{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":18,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":18,"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":"5345a4d347fe","filters":{"venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)"}},"results":[{"id":"W4318604478","doi":"10.1109/ssci51031.2022.10022206","title":"Increasing attacker engagement on SSH honeypots using semantic embeddings of cyber-attack patterns and deep reinforcement learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Thales (Canada)","funders":"","keywords":"Honeypot; Reinforcement learning; Computer science; Computer security; Reinforcement; Network security; Artificial intelligence; Engineering","authors":[{"name":"Junior Samuel López-Yépez","is_ca":true},{"name":"Antoine Fagette","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03010283877242323,"gpt":0.2791584439368711,"spread":0.2490556051644479,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00165904,0.001429535,0.0007967121,0.0005763774,0.0003338391,0.0008423774,0.001052308,0.000864682,0.001523924],"category_scores_gemma":[0.007962518,0.0003788894,0.0004025073,0.0003024026,0.0006491687,0.002320439,0.001456881,0.001616447,0.0005966076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006976557,"about_ca_system_score_gemma":0.0005583834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001590893,"about_ca_topic_score_gemma":0.002879662,"domain_scores_codex":[0.9990982,0.0002911777,0.0000509077,0.0002853324,0.0001546523,0.0001198315],"domain_scores_gemma":[0.9973186,0.001384395,0.0003264074,0.0003955116,0.0003426646,0.0002325237],"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":[0.0006130294,0.001596992,0.03844788,0.0002016366,0.0002057808,0.0002105856,0.0002845655,0.630478,0.01680935,0.001303398,0.003465082,0.3063837],"study_design_scores_gemma":[0.00001841092,0.0002054172,0.001827714,0.000008513525,0.00001144048,0.00003133763,0.00002847034,0.9927806,0.003524151,0.001232273,0.0003204777,0.00001119022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7270399,0.0005222418,0.2608615,0.0007491523,0.000194157,0.0002264464,0.0003546817,0.005990393,0.004061504],"genre_scores_gemma":[0.9656987,0.00004481357,0.03295305,0.0001278984,0.00001420853,0.00005062116,0.0002602547,0.00005497705,0.0007955249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.00165904,"threshold_uncertainty_score":0.008773923,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604462","doi":"10.1109/ssci51031.2022.10022021","title":"LSTM based Algorithmic Trading model for Bitcoin","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Cryptocurrency; Computer science; Trading strategy; Asset (computer security); Algorithmic trading; High-frequency trading; Financial market; Artificial neural network; Econometrics; Artificial intelligence; Finance; Economics; Computer security","authors":[{"name":"Japjeet Singh","is_ca":true},{"name":"Ruppa K. Thulasiram","is_ca":true},{"name":"A. Thavaneswaran","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02632286586507335,"gpt":0.2684879912966796,"spread":0.2421651254316063,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003742913,0.000655518,0.0005232976,0.0003934465,0.000289477,0.0008410376,0.0008082726,0.001003765,0.00331275],"category_scores_gemma":[0.0009964268,0.000232903,0.0004767969,0.0003599785,0.0003649349,0.001005126,0.0003623902,0.0009246798,0.0003278311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008602397,"about_ca_system_score_gemma":0.0006493973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009749243,"about_ca_topic_score_gemma":0.007525891,"domain_scores_codex":[0.9998535,0.00002660638,0.00001213744,0.00004994413,0.00002972393,0.0000281449],"domain_scores_gemma":[0.9997705,0.0001122542,0.00003748102,0.00001053111,0.0000574906,0.00001186681],"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":[0.0001034942,0.00004988177,0.00103043,0.00005093595,0.00004049667,0.0001398933,0.00005415736,0.9557161,0.003375487,0.007193262,0.0006667365,0.03157919],"study_design_scores_gemma":[0.000001699708,0.000008248962,0.00006502899,0.000001447163,0.000003219219,0.000006275923,0.000001938061,0.9986718,0.0001534705,0.001017832,0.00006726154,0.000001773849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.279541,0.001588538,0.6983947,0.001508422,0.0002437367,0.00009485899,0.0005047347,0.001635868,0.01648807],"genre_scores_gemma":[0.9822556,0.0001871389,0.01335524,0.00009354288,0.00002159942,0.00005992382,0.0001116256,0.00001959548,0.003895799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009749243,"threshold_uncertainty_score":0.01938498,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604517","doi":"10.1109/ssci51031.2022.10022250","title":"A Constraint Satisfaction Problem (CSP) Approach for the Nurse Scheduling Problem","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Mathematical optimization; Heuristics; Constraint satisfaction problem; Constraint satisfaction; Computer science; Nurse scheduling problem; Constraint programming; Job shop scheduling; Scheduling (production processes); Constraint satisfaction dual problem; Constraint (computer-aided design); Integer programming; Branch and