{"meta":{"query_hash":"0cdf1558bd4e","filters":{"venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)"},"cohort_total":15,"direct_labels_cover":0,"predictions_cover":15,"exported":15,"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/0cdf1558bd4e","api":"https://metacan.xera.ac/api/v1/cohort?venue=2021+IEEE+94th+Vehicular+Technology+Conference+%28VTC2021-Fall%29"},"results":[{"id":"W3165713359","doi":"10.1109/vtc2021-fall52928.2021.9625483","title":"Latency of Concatenating Unlicensed LPWAN with Cellular IoT: An Experimental QoE Study","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"IoT Networks and Protocols","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":"Sheridan College","funders":"Bell Canada Enterprises; Keysight Technologies","keywords":"LPWAN; Computer science; Latency (audio); Computer network; Internet of Things; Embedded system; Telecommunications","score_opus":0.015424556268265473,"score_gpt":0.24266687592221986,"score_spread":0.2272423196539544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165713359","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99151075,0.00073817844,0.002050772,0.00014455659,0.00033782143,0.0029602298,0.000013916671,0.0005306708,0.0017131232],"genre_scores_gemma":[0.99574643,0.000034364835,0.0024533765,0.000043485456,0.00012581485,0.0013090816,0.00004566126,0.000111127185,0.000130647],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997006,0.00016129937,0.0007340663,0.000842553,0.00046364436,0.00079242606],"domain_scores_gemma":[0.99791193,0.000051178213,0.00019708727,0.0011906044,0.00048192567,0.00016727256],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032638002,0.0005822725,0.00088840467,0.0002954833,0.00019518996,0.00009571887,0.0006851168,0.00059373834,0.00039103866],"category_scores_gemma":[0.000029185714,0.0005598689,0.00012667786,0.0011634015,0.00026758443,0.00017799198,0.00016714407,0.0009212439,0.00004361364],"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":[0.00011623136,0.0016458384,0.016701523,0.00027754923,0.0009620754,0.0030599039,0.0034975472,0.016713448,0.938839,0.00449726,0.0002052869,0.013484294],"study_design_scores_gemma":[0.0033072715,0.0014275702,0.0006145783,0.00039555156,0.00016581043,0.00010326145,0.012449481,0.052995488,0.92487633,0.0004391314,0.0020138843,0.0012116425],"about_ca_topic_score_codex":0.000076459124,"about_ca_topic_score_gemma":0.00030016358,"teacher_disagreement_score":0.03628204,"about_ca_system_score_codex":0.0001064586,"about_ca_system_score_gemma":0.00026509236,"threshold_uncertainty_score":0.9996853},"labels":[],"label_agreement":null},{"id":"W3197680405","doi":"10.1109/vtc2021-fall52928.2021.9625281","title":"Deep Reinforcement Learning Based Admission Control for Throughput Maximization in Mobile Edge Computing","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Shenzhen Fundamental Research Program","keywords":"Server; Computer science; Mobile edge computing; Cloud computing; Computer network; Edge computing; Reinforcement learning; Admission control; Enhanced Data Rates for GSM Evolution; Mobile computing; Mobile device; Distributed computing; Operating system; Quality of service; Artificial intelligence","score_opus":0.014097887138419241,"score_gpt":0.248571969144342,"score_spread":0.23447408200592273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197680405","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":[0.045711983,0.00077300647,0.9466451,0.0022734588,0.0028175225,0.00091528293,4.972701e-7,0.0004913938,0.00037176922],"genre_scores_gemma":[0.9599613,0.000072367875,0.03850677,0.0005161774,0.00041876512,0.00017680609,0.000073357325,0.000046429883,0.00022800318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958182,0.00028916151,0.0009408752,0.001360409,0.00046666327,0.0011247051],"domain_scores_gemma":[0.9971752,0.0003391814,0.000436859,0.0010528495,0.0008309778,0.00016491245],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008759851,0.0005143062,0.0007867236,0.00059798785,0.00056401355,0.0003067895,0.0012606583,0.00065627723,0.00003269259],"category_scores_gemma":[0.00054657925,0.0005550477,0.00023317503,0.0017875419,0.00013093904,0.00039796563,0.0005233947,0.0009733415,0.0000505007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006788808,0.00046696464,0.010934678,0.0003202137,0.00018054579