{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":15,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":15,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"8c3907e8a55b","filters":{"venue":"CityU Scholars"}},"results":[{"id":"W3113608201","doi":"","title":"High-dimensional quantile tensor regression","year":2020,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Shenzhen Research Institute, City University of Hong Kong; National Natural Science Foundation of China; City University of Hong Kong","keywords":"Quantile regression; Tensor (intrinsic definition); Regression; Econometrics; Quantile; Mathematics; Computer science; Statistics; Artificial intelligence; Pure mathematics","authors":[{"name":"Wenqi Lu","is_ca":false},{"name":"Zhongyi Zhu","is_ca":false},{"name":"Heng Lian","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07385700183305119,"gpt":0.3350480108015665,"spread":0.2611910089685153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001828582,0.0007993532,0.0008294027,0.000661178,0.0004129195,0.001094147,0.0008749402,0.0009890053,0.002115641],"category_scores_gemma":[0.006829949,0.0003858458,0.0006173169,0.001212839,0.0008449632,0.001418836,0.001003219,0.0013727,0.0004820302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007717399,"about_ca_system_score_gemma":0.0009340469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005632495,"about_ca_topic_score_gemma":0.003163598,"domain_scores_codex":[0.9990678,0.0004541013,0.00003729745,0.0001740606,0.0001879759,0.00007880726],"domain_scores_gemma":[0.9983571,0.0007347462,0.0002987478,0.000184579,0.000346911,0.00007792351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001170569,0.00005134158,0.004578607,0.0002241693,0.00009999261,0.0002034606,0.000120767,0.6716377,0.00450551,0.2003838,0.006168056,0.1119095],"study_design_scores_gemma":[0.000004004442,0.00001145056,0.0003977804,0.000009167568,0.000007016655,0.00002377025,0.00001007571,0.9757233,0.0004861754,0.02193724,0.001380819,0.000009336586],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007462221,0.0004350797,0.9904542,0.0003469921,0.00005394437,0.00001552745,0.0001086162,0.0001804674,0.0009429376],"genre_scores_gemma":[0.6597715,0.002820783,0.3284968,0.0003235813,0.0003605782,0.0001449546,0.0007174907,0.0001871333,0.007177217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005632495,"threshold_uncertainty_score":0.01119941,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3118218782","doi":"","title":"Ultra-High Dimensional Single-Index Quantile Regression","year":2020,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Index (typography); Quantile regression; Statistics; Regression; Mathematics; Quantile; Econometrics; Computer science","authors":[{"name":"Yuankun Zhang","is_ca":false},{"name":"Heng Lian","is_ca":true},{"name":"Yan Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1300614187877281,"gpt":0.3675802940496404,"spread":0.2375188752619123,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003545589,0.0007685851,0.001561938,0.0005431519,0.0004217009,0.001669638,0.002847746,0.001466968,0.002828882],"category_scores_gemma":[0.009815275,0.000600997,0.001070928,0.00163765,0.001571675,0.00209004,0.001939754,0.00281644,0.0005523633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022284,"about_ca_system_score_gemma":0.000961544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004649907,"about_ca_topic_score_gemma":0.003421873,"domain_scores_codex":[0.9983557,0.000677844,0.00006840199,0.0004308335,0.0002662957,0.0002008001],"domain_scores_gemma":[0.9950402,0.002625388,0.0009530968,0.0007203613,0.0004406493,0.0002202649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001131242,0.00008627651,0.009524441,0.0001846284,0.0001343215,0.000453367,0.0001824997,0.7126852,0.001288619,0.2371035,0.002543443,0.03570063],"study_design_scores_gemma":[0.000009902108,0.00002061808,0.001150997,0.00001361557,0.00001750105,0.00004927173,0.00002382578,0.9521083,0.0001864212,0.04536037,0.001042615,0.00001654143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03654206,0.0004954928,0.9596092,0.0008302653,0.00004718739,0.00003184065,0.0004603027,0.0001890304,0.001794684],"genre_scores_gemma":[0.8460268,0.001376693,0.1432397,0.0006111904,0.0002448246,0.0002109144,0.0009384796,0.00010539,0.007246015],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004649907,"threshold_uncertainty_score":0.01875108,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4416424338","doi":"10.22331/q-2025-11-20-1917","title":"A