{"meta":{"query_hash":"e5449fd77a16","filters":{"venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)"},"cohort_total":19,"direct_labels_cover":0,"predictions_cover":19,"exported":19,"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/e5449fd77a16","api":"https://metacan.xera.ac/api/v1/cohort?venue=2022+IEEE+International+Conference+on+Systems%2C+Man%2C+and+Cybernetics+%28SMC%29"},"results":[{"id":"W4309342487","doi":"10.1109/smc53654.2022.9945363","title":"Concurrent Consideration of Human and Machine Reliability in Human-Machine Systems - A Virtual Environment Approach","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"Computer science; Human–machine system; Reliability (semiconductor); Virtual machine; Reliability engineering; Human–computer interaction; Operating system; Engineering","score_opus":0.07234148971773913,"score_gpt":0.3437149249937347,"score_spread":0.27137343527599556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309342487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40132958,0.001392672,0.56521094,0.0015002228,0.00015260663,0.00016762316,0.00003425337,0.00017231636,0.030039815],"genre_scores_gemma":[0.97945625,0.00015447776,0.019464329,0.00004186312,0.000022011369,0.000044452187,0.000008052787,0.000014960316,0.0007936765],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99680674,0.0021442631,0.000086315056,0.00028536023,0.00048488338,0.00019247217],"domain_scores_gemma":[0.9927503,0.0043017883,0.000878948,0.00065065076,0.00079822645,0.00062002346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024604145,0.0006183712,0.00041731796,0.0008242579,0.0008238213,0.0025476834,0.00085604505,0.00086027704,0.0025146788],"category_scores_gemma":[0.00903311,0.00044461215,0.0005355778,0.00032345837,0.0026418879,0.0033675006,0.0030164632,0.0011133676,0.00016503349],"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":[0.0017569936,0.0013526449,0.05339719,0.0018617235,0.0007388808,0.0022755517,0.033636905,0.21793506,0.07907329,0.37178963,0.0022531368,0.23392887],"study_design_scores_gemma":[0.00016091503,0.0033147498,0.058403518,0.00043294186,0.00058222096,0.001638515,0.01572703,0.57382053,0.013933302,0.3093782,0.02223584,0.00037229105],"about_ca_topic_score_codex":0.0013109947,"about_ca_topic_score_gemma":0.0018110255,"teacher_disagreement_score":0.0025476834,"about_ca_system_score_codex":0.00090515625,"about_ca_system_score_gemma":0.0013236079,"threshold_uncertainty_score":0.013012111},"labels":[],"label_agreement":null},{"id":"W4309342498","doi":"10.1109/smc53654.2022.9945590","title":"Multi-Group Role Assignment with Constraints in Adaptive Collaboration","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Collaboration in agile enterprises","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"Nature; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Chongqing","keywords":"Task (project management); Computer science; Set (abstract data type); Group (periodic table); Process (computing); Mathematical optimization; Scheme (mathematics); Operations research; Engineering; Mathematics","score_opus":0.03347475054769768,"score_gpt":0.257146113009103,"score_spread":0.22367136246140532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309342498","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027646814,0.00024882902,0.96883744,0.00023118178,0.000050624567,0.00009828674,0.00003862562,0.0002736712,0.002574543],"genre_scores_gemma":[0.6475867,0.00024268417,0.34892052,0.00014198136,0.00006586954,0.0002240012,0.00012555529,0.00008856288,0.0026041404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976853,0.00086571876,0.000109173314,0.00064515014,0.00040075133,0.00029379383],"domain_scores_gemma":[0.997512,0.0010900783,0.0003428932,0.00040685653,0.00031978817,0.0003283554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002084461,0.00093412737,0.0011725515,0.0007321297,0.0013541514,0.0014905113,0.0024891745,0.00131368,0.002232505],"category_scores_gemma":[0.0053479844,0.00049375626,0.00069305074,0.0012570999,0.0010068892,0.0033878458,0.0024416777,0.0013422136,0.00040823768],"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":[0.0004389452,0.00036512042,0.0024048693,0.0003045174,0.00008297278,0.0004302291,0.0011342121,0.6518375,0.009007194,0.08447695,0.004522765,0.24499469],"study_design_scores_gemma":[0.000047414655,0.0000991221,0.00037201165,0.00001896739,0.000020033556,0.00013087437,0.00020144723,0.96777767,0.0018013815,0.026132742,0.0033709179,0.00002743752],"about_ca_topic_score_codex":0.0028714915,"about_ca_topic_score_gemma":0.0022330084,"teacher_disagreement_score":0.0028714915,"about_ca_system_score_codex":0.0007892366,"about_ca_system_score_gemma":0.001602687,"threshold_uncertainty_score":0.011023819},"labels":[],"label_agreement":null},{"id":"W4309342738","doi":"10.1109/smc53654.2022.9945530","title":"COVID-19 Self-Test Guidance System For Swab Collection Using Deep Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"COVID-19 diagnosis using AI","field":"Medicine","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":"Computer science; Artificial intelligence; Generalization; Data collection; Telehealth; Software deployment; Test (biology); Sample (material); Sampling (signal processing); Quality (philosophy); Inference; Machine learning; Computer vision; Telemedicine; Statistics; Health