{"meta":{"query_hash":"f2dd7f9a2141","filters":{"venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/f2dd7f9a2141","api":"https://metacan.xera.ac/api/v1/cohort?venue=2022+IEEE+31st+International+Symposium+on+Industrial+Electronics+%28ISIE%29"},"results":[{"id":"W4287595676","doi":"10.1109/isie51582.2022.9831502","title":"Loss Comparison of Electric Vehicle Fuel Cell Integration Methods","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Drivetrain; Inverter; Dual (grammatical number); Automotive engineering; Harmonics; Driving cycle; Voltage source inverter; Voltage; Electric vehicle; Computer science; Power (physics); Engineering; Electrical engineering; Torque; Physics","score_opus":0.025746924283265474,"score_gpt":0.2918162822024525,"score_spread":0.266069357919187,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287595676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96127933,0.0035309272,0.008458844,0.0013237271,0.0048745163,0.0009005573,0.00013439823,0.0011152563,0.018382458],"genre_scores_gemma":[0.9977198,0.0007135521,0.00028270337,0.00007361444,0.00034606282,0.00017439944,0.00010348175,0.00007288516,0.00051348907],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99706763,0.00022025096,0.00080864003,0.00043837348,0.00085271394,0.0006123757],"domain_scores_gemma":[0.9987858,0.0003376069,0.00029501726,0.0003855286,0.00012065834,0.00007537518],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007759409,0.00034538764,0.0004768317,0.0005043382,0.00020509414,0.00005951077,0.0010430774,0.00023869233,0.00033478902],"category_scores_gemma":[0.00007518607,0.00038849498,0.00020566642,0.0012051142,0.000040525523,0.00015988706,0.00012533675,0.0021735907,0.000024116274],"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":[0.0006264269,0.0009167083,0.0020806275,0.00004310034,0.0004385374,0.00001597587,0.0003903071,0.17670707,0.70090014,0.018468454,0.026638653,0.07277398],"study_design_scores_gemma":[0.0013812155,0.0012948421,0.00004457043,0.000012400107,0.000056797373,0.000017845174,0.00014110788,0.23036903,0.7308546,0.0012393588,0.034157664,0.0004305635],"about_ca_topic_score_codex":0.000055301636,"about_ca_topic_score_gemma":0.0000100785965,"teacher_disagreement_score":0.07234342,"about_ca_system_score_codex":0.0015558882,"about_ca_system_score_gemma":0.0001612133,"threshold_uncertainty_score":0.9998567},"labels":[],"label_agreement":null},{"id":"W4287848459","doi":"10.1109/isie51582.2022.9831643","title":"Monte Carlo Study of Jiles-Atherton Parameters on Hysteresis Area and Remnant Displacement","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Piezoelectric Actuators and Control","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Monte Carlo method; Hysteresis; Displacement (psychology); Parameter space; Statistical physics; Physics; Mathematics; Statistics; Condensed matter physics","score_opus":0.017292098813979404,"score_gpt":0.2181723443950831,"score_spread":0.20088024558110368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287848459","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956693,0.00015549983,0.00008817917,0.00032805643,0.001681474,0.0008070104,0.00012473254,0.00007924532,0.0010664691],"genre_scores_gemma":[0.99869543,0.00014489301,0.0000030362705,0.00011105996,0.00019814936,0.0003284297,0.000019207391,0.000057339825,0.00044242988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973902,0.0001520934,0.0005684274,0.0004498842,0.000980255,0.0004591055],"domain_scores_gemma":[0.99908507,0.0002315389,0.00019375641,0.00033913573,0.000046888734,0.00010360863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042464363,0.0003333506,0.0004057392,0.00024828519,0.00017878471,0.000060081446,0.0004942699,0.00010718087,0.00013717035],"category_scores_gemma":[0.00003457836,0.00032999096,0.00012740683,0.00029587516,0.000017657712,0.00008232545,0.00008393066,0.00089034846,0.000004514252],"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":[0.0058383476,0.0036198902,0.0041380674,0.00002983657,0.0037276852,0.00008415812,0.0028425837,0.86329657,0.020280395,0.00268812,0.020749725,0.0727046],"study_design_scores_gemma":[0.025648544,0.027763559,0.000457983,0.00017088233,0.0006422933,0.000060758273,0.002913563,0.8520554,0.021783436,0.00027959328,0.065831445,0.0023925889],"about_ca_topic_score_codex":0.00022510819,"about_ca_topic_score_gemma":0.000064813736,"teacher_disagreement_score":0.07031201,"about_ca_system_score_codex":0.0012099248,"about_ca_system_score_gemma":0.000075106,"threshold_uncertainty_score":0.99991524},"labels":[],"label_agreement":null},{"id":"W4287882643","doi":"10.1109/isie51582.2022.9831720","title":"Optimal