{"meta":{"query_hash":"e90da4c9436d","filters":{"venue":"Transportation Engineering"},"cohort_total":9,"direct_labels_cover":0,"predictions_cover":9,"exported":9,"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/e90da4c9436d","api":"https://metacan.xera.ac/api/v1/cohort?venue=Transportation+Engineering"},"results":[{"id":"W3041666796","doi":"10.1016/j.treng.2020.100013","title":"Smart transportation planning: Data, models, and algorithms","year":2020,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ryerson University","keywords":"Exponential smoothing; Cluster analysis; Computer science; Autoregressive integrated moving average; Intelligent transportation system; Machine learning; Field (mathematics); Population; Artificial intelligence; Transportation planning; Time series; Kalman filter; Engineering; Transport engineering","score_opus":0.03778781692137233,"score_gpt":0.22412082721000973,"score_spread":0.1863330102886374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041666796","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":[0.02872913,0.0003475775,0.9641621,0.0001396105,0.00012567449,0.00016520992,0.00013428307,0.0059803682,0.00021603421],"genre_scores_gemma":[0.98888975,0.00020251876,0.009935753,0.0000756599,0.000052300693,0.000018244164,0.00078235724,0.000039964325,0.0000034468349],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999286,0.0000025750026,0.00022655539,0.00021233223,0.00012864782,0.00014390826],"domain_scores_gemma":[0.99971277,0.000010513934,0.00001625408,0.00013565038,0.000011863714,0.000112948874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000054096872,0.0001463463,0.0001320194,0.00006754389,0.000023581933,0.000019410298,0.00012663982,0.000056276138,0.000007160863],"category_scores_gemma":[0.0000021765798,0.00017516053,0.000021119957,0.00014702816,0.0000088292,0.00052235316,0.0000018262077,0.00012688374,0.0000025325198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006091053,0.0000062893164,0.00028592808,0.00024424595,0.000047626698,0.000015171352,0.0016388606,0.9875162,0.0011217828,0.0016956272,0.002822189,0.0045999694],"study_design_scores_gemma":[0.00024896552,0.000016173703,0.011908016,0.000025688938,0.000036605226,4.514134e-7,0.00007483709,0.9757728,0.00038193463,0.000015200497,0.011335833,0.00018352293],"about_ca_topic_score_codex":0.0000067144615,"about_ca_topic_score_gemma":0.0000069106864,"teacher_disagreement_score":0.9601606,"about_ca_system_score_codex":0.000008700368,"about_ca_system_score_gemma":0.000003792987,"threshold_uncertainty_score":0.7142839},"labels":[],"label_agreement":null},{"id":"W3086620783","doi":"10.1016/j.treng.2020.100021","title":"Impacts of road and rail temporal traffic variations on grade crossings exposure, design, and regulation in Manitoba","year":2020,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Traffic and Road Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Level crossing; Train; Product (mathematics); Transport engineering; Computer science; Environmental science; Warning system; Engineering; Geography; Mathematics; Telecommunications; Cartography","score_opus":0.014838141743727095,"score_gpt":0.19417302005411208,"score_spread":0.17933487831038497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086620783","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9030349,0.00020069472,0.09628236,0.00006114236,0.00004852661,0.0001685775,0.000010589435,0.00018476057,0.00000845294],"genre_scores_gemma":[0.9948777,0.000051396106,0.004968068,0.000010360737,0.00002447894,0.000007773431,0.000032545595,0.00002636827,0.0000013252492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99936414,0.000007863415,0.00027685871,0.00012965158,0.00009619391,0.00012527034],"domain_scores_gemma":[0.99979377,0.000034692424,0.00002889526,0.000051870233,0.00001240359,0.00007835288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076642296,0.00012969002,0.0001573206,0.000098042074,0.0000241857,0.000014844695,0.000030634696,0.00006996841,0.000003819132],"category_scores_gemma":[0.000007932086,0.00014151826,0.00002115275,0.00020124017,0.000015333466,0.00016531366,8.6317414e-7,0.00010694539,6.112722e-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":[0.000020945423,0.00