,0.000604829,0.0012335891,0.67866004,0.021796828,0.014183737,0.0007687606,0.27078196],"study_design_scores_gemma":[0.0025480501,0.0002605802,0.00022890359,0.00034207915,0.00003428965,0.000039740447,0.00023789333,0.9625271,0.019555857,0.0013534592,0.012267056,0.00060502323],"about_ca_topic_score_codex":0.000029757039,"about_ca_topic_score_gemma":0.000046895726,"teacher_disagreement_score":0.91424936,"about_ca_system_score_codex":0.0002628528,"about_ca_system_score_gemma":0.0008331162,"threshold_uncertainty_score":0.9996901},"labels":[],"label_agreement":null},{"id":"W4200004457","doi":"10.1109/vtc2021-fall52928.2021.9625536","title":"Artificial Neural Networks-based Ambient RF Energy Harvesting with Environment Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Energy Harvesting in Wireless Networks","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":"Alberta University of the Arts; University of Calgary","funders":"","keywords":"Artificial neural network; Computer science; Radio frequency; Energy (signal processing); Wireless sensor network; Real-time computing; Electronic engineering; Artificial intelligence; Telecommunications; Engineering; Computer network; Mathematics; Statistics","score_opus":0.009712985143622265,"score_gpt":0.18224379315312256,"score_spread":0.1725308080095003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200004457","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52716786,0.00093457627,0.46849132,0.0005082247,0.0010139248,0.00019732778,0.000007907655,0.0012605629,0.00041830115],"genre_scores_gemma":[0.99436355,0.00021234984,0.003999346,0.00016140555,0.0004699259,0.00027822098,0.000099775076,0.00017394937,0.00024145753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961719,0.00017299224,0.00077023904,0.0011436328,0.0005316365,0.0012095924],"domain_scores_gemma":[0.9979402,0.00015128149,0.00021857498,0.0012283012,0.00022620434,0.00023543707],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026033385,0.0007538929,0.0007205616,0.00036941943,0.00036006476,0.00021164832,0.00058090105,0.000925127,0.00012880424],"category_scores_gemma":[0.00008878644,0.00080656476,0.00017665858,0.0012730088,0.00040523717,0.00023399893,0.00016815322,0.0013403975,0.00003700487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019486673,0.00009262232,0.0010846236,0.00003803315,0.00015026232,0.0007160791,0.000016189844,0.8533677,0.050787725,0.0015021263,0.00004021648,0.092184976],"study_design_scores_gemma":[0.0004204659,0.00014435717,0.00029756266,0.00020811503,0.00010824576,0.0001473167,0.00007834693,0.8070714,0.18537895,0.0001981961,0.005150434,0.0007966242],"about_ca_topic_score_codex":0.00012837395,"about_ca_topic_score_gemma":0.0027728854,"teacher_disagreement_score":0.46719572,"about_ca_system_score_codex":0.00031342288,"about_ca_system_score_gemma":0.00015420746,"threshold_uncertainty_score":0.9994385},"labels":[],"label_agreement":null},{"id":"W4200065057","doi":"10.1109/vtc2021-fall52928.2021.9625332","title":"Secrecy Outage Probability and Secrecy Capacity for Autonomous Driving in a Cascaded Rayleigh Fading Environment","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Wireless Communication Security Techniques","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 Calgary","funders":"","keywords":"Rayleigh fading; Secrecy; Relay; Fading; Computer science; Transmitter; Computer network; Secure communication; Transmission (telecommunications); Wireless; Communications system; Electronic engineering; Computer security; Telecommunications; Engineering; Channel (broadcasting); Encryption","score_opus":0.02327316765091591,"score_gpt":0.227065883454158,"score_spread":0.2037927158032421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200065057","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89017814,0.0010814399,0.10467641,0.0013957021,0.00014988863,0.0011443439,0.00004608349,0.0009326572,0.00039533956],"genre_scores_gemma":[0.9568342,0.00097422395,0.04098279,0.00005446619,0.000036579593,0.00090814877,0.00006188111,0.00008228099,0.00006541541],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99689806,0.00021465181,0.0008340925,0.0009751523,0.0002692558,0.0008088018],"domain_scores_gemma":[0.9977118,0.00022670101,0.00015748062,0.001572085,0.00017580317,0.00015612881],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