care","score_opus":0.07361408800445832,"score_gpt":0.3460364946927795,"score_spread":0.2724224066883212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309342738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12537022,0.0021016637,0.6746691,0.0007519531,0.00046815924,0.0010570969,0.01100981,0.1751478,0.009424233],"genre_scores_gemma":[0.56819916,0.0009707155,0.38176826,0.0019781615,0.00009569354,0.0011236692,0.030727472,0.0015317684,0.013605089],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970895,0.00002801116,0.000018502315,0.00010820681,0.00009116612,0.000045166456],"domain_scores_gemma":[0.9997093,0.000064932625,0.00003148521,0.000044159002,0.000108060514,0.000041977743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003576207,0.0012196358,0.000656619,0.0007187685,0.00022464908,0.0004573366,0.0017307395,0.00078186323,0.0036402163],"category_scores_gemma":[0.0011581668,0.0004522167,0.00045609567,0.00032289303,0.00016764714,0.00076171046,0.0010839867,0.0008078345,0.0016844815],"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":[0.0012006524,0.0009527096,0.014021531,0.0006054797,0.00019318753,0.00068110984,0.0001660925,0.049661737,0.039330386,0.0010827092,0.11684118,0.77526325],"study_design_scores_gemma":[0.00012200414,0.00032802147,0.0050659114,0.00006997194,0.00004019576,0.00026099454,0.000046803383,0.9486455,0.027132832,0.0012848396,0.01694865,0.000054310847],"about_ca_topic_score_codex":0.011258582,"about_ca_topic_score_gemma":0.016408088,"teacher_disagreement_score":0.011258582,"about_ca_system_score_codex":0.00099556,"about_ca_system_score_gemma":0.001086395,"threshold_uncertainty_score":0.022386074},"labels":[],"label_agreement":null},{"id":"W4309344223","doi":"10.1109/smc53654.2022.9945513","title":"Multimodal Human Activity Recognition for Smart Healthcare Applications","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","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 Waterloo","funders":"","keywords":"Activity recognition; Modalities; Convolutional neural network; Computer science; Wearable computer; Robustness (evolution); Sensor fusion; Artificial intelligence; Deep learning; Assisted living; Machine learning; Human–computer interaction; Embedded system","score_opus":0.1138413629557532,"score_gpt":0.33483149302410964,"score_spread":0.22099013006835644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309344223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07519379,0.0063764155,0.9023544,0.0008858696,0.000379202,0.00024256986,0.001703186,0.005519549,0.007345076],"genre_scores_gemma":[0.8294284,0.0029380457,0.1598527,0.00074704876,0.0002223186,0.0002528703,0.002379674,0.00009756858,0.0040814755],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996886,0.000080694386,0.000026707483,0.00008841406,0.00007728354,0.000038261118],"domain_scores_gemma":[0.9998029,0.000056912955,0.000029510176,0.000032392203,0.00006187524,0.000016429334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054291514,0.0007019259,0.000599619,0.0006514998,0.00010325374,0.00043219066,0.00047635444,0.00061045383,0.0028888586],"category_scores_gemma":[0.0010694044,0.00013361734,0.00042642734,0.000566198,0.00014730264,0.00060036074,0.00067287014,0.000504554,0.0011781286],"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":[0.00060418126,0.00026899713,0.005017035,0.00036630352,0.00014367269,0.00024440992,0.000094674026,0.015658459,0.060498416,0.0014239171,0.009930951,0.9057489],"study_design_scores_gemma":[0.00007365694,0.0007397619,0.029914558,0.0001974413,0.00022715084,0.001236357,0.00027416088,0.84047276,0.08889847,0.012296779,0.025580255,0.00008873308],"about_ca_topic_score_codex":0.0009772885,"about_ca_topic_score_gemma":0.0015556559,"teacher_disagreement_score":0.0028888586,"about_ca_system_score_codex":0.00024807797,"about_ca_system_score_gemma":0.00025396212,"threshold_uncertainty_score":0.009664178},"labels":[],"label_agreement":null},{"id":"W4309344674","doi":"10.1109/smc53654.2022.9945351","title":"A Formal Theory of AI Trustworthiness for Evaluating Autonomous AI Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Cognitive Computing and Networks","field":"Computer Science","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 Calgary","funders":"","keywords":"Novelty; Computer science; Trustworthiness; Artificial intelligence; Context (archaeology); Robot; Human–computer interaction; Cognition; Intelligent decision support system; Psychology; Computer security","score_opus":0.07171883577656224,"score_gpt":0.32802463356878825,"score_spread":0.256305797792226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309344674","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071953926,0.00044403173,0.98332685,0.0008963804,0.00010879203,0.000121683246,0.00009706164,0.00012738013,0.007682417],"genre_scores_gemma":[0.6745324,0.000773191,0.31944564,0.00034751778,0.00047239117,0.0008153878,0.0002863307,0.00010830206,0.0032187393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9846214,0.006447655,0.0014720728,0.0016000608,0.0051060147,0.0007527674],"domain_scores_gemma":[0.9521315,0.030170737,0.005172695,0.004841064,0.0066821403,0.0010018403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015623543,0.0015619113,0.0012563817,0.0042132563,0.0016938872,0.0059475726,0.00231879,0.0020292944,0.0036808408],