bound; Linear programming; Combinatorial optimization; Constraint logic programming; Mathematics; Artificial intelligence; Routing (electronic design automation); Flow shop scheduling","authors":[{"name":"Aymen Ben Said","is_ca":true},{"name":"Malek Mouhoub","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08505266767852177,"gpt":0.3530456267598732,"spread":0.2679929590813514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001957148,0.001957766,0.001049676,0.001207486,0.0009202299,0.00153973,0.002081365,0.001639175,0.00532696],"category_scores_gemma":[0.00511501,0.0005975842,0.001923904,0.003794577,0.001053029,0.001660779,0.001390513,0.003862244,0.0009798991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001466139,"about_ca_system_score_gemma":0.004429433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007211177,"about_ca_topic_score_gemma":0.008185652,"domain_scores_codex":[0.996696,0.001579934,0.0001645039,0.000497589,0.0008988177,0.0001630593],"domain_scores_gemma":[0.9972376,0.001903079,0.0002082625,0.0001588261,0.0004039058,0.00008828867],"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":[0.00006981046,0.0002205156,0.000501458,0.0008054834,0.0001337689,0.0003434018,0.0001864446,0.7044852,0.002591327,0.1692889,0.01321782,0.1081558],"study_design_scores_gemma":[0.00004075316,0.00009210591,0.0001554949,0.00008393612,0.00003578809,0.0002290675,0.00008353819,0.9182563,0.001254485,0.0598119,0.01993206,0.00002451845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001272031,0.0003367321,0.9935509,0.0005170836,0.00007476669,0.0001563844,0.0002079889,0.00009258244,0.003791451],"genre_scores_gemma":[0.05765984,0.001508529,0.93588,0.0003938706,0.0002013414,0.0007316535,0.0006939289,0.00009232984,0.002838536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007211177,"threshold_uncertainty_score":0.01782042,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604490","doi":"10.1109/ssci51031.2022.10022181","title":"Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Benchmark (surveying); Abstraction; Encoder; Graph; Recurrent neural network; Convolutional neural network; Data mining; Sequence (biology); Artificial intelligence; Machine learning; Theoretical computer science; Artificial neural network","authors":[{"name":"Naghmeh Shafiee Roudbari","is_ca":true},{"name":"Zachary Patterson","is_ca":true},{"name":"Ursula Eicker","is_ca":true},{"name":"Charalambos Poullis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01676661914422996,"gpt":0.2330853682350549,"spread":0.216318749090825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002661158,0.0009281252,0.000620637,0.0005821788,0.0002068378,0.0005435083,0.001285592,0.000486663,0.002169478],"category_scores_gemma":[0.001050985,0.0003122533,0.0006081061,0.0006540879,0.0002600309,0.001368538,0.0007288529,0.0009888852,0.000769705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007477917,"about_ca_system_score_gemma":0.0006667462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01872523,"about_ca_topic_score_gemma":0.02974327,"domain_scores_codex":[0.9998449,0.00002277562,0.000006443633,0.00005707912,0.00004036349,0.00002832739],"domain_scores_gemma":[0.9998139,0.00005091712,0.00002120575,0.00004663291,0.00004982213,0.00001757043],"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":[0.0002271875,0.0001718598,0.003113826,0.00008025463,0.0001217556,0.000146513,0.0000785023,0.6551587,0.01088303,0.006167284,0.009259117,0.314592],"study_design_scores_gemma":[0.000003675552,0.00001865347,0.000180456,0.000002460484,0.000007270247,0.000006924733,0.000004700493,0.9967895,0.0006791593,0.001867497,0.0004361959,0.000003501421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.121075,0.001333076,0.8617099,0.0005021946,0.0001978085,0.00008802453,0.001276167,0.009440859,0.004377018],"genre_scores_gemma":[0.8194811,0.0005447774,0.1709467,0.0002932139,0.00007459828,0.00009813338,0.003245139,0.0002475487,0.005068759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01872523,"threshold_uncertainty_score":0.03723246,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604500","doi":"10.1109/ssci51031.2022.10022098","title":"Hierarchical Reinforcement Learning With Multi Discount Factors In A Differential Game","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Guidance and Control Systems","field":"Engineering","cited_by":5,"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":"Reinforcement learning; Computer science; Differential game; Differential (mechanical device); Function (biology); Path (computing); Artificial intelligence; Mathematical optimization; Mathematics; Engineering","authors":[{"name":"Amirhossein Asgharnia","is_ca":true},{"name":"Howard M. Schwartz","is_ca":true},{"name":"Mohamed