069502,0.0005560749,0.00087355915,0.0004598357,0.00020305884,0.00012588646,0.0007598083,0.0009692966,0.00009707612],"category_scores_gemma":[0.0003001289,0.0006663727,0.00016035893,0.0006183799,0.00037859724,0.0002977205,0.0004491973,0.0013221558,0.000017921879],"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":[0.000041069947,0.0009816998,0.05400995,0.0016527994,0.0007610343,0.0004755781,0.0055838483,0.006668006,0.61376137,0.18738446,0.00017322406,0.12850696],"study_design_scores_gemma":[0.0032755556,0.00024684804,0.007099785,0.0010784123,0.00021260619,0.00036353493,0.0020109504,0.18814974,0.64471006,0.11875193,0.031023744,0.003076859],"about_ca_topic_score_codex":0.000099476936,"about_ca_topic_score_gemma":0.0015124243,"teacher_disagreement_score":0.18148173,"about_ca_system_score_codex":0.00055609003,"about_ca_system_score_gemma":0.00017701446,"threshold_uncertainty_score":0.9995788},"labels":[],"label_agreement":null},{"id":"W4200094847","doi":"10.1109/vtc2021-fall52928.2021.9625271","title":"End-to-End Multi-View Fusion for Enhanced Perception and Motion Prediction","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Perception; Motion (physics); Artificial intelligence; Computer vision; Fusion; Sensor fusion; End-to-end principle","score_opus":0.022531578955769488,"score_gpt":0.2700151100072144,"score_spread":0.2474835310514449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200094847","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":[0.16811097,0.00037123405,0.82267743,0.0067489375,0.00046748726,0.0010177303,0.000032097723,0.0004911606,0.00008294463],"genre_scores_gemma":[0.8400657,0.0010892373,0.15689522,0.00040426318,0.0001301453,0.0008506079,0.00010945256,0.000034358472,0.00042097713],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967605,0.00013031493,0.0005639876,0.0015596924,0.0003457264,0.00063978933],"domain_scores_gemma":[0.9974471,0.00011256752,0.00022471783,0.0012888036,0.000732755,0.00019407207],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029581445,0.00040509537,0.00048247355,0.00036164833,0.0004935026,0.0001589497,0.0007887772,0.0005212948,0.00006441808],"category_scores_gemma":[0.00019193637,0.00043523897,0.00012870715,0.0015689057,0.00018865937,0.00055611687,0.0005029048,0.00052039686,0.00010111889],"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":[0.000009667521,0.00015117027,0.00028501346,0.00004956049,0.000035816673,0.000018827775,0.00021025224,0.0006459269,0.5238746,0.021559626,0.00015476941,0.45300478],"study_design_scores_gemma":[0.003630135,0.00075467577,0.011391765,0.0006184285,0.00021847934,0.00040509412,0.00092186854,0.51028997,0.39575207,0.027298408,0.046774253,0.0019448331],"about_ca_topic_score_codex":0.000013736504,"about_ca_topic_score_gemma":0.00024287602,"teacher_disagreement_score":0.67195475,"about_ca_system_score_codex":0.00015711838,"about_ca_system_score_gemma":0.00017834952,"threshold_uncertainty_score":0.9998099},"labels":[],"label_agreement":null},{"id":"W4200112441","doi":"10.1109/vtc2021-fall52928.2021.9625178","title":"Energy-Based Analysis of String Stability in Heterogeneous Platoons","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Traffic control and management","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":"Queen's University","funders":"","keywords":"Platoon; String (physics); Control theory (sociology); Passivity; Stability (learning theory); Computer science; Vehicle dynamics; Engineering; Aerospace engineering; Physics; Control (management); Artificial intelligence","score_opus":0.011557642762086241,"score_gpt":0.20217288297559724,"score_spread":0.190615240213511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200112441","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95578784,0.0015964266,0.040677607,0.00048486108,0.00026092635,0.00019627424,0.00006243917,0.00041291615,0.0005206907],"genre_scores_gemma":[0.99867934,0.00020893676,0.00077669317,0.000060499766,0.000017874936,0.00010671612,0.000077800294,0.000033781052,0.000038345566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975632,0.000094206756,0.0007175922,0.00069741096,0.00031792576,0.0006096524],"domain_scores_gemma":[0.9983614,0.00009157469,0.000109368535,0.001122663,0.00021784868,0.00009711613],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023851475,0.00038006