0.0006716543,0.00020455381,0.0003344022,0.0019006981,0.00014397904,0.00075613154],"category_scores_gemma":[0.00005998078,0.00017515688,0.00012529844,0.0014704971,0.00026327462,0.0007038318,0.000069155845,0.00051274674,0.0000063485063],"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":[0.000099198245,0.00024267739,0.50199,0.00028721176,0.00030379818,0.000029634044,0.00033320647,0.013531901,0.38580677,0.0044017476,0.017014263,0.07595953],"study_design_scores_gemma":[0.0006106521,0.000018868339,0.61996377,0.00048354783,0.00007074816,0.000017638733,0.00012501559,0.0020578243,0.2516765,0.0025748778,0.12180214,0.00059844035],"about_ca_topic_score_codex":0.000195377,"about_ca_topic_score_gemma":0.0013975461,"teacher_disagreement_score":0.13413028,"about_ca_system_score_codex":0.000024616025,"about_ca_system_score_gemma":0.000048206082,"threshold_uncertainty_score":0.827911},"labels":[],"label_agreement":null},{"id":"W7093083738","doi":"10.1080/03155986.2025.2571318","title":"An analytical approach to planning for and managing random disruption in rail intermodal networks","year":2025,"lang":"en","type":"article","venue":"INFOR Information Systems and Operational Research","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"McMaster University","funders":"","keywords":"Component (thermodynamics); Work (physics); Key (lock); Production (economics); Context (archaeology)","score_opus":0.035132281507093764,"score_gpt":0.33971794009051276,"score_spread":0.304585658583419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7093083738","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":[0.18844208,0.000327395,0.7824283,0.00009030441,0.00026563206,0.0011382863,0.0000138047335,0.000057804,0.027236354],"genre_scores_gemma":[0.998893,0.000019095774,0.00036820988,0.000046497782,0.000076637836,0.0003594843,0.00008419794,0.000005687929,0.00014717509],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988869,0.000039429924,0.0004651739,0.00011038293,0.00024512695,0.0002530267],"domain_scores_gemma":[0.99947524,0.00013836341,0.00001652268,0.0001016076,0.00017336522,0.00009490816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016931788,0.00009712209,0.00016828816,0.00061249675,0.00019877737,0.0006206583,0.000093321156,0.00008776854,9.2427774e-7],"category_scores_gemma":[0.000079610276,0.00008557158,0.000016676604,0.00035625475,0.000031376665,0.001059319,0.000033694923,0.00015104987,0.0000019099762],"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":[0.00006736892,0.000006528082,0.0007786314,0.00022407882,0.000011566528,1.6852431e-7,0.0013274908,0.91165704,0.000014321105,0.0786695,0.00052908587,0.0067142094],"study_design_scores_gemma":[0.0007645647,0.000038151607,0.0035650553,0.0001338034,0.0000015904222,0.0000044082326,0.0018614085,0.98253655,0.000003001502,0.00003052208,0.010970816,0.00009014597],"about_ca_topic_score_codex":0.00012495874,"about_ca_topic_score_gemma":0.000009155783,"teacher_disagreement_score":0.8104509,"about_ca_system_score_codex":0.000081924816,"about_ca_system_score_gemma":0.00003531502,"threshold_uncertainty_score":0.5985022},"labels":[],"label_agreement":null},{"id":"W7096364010","doi":"","title":"ABSTRACT A DISCRETE EVENT SIMULATION FOR THE CREW ASSIGNMENT PROCESS IN NORTH AMERICAN FREIGHT RAILROADS","year":2008,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Crew; Schedule; Crew scheduling; Process (computing); Discrete event simulation; Event (particle physics)","score_opus":0.01543961659974583,"score_gpt":0.24881526324215986,"score_spread":0.23337564664241403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096364010","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":[0.9187745,0.0001161387,0.07799488,0.000049731767,0.00012139762,0.00037316227,0.0000036321003,0.00009815399,0.0024684116],"genre_scores_gemma":[0.99943185,0.000027738492,0.00010557726,0.00001920246,0.00008108906,0.00015490802,0.000006127124,0.00002016764,0.00015337086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923915,0.000006113409,0.00025026954,0.00012961627,0.00015803512,0.00021681008],"domain_scores_gemma":[0.9996453,0.000105528125,0.00003863462,0.00015017214,0.000019648573,0.000040738127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009328035,0.00011680413,0.00013318956,0.000043291275,0.00008184618,0.000014429108,0.000113933835,0.000020534713,0.000025641528],"category_scores_gemma":[0.000014077671,0.00007353398,0.000053293643,0.00023266498,0.000029502637,0.00008077779,0.000006392225,0.00006466827,0.0000066973694],"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":[0.0000072848284,0.000015819876,0.004659377,0.000022714845,0.000011328836,0.0000015375613,0.00050860783,0.9911543,0.000034501383,0.00002179005,0.000073096526,0.0034896045],"study_design_scores_gemma":[0.0002775997,0.000029175888,0.26788792,0.000008627659,0.000003471694,0.0000011753303,0.00015033815,0.72936106,0.00013380453,0.0000050627336,0.0020216675,0.00012010621],"about_ca_topic_score_codex":0.0002063938,"about_ca_topic_score_gemma":0.0007796579,"teacher_disagreement_score":0.26322854,"about_ca_system_score_codex":0.00005915047,"about_ca_system_score_gemma":0.000015310809,"threshold_uncertainty_score":0.29986286},"labels":[],"label_agreement":null},{"id":"W7097296848","doi":"","title":"Even before the IEEE’s Rail Transit Vehicle Interface","year":2012,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transit (satellite); Rail transit; Public transport; Protocol (science); Interface (matter); De facto","score_opus":0.0081360661497199,"score_gpt":0.19990784007365534,"score_spread":0.19177177392393543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097296848","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":[0.8952207,0.0009367748,0.034852374,0.00029436714,0.0014927007,0.000096213815,0.0000018047087,0.00037119005,0.06673385],"genre_scores_gemma":[0.9960972,0.000004586914,0.000062975996,0.000037592465,0.00025486693,0.000009444097,4.6487014e-7,0.000020009235,0.003512836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994508,0.00001159223,0.00011933526,0.000059327893,0.000091926944,0.00026700934],"domain_scores_gemma":[0.99972904,0.000011420269,0.0000073131437,0.00018210305,0.00000879881,0.00006130056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014447121,0.000090245165,0.00008204042,0.000016951068,0.000051496787,0.000017894972,0.00014037183,0.00004406888,0.00016124571],"category_scores_gemma":[0.0000024343212,0.00005407178,0.000045365276,0.000090836766,0.000016958842,0.00012377086,0.0000067222436,0.000083404666,0.00023249252],"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":[0.000012177334,0.00016762475,0.005623424,0.00020520626,0.00025181487,0.0000035382118,0.038131468,0.7197081,0.06915341,0.019576807,0.07568859,0.071477845],"study_design_scores_gemma":[0.00078221643,0.00011800359,0.02109584,0.00007735951,0.000047868507,0.000055733748,0.0029194842,0.2815,0.07403812,0.0001777704,0.6184345,0.0007531258],"about_ca_topic_score_codex":0.00007718341,"about_ca_topic_score_gemma":0.00009517843,"teacher_disagreement_score":0.5427459,"about_ca_system_score_codex":0.00002182344,"about_ca_system_score_gemma":0.0000026502087,"threshold_uncertainty_score":0.29882985},"labels":[],"label_agreement":null},{"id":"W7097427820","doi":"","title":"SWITCHDOG – AN INTELLIGENT HEALTH MONITOR FOR POWER SWITCHES","year":2015,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Reliability (semiconductor); Signature (topology); Upload; Power (physics); Point (geometry); SIGNAL (programming language); Line (geometry); Interface (matter); Software deployment; Consistency (knowledge bases)","score_opus":0.04383569062359385,"score_gpt":0.27602928248099246,"score_spread":0.2321935918573986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097427820","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":[0.61121863,0.0023274373,0.3445841,0.0005102546,0.004709635,0.0006449493,0.000012110372,0.0014676629,0.034525182],"genre_scores_gemma":[0.9960403,0.000012503536,0.0025432766,0.0000808119,0.00019958222,0.000036515015,0.000005698967,0.00003394101,0.0010473736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923575,0.000007279257,0.0002197151,0.00013751775,0.000117467054,0.00028229912],"domain_scores_gemma":[0.9994332,0.000012806858,0.000018859371,0.00021061266,0.00004926589,0.00027523623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027196095,0.00011795694,0.00015752677,0.000051235736,0.00003747083,0.000034477092,0.00012034035,0.000048847407,0.000021378068],"category_scores_gemma":[0.000012832713,0.00009598711,0.00004143367,0.00008113071,0.00000783426,0.000105835854,0.00000952903,0.00004449492,0.0000480157],"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":[0.00006585042,0.00045469558,0.0016017862,0.0004074832,0.00016722585,0.0000049088994,0.014785785,0.5979791,0.005129443,0.034915473,0.20095135,0.14353685],"study_design_scores_gemma":[0.0010701874,0.0015739778,0.00077663816,0.00007666158,0.0000103887405,0.000021432876,0.009135235,0.2912484,0.018983928,0.0014215187,0.6746578,0.0010238015],"about_ca_topic_score_codex":0.0002529525,"about_ca_topic_score_gemma":0.000088942135,"teacher_disagreement_score":0.47370645,"about_ca_system_score_codex":0.000090099275,"about_ca_system_score_gemma":0.000036635833,"threshold_uncertainty_score":0.39142406},"labels":[],"label_agreement":null},{"id":"W7099558655","doi":"","title":"Bank of canada participation in the 2007 FSAP macro stress testing excercise","year":2007,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Macro; Stress testing (software); Stress (linguistics); Stress test; Component (thermodynamics); Financial sector","score_opus":0.012728039999042703,"score_gpt":0.22219493055229747,"score_spread":0.20946689055325476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099558655","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":[0.9234103,0.00009271162,0.002296639,0.000013583558,0.00021295862,0.000058860118,0.0000019028292,0.000028257959,0.0738848],"genre_scores_gemma":[0.9995725,0.0000013687539,0.00019920609,0.00001944869,0.00005118154,0.000004695012,0.0000011849244,0.000007183762,0.00014319153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99931693,0.000010060171,0.00023496037,0.000059429778,0.00015481171,0.00022380911],"domain_scores_gemma":[0.9996692,0.00013349892,0.00002041023,0.00012238428,0.000024864763,0.000029657078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038401582,0.00006137275,0.00007854104,0.000033947403,0.00002387438,0.000007374976,0.00009421613,0.000025389436,0.00003860855],"category_scores_gemma":[0.00004993232,0.00004361894,0.000010311073,0.00023473724,0.000008044693,0.000034310284,0.000005603939,0.000052710275,0.0000011114557],"study_design_candidate":"simulation_or_modeling","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":[0.000003731744,0.000039332608,0.04018615,0.00010415458,0.0000069728408,0.000027644734,0.0011041563,0.9378538,0.0019560605,0.00074385596,0.0033350594,0.01463908],"study_design_scores_gemma":[0.00036227074,0.000037241214,0.72512555,0.00013917843,0.000009207719,0.0000067097844,0.002274539,0.24995558,0.018426957,0.000042484815,0.0033017932,0.00031846756],"about_ca_topic_score_codex":0.2667422,"about_ca_topic_score_gemma":0.64496094,"teacher_disagreement_score":0.6878982,"about_ca_system_score_codex":0.000049381502,"about_ca_system_score_gemma":0.000031947908,"threshold_uncertainty_score":0.73814064},"labels":[],"label_agreement":null},{"id":"W7104024471","doi":"10.1109/tvt.2025.3629100","title":"Handoff