{"meta":{"query_hash":"f87e10df105e","filters":{"venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)"},"cohort_total":21,"direct_labels_cover":0,"predictions_cover":21,"exported":21,"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/f87e10df105e","api":"https://metacan.xera.ac/api/v1/cohort?venue=2021+IEEE%2FRSJ+International+Conference+on+Intelligent+Robots+and+Systems+%28IROS%29"},"results":[{"id":"W3084438944","doi":"10.1109/iros51168.2021.9635938","title":"Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Scale (ratio); Monocular; Artificial intelligence; Metric (unit); Computer science; Consistency (knowledge bases); Computer vision; Retraining; Pattern recognition (psychology); Artificial neural network; Geography","score_opus":0.049959121019413286,"score_gpt":0.3102498759552751,"score_spread":0.2602907549358618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084438944","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":[0.040859573,0.000171334,0.95669,0.000104767845,0.000021164238,0.00004209778,0.00007110695,0.0010238444,0.0010161796],"genre_scores_gemma":[0.7796127,0.00017932996,0.21532181,0.00016764928,0.000055897348,0.00013109829,0.0003330856,0.00020711144,0.0039912923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996247,0.00009706774,0.000014362101,0.00010250861,0.00010879762,0.00005250017],"domain_scores_gemma":[0.99937844,0.00020310254,0.00012001738,0.00013467754,0.00012864152,0.000035087527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008366526,0.0008111065,0.0006044815,0.00040537104,0.00021821648,0.00039192595,0.0012964741,0.0007996019,0.0013864718],"category_scores_gemma":[0.0030022212,0.0004376253,0.00038912747,0.00046277005,0.0006071644,0.0012660549,0.0011160966,0.0010882113,0.0004399316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024187636,0.0001592533,0.0019544587,0.00009667882,0.00008070672,0.000088849905,0.0001212268,0.5985023,0.02691397,0.005277949,0.004031323,0.36253142],"study_design_scores_gemma":[0.0000043812356,0.000023445977,0.000301045,0.0000027300312,0.0000025200768,0.000017412041,0.0000066696366,0.9959331,0.0023327903,0.001130522,0.00024236311,0.000003109162],"about_ca_topic_score_codex":0.0028239687,"about_ca_topic_score_gemma":0.0037699402,"teacher_disagreement_score":0.0028239687,"about_ca_system_score_codex":0.0004906646,"about_ca_system_score_gemma":0.00053740415,"threshold_uncertainty_score":0.005614996},"labels":[],"label_agreement":null},{"id":"W3122520957","doi":"10.1109/iros51168.2021.9636080","title":"Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Robot; Imitation; Task (project management); Salient; Representation (politics); Unsupervised learning; Deep learning; Machine learning; Human–computer interaction","score_opus":0.0406571803570758,"score_gpt":0.3241998143792468,"score_spread":0.28354263402217095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122520957","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":[0.030215573,0.00020440907,0.96607053,0.00014484272,0.000026293003,0.000086086635,0.00010948347,0.0018260094,0.0013167098],"genre_scores_gemma":[0.7478623,0.00027498315,0.24765925,0.00018736385,0.000047262536,0.00023531953,0.000453504,0.00023399228,0.0030460323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960417,0.00010944486,0.0000160954,0.00014994074,0.00007878974,0.00004163136],"domain_scores_gemma":[0.9986332,0.00079150137,0.00017726424,0.00023438386,0.00008982577,0.00007370835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007940158,0.00080767943,0.0006711182,0.00044237848,0.00024034388,0.0005592462,0.0015352776,0.0009257795,0.0015004992],"category_scores_gemma":[0.004666551,0.00048396992,0.0005115777,0.0003226859,0.00096758496,0.001253252,0.00090378476,0.0011542041,0.0003244807],"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":[0.000267412,0.0002713365,0.0031390765,0.00024387643,0.00010106628,0.00033371523,0.00026411086,0.49073395,0.029651776,0.013087887,0.0036009862,0.45830485],"study_design_scores_gemma":[0.00001394732,0.000099689794,0.00046710548,0.000010192125,0.0000080815025,0.000057325484,0.000017124668,0.98719704,0.0052124714,0.0061445814,0.00076047675,0.000012080719],"about_ca_topic_score_codex":0.0041084834,"about_ca_topic_score_gemma":0.004675958,"teacher_disagreement_score":0.0041084834,"about_ca_system_score_codex":0.00061868096,"about_ca_system_score_gemma":0.0007998443,"threshold_uncertainty_score":0.008169174},"labels":[],"label_agreement":null},{"id":"W3125605478","doi":"10.1109/iros51168.2021.9636035","title":"LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; ENCODE; Graph; Benchmark (surveying); Theoretical computer science; Artificial intelligence; Encoder; Representation (politics); Robotics; Motion (physics); Robot","score_opus":0.06709133067816217,"score_gpt":0.28068348370138163,"score_spread":0.21359215302321946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125605478","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":[0.01991689,0.00027694314,0.9734172,0.0003170953,0.000084270985,0.00004512732,0.000637763,0.0040748282,0.0012297736],"genre_scores_gemma":[0.6818087,0.00042826965,0.30654016,0.0003506838,0.00012327616,0.00024309185,0.0045282547,0.0005467043,0.0054308935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999764,0.000048309103,0.0000098800265,0.00009351443,0.00005327651,0.000030948147],"domain_scores_gemma":[0.99928683,0.00030187043,0.00008192868,0.00016274455,0.00012160151,0.000045029436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049884606,0.0010245553,0.00077633106,0.0006916094,0.00033692102,0.0006841006,0.0024665736,0.0012284595,0.0022605138],"category_scores_gemma":[0.0025000765,0.00048720997,0.0006016487,0.00093010464,0.00044048612,0.0016825012,0.0009169901,0.0019278242,0.00091007905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009152551,0.00007346276,0.00060755824,0.000039705694,0.000031580304,0.000041724663,0.000038235175,0.89188355,0.0019438016,0.004489385,0.004878908,0.09588056],"study_design_scores_gemma":[0.0000028399427,0.0000062993545,0.00003865957,0.0000015455115,0.0000016759161,0.000003245349,0.0000027446793,0.99755585,0.00025211927,0.0018745098,0.0002585817,0.0000019195775],"about_ca_topic_score_codex":0.017120467,"about_ca_topic_score_gemma":0.024029352,"teacher_disagreement_score":0.017120467,"about_ca_system_score_codex":0.00103302,"about_ca_system_score_gemma":0.00093300623,"threshold_uncertainty_score":0.034041643},"labels":[],"label_agreement":null},{"id":"W3136737068","doi":"10.1109/iros51168.2021.9636638","title":"Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Point