001097844,0.0031520012,0.00016939684,0.0000134723405,0.0000027840717,0.0030928494,0.9842196,0.00671143,0.00023253419,0.000008430794,0.002365541],"study_design_scores_gemma":[0.00056284183,0.000044668395,0.6158599,0.000052067604,0.000011468889,8.341551e-7,0.00005654813,0.38231468,0.0009398136,0.0000052317473,0.000040748546,0.00011116977],"about_ca_topic_score_codex":0.00002602533,"about_ca_topic_score_gemma":0.00006287503,"teacher_disagreement_score":0.6127079,"about_ca_system_score_codex":0.000016366837,"about_ca_system_score_gemma":0.000010150371,"threshold_uncertainty_score":0.57709473},"labels":[],"label_agreement":null},{"id":"W3185095324","doi":"10.1016/j.treng.2021.100087","title":"A data-driven model for safety risk identification from flight data analysis","year":2021,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Advanced Statistical Methods and Models","field":"Mathematics","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":"Group for Research in Decision Analysis; Polytechnique Montréal","funders":"Mitacs","keywords":"Runway; Aviation; Fault tree analysis; Aviation accident; Computer science; Aviation safety; Identification (biology); Risk analysis (engineering); Aeronautics; Data mining; Engineering; Reliability engineering; Geography","score_opus":0.17119230247033312,"score_gpt":0.4046555615509826,"score_spread":0.23346325908064947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185095324","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037524262,0.00006220383,0.9604376,0.00003984929,0.00006483728,0.0001580114,0.03538548,0.00009297274,0.000006654739],"genre_scores_gemma":[0.13737118,0.00006397605,0.8391118,0.000007423778,0.00003734963,0.000019430714,0.023287201,0.000025485937,0.00007611263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986768,0.000021015023,0.00047411936,0.00051882945,0.00016366552,0.00014557026],"domain_scores_gemma":[0.9978198,0.0006709737,0.00012122665,0.0012211979,0.00010263665,0.00006420393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032237408,0.00012229856,0.00027648895,0.000059486527,0.00006083972,0.00002406839,0.00032003148,0.00005435334,0.00003198938],"category_scores_gemma":[0.00054876646,0.00013081374,0.00006230847,0.0002659953,0.00000773255,0.00034872888,0.000016550204,0.000093436516,0.0000013597781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016839742,0.00003997743,0.000064449036,0.00007319005,0.00046184676,0.0000032064236,0.00038668918,0.95433396,0.0017276531,0.040024538,0.00006873249,0.0027989382],"study_design_scores_gemma":[0.0002447245,0.0000020808427,0.001573177,0.00001267085,0.0016161054,8.624581e-8,0.000028193612,0.9566551,0.0002908699,0.039068047,0.00036491666,0.00014402947],"about_ca_topic_score_codex":0.000014234295,"about_ca_topic_score_gemma":0.00040110268,"teacher_disagreement_score":0.13361876,"about_ca_system_score_codex":0.000018372117,"about_ca_system_score_gemma":0.00002975397,"threshold_uncertainty_score":0.533443},"labels":[],"label_agreement":null},{"id":"W4224273618","doi":"10.1016/j.treng.2022.100115","title":"3D object detection for autonomous driving: Methods, models, sensors, data, and challenges","year":2022,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":42,"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":"Computer science; Object detection; Categorization; Artificial intelligence; Object (grammar); Focus (optics); Computer vision; Data mining; Pattern recognition (psychology)","score_opus":0.05284242472348057,"score_gpt":0.2838832401896235,"score_spread":0.23104081546614294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224273618","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003478526,0.0009806125,0.9943726,0.0003069711,0.00015341272,0.00029344184,0.000027584034,0.00037546764,0.0000113788965],"genre_scores_gemma":[0.49852508,0.00020929993,0.5009311,0.000014206733,0.000029014342,0.0002129663,0.000050532482,0.000016450154,0.00001134247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991389,0.000021017533,0.00017555858,0.00039742218,0.00010324724,0.00016386407],"domain_scores_gemma":[0.99930364,0.00016418146,0.00005479986,0.00040815843,0.000021142714,0.00004807773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024640