Decision Optimization in Train Autonomous Control Systems Using a Rolling Prediction-Decision Framework","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Handover; Channel (broadcasting); Reliability (semiconductor); Control (management); Path (computing); Scheme (mathematics); Decision model; State (computer science)","score_opus":0.006633378201355161,"score_gpt":0.22604392116627875,"score_spread":0.2194105429649236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104024471","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":[0.12786831,0.0043583876,0.8577046,0.00013922564,0.007638545,0.0012901584,0.00007879565,0.0008037171,0.00011825367],"genre_scores_gemma":[0.9757693,0.0006872306,0.022903362,0.000032366228,0.00009788001,0.00030574895,0.000003892725,0.00011664245,0.0000836048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99515164,0.00018166294,0.0019986068,0.0011468923,0.0005270133,0.000994212],"domain_scores_gemma":[0.9976217,0.00061495195,0.00023263945,0.0011590876,0.00023127308,0.00014030428],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008617075,0.00076952734,0.0012180613,0.0042533143,0.0006659914,0.00022460887,0.00055189594,0.002655664,0.00005552732],"category_scores_gemma":[0.000114329116,0.00085067766,0.00036627913,0.0044342256,0.00019364503,0.000312416,0.0000057747043,0.0018566152,0.000025790634],"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":[0.00018967739,0.00035349425,0.00005587641,0.00014323843,0.0002423905,0.000057017925,0.00014749671,0.9560862,0.0023835327,0.0009293408,0.00000891789,0.039402775],"study_design_scores_gemma":[0.0033357225,0.0002357554,0.00002139755,0.004650402,0.00024387446,0.0000794474,0.0004380964,0.9872587,0.002001036,0.0006514686,0.000535159,0.0005489519],"about_ca_topic_score_codex":0.00026766048,"about_ca_topic_score_gemma":0.000073133386,"teacher_disagreement_score":0.847901,"about_ca_system_score_codex":0.0013201518,"about_ca_system_score_gemma":0.000265471,"threshold_uncertainty_score":0.9993944},"labels":[],"label_agreement":null},{"id":"W7104179839","doi":"10.2139/ssrn.5711826","title":"A Mathematical Analysis Combined with Machine-Learning Optimal Control Method for Bidirectional Converter Devices of Urban Rail Transit System","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Nexen (Canada)","funders":"","keywords":"Voltage droop; Optimal control; Power (physics); Urban rail transit; Control theory (sociology); Energy (signal processing); Energy conservation; Work (physics); Reduction (mathematics)","score_opus":0.00493043594573802,"score_gpt":0.22122661877789776,"score_spread":0.21629618283215973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104179839","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":[0.009967962,0.0061492366,0.98099124,0.00013864752,0.00054046966,0.000992876,0.0001323377,0.00015519546,0.0009320349],"genre_scores_gemma":[0.98503035,0.0004889181,0.01161114,0.000012258667,0.00034486665,0.00020337795,0.000057746784,0.0001159312,0.002135406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9918174,0.00076451345,0.0023849648,0.0008970791,0.00093166454,0.0032043864],"domain_scores_gemma":[0.99635863,0.00089522346,0.0012068496,0.0005226118,0.0007374752,0.00027919523],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006422259,0.001081312,0.0032213514,0.0013612701,0.00052821374,0.00021809268,0.00085336587,0.0006706894,0.00008502954],"category_scores_gemma":[0.00007366275,0.00089945947,0.0019491834,0.0012445641,0.000102886486,0.00018018125,0.00006351783,0.0046581803,0.000004514113],"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":[0.0016309733,0.0001962549,0.00074390846,0.0027246575,0.03172095,0.00000651724,0.0009191424,0.86148214,0.0002261113,0.09857272,0.0000074175455,0.0017692065],"study_design_scores_gemma":[0.0050067375,0.001571647,0.00006295771,0.0012772419,0.0107473,0.00044890124,0.0029313604,0.9755674,0.00014695806,0.0009836918,0.00042700762,0.0008287992],"about_ca_topic_score_codex":0.00043529517,"about_ca_topic_score_gemma":0.00082937774,"teacher_disagreement_score":0.9750624,"about_ca_system_score_codex":0.0019719847,"about_ca_system_score_gemma":0.0032131963,"threshold_uncertainty_score":0.9993456},"labels":[],"label_agreement":null},{"id":"W7104471743","doi":"10.71781/9704","title":"Machine learning accelerated stochastic optimization and applications to railway operations","year":2025,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Montréal; Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Context (archaeology); Scheduling (production processes); Rail transportation; Single-machine