cloud; Computer science; Artificial intelligence; Computer vision; Adversarial system; Object detection; Benchmark (surveying); RGB color model; Lidar; Key (lock); Domain (mathematical analysis); Point (geometry); Image (mathematics); Object (grammar); Vulnerability (computing); Pattern recognition (psychology); Computer security; Remote sensing; Mathematics; Geography","score_opus":0.06681095259077346,"score_gpt":0.31419538868519464,"score_spread":0.24738443609442118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136737068","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":[0.191846,0.000782249,0.7971965,0.0010695125,0.00015708801,0.0000947431,0.00024138809,0.001322279,0.007290267],"genre_scores_gemma":[0.97515565,0.00016627072,0.022533534,0.00017725203,0.00002760933,0.000030004534,0.00012830431,0.0000588345,0.0017224923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985474,0.00047397887,0.000043205044,0.00027259128,0.00045919543,0.00020351817],"domain_scores_gemma":[0.99728644,0.0017799824,0.00026922414,0.00039331612,0.00017122853,0.00009981559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014725036,0.001163167,0.00079616904,0.00050609367,0.00048673348,0.00063705735,0.0010610062,0.0012627376,0.0014503483],"category_scores_gemma":[0.0059976825,0.00045923216,0.001038772,0.0003190022,0.0017037912,0.0015305541,0.002747903,0.0020359368,0.00030527558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012641095,0.000023679742,0.0007645617,0.000024435705,0.000050745362,0.00009448684,0.000041075535,0.97298753,0.0028661182,0.009589991,0.00088063604,0.012550344],"study_design_scores_gemma":[0.0000024890953,0.00002218274,0.00011512057,0.000004351418,0.000004054888,0.000027819593,0.000004840798,0.9952715,0.0011964134,0.0031537833,0.0001923347,0.000005164997],"about_ca_topic_score_codex":0.004045431,"about_ca_topic_score_gemma":0.0031325924,"teacher_disagreement_score":0.004045431,"about_ca_system_score_codex":0.0012454683,"about_ca_system_score_gemma":0.0005863718,"threshold_uncertainty_score":0.009036541},"labels":[],"label_agreement":null},{"id":"W3157951743","doi":"10.1109/iros51168.2021.9636440","title":"Seeing All the Angles: Learning Multiview Manipulation Policies for Contact-Rich Tasks from Demonstrations","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Reinforcement Learning in Robotics","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 Toronto","funders":"","keywords":"Viewpoints; Computer science; Task (project management); Artificial intelligence; Perspective (graphical); Human–computer interaction; Robot; Variety (cybernetics); Computer vision","score_opus":0.11316112666620681,"score_gpt":0.3296329576508928,"score_spread":0.21647183098468598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157951743","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":[0.16437043,0.00034551302,0.83308685,0.00023004433,0.000026425378,0.00007563468,0.00007912323,0.0006906469,0.001095348],"genre_scores_gemma":[0.942302,0.00010588301,0.05644787,0.00009095153,0.0000157559,0.00008766147,0.0001114861,0.000053793447,0.00078455237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957794,0.00012759423,0.000024437722,0.0001239777,0.00008997971,0.000056076362],"domain_scores_gemma":[0.9975923,0.0015527578,0.0003304956,0.00021575604,0.00014110668,0.00016752616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014773309,0.0006998122,0.0008556784,0.00035514377,0.0002429918,0.00056252757,0.0010862035,0.00077930297,0.0011183268],"category_scores_gemma":[0.005604007,0.00047068507,0.00035878315,0.00026276565,0.00079065596,0.0010843597,0.0011666407,0.0012044378,0.00025023113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041083852,0.000194019,0.0033018477,0.000091753376,0.00006139725,0.00009266997,0.0001493708,0.88844484,0.008166262,0.003328541,0.00063813856,0.09512033],"study_design_scores_gemma":[0.000020602916,0.000083017956,0.0003635237,0.000007792507,0.0000048926645,0.000014129932,0.0000117552645,0.9954426,0.0014999923,0.0023946196,0.00015038445,0.000006664],"about_ca_topic_score_codex":0.0033704978,"about_ca_topic_score_gemma":0.0032176434,"teacher_disagreement_score":0.0033704978,"about_ca_system_score_codex":0.000698257,"about_ca_system_score_gemma":0.0009297078,"threshold_uncertainty_score":0.007812977},"labels":[],"label_agreement":null},{"id":"W3203124669","doi":"10.1109/iros51168.2021.9636480","title":"Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Huawei Technologies (Canada)","funders":"","keywords":"Reinforcement learning; Computer science; Range (aeronautics); Motion planning; Artificial intelligence; Computation; Field (mathematics); Motion (physics); Machine learning; Mathematical