855,0.00094518036,0.0011137236,0.00005472346,0.00004006307,0.00048708273,0.00043406856,0.00030897203],"category_scores_gemma":[0.000058921672,0.00042828982,0.00033672305,0.0029180087,0.00014622047,0.00007951087,0.0001390809,0.0004473632,0.000009781428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033303488,0.00050633424,0.019938812,0.00025279928,0.0034820365,0.0010963434,0.00015876656,0.8091812,0.07423703,0.013402147,0.00006734239,0.07764386],"study_design_scores_gemma":[0.0020532876,0.000098082135,0.007634331,0.00016227657,0.001370558,0.0000128433,0.00064156216,0.81057936,0.16950129,0.00078922213,0.0061518503,0.0010053572],"about_ca_topic_score_codex":0.00015701265,"about_ca_topic_score_gemma":0.013203878,"teacher_disagreement_score":0.095264256,"about_ca_system_score_codex":0.0001715175,"about_ca_system_score_gemma":0.0001872129,"threshold_uncertainty_score":0.9998169},"labels":[],"label_agreement":null},{"id":"W4200249621","doi":"10.1109/vtc2021-fall52928.2021.9625372","title":"A Multi-Factor Authenticated Blockchain-Based OTA Update Framework for Connected Autonomous Vehicles","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Firmware; Authentication (law); Software; Upload; Scalability; Computer security; Embedded system; Operating system; Computer network","score_opus":0.021632543973088433,"score_gpt":0.26311272185818996,"score_spread":0.2414801778851015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200249621","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":[0.33776754,0.00074364856,0.63189876,0.026270047,0.00048169337,0.0009390074,0.00011579466,0.0017610827,0.000022418862],"genre_scores_gemma":[0.7552966,0.00005976828,0.24255319,0.000859415,0.00006233115,0.0008830133,0.00006864776,0.000062748804,0.00015427986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99451053,0.0002389503,0.0010253828,0.0023412602,0.00045032834,0.0014335741],"domain_scores_gemma":[0.99349767,0.0004790188,0.00052988756,0.003702977,0.0015060762,0.00028437568],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0004818371,0.00080977473,0.0010916642,0.00080587505,0.0007577616,0.00030026503,0.0034428847,0.0022059595,0.0001606744],"category_scores_gemma":[0.0008783612,0.00086621207,0.00039873418,0.0031420111,0.00079095655,0.000109536406,0.0007598039,0.0016927469,0.00022962906],"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":[0.000032262913,0.001457241,0.0017188503,0.00012685903,0.0005297558,0.00041853142,0.00038276988,0.00025240073,0.06722628,0.8682101,0.00037240793,0.059272557],"study_design_scores_gemma":[0.0024830368,0.00022054187,0.0006949195,0.00021142051,0.00015102798,0.00012585417,0.00028521373,0.67157346,0.20296581,0.101981446,0.01784307,0.0014641853],"about_ca_topic_score_codex":0.00004211782,"about_ca_topic_score_gemma":0.00027306154,"teacher_disagreement_score":0.7662286,"about_ca_system_score_codex":0.00019271745,"about_ca_system_score_gemma":0.0012739908,"threshold_uncertainty_score":0.99937886},"labels":[],"label_agreement":null},{"id":"W4200263087","doi":"10.1109/vtc2021-fall52928.2021.9625387","title":"Rigid Body Localization and Environment Sensing with 5G Millimeter Wave MIMO","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Indoor and Outdoor Localization Technologies","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":"Western University","funders":"","keywords":"Non-line-of-sight propagation; Computer science; Singular value decomposition; Specular reflection; Rigid body; Computer vision; Position (finance); Channel (broadcasting); Compressed sensing; Artificial intelligence; Algorithm; Wireless; Telecommunications; Physics; Optics","score_opus":0.008840740403978978,"score_gpt":0.18469389803806951,"score_spread":0.17585315763409054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200263087","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":[0.4153798,0.002429745,0.5763195,0.0016972396,0.00041539798,0.00050612824,0.000029954248,0.0015313706,0.0016908617],"genre_scores_gemma":[0.9904963,0.002148919,0.0066477624,0.00016112099,0.00005277292,0.00003748757,0.00009388941,0.00008934702,0.00027240993],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976513,0.00006860043,0.00047564245,0.0007858951,0.00034987752,0.00066867593],"domain_scores_gemma":[0.99865115,0.00004573,0.00010234972,0.0008768626,0.00021808797,0.000