scheduling","score_opus":0.02022518244406539,"score_gpt":0.27590065442802786,"score_spread":0.25567547198396245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104471743","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":[0.02596646,0.0021145367,0.76910883,0.000059170427,0.0010450352,0.003912824,0.00018121651,0.00008761351,0.19752432],"genre_scores_gemma":[0.71214336,0.00031094652,0.034740437,0.00003431825,0.00035733686,0.0025742617,0.0137934955,0.00021135717,0.23583451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990667,0.000022047952,0.0003099736,0.00032859735,0.00010263082,0.00017001107],"domain_scores_gemma":[0.9995526,0.000027118416,0.00003342181,0.00019496451,0.00010113806,0.00009076332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000111297646,0.00022528153,0.00026169443,0.0002156409,0.00026311158,0.00043845872,0.00026953974,0.00017158898,0.0005127825],"category_scores_gemma":[0.000027573347,0.00023902925,0.000024851504,0.0004465434,0.0000055195414,0.00014457194,0.000042144122,0.00021526137,0.00007959899],"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":[0.000004405768,0.000011089599,0.0000013992994,0.00003228653,0.000027174408,3.7665043e-7,0.0004214388,0.93475896,0.00043612465,0.000053328335,0.0000604643,0.064192966],"study_design_scores_gemma":[0.00022457253,0.0000267941,0.000025439753,0.00018899748,0.000055532295,0.0000018631296,0.00041213763,0.9603546,0.0005082749,0.0000024860499,0.037850663,0.00034861398],"about_ca_topic_score_codex":0.00015968728,"about_ca_topic_score_gemma":0.0011324568,"teacher_disagreement_score":0.7343684,"about_ca_system_score_codex":0.0000510551,"about_ca_system_score_gemma":0.000077362914,"threshold_uncertainty_score":0.974733},"labels":[],"label_agreement":null},{"id":"W7114793754","doi":"10.1155/atr/8828434","title":"Prediction of Traction Power Consumption for Rail Transit Based on Ensemble Learning Hybrid Time Series Models","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Traction (geology); Random forest; Energy consumption; Electricity; Ensemble learning; Sliding window protocol; Traction motor; Mean absolute percentage error; Mean absolute error; Energy (signal processing)","score_opus":0.008635384076985917,"score_gpt":0.2085522766478459,"score_spread":0.19991689257085998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7114793754","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":[0.51076764,0.00010675471,0.48828378,0.000026277497,0.00041808977,0.00011058816,0.000018945528,0.000032135442,0.00023578828],"genre_scores_gemma":[0.9971815,0.00007204711,0.0025511715,0.00000651534,0.000029047358,0.000009386951,0.000035641653,0.000016334625,0.000098376666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990947,0.00001641242,0.0005311997,0.00008426467,0.00017066857,0.00010272531],"domain_scores_gemma":[0.9994977,0.000059259626,0.00018267627,0.000058542977,0.0001733128,0.000028524024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019139468,0.000105602856,0.00021683052,0.00021655804,0.000050396076,0.000008423452,0.000040502655,0.00005568655,0.000009806391],"category_scores_gemma":[0.000010840688,0.00010496867,0.00012799304,0.000108723994,0.000013584007,0.0004169103,1.5382165e-7,0.00013010102,5.2062984e-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":[0.00037552018,0.000034603483,0.00006704124,0.00016613428,0.000028554769,0.0000014518829,0.0002746362,0.8734207,0.12284505,0.00025666898,0.000029863364,0.002499735],"study_design_scores_gemma":[0.0042491085,0.0013444892,0.02243753,0.0010881887,0.00020694942,0.000011764842,0.000420473,0.8334901,0.13260455,0.0012239029,0.0026711859,0.00025177273],"about_ca_topic_score_codex":0.0000013210014,"about_ca_topic_score_gemma":0.0000037522582,"teacher_disagreement_score":0.48641387,"about_ca_system_score_codex":0.00005589643,"about_ca_system_score_gemma":0.000029310639,"threshold_uncertainty_score":0.42804983},"labels":[],"label_agreement":null},{"id":"W7117543819","doi":"10.1109/icecst66106.2025.11307515","title":"Hyper Rail: Partial Vacuum-Powered Railway with Dynamic Demand Response Solar Energy Management","year":2025,"lang":"","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Photovoltaic system; Energy management; Fuel efficiency; Propulsion; Energy consumption; Electricity","score_opus":0.0032593726726892753,"score_gpt":0.19670324458690666,"score_spread":0.19344387191421739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117543819","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":[0.19396931,0.007418138,0.6615576,0.0011174239,0.004289375,0.0008381021,0.00002417557,0.0009011308,0.12988475],"genre_scores_gemma":[0.88383776,0.0008510192,0.0015415661,0.0002449637,0.00009589806,0.00015183823,0.000013525039,0.00013022484,0.11313321],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9951891,0.00037576078,0.0011702128,0.0011795661,0.0006436296,0.0014417343],"domain_scores_gemma":[0.997777,0.00020956536,0.00013508144,0.0014223215,0.00012105857,0.00033500258],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010847385,0.0009647835,0.00092015375,0.0007466127,0.0004624503,0.0003490995,0.00070169085,0.0004180582,0.0008457546],"category_scores_gemma":[0.00002429921,0.0008274239,0.000294347,0.001346433,0.00017174134,0.0003176792,0.00027220565,0.00034402043,0.00015574382],"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":[0.009273909,0.0011612095,0.0006009599,0.0018879609,0.0052492986,0.00086267095,0.0015993911,0.789571,0.019368298,0.088816985,0.015888596,0.06571975],"study_design_scores_gemma":[0.005474017,0.0005547062,0.0038562776,0.0010254269,0.0005473444,0.000043866152,0.00096373056,0.45269495,0.005286137,0.00019357848,0.52757895,0.001781009],"about_ca_topic_score_codex":0.00025443232,"about_ca_topic_score_gemma":0.00025854062,"teacher_disagreement_score":0.68986845,"about_ca_system_score_codex":0.00042138013,"about_ca_system_score_gemma":0.00017672473,"threshold_uncertainty_score":0.99941766},"labels":[],"label_agreement":null},{"id":"W7117674184","doi":"10.1155/atr/6893165","title":"Optimization