optimization; Algorithm; Engineering; Mathematics; Robot","score_opus":0.06493866033802373,"score_gpt":0.29827904322557747,"score_spread":0.23334038288755374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203124669","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":[0.051852297,0.00027856737,0.94477373,0.00031516157,0.000037440837,0.00004902982,0.00003113863,0.00034890595,0.0023138237],"genre_scores_gemma":[0.9607961,0.00011219727,0.03786557,0.00007152617,0.000022276594,0.00007026009,0.000052828836,0.000032467415,0.000976789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963176,0.00011835153,0.000019381921,0.000082650426,0.000090171234,0.000057595662],"domain_scores_gemma":[0.9986553,0.0008645014,0.0001764804,0.000053831496,0.00017434136,0.00007562597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009447779,0.0008220385,0.0007560538,0.00032843676,0.00035863882,0.0006575916,0.00081690075,0.0007058097,0.00090950826],"category_scores_gemma":[0.0027215406,0.0003946834,0.00042359898,0.00024134667,0.0009479825,0.00069939386,0.0008958939,0.0010974904,0.00014101964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025301626,0.0000130681055,0.00033176425,0.000021259219,0.000011196189,0.00003427485,0.000027124937,0.989082,0.00058681745,0.0016130795,0.00013517622,0.008118944],"study_design_scores_gemma":[0.000004104171,0.000011332476,0.000040453127,0.000002229978,0.0000019087825,0.0000034879943,0.000003825734,0.99858814,0.00012842583,0.0011359096,0.00007879993,0.000001390097],"about_ca_topic_score_codex":0.008426274,"about_ca_topic_score_gemma":0.00560545,"teacher_disagreement_score":0.008426274,"about_ca_system_score_codex":0.0008831132,"about_ca_system_score_gemma":0.0013579457,"threshold_uncertainty_score":0.016754448},"labels":[],"label_agreement":null},{"id":"W3215972260","doi":"10.1109/iros51168.2021.9636755","title":"Towards Efficient Learning-Based Model Predictive Control via Feedback Linearization and Gaussian Process Regression","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Advanced Control Systems Optimization","field":"Engineering","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":"Queen's University","funders":"NCR","keywords":"Control theory (sociology); Model predictive control; Kriging; Controller (irrigation); Feedback linearization; Gaussian process; Computer science; Linearization; Inverse dynamics; Nonlinear system; Trajectory; Robotics; Artificial intelligence; Control engineering; Gaussian; Robot; Engineering; Machine learning; Control (management)","score_opus":0.019374848110641205,"score_gpt":0.26377351617585365,"score_spread":0.24439866806521243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215972260","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":[0.0049105175,0.00017833213,0.99333596,0.00007275402,0.000014712471,0.00001843468,0.0000100318475,0.00034056467,0.0011186575],"genre_scores_gemma":[0.7885167,0.0004778544,0.20713837,0.00012724003,0.000086637614,0.00019544095,0.00010719643,0.000113656446,0.0032368635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955624,0.00012721661,0.000018940393,0.000078294004,0.00017244414,0.000046877925],"domain_scores_gemma":[0.9994498,0.00027917887,0.00008361113,0.000052466818,0.0001197126,0.000015154877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095861964,0.00094703736,0.000810417,0.00035226546,0.00033815895,0.000724639,0.000913364,0.000651484,0.0007871523],"category_scores_gemma":[0.0016895593,0.00041217564,0.00048029152,0.00050615566,0.0006949155,0.00064611156,0.0011062219,0.0013637383,0.0003617152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030162113,0.000027591923,0.00013532116,0.000060270668,0.000019401126,0.000037235866,0.00004791377,0.95230335,0.002900823,0.007165525,0.00053510815,0.03673728],"study_design_scores_gemma":[0.0000025930958,0.000009923615,0.000021190679,0.0000016991897,0.0000014323111,0.0000027589572,0.0000013336801,0.99880624,0.00030618953,0.0006664453,0.00017849678,0.000001606464],"about_ca_topic_score_codex":0.0066722077,"about_ca_topic_score_gemma":0.004008071,"teacher_disagreement_score":0.0066722077,"about_ca_system_score_codex":0.00047477442,"about_ca_system_score_gemma":0.00093224336,"threshold_uncertainty_score":0.013266742},"labels":[],"label_agreement":null},{"id":"W4200056893","doi":"10.1109/iros51168.2021.9636479","title":"A Marginal Log-Likelihood Approach for the Estimation of Discount Factors of Multiple Experts in Inverse Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Decision-Making and Behavioral Economics","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 Toronto","funders":"","keywords":"Marginal likelihood; Computer science; Latent variable; Machine learning; Probabilistic logic; Hyperparameter; Expectation–maximization algorithm; Markov decision process; Artificial intelligence; Principle of maximum entropy; Reinforcement learning; Latent variable model; Likelihood function; Variable (mathematics); Markov process; Bayesian probability; Mathematical optimization; Mathematics; Maximum likelihood; Estimation theory; Statistics; Algorithm","score_opus":0.21327069326339732,"score_gpt":0.3892692312640018,"score_spread":0.1759985380006045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200056893","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":[0.0034138951,0.00008172862,0.9958747,0.00012942345,0.00000877043,0.000029311761,0.000020629222,0.00007741838,0.00036401948],"genre_scores_gemma":[0.5344641,0.00037211267,0.46031335,0.00029529605,0.000104321625,0.00051441614,0.00026339336,0.00022053908,0.003452456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997471,0.0015198195,0.0000901028,0.0003963087,0.00036275503,0.00016000641],"domain_scores_gemma":[0.9920596,0.0061220713,0.0005949241,0.00037128857,0.00057365064,0.00027835227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061327885,0.0015457363,0.0019994103,0.0010274422,0.0004676502,0.0017319386,0.0032118491,0.0018527339,0.0033156467],"category_scores_gemma":[0.02345035,0.0011719686,0.001171197,0.00093475636,0.0024083743,0.003218731,0.0022636608,0.0034464772,0.00053551735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009388897,0.00006406177,0.00089418475,0.00010222828,0.00007071064,0.00009116927,0.00012814964,0.9327578,0.0007332251,0.03982279,0.0006939997,0.024547761],"study_design_scores_gemma":[0.000007709553,0.000023769027,0.00008501827,0.000011333044,0.0000061197684,0.000018343668,0.0000067153173,0.981199,0.00020693662,0.018193629,0.00023082056,0.0000105828885],"about_ca_topic_score_codex":0.0034330545,"about_ca_topic_score_gemma":0.0029528292,"teacher_disagreement_score":0.0061327885,"about_ca_system_score_codex":0.0021180343,"about_ca_system_score_gemma":0.0021378319,"threshold_uncertainty_score":0.03243363},"labels":[],"label_agreement":null},{"id":"W4200066496","doi":"10.1109/iros51168.2021.9636422","title":"Trajectory-Constrained