Atia","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01383266406375146,"gpt":0.2298687803865991,"spread":0.2160361163228476,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00168209,0.0007712957,0.0009844758,0.0003916027,0.000351914,0.0007771545,0.001195873,0.0008270646,0.001647876],"category_scores_gemma":[0.003388572,0.0003719297,0.0004747928,0.0003152701,0.001263592,0.0009958716,0.001112139,0.001247937,0.000168308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001445127,"about_ca_system_score_gemma":0.001175369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005129432,"about_ca_topic_score_gemma":0.003683539,"domain_scores_codex":[0.9991304,0.00032576,0.00004077781,0.0001809534,0.0002100853,0.0001119951],"domain_scores_gemma":[0.9987388,0.0007799153,0.0001521758,0.00006322841,0.0001445813,0.0001213876],"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":[0.00009461135,0.00006732555,0.000470863,0.00004833722,0.00003040864,0.00009520592,0.00008734595,0.9463673,0.001640662,0.03420153,0.0003424014,0.01655404],"study_design_scores_gemma":[0.00001391474,0.00002756212,0.00004319271,0.000001843491,0.000004162746,0.000005973685,0.000002538198,0.9956191,0.0001548185,0.003985118,0.0001385598,0.000003201917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03792588,0.0001452079,0.9583173,0.000161129,0.00002886542,0.00006839058,0.0000245279,0.0001623439,0.003166328],"genre_scores_gemma":[0.9409469,0.00009733241,0.05653296,0.00005878576,0.00001832037,0.0001097181,0.0000218759,0.00001647844,0.002197754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005129432,"threshold_uncertainty_score":0.01048517,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318606478","doi":"10.1109/ssci51031.2022.10022278","title":"Beta-Liouville and Inverted Beta-Liouville Based Predictive Models for Occupancy Detection using Small Training Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"BETA (programming language); Computer science; Training set; Gaussian; Flexibility (engineering); Alpha (finance); Artificial intelligence; Gaussian process; Mixture model; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics; Statistics; Physics","authors":[{"name":"Jiaxun Guo","is_ca":true},{"name":"Manar Amayri","is_ca":false},{"name":"Wentao Fan","is_ca":false},{"name":"Nizar Bouguila","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1645808759528356,"gpt":0.3284654171282836,"spread":0.1638845411754481,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002977384,0.00135706,0.00170667,0.001366171,0.0006041019,0.001945395,0.005057519,0.002229167,0.002034627],"category_scores_gemma":[0.01044886,0.001207447,0.001776394,0.001427734,0.001711778,0.003669961,0.002127348,0.003590131,0.0009964178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001223614,"about_ca_system_score_gemma":0.001026896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007866559,"about_ca_topic_score_gemma":0.007105684,"domain_scores_codex":[0.9988751,0.0003880044,0.00004670582,0.0003244531,0.0002353629,0.0001303795],"domain_scores_gemma":[0.9964229,0.002494216,0.0002389409,0.0002764419,0.0004404823,0.0001269272],"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":[0.000161551,0.00009646116,0.003127548,0.0001214294,0.00009040781,0.0001143807,0.0002286043,0.8610156,0.002362252,0.04742987,0.001795607,0.08345635],"study_design_scores_gemma":[0.000002097717,0.000009349189,0.0001078833,0.000006842851,0.000004683499,0.00001390836,0.000004933936,0.9936594,0.0002091295,0.005711893,0.0002624783,0.000007336051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007347358,0.0003232076,0.9910893,0.0001752978,0.00003239264,0.0000196506,0.00007006733,0.0002299557,0.0007127891],"genre_scores_gemma":[0.6646724,0.001547906,0.3214498,0.0007194512,0.0003412119,0.0004322518,0.001680452,0.000402717,0.008753909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007866559,"threshold_uncertainty_score":0.01574612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318603345","doi":"10.1109/ssci51031.2022.10022086","title":"Discrete-time Linear and Nonlinear Observers for an Electromechanical Plant with State Feedback Control","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Control theory (sociology); State observer; Kalman filter; Observer (physics); Control engineering; Nonlinear system; Mechatronics; Sliding mode control; Extended Kalman filter; Computer science; Engineering; Alpha beta filter; Separation principle; Control (management); Artificial intelligence","authors":[{"name":"Alexandra-Iulia Szedlak-Stinean","is_ca":false},{"name":"Radu‐Emil Precup","is_ca":false},{"name":"Raul‐Cristian Roman","is_ca":false},{"name":"Emil M. Petriu","is_ca":true},{"name":"Claudia‐Adina Bojan‐Dragos","is_ca":false},{"name":"Elena‐Lorena Hedrea","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01469442998437528,"gpt":0.2313208976008965,"spread":0.2166264676165212,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004852576,0.0003811512,0.0003214856,0.0001721482,0.0001641052,0.0004886159,0.0005996356,0.0005094153,0.001116531],"category_scores_gemma":[0.001505199,0.0001509263,0.0003200473,0.00015677,0.0003503957,0.0006638144,0.0003514446,0.0008141056,0.0002926538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003143157,"about_ca_system_score_gemma":0.000469929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537506,"about_ca_topic_score_gemma":0.001573923,"domain_scores_codex":[0.9996841,0.00006078431,0.00002352499,0.00006274001,0.000151888,0.00001698849],"domain_scores_gemma":[0.9996036,0.0001423891,0.00007535973,0.00005359295,0.0001173465,0.000007752175],"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