Randomized Method for Simulating Lindblad Equations and Thermal State Preparation","year":2025,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Quantum many-body systems","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Hamiltonian (control theory); Quantum; Quantum system; Spectral gap; Open quantum system; Jump; Lindblad equation; Quantum algorithm; Convergence (economics)","authors":[{"name":"Hongrui Chen","is_ca":false},{"name":"Bowen Li","is_ca":true},{"name":"Jianfeng Lu","is_ca":false},{"name":"Lexing Ying","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01974956975845883,"gpt":0.3544653298530401,"spread":0.3347157600945813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304892,0.0003810519,0.0006180992,0.0003140138,0.0006450815,0.0006907866,0.002146047,0.001229713,0.00397457],"category_scores_gemma":[0.003126896,0.0003180481,0.0005640975,0.0003138827,0.001214881,0.001152714,0.001072311,0.001308737,0.0005415223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009934041,"about_ca_system_score_gemma":0.001434427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003909301,"about_ca_topic_score_gemma":0.00444888,"domain_scores_codex":[0.999531,0.0001964044,0.00001813629,0.00006145042,0.0001228409,0.00007016405],"domain_scores_gemma":[0.9987321,0.0007256014,0.00008887769,0.0002332246,0.0001394684,0.00008063671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001622228,0.0001228449,0.0007992385,0.00007673428,0.0000468231,0.0001020269,0.00009699484,0.8146952,0.005344484,0.1604963,0.001105391,0.0169517],"study_design_scores_gemma":[0.00001319318,0.00001085483,0.00001514829,0.000001982737,0.000001797726,0.000004590313,0.000003250906,0.9944184,0.0004593547,0.004827435,0.0002403975,0.000003557533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04963388,0.0001263975,0.9441422,0.0003142554,0.00006993554,0.00008876991,0.00008148021,0.0006841834,0.004858879],"genre_scores_gemma":[0.5530142,0.0001147468,0.4412936,0.000260652,0.00004404096,0.0004109653,0.0001403939,0.0003201685,0.004401322],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00397457,"threshold_uncertainty_score":0.01329625,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3038640622","doi":"","title":"Distributed Kernel Ridge Regression with Communications","year":2020,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Generalization; Computer science; Kernel (algebra); Ridge; Regression; Artificial intelligence; Kernel regression; Kernel method; Machine learning; Regression analysis; Mathematical optimization; Mathematics; Support vector machine; Statistics; Discrete mathematics","authors":[{"name":"Shao-Bo Lin","is_ca":false},{"name":"Di Wang","is_ca":false},{"name":"Ding‐Xuan Zhou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03755810653255733,"gpt":0.2517889123443033,"spread":0.214230805811746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003527836,0.0008147477,0.001129727,0.0004531429,0.0005073601,0.001025868,0.00134972,0.001303971,0.001709175],"category_scores_gemma":[0.01722719,0.0003604713,0.0004460904,0.0009866337,0.001532213,0.002857041,0.002040038,0.002095284,0.0005671392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008031536,"about_ca_system_score_gemma":0.0009612591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009042266,"about_ca_topic_score_gemma":0.0008510711,"domain_scores_codex":[0.9962764,0.001675508,0.000128055,0.0007024391,0.0009243282,0.0002931801],"domain_scores_gemma":[0.987756,0.00778399,0.0009629065,0.002129314,0.001197204,0.0001705602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003525184,0.0001133663,0.0009253954,0.0001370267,0.00005946343,0.0001193087,0.0001134443,0.8057627,0.006676074,0.07618441,0.001871738,0.1076845],"study_design_scores_gemma":[0.00001211107,0.00004771907,0.00007996031,0.000003359238,0.000004455606,0.00002808445,0.00001031234,0.9872953,