744,0.00010496102,0.00010726158,0.000081737526,0.0001804555,0.000019424873,0.00034258366,0.000021745738,0.0000015064531],"category_scores_gemma":[0.000008063073,0.00012935679,0.000021780004,0.00019021612,0.0000064279525,0.00047949102,0.000033459386,0.00011515867,2.5579837e-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":[0.0000020014436,0.000009064768,0.0000025736038,0.000026048536,0.000008611065,8.5234245e-7,0.00053562137,0.88406044,0.0023114039,0.012635777,0.0000035120722,0.1004041],"study_design_scores_gemma":[0.00013927548,0.000028565593,0.00056581205,0.000002675463,0.000010628623,0.0000066526313,0.000038705803,0.98105055,0.00080138684,0.0012083787,0.016009562,0.00013782625],"about_ca_topic_score_codex":0.000003606176,"about_ca_topic_score_gemma":0.000016738079,"teacher_disagreement_score":0.49504656,"about_ca_system_score_codex":0.000032476295,"about_ca_system_score_gemma":0.000013051972,"threshold_uncertainty_score":0.52750164},"labels":[],"label_agreement":null},{"id":"W4284969520","doi":"10.1016/j.treng.2022.100124","title":"Ultra-low NOx diesel aftertreatment: An assessment by simulation","year":2022,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Catalytic Processes in Materials Science","field":"Materials Science","cited_by":9,"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":"General Motors of Canada","keywords":"Robustness (evolution); NOx; Context (archaeology); Computer science; Diesel fuel; Automotive engineering; Procurement; Process (computing); Environmental science; Systems engineering; Simulation; Combustion; Engineering; Business","score_opus":0.007020296165010977,"score_gpt":0.2678479680439302,"score_spread":0.2608276718789192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284969520","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92282283,0.0000374884,0.07578184,0.000030457793,0.00044493674,0.00019207716,0.00033779469,0.0002655844,0.00008701105],"genre_scores_gemma":[0.99630904,0.000002569836,0.0028027755,0.000037230388,0.000032706124,0.00017796965,0.00047504267,0.000025177342,0.00013750489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998613,0.000021961197,0.00029354144,0.00034573107,0.00047135615,0.00025439062],"domain_scores_gemma":[0.99950665,0.00005531123,0.00008451045,0.00022719486,0.00004117552,0.000085150365],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00032958554,0.00015232945,0.00014391047,0.000067959256,0.00020174615,0.00007404381,0.00025906067,0.000023743156,0.002930989],"category_scores_gemma":[0.000012555335,0.0001672484,0.000031283773,0.00022190355,0.000023902072,0.00056313956,0.0000073878205,0.00006905664,0.000026055883],"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":[0.000007276576,0.000050239938,0.0000849735,0.000026591171,0.000001927575,0.0000037638242,0.0003453512,0.5040226,0.49529073,0.00008323367,0.000016567192,0.000066751985],"study_design_scores_gemma":[0.0011682278,0.00036361642,0.013995557,0.00003201523,0.00006455179,0.0000075585876,0.0003880343,0.32801673,0.6505037,0.0001538534,0.0044786897,0.00082746847],"about_ca_topic_score_codex":0.000033022156,"about_ca_topic_score_gemma":0.000004204011,"teacher_disagreement_score":0.17600589,"about_ca_system_score_codex":0.00016316253,"about_ca_system_score_gemma":0.0000438966,"threshold_uncertainty_score":0.9979805},"labels":[],"label_agreement":null},{"id":"W4387642787","doi":"10.1016/j.treng.2023.100208","title":"Macroscopic traffic characterization based on driver memory and traffic stimuli","year":2023,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Traffic control and management","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 Victoria","funders":"Higher Education Commision, Pakistan; University of Engineering and Technology, Peshawar","keywords":"Bottleneck; Headway; Zhàng; Traffic flow (computer networking); Traffic bottleneck; Traffic model; Computer science; Function (biology); Traffic wave; Simulation; Flow (mathematics); Traffic congestion reconstruction with Kerner's three-phase theory; Characterization (materials science); Microscopic traffic flow model; Traffic generation model; Real-time computing; Floating car data; Traffic optimization; Engineering; Traffic congestion; Mechanics; Physics; Computer