":[0.0003097192,0.0001316521,0.001747993,0.0009448546,0.0001067716,0.00023287,0.000559006,0.3550888,0.09911007,0.07570118,0.002817051,0.4632501],"study_design_scores_gemma":[0.00002597262,0.0001506967,0.0005769823,0.00003266058,0.000025275,0.00005230173,0.00002582621,0.9771988,0.009713021,0.002786413,0.009395496,0.00001646339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003520378,0.000221673,0.9949548,0.00004403852,0.00004483405,0.00001773594,0.00001160122,0.0002197292,0.0009652037],"genre_scores_gemma":[0.727141,0.0009591671,0.2647022,0.0001110042,0.00009648599,0.0002403439,0.0001570117,0.00003862612,0.006554156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001537506,"threshold_uncertainty_score":0.003735185,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320031228","doi":"10.1109/ssci51031.2022.10022198","title":"An Improvement of Consensus in Group Decision-Making Through an Optimal Distribution of Information Granularity","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Granularity; Computer science; Granular computing; Group decision-making; Preference; Data mining; Process (computing); Group (periodic table); Fuzzy logic; Mathematical optimization; Theoretical computer science; Mathematics; Artificial intelligence; Statistics; Rough set","authors":[{"name":"Francisco Javier Cabrerizo","is_ca":false},{"name":"Juan Carlos Gonzalez-Quesada","is_ca":false},{"name":"Juan Antonio Morente-Molinera","is_ca":false},{"name":"Ignacio Javier Pérez","is_ca":false},{"name":"Enrique Herrera‐Viedma","is_ca":false},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05513989581785366,"gpt":0.3851552041242278,"spread":0.3300153083063742,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008025653,0.0006790461,0.001303698,0.001340215,0.0009812887,0.001862481,0.001139964,0.00115952,0.001033441],"category_scores_gemma":[0.02069042,0.000373409,0.0008996787,0.001553769,0.001830301,0.003536533,0.003071625,0.001521924,0.0001654515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048702,"about_ca_system_score_gemma":0.001408323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007248379,"about_ca_topic_score_gemma":0.0004882514,"domain_scores_codex":[0.9938185,0.003118795,0.0003925537,0.0009061785,0.00130478,0.0004592769],"domain_scores_gemma":[0.990396,0.00632631,0.001003678,0.001107574,0.0009074036,0.0002590874],"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":[0.0004540714,0.000228236,0.002544877,0.0004421651,0.0001782203,0.0002982187,0.002079889,0.5904561,0.0150158,0.2304573,0.0008695399,0.1569756],"study_design_scores_gemma":[0.00008796935,0.0002801553,0.001047216,0.00007144617,0.00007626309,0.00009723771,0.0003984089,0.829396,0.006027338,0.160795,0.001666375,0.00005665089],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05281684,0.0001929907,0.9438613,0.0002168161,0.00002348085,0.0000841566,0.00001292017,0.0000983591,0.002693159],"genre_scores_gemma":[0.8324124,0.0001662829,0.1667299,0.00005307984,0.00002623931,0.0001231548,0.00001876026,0.00002234002,0.0004479079],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008025653,"threshold_uncertainty_score":0.04244417,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604573","doi":"10.1109/ssci51031.2022.10022190","title":"Training and Testing Cascades for Imbalanced Data Classification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Cascade; Computer science; Artificial intelligence; Training set; Machine learning; Data modeling; Data mining; Data set; Class (philosophy); Set (abstract data type); Synthetic data; Test data; Recall rate; Pattern recognition (psychology); Database; Engineering","authors":[{"name":"Armin Sadreddin","is_ca":true},{"name":"Samira Sadaoui","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1728539108943217,"gpt":0.3439910303025261,"spread":0.1711371194082045,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007621484,0.002088299,0.001501076,0.002078569,0.001164995,0.0009869138,0.00289645,0.001501235,0.002922348],"category_scores_gemma":[0.02183869,0.0007882854,0.001013775,0.0009726672,0.000769843,0.004194192,0.002159705,0.002486309,0.00182249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009552904,"about_ca_system_score_gemma":0.001385316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002272583,"about_ca_topic_score_gemma":0.00429502,"domain_scores_codex":[0.9967321,0.00087755,0.0002465284,0.0008634963,0.001033044,0.0002473354],"domain_scores_gemma":[0.9876744,0.005624623,0.0008945186,0.003177739,0.002087257,0.0005414682],"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":[0.002133553,0.002551196,0.04993428,0.0003467902,0.0003714502,0.0007510295,0.0007560701,0.1410042,0.02770375,0.008570882,0.01566347,0.7502133],"study_design_scores_gemma":[0.00005850881,0.0003679645,0.002580175,0.00002946276,0.00004994997,0.0001793682,0.0001055062,0.9747217,0.01139739,0.008375159,0.002114163,0.00002071248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2205865,0.0008707326,0.7612559,0.0007490281,0.0004362763,0.001816171,0.0007026222,0.008166374,0.005416333],"genre_scores_gemma":[0.6717175,0.0002535847,0.3221374,0.0003463469,0.0002217079,0.0009406859,0.001309194,0.0002738873,0.00279966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007621484,"threshold_uncertainty_score":0.04030675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604520","doi":"10.1109/ssci51031.2022.10022205","title":"Pressure