"category_scores_gemma":[0.06381933,0.0006736497,0.002077146,0.0020756084,0.009181701,0.009676054,0.0031786829,0.0037731796,0.00071117445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031359225,0.00003730822,0.0011318936,0.00014407837,0.000076965,0.00010951219,0.0005149511,0.04712457,0.00091441936,0.9334518,0.0009880355,0.015475143],"study_design_scores_gemma":[0.000022549848,0.00008627884,0.0005675839,0.000106893094,0.0000440628,0.00012602365,0.00021113083,0.22246991,0.0006314588,0.7721237,0.0035640667,0.000046479578],"about_ca_topic_score_codex":0.0031676167,"about_ca_topic_score_gemma":0.0017970266,"teacher_disagreement_score":0.015623543,"about_ca_system_score_codex":0.00432313,"about_ca_system_score_gemma":0.0031599125,"threshold_uncertainty_score":0.082626164},"labels":[],"label_agreement":null},{"id":"W4309344693","doi":"10.1109/smc53654.2022.9945117","title":"A Machine Learning Based Approach to Detect Fault Injection Attacks in IoT Software Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Advanced Malware Detection Techniques","field":"Computer Science","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":"Ontario Tech University","funders":"","keywords":"Computer science; Software; Fault injection; Exploit; Embedded system; Software system; Attack surface; Software fault tolerance; Fault (geology); Machine learning; Artificial intelligence; Real-time computing; Computer security; Operating system","score_opus":0.03888005311913169,"score_gpt":0.2813813537436144,"score_spread":0.24250130062448272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309344693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13717295,0.0005214719,0.8548292,0.00072734785,0.0001017944,0.00025398252,0.00050520484,0.0030824249,0.0028055308],"genre_scores_gemma":[0.84844524,0.00015055572,0.14788178,0.00020298146,0.00007267982,0.00023394483,0.0008001298,0.000055883076,0.0021567377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999286,0.0001321558,0.00006585315,0.00021194419,0.00021298934,0.0000910941],"domain_scores_gemma":[0.99843055,0.00076381996,0.0002339828,0.00014749539,0.0003714626,0.000052681462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006908841,0.00079613726,0.00070223265,0.0015948731,0.0004492039,0.0007730552,0.0010947108,0.0011823942,0.00086653896],"category_scores_gemma":[0.0031644476,0.00026580927,0.00066986994,0.00072244625,0.00048009903,0.00083315093,0.0004696983,0.0010974653,0.00041192307],"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":[0.00023479348,0.0008911887,0.016799737,0.0001519426,0.00013052333,0.00029966317,0.000113212816,0.65638775,0.0118765915,0.003508364,0.0029454238,0.30666083],"study_design_scores_gemma":[0.0000030998717,0.0000396765,0.00081522274,0.0000052648084,0.00000530076,0.000027142514,0.0000070369138,0.99611557,0.0014393158,0.001332977,0.00020465194,0.000004752649],"about_ca_topic_score_codex":0.003876444,"about_ca_topic_score_gemma":0.004008277,"teacher_disagreement_score":0.003876444,"about_ca_system_score_codex":0.0009272855,"about_ca_system_score_gemma":0.0007409888,"threshold_uncertainty_score":0.0077077746},"labels":[],"label_agreement":null},{"id":"W4309344919","doi":"10.1109/smc53654.2022.9945095","title":"Constructing Digital Twins for IEC61499 Based Distributed Control Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Digital Transformation in Industry","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":"University of Calgary","funders":"","keywords":"Construct (python library); Computer science; Architecture; Automation; Digital control; Control (management); Distributed computing; Set (abstract data type); Reference architecture; Software architecture; Engineering; Artificial intelligence; Computer network; Operating system","score_opus":0.034797015607449584,"score_gpt":0.24531838277775175,"score_spread":0.21052136717030218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309344919","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026545476,0.000057989047,0.96423477,0.000121397396,0.00006526285,0.00006797611,0.000032376698,0.00052798295,0.008346844],"genre_scores_gemma":[0.60914606,0.00027170757,0.38016996,0.00012907542,0.000037714137,0.0002027961,0.0002110767,0.00014489355,0.009686844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994979,0.00012324376,0.000037520364,0.00006902683,0.00022576889,0.000046506677],"domain_scores_gemma":[0.99975663,0.00003807095,0.00002166726,0.00008466023,0.00007451755,0.00002446497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005336018,0.00043000665,0.00042125053,0.0003913816,0.00067868544,0.0014543288,0.00095746905,0.00084121356,0.0023272322],"category_scores_gemma":[0.0010831155,0.00033642768,0.0006454856,0.00028976053,0.000965415,0.002099653,0.0018317961,0.001260532,0.00087964017],"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":[0.00021570573,0.00013933457,0.0015405271,0.00015879642,0.00004920841,0.0003935883,0.0005379789,0.2737094,0.038170163,0.6172013,0.0018571434,0.06602687],"study_design_scores_gemma":[0.000035253994,0.00024375647,0.00027553216,0.00004387702,0.00004279689,0.00016873184,0.000090374066,0.8697177,0.022313999,0.07178355,0.03525388,0.000030559087],"about_ca_topic_score_codex":0.0012937955,"about_ca_topic_score_gemma":0.0014849352,"teacher_disagreement_score":0.0023272322,"about_ca_system_score_codex":0.0006899468,"about_ca_system_score_gemma":0.00089545693,"threshold_uncertainty_score":0.00778538},"labels":[],"label_agreement":null},{"id":"W4309345270","doi":"10.1109/smc53654.2022.9945231","title":"Improving