0.001546927,0.01061829,0.0003475102,0.000005958582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01809461,0.0003021326,0.9790092,0.000369787,0.0000439287,0.00002627597,0.00002285904,0.0002758252,0.001855402],"genre_scores_gemma":[0.8652473,0.0004168205,0.1303034,0.0002545022,0.00019068,0.0001501514,0.00007374135,0.00008992531,0.003273406],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003527836,"threshold_uncertainty_score":0.01865721,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133392047","doi":"","title":"More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization","year":2024,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Shenzhen Research Institute, City University of Hong Kong; Fujian Normal University; Shanghai University of Finance and Economics; Yunnan University; National Natural Science Foundation of China; Hong Kong Polytechnic University; City University of Hong Kong; National Science Foundation","keywords":"Estimator; Additive model; Multivariate statistics; Spline (mechanical); Nonparametric statistics; Tensor decomposition; Nonparametric regression; Tensor (intrinsic definition); Regression; Convergence (economics)","authors":[{"name":"Xu Liu","is_ca":false},{"name":"Heng Lian","is_ca":true},{"name":"Jian Huang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03504824305842081,"gpt":0.3512575918807643,"spread":0.3162093488223435,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004525563,0.001398048,0.001300017,0.001142395,0.0004207609,0.00112929,0.001282134,0.0009386485,0.001345427],"category_scores_gemma":[0.0128978,0.0005510529,0.001871915,0.00164288,0.0009820599,0.001822174,0.001387013,0.001881729,0.000431637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005386996,"about_ca_system_score_gemma":0.001363718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003883923,"about_ca_topic_score_gemma":0.003563185,"domain_scores_codex":[0.997888,0.001397454,0.00008747219,0.0002648984,0.000263579,0.00009858314],"domain_scores_gemma":[0.995235,0.003169534,0.0005271524,0.0004990232,0.0004499283,0.000119424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001127038,0.0001424218,0.003555822,0.0002364512,0.0002537437,0.0002414788,0.0002503608,0.7283832,0.00842522,0.1231667,0.001801333,0.1334305],"study_design_scores_gemma":[0.000002880877,0.00001871636,0.0001615748,0.000005415498,0.00001051797,0.00001992365,0.000006469208,0.9912787,0.0004116063,0.007670578,0.000403937,0.000009532981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003661363,0.0000761897,0.995963,0.0000691087,0.00001265971,0.000008513564,0.00001846772,0.00004602284,0.0001446417],"genre_scores_gemma":[0.2146008,0.0009395231,0.7808605,0.0001434434,0.0001630647,0.0001631238,0.0003625972,0.0001742648,0.002592645],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004525563,"threshold_uncertainty_score":0.02393371,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7136098884","doi":"","title":"Optimal subsampling for high-dimensional partially linear models via machine learning methods","year":2025,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Endogeneity; Estimator; Instrumental variable; Linear model; Leverage (statistics); Martingale (probability theory); Parametric statistics; Asymptotic distribution; Consistency (knowledge bases); Additive model","authors":[{"name":"Yujing Shao","is_ca":false},{"name":"Lei Wang","is_ca":false},{"name":"Heng Lian","is_ca":true},{"name":"Haiying Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1317479525967904,"gpt":0.4403878970745448,"spread":0.3086399444777544,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01742828,0.001533412,0.002763455,0.001690782,0.0009854824,0.001513184,0.002617752,0.001493519,0.001590837],"category_scores_gemma":[0.05256772,0.001224397,0.001881877,0.001322562,0.002474785,0.002064677,0.002246958,0.002142929,0.0003453223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001421133,"about_ca_system_score_gemma":0.002430052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005969631,"about_ca_topic_score_gemma":0.005077194,"domain_scores_codex":[0.9896319,0.007921256,0.0003385914,0.0009313293,0.0009137574,0.0002631656],"domain_scores_gemma":[0.9