Sensor Data Analysis for Motor Function Assessment of Stroke Patients","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Motor function; Stroke (engine); Histogram; Function (biology); Physical medicine and rehabilitation; Computer science; Artificial intelligence; Data mining; Medicine; Engineering; Image (mathematics)","authors":[{"name":"Helder C. R. Oliveira","is_ca":false},{"name":"Haaziq Altaf","is_ca":false},{"name":"Mohammed Almekhlafi","is_ca":true},{"name":"Svetlana Yanushkevich","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03436597177864332,"gpt":0.3240470125934061,"spread":0.2896810408147628,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005338528,0.0007064473,0.0006755586,0.001710485,0.0001608771,0.0006826707,0.0004143841,0.0005822792,0.00107714],"category_scores_gemma":[0.003103833,0.0001060086,0.0004199128,0.001404297,0.0001361098,0.0004478161,0.0003978759,0.0003866419,0.0006527288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002198221,"about_ca_system_score_gemma":0.0003964981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002350438,"about_ca_topic_score_gemma":0.002259853,"domain_scores_codex":[0.9993807,0.0001177349,0.00009864561,0.0001380568,0.0002147754,0.00005007643],"domain_scores_gemma":[0.9990528,0.0002679842,0.0002234501,0.0001096422,0.0002773009,0.00006888643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003771735,0.0008463917,0.3710578,0.001321985,0.000613251,0.001057755,0.0001799548,0.02073819,0.02555352,0.000688618,0.02085462,0.5533163],"study_design_scores_gemma":[0.000163588,0.001739968,0.6585766,0.0002320485,0.0002887852,0.002032735,0.0006069704,0.2932554,0.02658615,0.002232708,0.01416655,0.0001185215],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8820315,0.004292213,0.0550205,0.001092555,0.0003164428,0.0003664376,0.05032807,0.0026882,0.003864125],"genre_scores_gemma":[0.9582881,0.0008712278,0.0166619,0.0001053807,0.000133729,0.0002343813,0.02303486,0.00002601385,0.0006443828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002350438,"threshold_uncertainty_score":0.004673541,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318602947","doi":"10.1109/ssci51031.2022.10022239","title":"Planning for millions of NPCs in Real-Time","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Frame (networking); Unary operation; Computer science; Action (physics); Time complexity; Algorithm; Discrete mathematics; Mathematics","authors":[{"name":"Éric Jacopin","is_ca":false},{"name":"Tristan Cazenave","is_ca":false},{"name":"Christophe Guettier","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03006694627490485,"gpt":0.2902722793417352,"spread":0.2602053330668304,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007035006,0.0007948747,0.0007287956,0.0003535662,0.000512937,0.001065303,0.001418796,0.0007617471,0.007243749],"category_scores_gemma":[0.003805113,0.0004813477,0.0007163229,0.0004631948,0.001227531,0.001938522,0.001530809,0.001265904,0.0009876926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001125804,"about_ca_system_score_gemma":0.001251013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00873231,"about_ca_topic_score_gemma":0.01100663,"domain_scores_codex":[0.9992352,0.0001520606,0.00004390987,0.0002784774,0.0001838232,0.0001065516],"domain_scores_gemma":[0.9986293,0.0007649231,0.0001589823,0.0002168494,0.000121442,0.0001085944],"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":[0.0001932903,0.0001260163,0.0009147204,0.0001667392,0.00004695862,0.0002039867,0.0002528778,0.8774198,0.004383859,0.03451093,0.003277899,0.078503],"study_design_scores_gemma":[0.00002450489,0.00006142308,0.0001637207,0.000008329517,0.00001551136,0.00003671764,0.00006049757,0.9680015,0.001895292,0.02652453,0.00319938,0.000008526364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07497448,0.000230662,0.9085199,0.000688567,0.00009476636,0.0001212537,0.0002679357,0.0020865,0.01301594],"genre_scores_gemma":[0.6279935,0.0003024032,0.361448,0.0002023305,0.00005772257,0.0003409676,0.0006197226,0.0003198581,0.008715449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00873231,"threshold_uncertainty_score":0.02423275,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318605527","doi":"10.1109/ssci51031.2022.10022301","title":"Explainable AI Applied to the Analysis of the Climatic Behavior of 11 Years of Meteosat Water Vapor Images","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Satellite; Environmental science; Troposphere; Meteorology; Water vapor; Convolutional neural network; Remote sensing; Climatology; Atmospheric model; Artificial neural network; Computer science; Geography; Artificial intelligence; Geology","authors":[{"name":"Julio J. Valdés","is_ca":true},{"name":"Antonio