Time Series Generation of GANs through Soft Dynamic Time Warping Loss","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Dynamic time warping; Computer science; Series (stratigraphy); Metric (unit); Image warping; Time series; Sequence (biology); Function (biology); Algorithm; Generative grammar; Real-time computing; Artificial intelligence; Machine learning","score_opus":0.033170696498121495,"score_gpt":0.24981932993604702,"score_spread":0.21664863343792554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309345270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059530575,0.00063571375,0.9336759,0.00041483084,0.00020260872,0.00007360465,0.00023731722,0.0026796102,0.0025498827],"genre_scores_gemma":[0.824339,0.0004029192,0.169225,0.00042519672,0.000136293,0.00013641914,0.0012215117,0.00045828472,0.0036554094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994904,0.00018461356,0.000024605424,0.00013161075,0.000119986755,0.00004873277],"domain_scores_gemma":[0.9981895,0.001080525,0.00014330386,0.00025284686,0.000252635,0.000081281716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001887156,0.0013364148,0.00069272815,0.00057729526,0.00023869437,0.00078926637,0.0009715955,0.0008810427,0.0020629517],"category_scores_gemma":[0.006039831,0.00036809838,0.00067390886,0.00052824244,0.00044679467,0.0014062733,0.0008444682,0.002075325,0.00088042964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015051915,0.00009353908,0.0013304217,0.00005538156,0.0000744814,0.000112342546,0.000045467776,0.8872716,0.006264858,0.0048481207,0.0031725208,0.09658077],"study_design_scores_gemma":[0.0000030852386,0.000016688251,0.00008245742,0.0000025169604,0.0000030247916,0.000015343252,0.0000020114303,0.99763906,0.0010634704,0.00095484505,0.00021484197,0.0000027505946],"about_ca_topic_score_codex":0.0019401183,"about_ca_topic_score_gemma":0.0022874223,"teacher_disagreement_score":0.0020629517,"about_ca_system_score_codex":0.0005707257,"about_ca_system_score_gemma":0.00049083936,"threshold_uncertainty_score":0.009980381},"labels":[],"label_agreement":null},{"id":"W4309345280","doi":"10.1109/smc53654.2022.9945274","title":"A Deep Averaged Reinforcement Learning Approach for the Traveling Salesman Problem","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Reinforcement learning; Travelling salesman problem; Heuristics; Forgetting; Computer science; Artificial intelligence; Convergence (economics); Generalization; Mathematical optimization; Process (computing); Machine learning; Mathematics; Algorithm","score_opus":0.04134696800989715,"score_gpt":0.2538691343529126,"score_spread":0.21252216634301543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309345280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022346046,0.00030221968,0.97405213,0.00016324222,0.000051675594,0.000039665094,0.000031146585,0.0005895241,0.0024244231],"genre_scores_gemma":[0.792967,0.00019767771,0.2031843,0.0001734384,0.00005123247,0.000101326485,0.00009215022,0.00007747692,0.0031554212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969256,0.00008629121,0.000017928829,0.00007059378,0.00007815904,0.000054571254],"domain_scores_gemma":[0.9995908,0.00016571843,0.000052722167,0.00003486833,0.00011479647,0.00004116899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075555715,0.0006889123,0.0008162666,0.0003752865,0.00028880048,0.00045961683,0.0011515148,0.00071511755,0.0019530971],"category_scores_gemma":[0.0015877836,0.00032294256,0.00042726065,0.00030345327,0.0004774345,0.00074263767,0.0006505942,0.0011858913,0.0002067528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036092377,0.000051255065,0.00042210572,0.000034553876,0.000029724079,0.000038909257,0.00002954941,0.93523896,0.0012529471,0.0054336647,0.0006352498,0.05679695],"study_design_scores_gemma":[0.0000028040886,0.0000147985875,0.000027747194,0.000001305376,0.0000022947368,0.000004243782,0.0000011381502,0.9988558,0.00014738525,0.00080509373,0.00013606399,0.0000013458977],"about_ca_topic_score_codex":0.008205516,"about_ca_topic_score_gemma":0.0069736717,"teacher_disagreement_score":0.008205516,"about_ca_system_score_codex":0.00077026675,"about_ca_system_score_gemma":0.0013053679,"threshold_uncertainty_score":0.01631546},"labels":[],"label_agreement":null},{"id":"W4309345632","doi":"10.1109/smc53654.2022.9945528","title":"Improving imbalanced dataset classification using Conditional Classifier-Generator (cCGen)","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Energy Load and Power Forecasting","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Classifier (UML); Machine learning; Artificial intelligence; Generator (circuit theory); Sampling (signal processing); Oversampling; Synthetic data; Data mining; Bandwidth (computing)","score_opus":0.07074704000648549,"score_gpt":0.2766460503492664,"score_spread":0.20589901034278094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309345632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2511547,0.0007659799,0.73035735,0.00093398744,0.00044087777,0.0003547343,0.0011308719,0.0114339795,0.0034274668],"genre_scores_gemma":[0.78745013,0.00013959128,0.20465665,0.00061160617,0.000110539164,0.00028892778,0.0040597455,0.00037904372,0.0023037102],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990146,0.000312952,0.00003984909,0.00026557533,0.00025944508,0.00010742676],"domain_scores_gemma":[0.99723154,0.0012368652,0.00018596456,0.0006174937,0.00061192544,0.00011632272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026599485,0.0011801915,0.0008722831,0.0010025798,0.00045446405,0.0006683701,0.0013569633,0.0008683545,0.0012503499],"category_scores_gemma":[0.006921115,0.00024310325,0.0006465889,0.00067119783,0.0005717572,0.0013474057,0.001391814,0.0017401252,0.0008072786],"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":[0.0009198591,0.00060348015,0.018269487,0.00017463233,0.00015410793,0.00029670712,0.00024167167,0.4009281,0.025014952,0.0052500437,0.02317007,0.5249769],"study_design_scores_gemma":[0.000024547031,0.00008967866,0.0010888431,0.000007347175,0.0000102639315,0.00005233175,0.00002342075,0.98929745,0.0060306676,0.002091266,0.0012736823,0.000010473079],"about_ca_topic_score_codex":0.0033297054,"about_ca_topic_score_gemma":0.0039993986,"teacher_disagreement_score":0.0033297054,"about_ca_system_score_codex":0.00070173125,"about_ca_system_score_gemma":0.0009595435,"threshold_uncertainty_score":0.014067292},"labels":[],"label_agreement":null},{"id":"W4309345821","doi":"10.1109/smc53654.2022.9945362","title":"Enhancing Fresh Produce Yield Forecasting Using Vegetation Indices from Satellite Images","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Remote Sensing in Agriculture","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":"University of Waterloo","funders":"Zayed University","keywords":"Normalized Difference Vegetation Index; Artificial neural network; Deep learning; Interpolation (computer graphics); Computer science; Satellite; Feed forward; Artificial intelligence; Feedforward neural network; Machine learning; Leaf area index; Engineering","score_opus":0.056503444764418216,"score_gpt":0.2576504104452648,"score_spread":0.2011469656808466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309345821","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92136216,0.00042160804,0.07257073,0.00015570337,0.000054124008,0.00002378992,0.00064784783,0.0008375263,0.003926506],"genre_scores_gemma":[0.9803376,0.00027917975,0.017375879,0.000014649764,0.00002192052,0.0000074188943,0.00087403314,0.000021877415,0.0010672857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999355,0.0000058707164,0.000002957008,0.000020123674,0.000024417035,0.000011118044],"domain_scores_gemma":[0.99987674,0.000030001187,0.000019827834,0.000015475118,0.00004875947,0.000009275588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020486329,0.0004281507,0.00022394805,0.00056676037,0.00012881437,0.00046602596,0.00025206123,0.00025436244,0.0005320305],"category_scores_gemma":[0.00064024376,0.00012756368,0.00029031304,0.00062799733,0.00008056061,0.0007335117,0.0002236344,0.0002581447,0.00030048884],"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":[0.00028316816,0.00028163922,0.09063036,0.0000907794,0.000105442916,0.00019367822,0.00010012348,0.42027658,0.05219922,0.00049751333,0.0025323695,0.43280917],"study_design_scores_gemma":[0.0000041852454,0.00004918792,0.031154197,0.0000060953175,0.00002482521,0.000016138598,0.000054697328,0.9577589,0.0099688405,0.00023406254,0.0007172221,0.00001174399],"about_ca_topic_score_codex":0.010865931,"about_ca_topic_score_gemma":0.016871547,"teacher_disagreement_score":0.010865931,"about_ca_system_score_codex":0.00030563728,"about_ca_system_score_gemma":0.00020969348,"threshold_uncertainty_score":0.021605372},"labels":[],"label_agreement":null},{"id":"W4309346036","doi":"10.1109/smc53654.2022.9945386","title":"Investigating the addition of singing imagery as a control task in motor imagery BCI","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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":"Memorial University of Newfoundland","funders":"","keywords":"Motor imagery; Brain–computer interface; Singing; Computer science; Task (project management); Mental image; Electroencephalography; Psychology; Cognition; Engineering; Acoustics","score_opus":0.04224956867296505,"score_gpt":0.2796932368182106,"score_spread":0.23744366814524556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309346036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9778146,0.00022601396,0.017532459,0.000094057374,0.000056988214,0.00016470157,0.00008065819,0.00009561377,0.003934895],"genre_scores_gemma":[0.9794829,0.00014373907,0.01819495,0.00005547991,0.000026579317,0.00011221624,0.00015436018,0.000021005428,0.0018088395],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994079,0.00016157342,0.000040136845,0.00014411467,0.00018416154,0.00006211722],"domain_scores_gemma":[0.99868363,0.0007477889,0.000113017035,0.000111252564,0.00026986192,0.00007450689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055248657,0.00043904598,0.00031757387,0.0002452986,0.00020264022,0.00039567906,0.0003128645,0.0004458672,0.0017105615],"category_scores_gemma":[0.004027978,0.00010057407,0.0002288335,0.00016948556,0.00035144063,0.0004885612,0.0004070796,0.00035586735,0.00036965517],"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":[0.0026145831,0.0012064757,0.015190907,0.001093199,0.00011608313,0.00032082616,0.001030635,0.0066641676,0.6498622,0.00091166014,0.0009882896,0.3200011],"study_design_scores_gemma":[0.0003363012,0.022587048,0.27089778,0.00021525307,0.00059379434,0.0023119715,0.0012414431,0.19373268,0.49425846,0.0022850502,0.011389106,0.00015110595],"about_ca_topic_score_codex":0.00094423164,"about_ca_topic_score_gemma":0.00176494,"teacher_disagreement_score":0.0017105615,"about_ca_system_score_codex":0.0001521003,"about_ca_system_score_gemma":0.00018838693,"threshold_uncertainty_score":0.0057224035},"labels":[],"label_agreement":null},{"id":"W4309374308","doi":"10.1109/smc53654.2022.9945151","title":"Portfolio