Deep Latent Visual Attention for Improved Local Planning in Presence of Heterogeneous Terrain","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Multimodal Machine Learning Applications","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":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Trajectory; Generalization; Terrain; Constraint (computer-aided design); Feature (linguistics); Task (project management); Computer vision; Collision avoidance; Visual space; Machine learning; Collision; Mathematics; Engineering; Psychology","score_opus":0.042992689432445066,"score_gpt":0.328001037531914,"score_spread":0.28500834809946896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200066496","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":[0.034747362,0.00018150597,0.96211404,0.00016363454,0.000033040415,0.000020213714,0.00006779046,0.0015554943,0.0011168877],"genre_scores_gemma":[0.90849787,0.00010914715,0.087676644,0.00016762996,0.000039809187,0.00005239041,0.00019467217,0.00019615449,0.0030656199],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998149,0.00003014235,0.000006375329,0.000062927225,0.00004261147,0.000043010354],"domain_scores_gemma":[0.9995171,0.00021899705,0.00006167777,0.000076586366,0.000072513285,0.000053051903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040832715,0.0007104967,0.00070017844,0.0003371662,0.00026402404,0.0005854791,0.0015065696,0.00072740164,0.0017341916],"category_scores_gemma":[0.0018478595,0.00041124783,0.0004992009,0.00039698975,0.0005263311,0.00091798895,0.0013248514,0.001264693,0.0003099733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014661097,0.00007599305,0.0009104862,0.000047284033,0.000045234246,0.00007966396,0.00009020319,0.86336404,0.009212171,0.0041326224,0.0017784901,0.12011721],"study_design_scores_gemma":[0.0000047634935,0.000017450973,0.00009352801,0.0000019077838,0.0000036670149,0.000008927974,0.000002801641,0.99734735,0.000847659,0.0015479178,0.00012189397,0.000002163805],"about_ca_topic_score_codex":0.013959937,"about_ca_topic_score_gemma":0.015741313,"teacher_disagreement_score":0.013959937,"about_ca_system_score_codex":0.0009062807,"about_ca_system_score_gemma":0.0012491245,"threshold_uncertainty_score":0.027757406},"labels":[],"label_agreement":null},{"id":"W4200128685","doi":"10.1109/iros51168.2021.9636439","title":"Image-Based Joint State Estimation Pipeline for Sensorless Manipulators","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Pipeline (software); Artificial intelligence; Joint (building); Convolutional neural network; Robot; Excavator; Computer vision; State (computer science); Image (mathematics); Monocular; Engineering; Algorithm","score_opus":0.09430288392073351,"score_gpt":0.3138369955816506,"score_spread":0.21953411166091713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200128685","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":[0.005362201,0.000047833422,0.9914522,0.000061641025,0.00001233919,0.000023457062,0.00004167818,0.002302172,0.0006964234],"genre_scores_gemma":[0.5501852,0.00015948399,0.44319382,0.00014953087,0.000030379237,0.00011721788,0.00047057983,0.00034300005,0.0053508244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995969,0.000044601245,0.000016865268,0.0001356095,0.00015548285,0.000050521237],"domain_scores_gemma":[0.99955946,0.00012458475,0.00007662184,0.00011821629,0.000091988215,0.000029149593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000575235,0.00077806413,0.00057391793,0.00037103688,0.00033213373,0.00061550504,0.0015381075,0.00084114465,0.005486024],"category_scores_gemma":[0.0013895446,0.0007180424,0.0006291518,0.0002711523,0.0007455887,0.0012632001,0.0013541101,0.0014707504,0.0014256388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003085496,0.00009214046,0.00091920735,0.000101947204,0.000058142527,0.00023660649,0.00015507542,0.6607642,0.03980635,0.0092681395,0.0022552316,0.2860344],"study_design_scores_gemma":[0.0000069512494,0.00003569667,0.0002159136,0.000005529861,0.000004995865,0.000036761936,0.00000738584,0.9837923,0.011796739,0.00312729,0.00096140365,0.00000899321],"about_ca_topic_score_codex":0.0038895612,"about_ca_topic_score_gemma":0.0051458343,"teacher_disagreement_score":0.005486024,"about_ca_system_score_codex":0.0007471652,"about_ca_system_score_gemma":0.0010343223,"threshold_uncertainty_score":0.018352509},"labels":[],"label_agreement":null},{"id":"W4200140111","doi":"10.1109/iros51168.2021.9636140","title":"Memory-based Deep Reinforcement Learning for POMDPs","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Compute Canada","keywords":"Reinforcement learning; Markov decision process; Computer science; Observable; Artificial intelligence; Partially observable Markov decision process; Noise (video); Robotics; Component (thermodynamics); Sensitivity (control systems); Feature (linguistics); Deep learning; Markov process; Markov chain; Machine learning; Robot; Markov