665493,0.02822324,0.001527186,0.002112512,0.001275933,0.0003117721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003397787,0.0002266493,0.006262795,0.0004964824,0.0005645198,0.0003211389,0.000402794,0.6575722,0.002277281,0.1894134,0.002109122,0.1400139],"study_design_scores_gemma":[0.00002342479,0.00003801685,0.0002795569,0.00001754621,0.00002501249,0.00002129368,0.00001778593,0.9648257,0.000494392,0.03371111,0.0005326939,0.00001350886],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003158598,0.0001613451,0.9963132,0.00007975772,0.00001443055,0.0000398742,0.00002086313,0.000100387,0.0001115323],"genre_scores_gemma":[0.2531459,0.000722421,0.7428813,0.0003377679,0.0001913457,0.0007778837,0.0005783239,0.0001803549,0.001184806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01742828,"threshold_uncertainty_score":0.09217066,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133391277","doi":"","title":"Divide-and-Conquer for Debiased <i>l</i><sub>1</sub>-norm Support Vector Machine in Ultra-high Dimensions","year":2018,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"City University of Hong Kong","keywords":"Support vector machine; Hessian matrix; Estimator; Extension (predicate logic); Convergence (economics); Set (abstract data type); Rate of convergence; Relevance vector machine; Matrix (chemical analysis)","authors":[{"name":"Heng Lian","is_ca":true},{"name":"Zengyan Fan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01589888579282165,"gpt":0.2463766793316547,"spread":0.2304777935388331,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005339747,0.001110658,0.001753323,0.001264153,0.0009159855,0.001464864,0.002098676,0.0016007,0.003154745],"category_scores_gemma":[0.01882681,0.0005948492,0.0007196026,0.001306265,0.002101289,0.002315832,0.002077844,0.002226616,0.001023533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091767,"about_ca_system_score_gemma":0.001505242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002535643,"about_ca_topic_score_gemma":0.003382004,"domain_scores_codex":[0.9980444,0.0006325942,0.0001503195,0.0004878441,0.0005207753,0.000164191],"domain_scores_gemma":[0.993678,0.003552507,0.0006151705,0.00098075,0.0009937162,0.0001798646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006014979,0.0002448649,0.003617343,0.000257277,0.000135646,0.0002273822,0.0004267657,0.2331003,0.00952219,0.05744369,0.005007103,0.689416],"study_design_scores_gemma":[0.00001671117,0.00005887224,0.0002785213,0.00001182764,0.00001192257,0.00004988052,0.00003099619,0.976905,0.003040481,0.01857116,0.001012917,0.00001167577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01040199,0.0004439737,0.9876222,0.0002324382,0.00003443812,0.0000495201,0.00002134058,0.0005705317,0.000623626],"genre_scores_gemma":[0.2532213,0.000364765,0.7424127,0.0003442169,0.0001840777,0.000318183,0.0001934631,0.0002239507,0.002737296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005339747,"threshold_uncertainty_score":0.02823961,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133373697","doi":"","title":"Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification","year":2022,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Statistical learning theory; Support vector machine; Rate of convergence; Reproducing kernel Hilbert space; Statistical learning; Kernel (algebra); Convergence (economics); Linear inequality","authors":[{"name":"Yingying Zhang","is_ca":false},{"name":"Yan-Yong Zhao","is_ca":false},{"name":"Heng Lian","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.230558225503552,"gpt":0.4320637594767343,"spread":0.2015055339731822,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02928981,0.001831706,0.001855932,0.002868239,0.0009853434,0.003061051,0.003236363,0.002640259,0.003011275],"category_scores_gemma":[0.1273703,0.0008922049,0.001652821,0.001755156,0.004806604,0.008932111,0.004377186,0.005661691,0.0009451494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002995486,"about_ca_system_score_gemma":0.001633818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001230359,"about_ca_topic_score_gemma":0.0006353181,"domain_scores_codex":[0.9930955,0.003774782,0.0003326105,0.0008533229,0.001535