network; Transport engineering; Embedded system; Geography","score_opus":0.006360435535643829,"score_gpt":0.18797610233603668,"score_spread":0.18161566680039284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387642787","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9812202,0.00002464114,0.015843,0.00007443175,0.00039963648,0.0002505414,0.000034264256,0.0021036523,0.00004966067],"genre_scores_gemma":[0.99914134,0.000047940073,0.00018097057,0.00003323055,0.000050638144,0.00005637805,0.0003573774,0.000050462648,0.000081673854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923897,0.0000044676785,0.00019674176,0.00018125241,0.0001493288,0.000229223],"domain_scores_gemma":[0.99975544,0.000032673834,0.000015193266,0.000112977,0.000011549126,0.00007215986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006561281,0.0001778338,0.00014712657,0.00024060038,0.000037079953,0.000026385907,0.000056782097,0.000055422013,0.00003606895],"category_scores_gemma":[0.000002418918,0.00020050349,0.000040519222,0.00030609698,0.000009260915,0.00010993463,0.0000010994996,0.00009894912,0.00004150797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000065452964,0.000009845132,0.000020862079,0.0001732257,0.000017559976,0.000013407572,0.00042552347,0.97104496,0.006580411,0.000048014776,0.000045997847,0.02161367],"study_design_scores_gemma":[0.0006412942,0.000021709937,0.1365036,0.000043928525,0.000023640874,1.7735185e-7,0.0000261495,0.86093503,0.00016995781,3.5501287e-7,0.0014551673,0.00017897386],"about_ca_topic_score_codex":7.7133313e-7,"about_ca_topic_score_gemma":0.000009988944,"teacher_disagreement_score":0.13648275,"about_ca_system_score_codex":0.000025458568,"about_ca_system_score_gemma":0.0000053927934,"threshold_uncertainty_score":0.8176295},"labels":[],"label_agreement":null},{"id":"W4403634878","doi":"10.1016/j.treng.2024.100284","title":"Ensemble-based model to investigate factors influencing road crash fatality for imbalanced data","year":2024,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Traffic and Road Safety","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":"Concordia University","funders":"","keywords":"Crash; Road accident; Computer science; Transport engineering; Statistics; Engineering; Mathematics","score_opus":0.037870010097343906,"score_gpt":0.254573342776216,"score_spread":0.2167033326788721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403634878","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":[0.45223555,0.00007883452,0.54555106,0.000031086747,0.00026579475,0.00021569883,0.00046751127,0.0011357573,0.000018702898],"genre_scores_gemma":[0.9827821,0.0000070335436,0.01587437,0.000032550262,0.00005917969,0.00005193761,0.0010918322,0.00008359174,0.00001735572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988696,0.000002894427,0.00033282404,0.00032995964,0.00015581165,0.00030888323],"domain_scores_gemma":[0.9993982,0.000052674393,0.000011957204,0.00034627947,0.000026653825,0.00016423548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012843324,0.00023787106,0.000199072,0.0001445092,0.000040526098,0.00004627837,0.0002355688,0.00009007318,0.0000053672584],"category_scores_gemma":[0.000016044505,0.00024628386,0.00006765213,0.0002771805,0.000008835966,0.00033620629,0.0000042673028,0.00015433668,0.000007694754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046595587,0.0000040537807,0.00035620702,0.00057757733,0.00003788566,0.0000028172472,0.00084346783,0.97902226,0.01764578,0.00037556278,0.00016173595,0.000967963],"study_design_scores_gemma":[0.00019975072,0.000009860455,0.02160898,0.00014112201,0.00004218472,2.1537217e-7,0.000031464173,0.9703422,0.00629759,0.000022749204,0.0010066772,0.00029720954],"about_ca_topic_score_codex":0.000016104957,"about_ca_topic_score_gemma":0.00009354579,"teacher_disagreement_score":0.5305466,"about_ca_system_score_codex":0.000065108776,"about_ca_system_score_gemma":0.000052506526,"threshold_uncertainty_score":0.9999989},"labels":[],"label_agreement":null},{"id":"W4407986324","doi":"10.1016/j.treng.2025.100312","title":"Effect of aging kinetics on the fatigue behavior of asphalt mixtures incorporating various RAP contents","year":2025,"lang":"en","type":"article","venue":"Transportation