model","score_opus":0.06087323939367912,"score_gpt":0.30000014092519434,"score_spread":0.23912690153151522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200140111","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":[0.0355684,0.00042117687,0.9604839,0.00027831268,0.000060675007,0.000050039438,0.00010474956,0.0010343855,0.001998362],"genre_scores_gemma":[0.9218271,0.00015889114,0.076022945,0.00015686249,0.00002236306,0.00013178293,0.00015984438,0.0000693151,0.001450848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996376,0.00009724244,0.000025467194,0.000082331666,0.000084002124,0.000073258176],"domain_scores_gemma":[0.99836856,0.0010971255,0.00015180415,0.00009766298,0.00019960561,0.00008525095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010855757,0.0009095628,0.0011618591,0.00035559217,0.00034679275,0.0006421455,0.0013223012,0.00087961834,0.0024530678],"category_scores_gemma":[0.0037351968,0.0005055127,0.0004843209,0.0003495932,0.00082275626,0.00096250133,0.001192551,0.0018087202,0.00027124616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005079453,0.00003341674,0.00043262113,0.000051893072,0.000019725901,0.000032336855,0.000028836277,0.9695182,0.00048808582,0.003692927,0.00043962232,0.02521153],"study_design_scores_gemma":[0.00000389851,0.000008045963,0.000016675678,0.0000019406145,0.0000013712558,0.0000020593293,0.000001501262,0.9984201,0.000110249624,0.0013774023,0.000055509314,0.0000011104621],"about_ca_topic_score_codex":0.009951817,"about_ca_topic_score_gemma":0.0095427595,"teacher_disagreement_score":0.009951817,"about_ca_system_score_codex":0.0013511986,"about_ca_system_score_gemma":0.0017001939,"threshold_uncertainty_score":0.019787788},"labels":[],"label_agreement":null},{"id":"W4200163943","doi":"10.1109/iros51168.2021.9636609","title":"Deadlock Prediction and Recovery for Distributed Collision Avoidance with Buffered Voronoi Cells","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Science and Engineering Research Council","keywords":"Deadlock; Heuristics; Computer science; Deadlock prevention algorithms; Voronoi diagram; Distributed computing; Simple (philosophy); Collision avoidance; Robot; Collision; Artificial intelligence; Mathematics; Computer security","score_opus":0.041217035884472,"score_gpt":0.26518750893579945,"score_spread":0.22397047305132745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200163943","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":[0.018181842,0.00016345274,0.98039263,0.00004636455,0.00002768287,0.000056740653,0.0000349546,0.0005331153,0.00056325947],"genre_scores_gemma":[0.561407,0.00013491284,0.43718752,0.00004640952,0.00001695442,0.00018788097,0.00014581437,0.00007784275,0.0007956052],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994973,0.00009679516,0.000028456421,0.00009019462,0.00019968401,0.00008758852],"domain_scores_gemma":[0.9989673,0.0004809262,0.00011175861,0.00011334882,0.00023608202,0.000090758556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007322476,0.0005477972,0.00069457485,0.0008649749,0.00063806755,0.0008685307,0.0020240287,0.00047721658,0.0010493626],"category_scores_gemma":[0.002771912,0.00030604313,0.0004077002,0.0006293347,0.000543437,0.0009962929,0.0016596667,0.00053466944,0.00024129904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041084067,0.000102397455,0.0039292825,0.00014162903,0.00007972356,0.00018160572,0.00027247233,0.74328125,0.019629473,0.031038897,0.001655827,0.19927663],"study_design_scores_gemma":[0.000032229913,0.000031024898,0.00012383053,0.000005997028,0.000009487667,0.000034592063,0.000024517121,0.9888849,0.005124369,0.0047238483,0.0009943077,0.000010858324],"about_ca_topic_score_codex":0.0054481016,"about_ca_topic_score_gemma":0.0046288613,"teacher_disagreement_score":0.0054481016,"about_ca_system_score_codex":0.00082867825,"about_ca_system_score_gemma":0.0015625836,"threshold_uncertainty_score":0.010832787},"labels":[],"label_agreement":null},{"id":"W4200200310","doi":"10.1109/iros51168.2021.9635875","title":"PLUMENet: Efficient 3D Object Detection from Stereo Images","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Point cloud; Computer vision; Lidar; Benchmark (surveying); Metric (unit); Object detection; Object (grammar); Feature (linguistics); Representation (politics); Exploit; Inference; Detector; Key (lock); Pattern recognition (psychology); Remote sensing; Engineering; Geography","score_opus":0.04345949719033659,"score_gpt":0.2905566820600707,"score_spread":0.2470971848697341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200200310","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":[0.017267948,0.0009842751,0.93564075,0.00026241588,0.00015387287,0.0003153381,0.0024997157,0.04000962,0.0028660158],"genre_scores_gemma":[0.1475309,0.00056801864,0.8361906,0.00043551644,0.00012237973,0.00038267107,0.009131808,0.0013502846,0.0042878445],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906534,0.00006576582,0.000021558877,0.00020486586,0.00055585353,0.000086713895],"domain_scores_gemma":[0.9995565,0.00008937826,0.00006129822,0.00009280689,0.00015694753,0.00004318997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004897189,0.0025290835,0.0017528572,0.0028522413,0.00045959212,0.0014313727,0.0052734087,0.0018545865,0.004897505],"category_scores_gemma":[0.0015699159,0.0012651255,0.0014448322,0.0018626153,0.0005660901,0.002258652,0.002669811,0.0015481345,0.003664285],"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":[0.0003382469,0.0003574953,0.0019670513,0.00031085062,0.0002479412,0.00026598788,0.000086004904,0.086332776,0.036591027,0.004741019,0.04197501,0.8267866],"study_design_scores_gemma":[0.000041043728,0.00007209145,0.0005194456,0.000014907955,0.00001494692,0.00014463026,0.000024559171,0.9812837,0.0091825295,0.0042638634,0.0044131344,0.000025071158],"about_ca_topic_score_codex":0.016892701,"about_ca_topic_score_gemma":0.029319756,"teacher_disagreement_score":0.016892701,"about_ca_system_score_codex":0.0011124156,"about_ca_system_score_gemma":0.0017734582,"threshold_uncertainty_score":0.033588767},"labels":[],"label_agreement":null},{"id":"W4200232909","doi":"10.1109/iros51168.2021.9636627","title":"Deep Neural Skill Assessment and Transfer: Application to Robotic Surgery Training","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada Foundation for Innovation; Health Research","keywords":"Computer science; Transfer of learning; Artificial