Selection for SAT Instances","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","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 Regina","funders":"","keywords":"Computer science; Portfolio; Solver; Context (archaeology); Cluster analysis; Complement (music); Mathematical optimization; Greedy algorithm; Set (abstract data type); Boolean satisfiability problem; Limit (mathematics); Selection (genetic algorithm); Theoretical computer science; Artificial intelligence; Mathematics; Algorithm","score_opus":0.04584683663469806,"score_gpt":0.2846385741773983,"score_spread":0.23879173754270022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309374308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031579565,0.0026887313,0.944536,0.0015546002,0.000245079,0.000616051,0.001069203,0.0021589382,0.015551905],"genre_scores_gemma":[0.40207094,0.0017705571,0.5787535,0.0011538992,0.00050748215,0.0014120804,0.0044886502,0.0007453862,0.009097443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99600935,0.0019703228,0.00018145949,0.0006805796,0.0007822811,0.00037592344],"domain_scores_gemma":[0.9909745,0.0068089217,0.00050292193,0.0006568412,0.00073787104,0.00031893697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044966014,0.0023205876,0.0025778115,0.0029033981,0.00091992004,0.0030810237,0.0026732907,0.002515757,0.014497011],"category_scores_gemma":[0.018012675,0.001131807,0.002240883,0.0036275906,0.0010519976,0.0029988305,0.0020607333,0.0028800964,0.0024009484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028116553,0.00029534736,0.0036258933,0.0005131541,0.00023343942,0.0001941668,0.00013446361,0.6174015,0.001365774,0.066452354,0.024571486,0.2849313],"study_design_scores_gemma":[0.000055629134,0.00008971732,0.00031009573,0.00005629416,0.000034755696,0.000056917284,0.00003652944,0.9487478,0.0005458213,0.046312395,0.0037429642,0.000011075678],"about_ca_topic_score_codex":0.0017530976,"about_ca_topic_score_gemma":0.003096913,"teacher_disagreement_score":0.014497011,"about_ca_system_score_codex":0.0020606255,"about_ca_system_score_gemma":0.0025595836,"threshold_uncertainty_score":0.04849732},"labels":[],"label_agreement":null},{"id":"W4309374654","doi":"10.1109/smc53654.2022.9945605","title":"Managing Inconsistency With an Optimal Distribution of Information Granularity in Fuzzy Preference Relations","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Junta de Andalucía","keywords":"Granularity; Consistency (knowledge bases); Pairwise comparison; Preference; Data mining; Computer science; Fuzzy logic; Reliability (semiconductor); Matrix (chemical analysis); Process (computing); Artificial intelligence; Mathematics; Statistics","score_opus":0.14323777273122654,"score_gpt":0.3576604413775825,"score_spread":0.21442266864635598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309374654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17391133,0.0005189325,0.82331866,0.00029244513,0.000035002682,0.0001969749,0.000051466854,0.00029872087,0.0013765063],"genre_scores_gemma":[0.72981465,0.00013082165,0.26949328,0.00006676429,0.00003146716,0.00016398542,0.000057743266,0.00004549552,0.00019571571],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9839671,0.008082969,0.001349857,0.002072369,0.0037507007,0.000776945],"domain_scores_gemma":[0.94638443,0.03517017,0.004629407,0.009013718,0.004113157,0.0006891684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01903167,0.0008219711,0.00152366,0.0020715934,0.0013263456,0.0030652988,0.0018027991,0.0014038933,0.0009556171],"category_scores_gemma":[0.06835855,0.00067919755,0.00096449105,0.002926515,0.0014743627,0.0060165254,0.0039515803,0.0024723313,0.00016353486],"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":[0.0030866617,0.0013130257,0.016800625,0.00094983063,0.00053413707,0.00063208136,0.005468888,0.28669113,0.027433302,0.12668386,0.0014939129,0.5289126],"study_design_scores_gemma":[0.00047518083,0.0009121258,0.005123813,0.00015838449,0.00030518114,0.0004297866,0.0008505795,0.79223716,0.02503216,0.17203122,0.0022905574,0.00015388145],"about_ca_topic_score_codex":0.0007506308,"about_ca_topic_score_gemma":0.0004938947,"teacher_disagreement_score":0.01903167,"about_ca_system_score_codex":0.0012693523,"about_ca_system_score_gemma":0.0011758299,"threshold_uncertainty_score":0.10065031},"labels":[],"label_agreement":null},{"id":"W4309374658","doi":"10.1109/smc53654.2022.9945481","title":"Optimal Robust Control For Tremor Suppression in Parkinson’s Disease","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Neurological disorders and treatments","field":"Medicine","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 Windsor","funders":"","keywords":"Parkinson's