462,0.0004083395],"domain_scores_gemma":[0.9023812,0.08090021,0.004101558,0.004582558,0.006613992,0.001420557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001978721,0.00007061413,0.00270771,0.0004346,0.0001227138,0.0001854219,0.0004030537,0.1665349,0.002105943,0.7924603,0.002094862,0.03268202],"study_design_scores_gemma":[0.00001174838,0.00008712454,0.0007497019,0.0001119019,0.00002582258,0.0001057563,0.00005457368,0.7853862,0.001319238,0.2106247,0.001483146,0.00004009806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02112569,0.002873,0.9702983,0.001613917,0.0001423574,0.00009811216,0.0001373444,0.0002092834,0.003502039],"genre_scores_gemma":[0.624135,0.007234826,0.3495303,0.001361848,0.001231899,0.001322989,0.001213018,0.001016191,0.01295396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02928981,"threshold_uncertainty_score":0.1549012,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133369314","doi":"","title":"Maturation of Cardiomyocytes Derived from Human Pluripotent Stem Cells:<i>Current Strategies and Limitations</i>","year":2018,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Pluripotent Stem Cells Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"City University of Hong Kong","keywords":"Induced pluripotent stem cell; Embryonic stem cell; Embryoid body; Stem cell; Human Induced Pluripotent Stem Cells; Drug discovery; Cellular differentiation","authors":[{"name":"Yanqing Jiang","is_ca":true},{"name":"Peter J. Park","is_ca":false},{"name":"Sangmin hong","is_ca":false},{"name":"Kiwon Ban","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0370471128409585,"gpt":0.284015056791248,"spread":0.2469679439502895,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009916697,0.0003411296,0.000310305,0.0004169209,0.0002428829,0.0007655108,0.0003866864,0.0004528679,0.0009568207],"category_scores_gemma":[0.0005395426,0.0001733868,0.0003053594,0.0004286963,0.0004020887,0.0006307292,0.0004488231,0.0007988925,0.0008119855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003099449,"about_ca_system_score_gemma":0.0006281692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000384087,"about_ca_topic_score_gemma":0.0005692051,"domain_scores_codex":[0.9995474,0.0000734414,0.00007645559,0.00008092511,0.0001878718,0.00003381752],"domain_scores_gemma":[0.9996407,0.0001139686,0.00007343075,0.00003917625,0.0001022641,0.00003050488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000131091,0.0000601077,0.002230633,0.00460255,0.00004496089,0.0004347719,0.0004219075,0.0009217688,0.6267787,0.0102676,0.002938017,0.351168],"study_design_scores_gemma":[0.00002665176,0.0005336417,0.006154709,0.0009909112,0.0001671148,0.002640771,0.0004303005,0.00223048,0.7030656,0.004791958,0.2789066,0.00006134738],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.15792,0.615068,0.1843607,0.004351907,0.001415622,0.000475532,0.0009223459,0.0006343908,0.03485142],"genre_scores_gemma":[0.3716072,0.5228248,0.09019534,0.002252107,0.0006579614,0.0004828286,0.001613127,0.0001406952,0.0102259],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.0009916697,"threshold_uncertainty_score":0.005244553,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133373432","doi":"","title":"Trust, Information Integration, and Coordination Costs:An Integrative Mode","year":2017,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Innovation and Knowledge Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Information system; Information sharing; Information integration; Joint (building); Mode (computer interface); Information technology; Goodwill","authors":[{"name":"Shaohan