105816005],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013980061,0.0005192581,0.00057314156,0.00041987916,0.0002345329,0.00012628495,0.00024856086,0.0008156723,0.00016472577],"category_scores_gemma":[0.00006950689,0.0005010573,0.0000837492,0.00079033885,0.00048024088,0.00018787921,0.00014070178,0.0006473615,0.00008845089],"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":[0.000121270125,0.0005429125,0.023770079,0.0012184,0.0027900198,0.007956152,0.0017086557,0.069071226,0.48276374,0.0478857,0.0034720558,0.3586998],"study_design_scores_gemma":[0.0014053686,0.00019228781,0.00043630353,0.0003170994,0.0002462329,0.00064841006,0.001663653,0.23778033,0.71858406,0.0036692268,0.033749066,0.0013079486],"about_ca_topic_score_codex":0.000024151686,"about_ca_topic_score_gemma":0.00015194187,"teacher_disagreement_score":0.5751165,"about_ca_system_score_codex":0.00014583439,"about_ca_system_score_gemma":0.00009493434,"threshold_uncertainty_score":0.9997441},"labels":[],"label_agreement":null},{"id":"W4200277452","doi":"10.1109/vtc2021-fall52928.2021.9625310","title":"SA-SGAN: A Vehicle Trajectory Prediction Model Based on Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Autonomous Vehicle Technology and Safety","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":"Queen's University","funders":"Science and Technology Department, Henan Province; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Sequence (biology); Generative grammar; Task (project management); Artificial intelligence; Displacement (psychology); Generative model; Adversarial system; Machine learning; Motion (physics); Engineering","score_opus":0.009742288950092161,"score_gpt":0.2003179927223472,"score_spread":0.19057570377225505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200277452","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":[0.47379985,0.0010655911,0.511881,0.00301244,0.001734109,0.0007398142,0.00014784669,0.003527772,0.004091583],"genre_scores_gemma":[0.99342406,0.0003745035,0.0045886855,0.00043041896,0.00026846977,0.00027003113,0.00017156925,0.000122114,0.00035017583],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99641407,0.00016651045,0.000756388,0.0011769735,0.00041987366,0.0010662122],"domain_scores_gemma":[0.99778736,0.00010436144,0.00015159574,0.0014160426,0.000356796,0.00018385093],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0003411703,0.000738967,0.0008498718,0.000550795,0.0004048684,0.000071132876,0.0007349569,0.0021964002,0.00025208667],"category_scores_gemma":[0.0001122208,0.0008284362,0.00029253948,0.0012590034,0.00046294212,0.00024033777,0.00014584884,0.0023261595,0.00014453846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072027244,0.00023212109,0.0010710001,0.000043790547,0.00032063393,0.00044357532,0.00012838162,0.9312931,0.035102855,0.0095989,0.0011717809,0.02052187],"study_design_scores_gemma":[0.0016230121,0.00018320799,0.0003946475,0.00011366607,0.00013214406,0.000042117445,0.00021590765,0.941527,0.051632557,0.0016147227,0.0018155485,0.00070543715],"about_ca_topic_score_codex":0.00001527114,"about_ca_topic_score_gemma":0.00028904967,"teacher_disagreement_score":0.5196242,"about_ca_system_score_codex":0.0004063352,"about_ca_system_score_gemma":0.0005928245,"threshold_uncertainty_score":0.9999755},"labels":[],"label_agreement":null},{"id":"W4200300506","doi":"10.1109/vtc2021-fall52928.2021.9625390","title":"Joint N ode- Link Embedding Algorithm based on Genetic Algorithm in Virtualization Environment","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Software-Defined Networks and 5G","field":"Computer Science","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":"Carleton University","funders":"","keywords":"Computer science; Network virtualization; Virtualization; Algorithm; Embedding; Distributed computing; Node (physics); Virtual network; Link (geometry); Overhead (engineering); Heuristic; Theoretical computer science; Computer network; Cloud computing; Operating