neural network; Training (meteorology); Artificial intelligence; Robot; Human–computer interaction","score_opus":0.11211322615996172,"score_gpt":0.359077710568045,"score_spread":0.24696448440808327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200232909","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":[0.35418442,0.001485332,0.63404554,0.00061022234,0.00019166738,0.00019099115,0.00034123103,0.0039568683,0.004993652],"genre_scores_gemma":[0.9281167,0.00025070642,0.06866036,0.0001012481,0.000024652754,0.00006894164,0.00022966374,0.000047755573,0.0024999436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998109,0.000046711913,0.000010499356,0.000040009538,0.000056437824,0.00003541222],"domain_scores_gemma":[0.9996562,0.00016002421,0.000033035463,0.00004074339,0.000077877645,0.00003208301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006243843,0.0006282479,0.0002843555,0.00043256118,0.00016511157,0.0003288842,0.00057711644,0.0006592916,0.001333946],"category_scores_gemma":[0.0019107659,0.00016719953,0.00029641174,0.00043465558,0.0002646324,0.00041082446,0.0007626979,0.0006705007,0.00023867207],"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":[0.0002947373,0.0003874973,0.0043181665,0.00012233338,0.000089273664,0.000230749,0.00012781059,0.43596208,0.022919264,0.0013636652,0.002008492,0.5321759],"study_design_scores_gemma":[0.000009783434,0.00011828605,0.00186418,0.000008546887,0.000009989599,0.000043376414,0.000018234281,0.9882569,0.0077399053,0.0012910345,0.00063119264,0.000008539474],"about_ca_topic_score_codex":0.00591201,"about_ca_topic_score_gemma":0.0046971035,"teacher_disagreement_score":0.00591201,"about_ca_system_score_codex":0.000562408,"about_ca_system_score_gemma":0.00061187363,"threshold_uncertainty_score":0.011755168},"labels":[],"label_agreement":null},{"id":"W4200294443","doi":"10.1109/iros51168.2021.9636449","title":"Latent Attention Augmentation for Robust Autonomous Driving Policies","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Domain Adaptation and Few-Shot Learning","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 Toronto; McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Pipeline (software); Artificial intelligence; Adaptability; Machine learning; Domain (mathematical analysis); Segmentation; Robot; State space; Latent semantic analysis; Adaptation (eye)","score_opus":0.10033025223318776,"score_gpt":0.3167034949752955,"score_spread":0.21637324274210773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200294443","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":[0.036372006,0.00016681834,0.9607194,0.00015959567,0.00003306841,0.000036749665,0.00004679582,0.0012396915,0.0012258876],"genre_scores_gemma":[0.93551683,0.0000709156,0.062413447,0.00010953467,0.000025678102,0.00008658289,0.000103899954,0.000091819755,0.001581249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967194,0.000089230845,0.000014736049,0.000100634934,0.0000647237,0.000058729904],"domain_scores_gemma":[0.9989819,0.0005903885,0.000106281324,0.00012953515,0.00013359441,0.000058312777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087759126,0.00068450725,0.00075234845,0.00034824616,0.0003176381,0.00054123957,0.0010047111,0.000726283,0.0017286317],"category_scores_gemma":[0.0036303543,0.00044784448,0.00044678999,0.00022941417,0.0008055564,0.0013480873,0.0012564504,0.0016528158,0.00038092077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013401694,0.00010389185,0.0005271165,0.00004352736,0.000024306464,0.000043820328,0.000082226456,0.91519153,0.0060374644,0.0062535927,0.00095271826,0.07060579],"study_design_scores_gemma":[0.0000030220954,0.000013414759,0.0000430549,0.000001687992,0.000001643439,0.0000035025307,0.0000022756674,0.99714917,0.00068913243,0.0019810176,0.000110042405,0.0000020469672],"about_ca_topic_score_codex":0.0035237016,"about_ca_topic_score_gemma":0.0034827474,"teacher_disagreement_score":0.0035237016,"about_ca_system_score_codex":0.00082108186,"about_ca_system_score_gemma":0.00093220687,"threshold_uncertainty_score":0.007006407},"labels":[],"label_agreement":null},{"id":"W4200304283","doi":"10.1109/iros51168.2021.9635900","title":"A Multimodal and Hybrid Framework for Human Navigational Intent Inference","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":"Simon Fraser University","funders":"","keywords":"Computer science; Trajectory; Artificial intelligence; Reachability; Inference; Set (abstract data type); Position (finance); Perspective (graphical); Motion (physics); Machine learning; Orientation (vector space); Perception; Computer vision; Motion planning; Motion capture; Robot; Algorithm; Mathematics","score_opus":0.04790299826362454,"score_gpt":0.3108265899219895,"score_spread":0.262923591658365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200304283","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":[0.02259681,0.0007439913,0.9694076,0.00027213438,0.00006828729,0.000047255868,0.0009792796,0.00333488,0.0025497274],"genre_scores_gemma":[0.6865583,0.00061869365,0.30315578,0.0003373507,0.000112208276,0.00013404724,0.003337378,0.00021077575,0.005535586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999765,0.000040027164,0.0000095043015,0.000109623965,0.000042271506,0.00003353625],"domain_scores_gemma":[0.99977773,0.00006112829,0.000027748445,0.000048827787,0.00006339779,0.000021193273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037649143,0.0012697598,0.0004228405,0.0009850074,0.00026096954,0.0005195418,0.0010856775,0.0008033878,0.0024147846],"category_scores_gemma":[0.0010449224,0.00029259213,0.00085532555,0.0006007977,0.0004088727,0.001234802,0.0009811461,0.0010059836,0.0007115195],"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":[0.00043252597,0.0003382801,0.0061925594,0.00023028188,0.00023230104,0.000454837,0.00028931512,0.2947957,0.021320747,0.012308197,0.011771293,0.65163404],"study_design_scores_gemma":[0.0000071655463,0.00006861644,0.0011211527,0.000021060592,0.000028964005,0.00007293407,0.00003104375,0.98419094,0.00259343,0.010227878,0.0016215199,0.000015262025],"about_ca_topic_score_codex":0.011583798,"about_ca_topic_score_gemma":0.016442677,"teacher_disagreement_score":0.011583798,"about_ca_system_score_codex":0.0005889497,"about_ca_system_score_gemma":0.000639875,"threshold_uncertainty_score":0.023032784},"labels":[],"label_agreement":null},{"id":"W4200356239","doi":"10.1109/iros51168.2021.9636461","title":"Map-Aided