Energy Saving Adaptive Cruise Control in Overtaking Scenarios for a Hybrid Electric Vehicle","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Traffic control and management","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Overtaking; Cruise control; Cruise; Automotive engineering; Electric vehicle; Computer science; Energy consumption; Fuel efficiency; Jerk; Powertrain; Optimal control; Energy (signal processing); Hybrid vehicle; Control theory (sociology); Control (management); Engineering; Torque; Mathematical optimization; Acceleration; Power (physics); Aerospace engineering","score_opus":0.012092151063988787,"score_gpt":0.21121720465759639,"score_spread":0.1991250535936076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287882643","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9427674,0.0015362798,0.026992863,0.003550601,0.011776618,0.003373926,0.0006609873,0.00095414167,0.008387176],"genre_scores_gemma":[0.9966523,0.00010044602,0.000028371694,0.00049439265,0.0008777538,0.0011526548,0.000084427345,0.00008526796,0.0005243797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972411,0.00010795233,0.0005919869,0.00053111,0.0006995693,0.00082829595],"domain_scores_gemma":[0.999135,0.00031145097,0.00015324075,0.00023568072,0.00006498605,0.00009964993],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005113546,0.00035138873,0.00038444044,0.00043546184,0.00023345913,0.00010386849,0.00063721393,0.0001039976,0.00019386628],"category_scores_gemma":[0.00004472368,0.00042595167,0.00022899573,0.00042783038,0.000012421894,0.00016871275,0.0000841915,0.0009414041,0.0000069866787],"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":[0.0014060815,0.00021513767,0.00003835395,0.0000049918654,0.00034583182,0.000033542263,0.0000881445,0.940918,0.028505137,0.011632657,0.0060225027,0.0107895825],"study_design_scores_gemma":[0.007382909,0.00088304566,0.000036066,0.000024405052,0.000060601815,0.000014983255,0.00006318014,0.8708779,0.0024917459,0.00013344741,0.11756667,0.00046499685],"about_ca_topic_score_codex":0.00016609282,"about_ca_topic_score_gemma":0.00012467803,"teacher_disagreement_score":0.11154417,"about_ca_system_score_codex":0.0027454083,"about_ca_system_score_gemma":0.00017256066,"threshold_uncertainty_score":0.9998192},"labels":[],"label_agreement":null},{"id":"W4287882695","doi":"10.1109/isie51582.2022.9831670","title":"Formation Shaping Control for Multi-Agent Systems with Obstacle Avoidance and Dynamic Leader Selection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Collision avoidance; Obstacle avoidance; Obstacle; Computer science; Mobile robot; Process (computing); Collision; Controller (irrigation); Control theory (sociology); Displacement (psychology); Robot; Trajectory; Control engineering; Control (management); Real-time computing; Artificial intelligence; Engineering","score_opus":0.041614744870765115,"score_gpt":0.2659374443167727,"score_spread":0.22432269944600758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287882695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06336519,0.0004562823,0.92361265,0.003215031,0.0053232154,0.003101794,0.00044813124,0.00032331565,0.00015436143],"genre_scores_gemma":[0.99637544,0.000029625679,0.00036551885,0.000330026,0.0003977672,0.0016152479,0.00018681263,0.000047501402,0.00065207126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99625176,0.00034297482,0.0007263134,0.0008306212,0.0011074189,0.0007409172],"domain_scores_gemma":[0.99828124,0.0002781442