system","score_opus":0.013932060346231685,"score_gpt":0.22400567650873837,"score_spread":0.2100736161625067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200300506","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012009017,0.0010051016,0.97994924,0.005056654,0.0008510618,0.00046500488,0.000017962615,0.00046628047,0.00017970256],"genre_scores_gemma":[0.42836654,0.0013494418,0.5672923,0.0016467286,0.000342543,0.00037432808,0.00013674467,0.000110244204,0.00038114033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9950359,0.00034655439,0.00091064756,0.0018458146,0.0008081272,0.0010529859],"domain_scores_gemma":[0.99700916,0.00016961043,0.00031238058,0.0020515565,0.00025128401,0.0002060085],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005070683,0.0006443002,0.0007965904,0.0008131073,0.00026491206,0.00027476193,0.0013230384,0.0009125453,0.00021142067],"category_scores_gemma":[0.00015743558,0.00069188897,0.00022409909,0.0019577665,0.00021635424,0.00027188964,0.000590796,0.0011161442,0.00028716793],"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":[0.0000059041895,0.0004364664,0.0013786068,0.00003074066,0.00007975769,0.001990467,0.00016767731,0.060923476,0.0022192374,0.009112939,0.00027935268,0.92337537],"study_design_scores_gemma":[0.0012199342,0.00024750765,0.0015169792,0.00028730097,0.00003484086,0.0000898469,0.00011392466,0.9742873,0.011710877,0.004508449,0.0052004005,0.0007826433],"about_ca_topic_score_codex":0.00005853949,"about_ca_topic_score_gemma":0.00005854051,"teacher_disagreement_score":0.92259276,"about_ca_system_score_codex":0.0003300311,"about_ca_system_score_gemma":0.00052045507,"threshold_uncertainty_score":0.9995532},"labels":[],"label_agreement":null},{"id":"W4200389689","doi":"10.1109/vtc2021-fall52928.2021.9625045","title":"Kalman Filtering to Track Changes in Pupil Size for Automated Driving Systems","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Human-Automation Interaction and Safety","field":"Psychology","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 Windsor","funders":"","keywords":"Distraction; Computer science; Cognitive load; Workload; Automation; Human multitasking; Situation awareness; Driving simulator; Kalman filter; Pupil; BitTorrent tracker; Cognition; Human–computer interaction; Task (project management); Real-time computing; Eye tracking; Simulation; Artificial intelligence; Engineering","score_opus":0.03050680192234762,"score_gpt":0.3303112034892812,"score_spread":0.2998044015669336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200389689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9602348,0.00043975527,0.009399357,0.014648885,0.0051019993,0.001648756,0.000090396374,0.002208809,0.0062272465],"genre_scores_gemma":[0.9882182,0.00005652913,0.0017109205,0.00063147274,0.00022410195,0.0014682647,0.00007465665,0.0000857469,0.0075301486],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960865,0.00034521855,0.0009585994,0.0012709808,0.00032650627,0.0010122304],"domain_scores_gemma":[0.9972629,0.00038242512,0.00031858205,0.0011870387,0.0006390797,0.00020996496],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00060245016,0.0005169101,0.0009001862,0.0008214511,0.00025510913,0.00019839343,0.00070873526,0.0009363019,0.0026960787],"category_scores_gemma":[0.0006778365,0.0005782296,0.00018869495,0.0013190459,0.00012877063,0.00018621306,0.00018896733,0.0008071578,0.00085672154],"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":[0.00041033322,0.0025571694,0.025864,0.0011302974,0.0021922635,0.004598258,0.014439801,0.0043381336,0.634012,0.1756455,0.04825318,0.086559065],"study_design_scores_gemma":[0.011224655,0.0014574857,0.059332926,0.0037178851,0.00039886372,0.0019323701,0.0434431,0.14713302,0.083670974,0.0020032136,0.6404898,0.0051957103],"about_ca_topic_score_codex":0.00017044105,"about_ca_topic_score_gemma":0.004235621,"teacher_disagreement_score":0.59223664,"about_ca_system_score_codex":0.00026023193,"about_ca_system_score_gemma":0.00022205178,"threshold_uncertainty_score":0.9999212},"labels":[],"label_agreement":null},{"id":"W4200463835","doi":"10.1109/vtc2021-fall52928.2021.9625532","title":"Conference Information","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Human auditory perception and evaluation","field":"Engineering","cited_by":0,"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":"Universitatea Tehnică din Cluj-Napoca; University of Science and Technology Beijing; Iran Telecommunication Research Center; Universidade Federal do Rio Grande do Sul; Independent University, Bangladesh; Nanjing University of Aeronautics and Astronautics; Universitat de València; Pusan National University; Edinburgh Napier