Train Navigation with IMU Measurements","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Indoor and Outdoor Localization Technologies","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":"McGill University","funders":"","keywords":"Inertial measurement unit; Computer science; Estimator; GNSS applications; Dead reckoning; Acceleration; Track (disk drive); Sensor fusion; Computer vision; Constraint (computer-aided design); Global Positioning System; Real-time computing; Artificial intelligence; Simulation; Engineering; Mathematics; Telecommunications; Statistics","score_opus":0.06689787562250461,"score_gpt":0.2691267012699457,"score_spread":0.2022288256474411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200356239","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":[0.06789179,0.00020895756,0.9206809,0.00008281643,0.00008833587,0.000048186397,0.00037496464,0.0027696202,0.007854546],"genre_scores_gemma":[0.85107946,0.00015746456,0.14319882,0.0000406912,0.000043031338,0.00010784969,0.00044782393,0.00009134772,0.004833472],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999788,0.00003931051,0.000008028691,0.000040021365,0.00009926414,0.000025448348],"domain_scores_gemma":[0.9998323,0.000033406635,0.000024489696,0.000040934214,0.000060798648,0.0000081376475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014983112,0.00068170915,0.00047739016,0.0005203766,0.00031873622,0.0006195339,0.00056722836,0.0004242447,0.0016614817],"category_scores_gemma":[0.00066828565,0.00023665669,0.00025453104,0.0005632006,0.00016678043,0.0006225173,0.0010020584,0.00031795955,0.0013446016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029576235,0.00007133884,0.0052854777,0.00023099274,0.00010479542,0.00020318969,0.00022362146,0.47219786,0.045999553,0.0043893666,0.0036558176,0.46734226],"study_design_scores_gemma":[0.000015815907,0.00010439943,0.0038242503,0.0000123602385,0.000026043615,0.00009686866,0.00006439589,0.97300875,0.015053244,0.0013648144,0.0064026215,0.000026409907],"about_ca_topic_score_codex":0.004318988,"about_ca_topic_score_gemma":0.0058438657,"teacher_disagreement_score":0.004318988,"about_ca_system_score_codex":0.00017137993,"about_ca_system_score_gemma":0.00057325314,"threshold_uncertainty_score":0.008587658},"labels":[],"label_agreement":null},{"id":"W4200366202","doi":"10.1109/iros51168.2021.9636741","title":"On Fault Classification in Connected Autonomous Vehicles Using Supervised Machine Learning","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadratic classifier; Artificial intelligence; Support vector machine; Machine learning; Classifier (UML); Computer science; Naive Bayes classifier; Margin classifier; Platoon; Discriminant; Bayes classifier; Pattern recognition (psychology)","score_opus":0.06005271555710401,"score_gpt":0.2761365916842959,"score_spread":0.21608387612719188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200366202","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":[0.25175056,0.0011699093,0.74447495,0.00029103176,0.00008412337,0.000113741764,0.00008396643,0.0006584422,0.0013734216],"genre_scores_gemma":[0.9458729,0.00027419764,0.05278445,0.000046839727,0.00005106904,0.000059961512,0.00015642976,0.000020927017,0.0007331572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990206,0.000397083,0.0000850556,0.0001752222,0.00023597802,0.00008613966],"domain_scores_gemma":[0.9943169,0.003907995,0.00039422867,0.00028857295,0.0010166545,0.000075672484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017423517,0.000709212,0.0008324236,0.00095195865,0.00040913548,0.00056591374,0.00068062986,0.000692127,0.00044574327],"category_scores_gemma":[0.005313326,0.00023105131,0.00061886763,0.00062315783,0.0005655039,0.000794775,0.0003909956,0.00056475867,0.00013119282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019271672,0.00023794548,0.0056176186,0.00011986636,0.00011329815,0.000060698632,0.000105232,0.81419206,0.0021077325,0.0011856498,0.0004598906,0.17560732],"study_design_scores_gemma":[0.0000031881937,0.000046356174,0.0006145595,0.0000043792743,0.000005035402,0.0000102440845,0.0000088537545,0.9980445,0.0006143817,0.0005715389,0.00007301096,0.0000037709535],"about_ca_topic_score_codex":0.007896892,"about_ca_topic_score_gemma":0.0049022133,"teacher_disagreement_score":0.007896892,"about_ca_system_score_codex":0.00069432333,"about_ca_system_score_gemma":0.0007372037,"threshold_uncertainty_score":0.01570189},"labels":[],"label_agreement":null},{"id":"W4200453233","doi":"10.1109/iros51168.2021.9636401","title":"Robot-assisted Breast Ultrasound Scanning Using Geometrical Analysis of the Seroma and Image Segmentation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Medical Image Segmentation Techniques","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 Alberta","funders":"","keywords":"Computer vision; Artificial intelligence; Imaging phantom; Computer science; Trajectory; Segmentation; Robot; Robotic arm; Orientation (vector space); Ultrasound; Visual