Train Stop Planning for High‐Speed Railway Considering Flexible Ticket Pricing and Elastic Demand","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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 Natural Science Foundation of China","keywords":"Ticket; Train; Revenue; Revenue management; Correctness; Range (aeronautics); Plan (archaeology); Simulated annealing; Optimization problem","score_opus":0.008100002758706742,"score_gpt":0.2337611587782018,"score_spread":0.22566115601949507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117674184","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":[0.42901328,0.0005838481,0.5697831,0.000026550984,0.00039424715,0.00009174334,0.0000029370083,0.000032247288,0.00007204897],"genre_scores_gemma":[0.9634858,0.000113401875,0.036254935,0.000014234006,0.000055395518,0.0000041698145,0.000009907572,0.00001709278,0.00004506192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991687,0.000009010161,0.0004884691,0.00009120443,0.000102711725,0.00013991338],"domain_scores_gemma":[0.99955195,0.00012126761,0.0001358586,0.000047171678,0.00009960283,0.000044171065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018911656,0.00011097211,0.00023003266,0.00020168428,0.00007288092,0.000025691656,0.00004262243,0.000053553187,0.0000028415368],"category_scores_gemma":[0.000036347687,0.00010735345,0.000043142612,0.00016279794,0.0000154195,0.00029856878,7.4006545e-7,0.00009137576,8.181783e-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":[0.000050801085,0.000007725634,0.000166162,0.00020255722,0.00003795431,0.0000047870076,0.001016258,0.9811044,0.013657092,0.0006543617,0.000019842179,0.00307808],"study_design_scores_gemma":[0.010440826,0.0006353221,0.054025717,0.0033612074,0.00047142763,0.000055067463,0.004964633,0.8788723,0.04074491,0.0025743588,0.0030145722,0.0008396699],"about_ca_topic_score_codex":0.0000018767088,"about_ca_topic_score_gemma":0.0000038939183,"teacher_disagreement_score":0.5344725,"about_ca_system_score_codex":0.000039334536,"about_ca_system_score_gemma":0.000026995906,"threshold_uncertainty_score":0.43777466},"labels":[],"label_agreement":null},{"id":"W7119509942","doi":"10.1115/icef2025-164660","title":"Next Generation Smart Railways Communications Based on 5G Radio Access Technology","year":2025,"lang":"","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; WSP (Canada)","funders":"","keywords":"Key (lock); Resilience (materials science); Service (business); Mobile telephony; Cellular network; Focus (optics); Information and Communications Technology; Telecommunications network","score_opus":0.04559075462784801,"score_gpt":0.27841349352672806,"score_spread":0.23282273889888006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7119509942","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":[0.02517357,0.0060045156,0.33067146,0.01642009,0.005296891,0.0010908231,0.000021164093,0.0014971244,0.61382437],"genre_scores_gemma":[0.9906021,0.00061242416,0.0026222265,0.00050993933,0.00015235489,0.00020316316,0.000055012308,0.000051140454,0.0051916447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976957,0.0001260399,0.0008392875,0.00053476996,0.0002385859,0.0005656515],"domain_scores_gemma":[0.9965359,0.00017810651,0.0001033743,0.0029152238,0.00016377849,0.00010361879],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040839176,0.00043339137,0.0004759577,0.0011993472,0.0006475377,0.00046968856,0.0018598819,0.00055458926,0.00040601922],"category_scores_gemma":[0.00012317217,0.000445614,0.00016211063,0.0023129273,0.00019931686,0.00036838773,0.00021765781,0.00054395286,0.00016906875],"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":[0.0000135357395,0.00040308517,0.0011820758,0.00019208349,0.00014634297,0.0000042983233,0.00009881068,0.684459,0.019601313,0.22208415,0.019961499,0.051853854],"study_design_scores_gemma":[0.00059876917,0.000072698866,0.00026833688,0.00019775186,0.00004581479,0.0000019533438,0.00009487895,0.88187134,0.008440631,0.000135656,0.10791076,0.00036139495],"about_ca_topic_score_codex":0.00038040933,"about_ca_topic_score_gemma":0.00049509533,"teacher_disagreement_score":0.96542853,"about_ca_system_score_codex":0.0003391568,"about_ca_system_score_gemma":0.00023222574,"threshold_uncertainty_score":0.99979955},"labels":[],"label_agreement":null},{"id":"W7129536761","doi":"10.1109/icecmsn68058.2025.11382598","title":"An Advanced AI-Driven Complaint Management System for RailMadad","year":2025,"lang":"","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Complaint; Grievance; Customer satisfaction; Scalability; Management