University; Fujitsu; Aalborg Universitet; Universitat Autònoma de Barcelona; Nanjing University; Queensland University of Technology; Old Dominion University; National Institute of Information and Communications Technology; University of Waterloo; Università degli Studi di Cagliari; Università degli Studi di Siena; TU Graz, Internationale Beziehungen und Mobilitätsprogramme; Kuwait College of Science and Technology; University of Windsor","keywords":"Computer science","score_opus":0.01932620051482186,"score_gpt":0.2362551114196865,"score_spread":0.21692891090486463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200463835","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.882805,0.00042610805,0.07978904,0.0038827606,0.0033140343,0.0005782882,0.000057818528,0.002104153,0.027042823],"genre_scores_gemma":[0.9954837,0.00061262865,0.0015516387,0.0002904366,0.00022202427,0.0001345273,0.0002674031,0.000041422976,0.001396214],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977484,0.000102860606,0.00064668176,0.00043606633,0.00048679087,0.00057922216],"domain_scores_gemma":[0.99803877,0.000032176104,0.00012513307,0.0008450437,0.00081225135,0.00014664172],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00029954908,0.0004058904,0.0004624074,0.00047279347,0.00020541031,0.0002222599,0.000471156,0.00072608877,0.013629988],"category_scores_gemma":[0.00016850718,0.00045975595,0.00013720337,0.00084021967,0.00019943078,0.00075858535,0.000108044114,0.0008253003,0.012196962],"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":[0.000042105148,0.00037478274,0.004136196,0.00080002885,0.0008622159,0.00046821413,0.0043073823,0.026049808,0.41633317,0.04475604,0.106014796,0.39585528],"study_design_scores_gemma":[0.0028360984,0.00017882646,0.006680797,0.0004789365,0.00024497538,0.00029342007,0.0055009127,0.2831868,0.090375535,0.008465912,0.5993636,0.0023941665],"about_ca_topic_score_codex":0.0000048550423,"about_ca_topic_score_gemma":0.0002703378,"teacher_disagreement_score":0.49334884,"about_ca_system_score_codex":0.00019943144,"about_ca_system_score_gemma":0.0003345995,"threshold_uncertainty_score":0.9997854},"labels":[],"label_agreement":null},{"id":"W4200477656","doi":"10.1109/vtc2021-fall52928.2021.9625311","title":"Final Program","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Conferences and Exhibitions Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nanjing University; Shanghai Jiao Tong University; University of Waterloo","keywords":"Computer science; Programming language","score_opus":0.03883388310926826,"score_gpt":0.31061307827023077,"score_spread":0.2717791951609625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200477656","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64587426,0.0023500079,0.009811066,0.11213711,0.0030697212,0.0024386807,0.000060394646,0.0024071857,0.22185156],"genre_scores_gemma":[0.98075575,0.0015689437,0.0037587495,0.00042490297,0.00033456946,0.00045315493,0.000072981355,0.00003363738,0.012597328],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961055,0.00032071583,0.0005527209,0.0010858224,0.00077126024,0.001163997],"domain_scores_gemma":[0.99749094,0.00007894846,0.00021005837,0.0010221032,0.00094955746,0.00024836094],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00064825296,0.00040499878,0.0005699425,0.00042037395,0.0010136567,0.00045090614,0.001035896,0.0006951795,0.0030604652],"category_scores_gemma":[0.00037696818,0.0004373735,0.00027170786,0.0021887901,0.0009660811,0.00028235602,0.00039672517,0.0008095363,0.0007802811],"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":[0.000013052849,0.0007251544,0.0044731884,0.00005508502,0.00022936145,0.0009783072,0.0011239079,0.000029368694,0.0014049879,0.49289745,0.0060391095,0.492031],"study_design_scores_gemma":[0.0008189474,0.00020447934,0.0010143956,0.00020800922,0.0001643918,0.000035485755,0.014159736,0.0011384755,0.0023787217,0.024907801,0.954099,0.0008705503],"about_ca_topic_score_codex":0.0015195401,"about_ca_topic_score_gemma":0.026566338,"teacher_disagreement_score":0.9480599,"about_ca_system_score_codex":0.00020178348,"about_ca_system_score_gemma":0.0014833198,"threshold_uncertainty_score":0.99999774},"labels":[],"label_agreement":null},{"id":"W4200507944","doi":"10.1109/vtc2021-fall52928.2021.9625196","title":"Deep Learning Based