Cai","is_ca":true},{"name":"Zhilin Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01649649831803211,"gpt":0.2722423383169171,"spread":0.255745839998885,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004896134,0.001349351,0.0008941464,0.004247119,0.001566137,0.008424502,0.001257945,0.00167968,0.008540954],"category_scores_gemma":[0.0246583,0.0009193959,0.001320905,0.003952108,0.004157175,0.01201884,0.005004222,0.001683939,0.0004246097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004926483,"about_ca_system_score_gemma":0.00435909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006408553,"about_ca_topic_score_gemma":0.00351253,"domain_scores_codex":[0.9940792,0.002469805,0.0003755402,0.0006933271,0.001607613,0.0007745115],"domain_scores_gemma":[0.9646572,0.01968321,0.008278025,0.002274517,0.003465495,0.001641498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000725513,0.0006912529,0.2534679,0.0009134682,0.001404788,0.001070363,0.01082028,0.02576013,0.001518061,0.6029236,0.002019748,0.09868482],"study_design_scores_gemma":[0.0002509727,0.001139614,0.2282267,0.001368609,0.0023777,0.001464667,0.02012045,0.1314183,0.00193864,0.6006005,0.01073535,0.0003585625],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7450722,0.003097805,0.0853769,0.006111268,0.0001074526,0.0003291713,0.0003434482,0.0001329532,0.1594288],"genre_scores_gemma":[0.9963472,0.0003366666,0.002039783,0.00006038841,0.00002467887,0.00005348362,0.00003182634,0.00000918408,0.001096646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008540954,"threshold_uncertainty_score":0.03574431,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133405170","doi":"","title":"Lexicographic Lipschitz Bandits:New Algorithms and a Lower Bound","year":2025,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; City University of Hong Kong","keywords":"Lexicographical order; Regret; Upper and lower bounds; Lipschitz continuity; Dimension (graph theory); Matching (statistics)","authors":[{"name":"Bo Xue","is_ca":false},{"name":"Cheng Ji","is_ca":false},{"name":"Fei Liu","is_ca":false},{"name":"Yimu Wang","is_ca":true},{"name":"Lijun Zhang","is_ca":false},{"name":"Qingfu Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07882936778791948,"gpt":0.4231396116865803,"spread":0.3443102438986608,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003663404,0.00174752,0.002239871,0.001210331,0.001137239,0.003304762,0.002625341,0.003622513,0.007462793],"category_scores_gemma":[0.01786356,0.001003609,0.001239524,0.002049498,0.002571231,0.005499525,0.003041601,0.006122175,0.001769019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002572191,"about_ca_system_score_gemma":0.002545004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002678812,"about_ca_topic_score_gemma":0.003950532,"domain_scores_codex":[0.9978696,0.0009177286,0.00009203365,0.0003167541,0.0005292334,0.0002745862],"domain_scores_gemma":[0.9907575,0.007498605,0.0004249508,0.0006523247,0.0004000734,0.0002665973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005604285,0.0003116851,0.001327651,0.0004413931,0.00009913192,0.0001891922,0.00024929,0.5829117,0.002053131,0.2609564,0.01393254,0.1369675],"study_design_scores_gemma":[0.00005327704,0.00005421936,0.00007913999,0.00005803833,0.00001767277,0.00005298812,0.00002730978,0.9230223,0.0006936686,0.07365016,0.002278759,0.0000126339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01361173,0.002053377,0.9660071,0.001966345,0.0001924005,0.0001492006,0.0001852613,0.0008254523,0.01500918],"genre_scores_gemma":[0.2572672,0.002122777,0.7184041,0.001375742,0.000563055,0.0006796398,0.000559233,0.000683683,0.01834462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007462793,"threshold_uncertainty_score":0.02496552,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133394521","doi":"","title":"Decimated Framelet System on Graphs and Fast <i>G</i>-Framelet Transforms","year":2022,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Shanghai Jiao Tong University; European Commission; City University of Hong Kong; National Science Foundation","keywords":"External Data Representation; Graph; Pattern recognition (psychology); Orthonormal basis; Voltage graph; Graph theory; Representation (politics); Artificial neural network","authors":[{"name":"Xuebin Zheng","is_ca":false},{"name":"Bingxin Zhou","is_ca":false},{"name":"Yu Guang Wang","is_ca":false},{"name":"Xiaosheng