,0.0006395587,0.0003634257,0.00029584297,0.00014177625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011264366,0.00037436755,0.00040345074,0.00026419133,0.00080466695,0.0005576898,0.0010440291,0.00015538723,0.000019336541],"category_scores_gemma":[0.00007199899,0.00038508695,0.00013171708,0.0004915578,0.000031045445,0.00082031795,0.0001172725,0.0008035609,0.000012561116],"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":[0.0041321027,0.0017420781,0.0014276274,0.00016777981,0.0021016148,0.000058650032,0.0023951484,0.63129693,0.20686227,0.12672931,0.008294026,0.014792432],"study_design_scores_gemma":[0.008301726,0.0010778117,0.00003644213,0.00005437031,0.000047167614,0.00019190939,0.00021986863,0.96778804,0.0025997404,0.0000388866,0.019206664,0.00043734643],"about_ca_topic_score_codex":0.00009527238,"about_ca_topic_score_gemma":0.000057441535,"teacher_disagreement_score":0.9330102,"about_ca_system_score_codex":0.002780861,"about_ca_system_score_gemma":0.00027764842,"threshold_uncertainty_score":0.9998601},"labels":[],"label_agreement":null},{"id":"W4287882769","doi":"10.1109/isie51582.2022.9831475","title":"Rao-Blackwellized Variational Bayesian Smoother for Mobile Robot Localization","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Robotics and Sensor-Based Localization","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":"York University","funders":"","keywords":"Covariance; Noise (video); Estimator; Mobile robot; Landmark; Artificial intelligence; Bayesian probability; Computer science; Computer vision; Covariance matrix; Posterior probability; Estimation of covariance matrices; Noise measurement; Algorithm; Mathematics; Robot; Statistics; Noise reduction","score_opus":0.013615456403759822,"score_gpt":0.23117831622536428,"score_spread":0.21756285982160445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287882769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008942715,0.00020759868,0.96969575,0.0015204358,0.010840475,0.0022857452,0.00066937244,0.00053541915,0.0053024674],"genre_scores_gemma":[0.9879241,0.00024593048,0.000584238,0.00091406156,0.0026038017,0.0014428722,0.0032342582,0.00026835492,0.00278236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972315,0.000137374,0.0006701395,0.0004970452,0.0009308002,0.00053315324],"domain_scores_gemma":[0.99899113,0.00023352365,0.00018744242,0.00029750736,0.00017259744,0.00011778167],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005312596,0.0003454938,0.0003068097,0.0002797496,0.00041365,0.0001462242,0.00052198535,0.00022700483,0.0016646747],"category_scores_gemma":[0.000051170537,0.00041156862,0.00021081431,0.0004990644,0.000025429097,0.0001575412,0.00004897604,0.0006406387,0.000031031785],"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":[0.00034611532,0.00018380077,0.00009235681,0.000009223132,0.00020622218,0.0000027044057,0.00010700496,0.95029277,0.0061472543,0.023096457,0.018376756,0.0011393146],"study_design_scores_gemma":[0.0024543756,0.0004547777,0.000004993499,0.000011711903,0.000044430773,0.000007808698,0.00004124363,0.78556913,0.0057395585,0.000969744,0.20431879,0.0003834463],"about_ca_topic_score_codex":0.000031614385,"about_ca_topic_score_gemma":0.000016999073,"teacher_disagreement_score":0.97898144,"about_ca_system_score_codex":0.0016750428,"about_ca_system_score_gemma":0.00019107653,"threshold_uncertainty_score":0.99983364},"labels":[],"label_agreement":null},{"id":"W4287883007","doi":"10.1109/isie51582.2022.9831530","title":"A Hierarchical Pitman-Yor mixture of Scaled Dirichlet Distributions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Bayesian Methods and Mixture Models","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixture model; Robustness (evolution); Computer science; Dirichlet distribution; Inference; Cluster analysis; Latent Dirichlet allocation; Dirichlet process; Artificial intelligence; Flexibility (engineering); Hierarchical clustering; Hierarchical Dirichlet process; Gaussian; Machine learning; Data mining; Topic