Pou","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01802066341080581,"gpt":0.2670007057219184,"spread":0.2489800423111126,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009595288,0.0004728477,0.0002280648,0.001379438,0.0002377733,0.0006190792,0.0004228274,0.0002972253,0.001022954],"category_scores_gemma":[0.005022436,0.0001062141,0.0007144323,0.0009707863,0.0003289124,0.0007410683,0.0005786474,0.0004888363,0.00008620403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005538461,"about_ca_system_score_gemma":0.000511509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057042,"about_ca_topic_score_gemma":0.009686194,"domain_scores_codex":[0.9997495,0.00009907703,0.00001602874,0.00006921099,0.00003896324,0.00002723057],"domain_scores_gemma":[0.9982343,0.001143794,0.0002892025,0.0001930331,0.000107969,0.00003182276],"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":[0.0003919087,0.0001503942,0.1813919,0.0002608116,0.0007719665,0.0004980351,0.000720626,0.5110382,0.007081785,0.02164998,0.001767995,0.2742763],"study_design_scores_gemma":[0.00001060153,0.00006338929,0.04424174,0.00001528894,0.0000555215,0.0000731839,0.000141332,0.9394277,0.001492001,0.01283297,0.001625673,0.00002070394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6219161,0.0009867619,0.3699569,0.0006325435,0.00005439776,0.00009620727,0.002167587,0.001193454,0.002995973],"genre_scores_gemma":[0.9624173,0.0001667225,0.03512525,0.00002681262,0.00002877572,0.00003747154,0.001763494,0.00003313203,0.0004010852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01057042,"threshold_uncertainty_score":0.02101785,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318603845","doi":"10.1109/ssci51031.2022.10022110","title":"A Particle Swarm Optimization Decomposition Strategy for Large Scale Global Optimization","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle swarm optimization; Decomposition; Benchmark (surveying); Metaheuristic; Mathematical optimization; Multi-swarm optimization; Computer science; Optimization problem; Function (biology); Global optimization; Scale (ratio); Mathematics; Biology; Ecology; Physics","authors":[{"name":"Liam J. S. McDevitt","is_ca":true},{"name":"Beatrice Ombuki-Berman","is_ca":true},{"name":"Andries P. Engelbrecht","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03094350290861493,"gpt":0.330977887943443,"spread":0.300034385034828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282027,0.001374407,0.001029389,0.0007643164,0.0004880455,0.001003717,0.001064001,0.001237088,0.002255474],"category_scores_gemma":[0.002055949,0.0005169548,0.001145626,0.0008078723,0.0006601464,0.0008637766,0.001455046,0.001625047,0.0007188068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007284335,"about_ca_system_score_gemma":0.001263239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004001981,"about_ca_topic_score_gemma":0.003254174,"domain_scores_codex":[0.9995652,0.0001555716,0.00002469034,0.00006770054,0.0001473538,0.00003941473],"domain_scores_gemma":[0.9995011,0.0002358219,0.00004866656,0.00004974327,0.0001255357,0.00003911948],"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":[0.0000388556,0.00004443538,0.0004590237,0.00009768808,0.00006667063,0.00006977512,0.00005999272,0.9083381,0.003084846,0.02390509,0.003115345,0.06072022],"study_design_scores_gemma":[0.000006866919,0.00001912959,0.0000425663,0.000005805944,0.000005183397,0.000008892981,0.000005190545,0.9963132,0.0002444803,0.002284621,0.001060689,0.000003307471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002655511,0.0002115016,0.9943777,0.0001220229,0.00005181226,0.00003703151,0.00002302095,0.0001213264,0.002399998],"genre_scores_gemma":[0.2095982,0.0006940957,0.781297,0.0002525581,0.0001330366,0.0004634539,0.00032906,0.000183066,0.007049569],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004001981,"threshold_uncertainty_score":0.007957339,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318603638","doi":"10.1109/ssci51031.2022.10022259","title":"A Data-Driven Forecasting and Solution Approach for the Dial-A-Ride Problem with Time Windows","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Transportation and Mobility Innovations","field":"Engineering","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":"Computer science; Tabu search; Artificial neural network; Benchmark (surveying); Mathematical optimization; Gradient descent; Simulated annealing; Travelling salesman problem; Operations research; Artificial intelligence; Machine learning; Algorithm; Engineering; Mathematics","authors":[{"name":"Slim Belhaiza","is_ca":false},{"name":"Rym M’Hallah","is_ca":false},{"name":"Munirah