Traffic Flow Prediction for Autonomous Vehicular Mobile Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Traffic Prediction and Management Techniques","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Deep learning; Intelligent transportation system; Traffic flow (computer networking); Artificial intelligence; Vehicular ad hoc network; Data modeling; Machine learning; Internet of Things; Wireless ad hoc network; Real-time computing; Computer network; Engineering; Wireless; Embedded system; Telecommunications; Transport engineering","score_opus":0.007288010201301744,"score_gpt":0.20345469843504108,"score_spread":0.19616668823373934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200507944","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":[0.066990815,0.0020994425,0.9151866,0.00052196515,0.0014778798,0.0012232335,0.000033233566,0.011851631,0.00061523356],"genre_scores_gemma":[0.9857748,0.0011133271,0.010129968,0.00014384835,0.00024143714,0.0016898457,0.00048347123,0.00013906712,0.00028419468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969086,0.00011980545,0.0007149259,0.000977084,0.00034461662,0.00093495264],"domain_scores_gemma":[0.9983004,0.00008983454,0.0001380774,0.0008967655,0.00039705422,0.00017789468],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004067994,0.0006053795,0.0006909678,0.0005726395,0.00037351358,0.00016328247,0.0005687738,0.0011090627,0.00015210695],"category_scores_gemma":[0.000101369245,0.0007085745,0.00034097157,0.0010661276,0.00018486596,0.00024630476,0.000115427996,0.0012533341,0.000050027997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000127949925,0.00011788516,0.00016235586,0.00013068532,0.0002473289,0.0001330744,0.00005413653,0.84584624,0.0027866384,0.00079188735,0.0027751022,0.14694187],"study_design_scores_gemma":[0.0010294458,0.0002082493,0.0001095622,0.00013894076,0.0001895751,0.000039833772,0.00034776176,0.89635336,0.006617598,0.00007958976,0.094322324,0.0005637273],"about_ca_topic_score_codex":0.000006721142,"about_ca_topic_score_gemma":0.00016498855,"teacher_disagreement_score":0.918784,"about_ca_system_score_codex":0.00025833552,"about_ca_system_score_gemma":0.00016365811,"threshold_uncertainty_score":0.9995365},"labels":[],"label_agreement":null},{"id":"W4200616981","doi":"10.1109/vtc2021-fall52928.2021.9625353","title":"Taxi Dispatch and AEV Management in AEV Taxi Services","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Adaptability; Scheduling (production processes); Computer science; Service (business); Idle; Transport engineering; Operations research; Business; Operations management; Engineering; Marketing","score_opus":0.007292265237174774,"score_gpt":0.21233062287952895,"score_spread":0.20503835764235417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200616981","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845238,0.00092634466,0.0074854637,0.0032191705,0.00042511633,0.0004390824,0.000050658586,0.00052218034,0.0024082004],"genre_scores_gemma":[0.99440646,0.0014129215,0.003047988,0.00022192037,0.000028154975,0.00024427805,0.00016120063,0.00004179692,0.00043529467],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99799454,0.000039082526,0.00056852656,0.0006355716,0.00024598237,0.0005162665],"domain_scores_gemma":[0.9988802,0.00002698542,0.000071067494,0.0007158264,0.00021644401,0.00008946258],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001721556,0.0003525291,0.00043242335,0.0004864344,0.00010372415,0.00008480998,0.0003590307,0.0004426152,0.00024344861],"category_scores_gemma":[0.000015073255,0.00040273284,0.00007127472,0.0015237976,0.0001478787,0.00020390717,0.00008257154,0.000618632,0.000079116806],"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":[0.00004957782,0.0012217178,0.20804557,0.0036394645,0.0019776386,0.004978547,0.003532008,0.017476078,0.11503196,0.4018435,0.0013605028,0.24084345],"study_design_scores_gemma":[0.010430808,0.00029085262,0.431737,0.0023499036,0.0008795055,0.00038953096,0.026811913,0.11204456,0.12155497,0.03228947,0.25539276,0.005828729],"about_ca_topic_score_codex":0.00010939288,"about_ca_topic_score_gemma":0.0071902657,"teacher_disagreement_score":0.369554,"about_ca_system_score_codex":0.0000771595,"about_ca_system_score_gemma":0.00007076364,"threshold_uncertainty_score":0.99984246},"labels":[],"label_agreement":null}]}