model; Mathematics; Statistics","score_opus":0.019243793984783892,"score_gpt":0.2698742535938535,"score_spread":0.2506304596090696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287883007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03278422,0.00023986367,0.9302598,0.020628946,0.006347348,0.0007683842,0.00078198884,0.0002043472,0.00798505],"genre_scores_gemma":[0.98304373,0.00014335854,0.0098362565,0.0013083443,0.001574078,0.0003938564,0.00037539992,0.00006043855,0.0032645387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956889,0.0006239975,0.0007383966,0.00083501864,0.0014417538,0.00067189854],"domain_scores_gemma":[0.9980487,0.000416733,0.00039315756,0.0007562426,0.00018001528,0.00020513401],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001228442,0.00034149212,0.00044194912,0.0002850635,0.00046737937,0.0001456833,0.002546446,0.000229171,0.00037913985],"category_scores_gemma":[0.00014294968,0.0003459415,0.00035236264,0.0010398414,0.00008000107,0.00025191688,0.00061392324,0.0019628957,0.000016725851],"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":[0.00040660563,0.0009230957,0.00013328146,0.000006488249,0.00027601453,0.00003957932,0.0002932227,0.0013470337,0.02716276,0.9143461,0.03557772,0.019488111],"study_design_scores_gemma":[0.008934397,0.0044386294,0.00019936205,0.00009983504,0.00017795643,0.00040801495,0.00005015548,0.09621053,0.08210206,0.12383622,0.6815548,0.00198805],"about_ca_topic_score_codex":0.000043069973,"about_ca_topic_score_gemma":0.000009107401,"teacher_disagreement_score":0.9502595,"about_ca_system_score_codex":0.0008947408,"about_ca_system_score_gemma":0.000548638,"threshold_uncertainty_score":0.99989927},"labels":[],"label_agreement":null},{"id":"W4287883046","doi":"10.1109/isie51582.2022.9831703","title":"Power Delivery Capability Improvement of Voltage Source Converters in Weak Power Grid Using Deep Reinforcement Learning with Continuous Action","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)","topic":"Microgrid Control and Optimization","field":"Engineering","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":"Carleton University","funders":"","keywords":"Control theory (sociology); Phase-locked loop; Voltage source; Computer science; Controller (irrigation); Converters; AC power; Grid; Engineering; Voltage; Electronic engineering; Electrical engineering; Mathematics; Control (management)","score_opus":0.007839375407568355,"score_gpt":0.20201533670325197,"score_spread":0.19417596129568362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287883046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9777134,0.00012269118,0.017225757,0.0001164536,0.0026275238,0.00075772434,0.000027381635,0.00011214505,0.001296942],"genre_scores_gemma":[0.99910915,0.0000997377,0.000018947967,0.0000756976,0.00016867994,0.00009163906,0.0001224603,0.000051521805,0.00026216081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775183,0.00009767524,0.0006368117,0.000367235,0.0007009924,0.00044547374],"domain_scores_gemma":[0.99925524,0.00007711759,0.00027393724,0.00020707522,0.00011934155,0.00006731808],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00056713074,0.00027736882,0.00032325668,0.00025682707,0.00014568807,0.000053307835,0.0003074495,0.00012594112,0.0007340195],"category_scores_gemma":[0.000022669854,0.00030304314,0.00011483771,0.0003216417,0.000030869054,0.0002013905,0.00007528817,0.0010402882,0.0000035115286],"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":[0.00068253046,0.00010652554,0.0010391618,0.000007745693,0.00019106887,0.0000038915227,0.00027850334,0.87810284,0.117397785,0.00009175276,0.00018329943,0.0019148717],"study_design_scores_gemma":[0.0055514816,0.0020665992,0.00008187652,0.000047958376,0.000060486913,0.000021364494,0.0010983467,0.9330019,0.035732012,0.000011221169,0.021780895,0.0005459087],"about_ca_topic_score_codex":0.0003092339,"about_ca_topic_score_gemma":0.0000620846,"teacher_disagreement_score":0.08166577,"about_ca_system_score_codex":0.0024641522,"about_ca_system_score_gemma":0.00013393254,"threshold_uncertainty_score":0.9999422},"labels":[],"label_agreement":null}]}