Al-Qarni","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04255682157573455,"gpt":0.2447510428787917,"spread":0.2021942213030571,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001042379,0.001150523,0.001167027,0.0007854058,0.000450696,0.001258756,0.001505981,0.001495839,0.001696117],"category_scores_gemma":[0.002543946,0.0007017052,0.0009324739,0.001037046,0.0004157963,0.001142727,0.0008145083,0.001948408,0.0001881819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148883,"about_ca_system_score_gemma":0.001873895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01861464,"about_ca_topic_score_gemma":0.01336705,"domain_scores_codex":[0.9996791,0.00008245876,0.00002330021,0.00009856615,0.00006688932,0.00004970536],"domain_scores_gemma":[0.9990175,0.0006268437,0.0001028758,0.00002952081,0.0001704723,0.00005287592],"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":[0.00001249143,0.00001603446,0.0002379143,0.00002833941,0.00001355659,0.00002027889,0.00001281413,0.9898092,0.0001302295,0.001532668,0.0002844902,0.00790203],"study_design_scores_gemma":[0.000001429221,0.000005441169,0.00002620731,0.000002283588,0.000001646505,0.000001929103,0.000004334877,0.9992323,0.00004763836,0.0005651196,0.0001104563,0.000001291901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02827206,0.0007485235,0.9667794,0.0006095071,0.00009670207,0.00009863116,0.0002952532,0.0002481016,0.002851853],"genre_scores_gemma":[0.6756417,0.001195761,0.3175107,0.0001711894,0.0001703722,0.0005574776,0.001004228,0.0000916366,0.003656948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01861464,"threshold_uncertainty_score":0.03701258,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318603555","doi":"10.1109/ssci51031.2022.10022070","title":"Mother Tree Optimization for Conditional Constraints and Qualitative Preferences","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Constraint Satisfaction and Optimization","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":"University of Regina","funders":"","keywords":"A priori and a posteriori; Set (abstract data type); Mathematical optimization; Constraint satisfaction problem; Constraint (computer-aided design); Tree (set theory); Computer science; Mathematics; Artificial intelligence; Combinatorics","authors":[{"name":"Wael Korani","is_ca":false},{"name":"Malek Mouhoub","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03213036417730307,"gpt":0.3027704748620733,"spread":0.2706401106847702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001570398,0.0009869166,0.0009673601,0.0006475077,0.0004971275,0.000959623,0.00114735,0.000937512,0.005789285],"category_scores_gemma":[0.004863968,0.0004633647,0.001295356,0.001276827,0.0008025622,0.001956406,0.0013339,0.001930005,0.0003888258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123793,"about_ca_system_score_gemma":0.002139749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004988597,"about_ca_topic_score_gemma":0.006907216,"domain_scores_codex":[0.9989762,0.0004186338,0.00004014699,0.0001974895,0.0002285406,0.0001389546],"domain_scores_gemma":[0.9972534,0.002080148,0.0001678644,0.000153576,0.0002217609,0.0001233143],"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":[0.00007488261,0.00006353362,0.0005284261,0.0001365715,0.00003364849,0.0001468069,0.00006892262,0.9052126,0.001511854,0.0620239,0.002773086,0.02742579],"study_design_scores_gemma":[0.00001110896,0.00001837228,0.00006398086,0.000006357138,0.000006022642,0.00001991799,0.00001976504,0.9773799,0.000359609,0.02114226,0.0009684635,0.000004404263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02824349,0.0002279628,0.9655776,0.0003749953,0.00004483369,0.00007163869,0.0003785916,0.0002667166,0.00481411],"genre_scores_gemma":[0.4873368,0.0004747814,0.5041144,0.000354344,0.00005346693,0.0003377645,0.001273296,0.0003240005,0.005731061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005789285,"threshold_uncertainty_score":0.0193671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318606113","doi":"10.1109/ssci51031.2022.10022116","title":"Improving Topic Quality with Interactive Beta-Liouville Mixture Allocation Model","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Topic Modeling","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":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Topic model; Computer science; Inference; Cluster analysis; Categorization; Artificial intelligence; Dirichlet distribution; Natural language processing; Task (project management); Machine learning; Set (abstract data type); Quality (philosophy); Hierarchical Dirichlet process; BETA (programming language); Mathematics","authors":[{"name":"Kamal Maanicshah","is_ca":true},{"name":"Manar Amayri","is_ca":false},{"name":"Nizar Bouguila","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02637901145546341,"gpt":0.2805629512311266,"spread":0.2541839397756632,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00650733,0.001221409,0.002115044,0.001814728,0.001106233,0.002677516,0.003410499,0.002435758,0.002357789],"category_scores_gemma":[0.01709834,0.0009379507,0.001927339,0.001885043,0.001149956,0.005460645,0.002930112,0.003153789,0.002025031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001521844,"about_ca_system_score_gemma":0.001604595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006553198,"about_ca_topic_score_gemma":0.007148977,"domain_scores_codex":[0.9968641,0.001542795,0.0001828049,0.0006116936,0.000557757,0.0002408176],"domain_scores_gemma":[0.992708,0.004762343,0.00035094,0.0008623204,0.001028405,0.0002879789],"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":[0.001338398,0.0006674926,0.007860336,0.0002760802,0.0003657572,0.000226339,0.001432741,0.5077273,0.01544493,0.03320675,0.009338727,0.4221152],"study_design_scores_gemma":[0.00003220499,0.00002987903,0.0002386845,0.000006805872,0.00002140777,0.00003276194,0.00002757222,0.9898545,0.00142226,0.007397053,0.000919444,0.00001743861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01702174,0.0005123237,0.9796708,0.0002357965,0.00005202465,0.00006300148,0.00006614479,0.001440713,0.0009373673],"genre_scores_gemma":[0.5535619,0.0008683401,0.4371777,0.000581591,0.0002652334,0.0005101773,0.00110938,0.0009022602,0.00502337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006553198,"threshold_uncertainty_score":0.03441441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4318604488","doi":"10.1109/ssci51031.2022.10022072","title":"Bayesian