system","score_opus":0.005887598008447537,"score_gpt":0.23780649898523387,"score_spread":0.23191890097678633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7129536761","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":[0.007176756,0.0008590063,0.8079287,0.00019554915,0.0053409864,0.0016496709,0.000031419266,0.0007233377,0.17609462],"genre_scores_gemma":[0.97194916,0.00005681514,0.015471517,0.000113091795,0.0001579749,0.00040094135,0.000025724408,0.00006655529,0.011758222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729127,0.0000530913,0.0009006926,0.00069841905,0.00025141463,0.0008050914],"domain_scores_gemma":[0.9985917,0.0000632448,0.00008048062,0.0009373881,0.00013190143,0.00019527816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031942586,0.0004890754,0.0006431234,0.00034062905,0.0003672294,0.00020608732,0.00058829633,0.00019088126,0.00006090544],"category_scores_gemma":[0.0000054772963,0.0004855987,0.00024139025,0.0005052457,0.00004112417,0.00024707481,0.00008977961,0.0001518312,0.00006215899],"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":[0.000037317954,0.00008445547,0.000024096444,0.0034449168,0.0002506632,0.000011680645,0.00012349607,0.62264115,0.0009671569,0.33327687,0.0017592422,0.03737895],"study_design_scores_gemma":[0.0017381776,0.00017207935,0.00025239863,0.0011307972,0.000120312434,0.000004771839,0.0030199508,0.826837,0.0011787321,0.00007348207,0.16495709,0.00051522854],"about_ca_topic_score_codex":0.000109081324,"about_ca_topic_score_gemma":0.000085221225,"teacher_disagreement_score":0.9647724,"about_ca_system_score_codex":0.0005056269,"about_ca_system_score_gemma":0.00003628881,"threshold_uncertainty_score":0.99975955},"labels":[],"label_agreement":null},{"id":"W7137255480","doi":"10.1109/itsc60802.2025.11423706","title":"Simulation-Based Pattern Timetabling for Short-Turning Trains in a Metro Network","year":2025,"lang":"","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Train; Line (geometry); Work (physics); Key (lock)","score_opus":0.018334959294181667,"score_gpt":0.26558463490133694,"score_spread":0.24724967560715527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7137255480","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":[0.04997984,0.0022392273,0.9400665,0.0000824241,0.001710714,0.0008253726,0.00001003751,0.00018099096,0.0049048597],"genre_scores_gemma":[0.9924661,0.000008632401,0.0057992875,0.0001864175,0.00042783355,0.00019908456,0.000022950999,0.00006513085,0.0008245498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971979,0.000070298775,0.001064724,0.0005240578,0.00020952252,0.00093347806],"domain_scores_gemma":[0.99729747,0.0020654802,0.00006193485,0.0003776851,0.00009476307,0.00010268686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009702131,0.00041978195,0.0006538223,0.0005560898,0.00020793497,0.00017517127,0.0002642928,0.00028007056,0.00015992108],"category_scores_gemma":[0.00026689935,0.000437708,0.00029371917,0.0015145979,0.000022790127,0.00014949747,0.00003079713,0.00028361732,0.0000080254495],"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":[0.000014260828,0.000054412343,0.010476537,0.00030452813,0.0000898634,0.0000026052483,0.00010878289,0.92156243,0.00013976805,0.0004784694,0.00009305403,0.06667527],"study_design_scores_gemma":[0.00088954647,0.000045473404,0.0024594206,0.0005780798,0.00006641274,4.863573e-8,0.00017411057,0.9872692,0.00017530785,0.000049879454,0.007878026,0.00041449408],"about_ca_topic_score_codex":0.00030125864,"about_ca_topic_score_gemma":0.001303015,"teacher_disagreement_score":0.9424863,"about_ca_system_score_codex":0.00023089579,"about_ca_system_score_gemma":0.0001277958,"threshold_uncertainty_score":0.9998075},"labels":[],"label_agreement":null},{"id":"W765329146","doi":"","title":"LRT for Canada's Capital","year":2013,"lang":"en","type":"article","venue":"Railway age","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Light rail transit; Light rail; Transport engineering; Transit (satellite); Rail transit; Right of way; Public transport; Rapid transit; Capital (architecture); Spring (device); Private capital; Engineering; Business; Telecommunications; Geography; Economics","score_opus":0.004737990839010995,"score_gpt":0.164913875595621,"score_spread":0.16017588475661002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W765329146","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":[0.94861996,0.00046078267,0.0034860563,0.00013950994,0.0016253532,0.00036916565,0.00002508754,0.00028681994,0.044987272],"genre_scores_gemma":[0.99468,0.0000048614397,0.0003621828,0.00005628639,0.00021092393,0.000120008575,0.0000134216125,0.00003170041,0.004520602],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99931365,0.000004551777,0.00015316786,0.00011691859,0.00010921784,0.00030246284],"domain_scores_gemma":[0.99966156,0.000032651664,0.000012826779,0.00017069156,0.000026792291,0.00009549179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000047447476,0.000116868694,0.00013287879,0.000028054159,0.00005435484,0.000038407765,0.00012645246,0.000048900594,0.00017325638],"category_scores_gemma":[0.000013891976,0.00010620354,0.00004411724,0.00006604891,0.000011474715,0.0000738812,0.000009197284,0.000053063457,0.000069199974],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004149571,0.000043906766,0.0003814613,0.00040367886,0.00014132039,0.000059722803,0.0012056096,0.11398758,0.034513786,0.009621907,0.80789936,0.03173753],"study_design_scores_gemma":[0.0006758352,0.000059647806,0.008904013,0.0000408945,0.000013565421,0.000016105017,0.0004083347,0.0649048,0.0050890376,0.00043194578,0.918717,0.0007388196],"about_ca_topic_score_codex":0.20676902,"about_ca_topic_score_gemma":0.22789787,"teacher_disagreement_score":0.110817656,"about_ca_system_score_codex":0.00008163797,"about_ca_system_score_gemma":0.000042154767,"threshold_uncertainty_score":0.7985132},"labels":[],"label_agreement":null},{"id":"W796039993","doi":"","title":"Diesel