disease; Control (management); Computer science; Control theory (sociology); Physical medicine and rehabilitation; Robust control; Disease; Neuroscience; Medicine; Psychology; Control system; Engineering; Artificial intelligence; Electrical engineering; Internal medicine","score_opus":0.056375428970935046,"score_gpt":0.29660321956841657,"score_spread":0.2402277905974815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309374658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017869294,0.0011437867,0.97472626,0.00021402592,0.000077262346,0.000024384864,0.000016198805,0.0002163465,0.0057125445],"genre_scores_gemma":[0.9616938,0.0008218035,0.0339979,0.000090449474,0.00005764153,0.00006427974,0.000038808234,0.000032605098,0.0032028311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978584,0.000049260638,0.000013432682,0.000052976924,0.00007230839,0.000026131607],"domain_scores_gemma":[0.9997856,0.00010221213,0.000044866254,0.0000110274395,0.0000477587,0.00000858194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043609197,0.0006181918,0.00046908716,0.0002728816,0.00023157091,0.0006113237,0.0004011677,0.0004937186,0.0011546264],"category_scores_gemma":[0.0008518464,0.00019771309,0.0004315291,0.00019130559,0.00058151025,0.00035158888,0.0005263953,0.0005447457,0.00012928309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011051142,0.000034927845,0.00033077868,0.00022009555,0.00004787064,0.00011135575,0.00011729611,0.90639514,0.011614575,0.020384006,0.00089838856,0.059735145],"study_design_scores_gemma":[0.000010026427,0.000070162794,0.00011747793,0.00000861336,0.00000917225,0.000012937031,0.000009211784,0.9956618,0.0008699426,0.0024668255,0.00075813115,0.000005609665],"about_ca_topic_score_codex":0.0038208012,"about_ca_topic_score_gemma":0.002107696,"teacher_disagreement_score":0.0038208012,"about_ca_system_score_codex":0.00035896277,"about_ca_system_score_gemma":0.0005834147,"threshold_uncertainty_score":0.0075971484},"labels":[],"label_agreement":null},{"id":"W4309676745","doi":"10.1109/smc53654.2022.9945394","title":"Fault-Resilience Role Engine for an Autonomous Cooperative Multi-Robot System using E-CARGO","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Distributed systems and fault tolerance","field":"Computer Science","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":"Nipissing University","funders":"","keywords":"Robustness (evolution); Computer science; Redundancy (engineering); Fault tolerance; Distributed computing; Mobile robot; Resilience (materials science); Process (computing); Robot; Motion planning; Reliability engineering; Engineering; Artificial intelligence","score_opus":0.06881004630316598,"score_gpt":0.31016865509495783,"score_spread":0.24135860879179186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309676745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07048078,0.00010033554,0.92443764,0.00019636222,0.000051591254,0.00011479963,0.00003235359,0.0008485644,0.0037375668],"genre_scores_gemma":[0.9415261,0.00008366729,0.054835763,0.00005261082,0.0000124021235,0.00011838597,0.000036352405,0.000022725077,0.0033120418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996929,0.00005394244,0.000025522046,0.00008366043,0.00009355728,0.000050503353],"domain_scores_gemma":[0.9996979,0.00007852332,0.000049069982,0.000049795308,0.000081244165,0.000043401353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005068965,0.00050626084,0.00045505422,0.0003149363,0.0005869628,0.00071517215,0.0012696015,0.00061386696,0.0016314138],"category_scores_gemma":[0.0008298493,0.0001737188,0.00046765892,0.0001913917,0.00068829936,0.0010484698,0.00094250555,0.0005284653,0.00030166053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029418708,0.00019317628,0.0016907534,0.00013684225,0.00005000088,0.0007771326,0.0005272176,0.85321504,0.031984318,0.058869638,0.001174599,0.05108711],"study_design_scores_gemma":[0.000014786171,0.00006894289,0.00009982803,0.0000043270247,0.000010324164,0.000040419425,0.000021075317,0.9943118,0.0022382191,0.0022899031,0.0008915345,0.000008804371],"about_ca_topic_score_codex":0.005406336,"about_ca_topic_score_gemma":0.0027073591,"teacher_disagreement_score":0.005406336,"about_ca_system_score_codex":0.0006388543,"about_ca_system_score_gemma":0.0009010591,"threshold_uncertainty_score":0.010749757},"labels":[],"label_agreement":null},{"id":"W4309679321","doi":"10.1109/smc53654.2022.9945270","title":"A Data-Centric Approach to Evaluate Requirements Engineering in Multidisciplinary Projects","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Multidisciplinary approach; Computer science; Requirements engineering; Systems engineering; Engineering management; Requirements analysis; Software