Folding-In Using Generalized Dirichlet and Beta-Liouville Kernels for Information Retrieval","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Text and Document Classification Technologies","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":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Probabilistic latent semantic analysis; Dirichlet distribution; Computer science; Kernel (algebra); Bayesian probability; Topic model; Mixture model; Folding (DSP implementation); Artificial intelligence; Pattern recognition (psychology); Mathematics","authors":[{"name":"Sahar Salmanzade Yazdi","is_ca":true},{"name":"Fatma Najar","is_ca":true},{"name":"Nizar Bouguila","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0287169286179947,"gpt":0.2829308803661048,"spread":0.2542139517481101,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00505355,0.0008643343,0.002021387,0.002865177,0.0009325054,0.002629866,0.002109993,0.001916407,0.001880766],"category_scores_gemma":[0.01596806,0.0007340218,0.001942171,0.003894628,0.00150427,0.005674833,0.002384468,0.001889949,0.001070555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002673757,"about_ca_system_score_gemma":0.002291246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00801696,"about_ca_topic_score_gemma":0.005623979,"domain_scores_codex":[0.995417,0.002055105,0.0003161781,0.0007378423,0.001148816,0.0003249878],"domain_scores_gemma":[0.996078,0.002301923,0.0002966954,0.0005343432,0.0006591377,0.0001298604],"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":[0.0003849703,0.0003406611,0.003128523,0.0004270664,0.0002305664,0.0001806015,0.0007191582,0.2541259,0.008714218,0.3024294,0.00607063,0.4232484],"study_design_scores_gemma":[0.00001859987,0.0000310693,0.0003701955,0.00001614572,0.00002444287,0.00007740979,0.00003332117,0.9191942,0.001282334,0.07642027,0.002494155,0.00003785728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004808304,0.0005047363,0.9933136,0.0001802965,0.00002614885,0.00004614748,0.00005525638,0.0002837418,0.0007816789],"genre_scores_gemma":[0.4247935,0.002394324,0.5638268,0.000447164,0.0003431374,0.000494034,0.0008456622,0.0002454033,0.006609989],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00801696,"threshold_uncertainty_score":0.02672607,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320031295","doi":"10.1109/ssci51031.2022.10022203","title":"Rodent Tracking and Abnormal Behavior Classification in Live Video using Deep Neural Networks","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Neuroendocrine regulation and behavior","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Frame (networking); Artificial neural network; Tracking (education); Opioid; Artificial intelligence; Computer vision; Neuroscience; Audiology; Medicine; Psychology; Internal medicine","authors":[{"name":"Sudarsini Tekkam Gnanasekar","is_ca":true},{"name":"Svetlana Yanushkevich","is_ca":true},{"name":"Nynke J. van den Hoogen","is_ca":true},{"name":"Tuan Trang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05898141416514661,"gpt":0.3398692318174953,"spread":0.2808878176523487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003194152,0.0006236507,0.0002698278,0.0008387299,0.0001138494,0.0002348369,0.0004998753,0.0004339021,0.000932269],"category_scores_gemma":[0.0006805196,0.0001452567,0.0002923529,0.0004159168,0.0001294781,0.000291851,0.000291492,0.000364088,0.0003073323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004152253,"about_ca_system_score_gemma":0.0002830882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005843306,"about_ca_topic_score_gemma":0.01087788,"domain_scores_codex":[0.9998386,0.00001987122,0.000007282353,0.00005788734,0.00003881128,0.00003754955],"domain_scores_gemma":[0.9998012,0.00004702582,0.00004378304,0.00002661163,0.00005703609,0.00002428905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001076539,0.0006509785,0.06603453,0.0003820896,0.0003271656,0.0007778282,0.0001718157,0.124245,0.2062331,0.001039956,0.007235412,0.5918255],"study_design_scores_gemma":[0.00001710463,0.000328954,0.04159754,0.00003398614,0.00004695507,0.0002290852,0.00005960857,0.917262,0.03834981,0.0006962728,0.001356976,0.00002179885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7722109,0.0008048018,0.2151444,0.0002106141,0.0001716178,0.0001378054,0.003097976,0.005245429,0.002976485],"genre_scores_gemma":[0.9167958,0.0004332191,0.07610594,0.0001044636,0.00002885535,0.0001074444,0.003855869,0.00008036579,0.002488019],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005843306,"threshold_uncertainty_score":0.01161861,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}