Zhuang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008474589290824047,"gpt":0.217582563188075,"spread":0.209107973897251,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004176344,0.0004847643,0.0003485654,0.0005664317,0.0002551825,0.0006786114,0.0005953267,0.000752309,0.00169062],"category_scores_gemma":[0.00162932,0.0002254172,0.0003822659,0.0007473066,0.0006102985,0.001155587,0.0005357093,0.000968858,0.0006977422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004911925,"about_ca_system_score_gemma":0.0004041043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001875581,"about_ca_topic_score_gemma":0.001514499,"domain_scores_codex":[0.9998007,0.00004689534,0.000008519491,0.00004147353,0.00007852001,0.00002388521],"domain_scores_gemma":[0.9995632,0.0001722028,0.00005361701,0.00009047548,0.00009903968,0.00002156185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001937534,0.00005560962,0.0009353095,0.0001200306,0.00002792933,0.0003905362,0.0003031371,0.4095856,0.07788967,0.2194113,0.005621472,0.2854657],"study_design_scores_gemma":[0.000005739517,0.00002797482,0.0001700625,0.000006082963,0.000004036791,0.00009801323,0.0000227384,0.9711566,0.009853861,0.01548719,0.003153542,0.00001404632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01115374,0.0001229931,0.9873979,0.0001079337,0.00003734723,0.00001279699,0.00003602028,0.0001573418,0.0009738813],"genre_scores_gemma":[0.2411592,0.0007092892,0.7527977,0.0001418262,0.00009601908,0.00006788984,0.0003291429,0.0001430806,0.004555766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001875581,"threshold_uncertainty_score":0.005655706,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133419607","doi":"","title":"Allocation of Cases Based on Geography","year":2024,"lang":"en","type":"book-chapter","venue":"CityU Scholars","topic":"Conflict of Laws and Jurisdiction","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Jurisdiction; Plaintiff; Mainland; Focus (optics); Mainland China; Section (typography)","authors":[{"name":"Peter C. H. Chan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03082748132339836,"gpt":0.2992627320042393,"spread":0.2684352506808409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007244733,0.0005120092,0.0007028723,0.006666685,0.003569071,0.008049155,0.002076642,0.001624033,0.01726618],"category_scores_gemma":[0.01754971,0.0004908066,0.000682526,0.004441009,0.008081103,0.006031078,0.007656989,0.002382664,0.002951125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007580881,"about_ca_system_score_gemma":0.004436736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003669011,"about_ca_topic_score_gemma":0.008194034,"domain_scores_codex":[0.9822282,0.008310331,0.000619332,0.000988125,0.006189119,0.001664956],"domain_scores_gemma":[0.9949749,0.002836983,0.000384311,0.0005995517,0.0008697226,0.0003345133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001756027,0.0000181848,0.0007776434,0.00006840708,0.000007180889,0.0001272919,0.00180062,0.001470116,0.0003306283,0.9481511,0.01151466,0.03571661],"study_design_scores_gemma":[0.00003998376,0.00009811107,0.006077169,0.0009705441,0.00003166329,0.0006323204,0.01060026,0.004034794,0.001127963,0.4758935,0.5004228,0.00007089201],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02565889,0.006007798,0.0331829,0.005709267,0.0006261866,0.0008470204,0.0001867724,0.0001399234,0.9276412],"genre_scores_gemma":[0.6854524,0.01046453,0.0415662,0.005230509,0.0007729355,0.002305949,0.0005030907,0.0002978969,0.2534065],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01726618,"threshold_uncertainty_score":0.05776113,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133379698","doi":"","title":"Setting priorities for ageing research in Africa:A systematic mapping review of 512 studies from sub-Saharan Africa","year":2021,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Elder Abuse and Neglect","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge; University of Manitoba; McMaster University","funders":"","keywords":"Categorization; Observational study; Research design; Qualitative research; Population ageing; Narrative; Older people; Systematic review; Content analysis","authors":[{"name":"including Emerging Researchers and Professionals in Ageing-African Network","is_ca":false},{"name":"Michael Kalu","is_ca":true},{"name":"Blessing Ugochi Ojembe","is_ca":true},{"name":"Olayinka Akinrolie","is_ca":true},{"name":"Augustine C Okoh","is_ca":false},{"name":"Israel I. Adandom","is_ca":false},{"name":"Henrietta C Nwankwo","is_ca":true},{"name":"Michael S