Engineering","topic":"Asphalt Pavement Performance Evaluation","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":"Asphalt; Kinetics; Materials science; Composite material; Physics","score_opus":0.013265463395923785,"score_gpt":0.2562566467723457,"score_spread":0.24299118337642195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407986324","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9388487,0.00015797144,0.059888095,0.000016384667,0.00031743865,0.00046898404,0.000014142211,0.000119412376,0.00016886975],"genre_scores_gemma":[0.99924266,0.000013251465,0.0005463615,0.000011498216,0.0000150900305,0.000088146924,0.00004884194,0.000024633697,0.000009519614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990211,0.000022203689,0.0004702657,0.00010591474,0.00024217076,0.0001383371],"domain_scores_gemma":[0.9994399,0.00020824857,0.000096813754,0.00017109042,0.00006276702,0.000021211035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030670402,0.00016946105,0.00021654196,0.0001697131,0.00002680903,0.000007079647,0.00012441837,0.000060686045,0.000015479009],"category_scores_gemma":[0.000032552485,0.00014370133,0.00006452968,0.00031424104,0.000020070927,0.000094461,0.000002424259,0.00015018918,9.588829e-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":[0.000010422513,0.00001982748,0.011537282,0.00066193077,0.000057698508,0.0000011300162,0.00033585425,0.8007135,0.18344197,0.0006233546,0.000012995454,0.0025840844],"study_design_scores_gemma":[0.0005490056,0.000136797,0.05263749,0.00037047965,0.00012322416,1.5206334e-7,0.000026595266,0.1547629,0.7912617,0.000008217099,0.000008936613,0.00011454721],"about_ca_topic_score_codex":0.0000104629,"about_ca_topic_score_gemma":0.00000754234,"teacher_disagreement_score":0.64595056,"about_ca_system_score_codex":0.000039407496,"about_ca_system_score_gemma":0.000008297918,"threshold_uncertainty_score":0.585997},"labels":[],"label_agreement":null},{"id":"W4415070958","doi":"10.1016/j.treng.2025.100397","title":"Blended wing body designs for aerodynamic, stability, and control optimization: A comprehensive review","year":2025,"lang":"en","type":"review","venue":"Transportation Engineering","topic":"Advanced Aircraft Design and Technologies","field":"Environmental Science","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":"United Arab Emirates University","keywords":"Propulsion; Aerospace; Fuselage; Airworthiness; Payload (computing); Aviation; Aerodynamics; Control (management)","score_opus":0.026455799688963094,"score_gpt":0.27247036313493733,"score_spread":0.24601456344597425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415070958","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":[8.479577e-7,0.5564228,0.4419576,0.000026157873,0.00003976397,0.0013100537,0.0000714704,0.00015803568,0.000013256311],"genre_scores_gemma":[0.00009747531,0.9688835,0.030113239,0.000048697304,0.000008735902,0.00056771364,0.00022554773,0.00003265378,0.00002244505],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987094,0.000022983859,0.0005000806,0.00041869335,0.00012411935,0.0002247214],"domain_scores_gemma":[0.99923307,0.00033808962,0.00013927647,0.00022196029,0.000019245395,0.000048385697],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000114148825,0.0003359672,0.0009329183,0.000071288014,0.000066017215,0.000013455337,0.00016505655,0.00015260644,0.00008783407],"category_scores_gemma":[0.00005694129,0.00031974053,0.00018023675,0.00030556964,0.000051193143,0.00014959484,0.0000093342915,0.0001707827,0.00000267155],"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":[0.000013354678,0.000057887086,0.00002948114,0.262865,0.00024502858,0.000015434467,0.00006354772,0.20109217,0.0001226807,0.0006023234,0.00015548493,0.5347376],"study_design_scores_gemma":[0.0014272076,0.00014669084,0.00015796062,0.07164808,0.0034372772,0.000013034411,0.00003013891,0.036442913,0.000029553437,0.00016636492,0.8847213,0.0017794962],"about_ca_topic_score_codex":0.0000029094701,"about_ca_topic_score_gemma":0.0000042590086,"teacher_disagreement_score":0.8845658,"about_ca_system_score_codex":0.00010727334,"about_ca_system_score_gemma":0.000028895973,"threshold_uncertainty_score":0.9999255},"labels":[],"label_agreement":null}]}