servoing; Mathematics; Acoustics; Physics; Optics","score_opus":0.06402075817554106,"score_gpt":0.3326906825915315,"score_spread":0.26866992441599047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200453233","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":[0.02842739,0.0003833772,0.9690735,0.000069221285,0.000017809702,0.00004208212,0.00002378774,0.0010524885,0.00091041176],"genre_scores_gemma":[0.33848488,0.00038748095,0.6593512,0.00007031009,0.00003276728,0.00006090149,0.00009613815,0.00012094083,0.0013953717],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997421,0.000049723287,0.000013883874,0.00006999052,0.00010889085,0.000015458581],"domain_scores_gemma":[0.99972767,0.000090059984,0.000058260775,0.000048605267,0.000062993604,0.000012381578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020661624,0.0003648978,0.00030572584,0.00069825666,0.0001434083,0.00033743147,0.00045987807,0.00039308323,0.00080701936],"category_scores_gemma":[0.0006732688,0.00029209405,0.00039237697,0.00044259784,0.000323418,0.0004359914,0.00033292818,0.00020178167,0.0003330396],"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":[0.00021394176,0.00003836866,0.002079285,0.00021373031,0.000047267455,0.0002283,0.00017219197,0.050202876,0.49662405,0.002271672,0.0007540841,0.44715422],"study_design_scores_gemma":[0.000041322943,0.0005032058,0.010438078,0.000033506207,0.000087223096,0.003042777,0.000090065776,0.7530735,0.21896154,0.0027205592,0.010894786,0.0001133842],"about_ca_topic_score_codex":0.00089381094,"about_ca_topic_score_gemma":0.0011829749,"teacher_disagreement_score":0.00089381094,"about_ca_system_score_codex":0.00019713746,"about_ca_system_score_gemma":0.00033032653,"threshold_uncertainty_score":0.0026997328},"labels":[],"label_agreement":null},{"id":"W4200582041","doi":"10.1109/iros51168.2021.9636410","title":"Real-Time Hamilton-Jacobi Reachability Analysis of Autonomous System With An FPGA","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Formal Methods in Verification","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":"Simon Fraser University","funders":"","keywords":"Computer science; Reachability; Field-programmable gate array; Curse of dimensionality; Embedded system; Parallel computing; Real-time computing; Algorithm; Artificial intelligence","score_opus":0.056802871160548496,"score_gpt":0.3143942311967452,"score_spread":0.2575913600361967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200582041","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":[0.14992309,0.00017421086,0.8319055,0.00014252494,0.00005309383,0.00006828698,0.000259442,0.0041073184,0.013366514],"genre_scores_gemma":[0.80275035,0.00010271631,0.19364879,0.000043410564,0.000007764596,0.00011654999,0.00023880664,0.00011123855,0.002980415],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998826,0.000018737419,0.000007141841,0.00001763578,0.00005006213,0.000023821513],"domain_scores_gemma":[0.9998727,0.00006074362,0.000011024152,0.00002091777,0.000028178529,0.000006549608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017240363,0.00045778698,0.00022743508,0.0003252516,0.00024172918,0.00032823515,0.00039730113,0.00022479436,0.0036757365],"category_scores_gemma":[0.00038198647,0.0001645296,0.00035435663,0.00017244277,0.00026504704,0.00033155858,0.00028779614,0.000315353,0.00036695861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002259075,0.000047445315,0.0018458134,0.0001878407,0.000042348383,0.00031508607,0.00011782966,0.8607427,0.038619276,0.023326332,0.002072182,0.07245729],"study_design_scores_gemma":[0.000020223897,0.00004647703,0.00032105503,0.000007622973,0.000008644223,0.00003076609,0.00001558864,0.9851179,0.009020318,0.0035763204,0.001827754,0.0000074107666],"about_ca_topic_score_codex":0.004664817,"about_ca_topic_score_gemma":0.0051825955,"teacher_disagreement_score":0.004664817,"about_ca_system_score_codex":0.0005088836,"about_ca_system_score_gemma":0.0005778585,"threshold_uncertainty_score":0.012296557},"labels":[],"label_agreement":null},{"id":"W4200613207","doi":"10.1109/iros51168.2021.9636560","title":"Analytical Tip Force Estimation on Tendon-driven Catheters Through Inverse Solution of Cosserat Rod Model","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University","funders":"Science and Engineering Research Council","keywords":"Computation; Mathematics; Inverse problem; Kinematics; Inverse; Control theory (sociology); Applied mathematics; Computer science; Mathematical optimization; Algorithm; Mathematical analysis; Physics; Classical mechanics; Geometry","score_opus":0.07979828588617971,"score_gpt":0.30309946667693627,"score_spread":0.22330118079075656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200613207","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":[0.020487918,0.0001105188,0.9777764,0.000053202864,0.000015180496,0.000024461779,0.000021449332,0.00023117258,0.0012796049],"genre_scores_gemma":[0.619194,0.00047772884,0.37652507,0.0000515102,0.000020190275,0.00011751355,0.00008703276,0.000077304954,0.0034496314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997433,0.00005375412,0.000017319562,0.000047694233,0.000121941644,0.000016095093],"domain_scores_gemma":[0.9996599,0.000120766104,0.00007773368,0.000039396746,0.00009174758,0.000010430957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050522346,0.0005169755,0.0004047787,0.00039464768,0.00020192217,0.0004930691,0.00065381185,0.0009680105,0.0009629388],"category_scores_gemma":[0.0010246221,0.0002721536,0.0005287086,0.00032750843,0.00037334743,0.0005821896,0.00038941408,0.0004656139,0.0002914212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009670844,0.000057696605,0.001469018,0.0002474091,0.000023855024,0.00037288215,0.00028394416,0.8234024,0.08613587,0.012683646,0.00065191294,0.074574724],"study_design_scores_gemma":[0.0000028876218,0.000025092653,0.00013641501,0.0000070127235,0.0000024640437,0.00006294597,0.000010398984,0.99566275,0.00314881,0.00041870875,0.0005153389,0.000007179635],"about_ca_topic_score_codex":0.0019312717,"about_ca_topic_score_gemma":0.0016433923,"teacher_disagreement_score":0.0019312717,"about_ca_system_score_codex":0.00026402253,"about_ca_system_score_gemma":0.00065673044,"threshold_uncertainty_score":0.0038400292},"labels":[],"label_agreement":null}]}