engineering; Engineering; Software","score_opus":0.14774894760255738,"score_gpt":0.3454449707628305,"score_spread":0.1976960231602731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309679321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33380604,0.0008657154,0.57571197,0.0014910593,0.00022901251,0.011969975,0.015748821,0.0025418985,0.057635516],"genre_scores_gemma":[0.53240746,0.00027706605,0.4482687,0.00023775343,0.00004135702,0.009997573,0.0061652027,0.00018982601,0.0024149786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.95401055,0.024746692,0.0045556976,0.0032003075,0.012668251,0.00081851525],"domain_scores_gemma":[0.8777361,0.07105506,0.0114007145,0.012477821,0.025876878,0.0014533753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029054523,0.0012846966,0.0014890962,0.019107094,0.0014378793,0.004674309,0.0018836007,0.0012761835,0.003869709],"category_scores_gemma":[0.08141669,0.0005450802,0.0017115836,0.016827645,0.001278847,0.0035488901,0.0026968252,0.0017098547,0.00094376],"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":[0.0017018005,0.003593142,0.15088779,0.0044774343,0.0013129857,0.0008810577,0.011786808,0.051821366,0.017903155,0.0570304,0.0117172655,0.6868868],"study_design_scores_gemma":[0.00080337055,0.0088774795,0.2695791,0.003395253,0.0009808706,0.0014588536,0.03915552,0.4409003,0.051371567,0.06337865,0.11937819,0.00072074414],"about_ca_topic_score_codex":0.004719565,"about_ca_topic_score_gemma":0.0050885538,"teacher_disagreement_score":0.029054523,"about_ca_system_score_codex":0.0041143717,"about_ca_system_score_gemma":0.0035497,"threshold_uncertainty_score":0.15365684},"labels":[],"label_agreement":null},{"id":"W4309679509","doi":"10.1109/smc53654.2022.9945496","title":"Evolutionary Mapping with Multiple Unmanned Aerial Vehicles","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Robotic Path Planning Algorithms","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":"University of Regina","funders":"","keywords":"Drone; Computer science; Context (archaeology); Motion planning; Obstacle; Plan (archaeology); Real-time computing; Search and rescue; Evolutionary algorithm; Operations research; Artificial intelligence; Robot; Engineering","score_opus":0.04777489517899604,"score_gpt":0.2558853031340413,"score_spread":0.20811040795504526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309679509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0854007,0.0004938141,0.9085125,0.000205042,0.00007890357,0.00009770432,0.000035666075,0.0005267142,0.004649064],"genre_scores_gemma":[0.61503327,0.00025217896,0.38047197,0.00008868885,0.000025904272,0.00021078209,0.00007776111,0.000047411726,0.0037919744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995944,0.00012213796,0.000016251814,0.00007961818,0.00013596594,0.000051717114],"domain_scores_gemma":[0.99969053,0.00013216051,0.000049762893,0.000039320104,0.000063346204,0.000024767962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060323137,0.0008182232,0.0006192857,0.000659405,0.00051721674,0.0005402618,0.00094700756,0.00071223115,0.0011973948],"category_scores_gemma":[0.0010972791,0.00035113387,0.0006969602,0.00062484207,0.0003586325,0.00068527414,0.00097510876,0.00059894985,0.00012743469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030059591,0.00004727491,0.00060170697,0.000038347458,0.000052705385,0.000090901354,0.00006395222,0.9128588,0.00277948,0.0027353289,0.00032041004,0.08038097],"study_design_scores_gemma":[0.000008704744,0.000044591223,0.00019389321,0.0000047565104,0.000007504931,0.00002769638,0.000015079414,0.9965874,0.000913632,0.0010263319,0.0011657659,0.0000047538756],"about_ca_topic_score_codex":0.0052998634,"about_ca_topic_score_gemma":0.004019081,"teacher_disagreement_score":0.0052998634,"about_ca_system_score_codex":0.00050853565,"about_ca_system_score_gemma":0.00058475917,"threshold_uncertainty_score":0.010538042},"labels":[],"label_agreement":null},{"id":"W4309679519","doi":"10.1109/smc53654.2022.9945250","title":"Activity Ratio to Measure Physical Demand of Cognitive Workload","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates","keywords":"Workload; Measure (data warehouse); Computer science; Cognition; Psychology; Data mining; Operating system","score_opus":0.07856786339092965,"score_gpt":0.37034742297947865,"score_spread":0.291779559588549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309679519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8590192,0.0028442978,0.10236988,0.00042631867,0.00051305006,0.0014179312,0.0060601034,0.00084461353,0.02650453],"genre_scores_gemma":[0.9632896,0.00092877354,0.02685653,0.00028198343,0.00020504992,0.0012535806,0.002780506,0.00007502822,0.004328905],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9985538,0.00036063287,0.00019385327,0.0002449299,0.000540109,0.00010666531],"domain_scores_gemma":[0.99664766,0.0013679715,0.00076566875,0.00018973477,0.00077533606,0.00025367702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010787253,0.0009456096,0.00047133813,0.001546072,0.0002146369,0.0008430572,0.0005764582,0.00070347247,0.0052860933],"category_scores_gemma":[0.006259296,0.00018116209,0.00058071484,0.0011349795,0.00026992426,0.000747329,0.00063832547,0.0007281919,0.0014335102],"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":[0.0025342826,0.0026590026,0.543566,0.0027243353,0.0012112799,0.0006477671,0.0020796084,0.006226994,0.09652987,0.0042272424,0.009020777,0.32857278],"study_design_scores_gemma":[0.00013547548,0.004279024,0.9477108,0.00021923671,0.00037932873,0.0014163676,0.0013759144,0.015492958,0.01575015,0.002967478,0.010104042,0.00016934644],"about_ca_topic_score_codex":0.00073579507,"about_ca_topic_score_gemma":0.0014668816,"teacher_disagreement_score":0.0052860933,"about_ca_system_score_codex":0.00023176198,"about_ca_system_score_gemma":0.00021452866,"threshold_uncertainty_score":0.017683744},"labels":[],"label_agreement":null}]}