Ajulo","is_ca":false},{"name":"Chidinma A Omeje","is_ca":false},{"name":"Chukwuebuka Okeke","is_ca":false},{"name":"Ekezie M Uduonu","is_ca":false},{"name":"Chigozie Donatus Ezulike","is_ca":false},{"name":"Ebuka Miracle Anieto","is_ca":false},{"name":"Diameta Emofe","is_ca":false},{"name":"Ernest C Nwachukwu","is_ca":false},{"name":"Michael C Ibekaku","is_ca":false},{"name":"Perpetual C Obi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2662517741262786,"gpt":0.427603985127236,"spread":0.1613522110009574,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05495967,0.001825808,0.007045324,0.05027628,0.002185086,0.00483369,0.002329637,0.002181813,0.004224204],"category_scores_gemma":[0.1633048,0.002240888,0.007409513,0.03882013,0.001700068,0.006322172,0.005105814,0.001789631,0.0004578391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007600609,"about_ca_system_score_gemma":0.03375079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01129132,"about_ca_topic_score_gemma":0.03948097,"domain_scores_codex":[0.9437008,0.02059409,0.02557142,0.002527863,0.006456968,0.001148867],"domain_scores_gemma":[0.8486958,0.1119324,0.02042488,0.002784316,0.01507421,0.001088377],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001634247,0.00002162979,0.003304527,0.9368775,0.006044111,0.0003059815,0.002650787,0.0001392895,0.0002936375,0.0006250717,0.001957809,0.04761629],"study_design_scores_gemma":[0.00009194796,0.00008855048,0.004954141,0.9630406,0.01638449,0.0002209534,0.002248443,0.00006419927,0.0001548639,0.0003002437,0.01242082,0.00003076642],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.007702278,0.9804011,0.001427919,0.001567414,0.000316814,0.005031478,0.002533691,0.0000252963,0.0009939555],"genre_scores_gemma":[0.06380051,0.912657,0.007601754,0.001904737,0.0001236621,0.01224221,0.00139479,0.00002558561,0.0002498774],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.05495967,"threshold_uncertainty_score":0.290658,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7133401585","doi":"","title":"A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators","year":2024,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Johns Hopkins University; National Science Foundation","keywords":"Reproducing kernel Hilbert space; Prior probability; Posterior probability; Kernel (algebra); Hilbert space; Bayesian probability; Stability (learning theory); Inverse problem; Representer theorem","authors":[{"name":"Neil K. Chada","is_ca":true},{"name":"Quanjun Lang","is_ca":false},{"name":"Fei Lu","is_ca":false},{"name":"Xiong Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1848531222039198,"gpt":0.4356696791674936,"spread":0.2508165569635737,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006424348,0.0008702148,0.001018843,0.001225258,0.0006281036,0.00129977,0.001830271,0.001810746,0.001947412],"category_scores_gemma":[0.02295105,0.0008084807,0.001002279,0.0009603153,0.002649623,0.003139672,0.002718586,0.003307804,0.0007332793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070489,"about_ca_system_score_gemma":0.001688985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001833049,"about_ca_topic_score_gemma":0.002057109,"domain_scores_codex":[0.9971758,0.00149171,0.0001162031,0.0004575561,0.0006476193,0.0001110735],"domain_scores_gemma":[0.9901721,0.006211288,0.0007295192,0.001236456,0.001373264,0.0002774851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001909712,0.00008569005,0.001569684,0.0002364458,0.0000729533,0.0001043504,0.0002380833,0.652132,0.008430003,0.2607553,0.002134823,0.07404967],"study_design_scores_gemma":[0.000009081767,0.0000160077,0.0001454691,0.00001429476,0.000005255301,0.000020479,0.000007726258,0.9612575,0.0009699628,0.03697294,0.000565104,0.00001622954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003099342,0.00005990923,0.9963438,0.0001087858,0.0000100064,0.00001434419,0.00002979755,0.00007426749,0.000259784],"genre_scores_gemma":[0.2782168,0.0005409357,0.7177501,0.00027237,0.0001531721,0.0002834105,0.0004580854,0.0002044075,0.002120749],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006424348,"threshold_uncertainty_score":0.0339756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}