Railcar: A Look Ahead","year":2000,"lang":"en","type":"article","venue":"Railroad history","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Successor cardinal; Diesel fuel; Service (business); Engineering; Aeronautics; Business; Automotive engineering; Marketing","score_opus":0.007988842826739997,"score_gpt":0.16574453663104105,"score_spread":0.15775569380430104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W796039993","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":[0.21158384,0.01799091,0.00021981726,0.000032900956,0.0016660573,0.000107299136,0.000004265435,0.00095503306,0.7674399],"genre_scores_gemma":[0.8921834,0.0003038549,0.00018907645,0.00012629958,0.00039168133,0.00004207851,0.00000696509,0.00007271479,0.106683895],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989964,0.000020236997,0.00024116761,0.0002094943,0.00018505228,0.000347683],"domain_scores_gemma":[0.9994677,0.000017163222,0.000016802422,0.00036201402,0.000017013988,0.00011935759],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000112677655,0.00018725345,0.00021653326,0.00009155249,0.00006283771,0.00001078237,0.00019121583,0.0001032032,0.0042649675],"category_scores_gemma":[0.0000061658366,0.00019286355,0.000102869504,0.0000973014,0.00007443704,0.00011969521,0.0000085395695,0.0001496639,0.0018614546],"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":[0.000020069818,0.00008974467,0.00016093226,0.00016964266,0.00007997608,0.000066421126,0.004085582,0.06382723,0.0051593194,0.0013620327,0.7376798,0.18729927],"study_design_scores_gemma":[0.0002270671,0.000021138698,0.00057096226,0.000023929842,0.0000075158096,0.000013446156,0.000029803843,0.007119391,0.00006410237,0.000014695994,0.99166125,0.00024668724],"about_ca_topic_score_codex":0.00014489892,"about_ca_topic_score_gemma":0.00004979525,"teacher_disagreement_score":0.6805996,"about_ca_system_score_codex":0.0005138494,"about_ca_system_score_gemma":0.000037312104,"threshold_uncertainty_score":0.99891573},"labels":[],"label_agreement":null},{"id":"W799195926","doi":"","title":"CANADIAN RAIL LABOR URGES HEARINGS.","year":2000,"lang":"en","type":"article","venue":"Traffic world","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Business; Transport engineering; Labor relations; Engineering; Labour economics; Economics","score_opus":0.0043159822958364815,"score_gpt":0.1693898369002655,"score_spread":0.165073854604429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W799195926","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":[0.7930785,0.0006080886,0.000003082061,0.0003283918,0.00042499768,0.000061249266,0.0000082040715,0.00034918188,0.20513833],"genre_scores_gemma":[0.96007055,0.000041295865,0.00007389917,0.000092190865,0.00022689806,0.0000105593135,0.0000052813616,0.000032345917,0.039446976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991847,0.000008428736,0.00015938526,0.00014030124,0.00009886667,0.00040827628],"domain_scores_gemma":[0.999535,0.000012022566,0.0000062165377,0.00019573834,0.000010396017,0.0002406278],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000077063945,0.00013122606,0.00013892804,0.00015521166,0.00007809147,0.00004313244,0.00013282541,0.000043952543,0.004179795],"category_scores_gemma":[0.0000027000908,0.00012928864,0.000043834043,0.0004185237,0.000017669527,0.00006118052,0.0000022664265,0.00011314294,0.0009983802],"study_design_candidate":"not_applicable","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":[0.0000018443488,0.000011397906,0.00020016832,0.000019558654,0.000018863991,0.000023863378,0.0003434429,0.8213614,0.000052786945,0.0006291281,0.0409251,0.1364124],"study_design_scores_gemma":[0.0001439866,0.000008622702,0.0035332153,0.000030059535,0.000004840194,0.000006151929,0.000023944604,0.030903399,0.00007444875,0.0000044846347,0.96503687,0.00023000479],"about_ca_topic_score_codex":0.020954726,"about_ca_topic_score_gemma":0.3078378,"teacher_disagreement_score":0.9241117,"about_ca_system_score_codex":0.000118021206,"about_ca_system_score_gemma":0.000047661906,"threshold_uncertainty_score":0.99977946},"labels":[],"label_agreement":null},{"id":"W824061481","doi":"","title":"GETTING CLOSER : TRANSCEIVER CHIP PROBLEM SOLVED, ECP PROPONENTS ENVISION 'PATH TO SUCCESS'","year":2001,"lang":"en","type":"article","venue":"Progressive railroading","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Transceiver; Engineering; Reliability (semiconductor); Track (disk drive); Chip; Wireless; Automotive engineering; Telecommunications; Electrical engineering; Aeronautics; Mechanical engineering; Power (physics)","score_opus":0.008397853178780394,"score_gpt":0.22431000878117838,"score_spread":0.215912155602398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W824061481","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":[0.97129405,0.002047002,0.0071099196,0.00024765494,0.00063278514,0.00093353685,0.0000058286273,0.0007755926,0.016953634],"genre_scores_gemma":[0.995024,0.00007179345,0.0035891568,0.00006655482,0.00043422563,0.00021205537,0.000012242394,0.000109187364,0.00048076856],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975387,0.00005244673,0.00051664637,0.0005278485,0.00045687432,0.0009074949],"domain_scores_gemma":[0.99915034,0.00003964366,0.000094369505,0.00032265444,0.00009335546,0.0002996374],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037294827,0.00040311206,0.0003758376,0.00024855908,0.000303403,0.0001759789,0.00038695944,0.00016222193,0.0001509332],"category_scores_gemma":[0.000036121455,0.00036000443,0.00014114147,0.00054359337,0.000042158532,0.00040574555,0.000055467022,0.0002887035,0.00016961951],"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":[0.0001823739,0.0005031499,0.05565508,0.0016226736,0.00042166072,0.00087641185,0.018517427,0.16837846,0.25058645,0.0014592168,0.003600713,0.4981964],"study_design_scores_gemma":[0.009887019,0.0017113328,0.06566025,0.013288347,0.00041819908,0.0012016518,0.0029207163,0.47601146,0.13467355,0.0015516698,0.28214765,0.010528159],"about_ca_topic_score_codex":0.00004122039,"about_ca_topic_score_gemma":0.000010429355,"teacher_disagreement_score":0.48766825,"about_ca_system_score_codex":0.00013540358,"about_ca_system_score_gemma":0.000022716033,"threshold_uncertainty_score":0.9998852},"labels":[],"label_agreement":null},{"id":"W849611940","doi":"","title":"PRINCE EDWARD ISLAND: A PROVINCE WITHOUT RAILS","year":2001,"lang":"en","type":"article","venue":"RAIL TRAVEL NEWS","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Geography; History; Geology","score_opus":0.009725093906732955,"score_gpt":0.21143506817452745,"score_spread":0.2017099742677945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W849611940","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":[0.5948471,0.00097191805,0.07911677,0.00022923954,0.0013197374,0.00053785695,0.000014371666,0.00093955465,0.32202348],"genre_scores_gemma":[0.9829707,0.00018501382,0.0008505862,0.00008197484,0.0003999768,0.000038898193,0.000005586361,0.00006353985,0.015403692],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986627,0.000022725439,0.00032500035,0.00028099472,0.00023321316,0.0004753386],"domain_scores_gemma":[0.9993846,0.000022298278,0.00003986829,0.00038260702,0.000025652822,0.00014500726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013934722,0.00024548304,0.0002896619,0.00008381944,0.000072481525,0.00006400575,0.00025472985,0.00011193672,0.00011577693],"category_scores_gemma":[0.000017619774,0.00021698495,0.00008763303,0.00021131238,0.000035690635,0.00015490026,0.00002095022,0.00016691888,0.0002532684],"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":[0.00021459081,0.00062576093,0.059155896,0.0011306814,0.00047366647,0.00073711446,0.008301763,0.31477883,0.107059754,0.010221927,0.07582348,0.42147654],"study_design_scores_gemma":[0.001529148,0.00012728591,0.013538026,0.00017297996,0.000026520349,0.00024203156,0.00029571928,0.05685983,0.0034829187,0.00012513506,0.92257965,0.0010207301],"about_ca_topic_score_codex":0.00029499663,"about_ca_topic_score_gemma":0.0015801038,"teacher_disagreement_score":0.8467562,"about_ca_system_score_codex":0.00006423982,"about_ca_system_score_gemma":0.000050336224,"threshold_uncertainty_score":0.88483894},"labels":[],"label_agreement":null},{"id":"W880153358","doi":"","title":"CBAA expands service for Canadian operators","year":2007,"lang":"en","type":"article","venue":"Aviation International News","topic":"Railway Systems and Energy Efficiency","field":"Engineering","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":"Service (business); Computer science; Business; Marketing","score_opus":0.0094479364869353,"score_gpt":0.22916276236795252,"score_spread":0.21971482588101723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W880153358","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":[0.64266884,0.000091230264,0.20685974,0.001964153,0.008636062,0.0003668778,0.000084478546,0.000314292,0.13901433],"genre_scores_gemma":[0.9970897,0.0000057945163,0.00081800396,0.0005274563,0.00047444509,0.000024524861,0.00009503872,0.000017593211,0.0009474876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994221,0.0000026112828,0.00019365689,0.00009442798,0.00011860249,0.00016863499],"domain_scores_gemma":[0.99964267,0.000026697535,0.000019899362,0.00007818363,0.0001311099,0.00010145473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013938185,0.00007397609,0.00005859782,0.00015735731,0.00005975308,0.00004252272,0.00013546803,0.000047273228,0.000086779735],"category_scores_gemma":[0.00002015383,0.00007663902,0.000030681735,0.00012623939,0.0000035212659,0.00013933358,0.000004626995,0.000037654943,0.00006808492],"study_design_candidate":"not_applicable","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":[0.00006052503,0.00010382209,0.04390376,0.00015175059,0.00043517892,0.00002158839,0.0061540194,0.45835468,0.02050273,0.21642844,0.12910257,0.12478093],"study_design_scores_gemma":[0.00059972756,0.000023386396,0.021056563,0.000026103606,0.0000066007847,0.000008039708,0.000349404,0.09717807,0.003401539,0.00032071356,0.87671757,0.00031228244],"about_ca_topic_score_codex":0.024178043,"about_ca_topic_score_gemma":0.1891985,"teacher_disagreement_score":0.747615,"about_ca_system_score_codex":0.00018213218,"about_ca_system_score_gemma":0.000032084616,"threshold_uncertainty_score":0.98232},"labels":[],"label_agreement":null}]}