{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":560,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":560,"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":"6f8398f419cb","filters":{"topic":"Time Series Analysis and Forecasting"}},"results":[{"id":"W2145487065","doi":"10.1016/j.dsp.2007.12.004","title":"Time–frequency feature representation using energy concentration: An overview of recent advances","year":2008,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":746,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Feature (linguistics); Computer science; Signal processing; Frequency domain; Energy (signal processing); SIGNAL (programming language); Time–frequency analysis; Representation (politics); Artificial intelligence; Time domain; Domain (mathematical analysis); Pattern recognition (psychology); Machine learning; Data mining; Digital signal processing; Mathematics; Statistics; Telecommunications","authors":[{"name":"Ervin Sejdić","is_ca":true},{"name":"Igor Djurović","is_ca":false},{"name":"Jin Jiang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06821636393239666,"gpt":0.2987034494592146,"spread":0.230487085526818,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008742531,0.000910884,0.001540623,0.002035862,0.0001772041,0.001837546,0.001074243,0.0009256992,0.001861674],"category_scores_gemma":[0.00163817,0.0003119798,0.001025906,0.003427627,0.0003247871,0.001604836,0.0004189295,0.0007157855,0.001157445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002146586,"about_ca_system_score_gemma":0.000331884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001398518,"about_ca_topic_score_gemma":0.0008283952,"domain_scores_codex":[0.9997024,0.00005439497,0.00003194496,0.00009378391,0.00009996373,0.00001743695],"domain_scores_gemma":[0.9992575,0.0003827594,0.00008237817,0.00005929003,0.0001929697,0.0000250265],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000106326,0.00008622329,0.001229809,0.000746378,0.0001252982,0.00007414645,0.00005022343,0.009757609,0.01036879,0.004482285,0.002692865,0.9702801],"study_design_scores_gemma":[0.0001135392,0.0006833766,0.01226272,0.0006471209,0.0006787774,0.001807182,0.0003470667,0.7801331,0.04293749,0.05408886,0.1059239,0.0003768508],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01207347,0.06817464,0.9135365,0.000667736,0.0003219353,0.00006358005,0.0002454236,0.0008280419,0.004088645],"genre_scores_gemma":[0.2073098,0.1196909,0.6610339,0.0004591763,0.002737938,0.0002308531,0.001386725,0.0002841205,0.006866594],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002035862,"threshold_uncertainty_score":0.006227851,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1597504361","doi":"10.1016/b978-012088469-8.50070-x","title":"On The Marriage of Lp-norms and Edit Distance","year":2004,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":698,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Triangle inequality; Edit distance; Search engine indexing; Subsequence; Mathematics; Earth mover's distance; Metric (unit); Series (stratigraphy); Function (biology); Nearest neighbor search; Computer science; Tree (set theory); Upper and lower bounds; Algorithm; Combinatorics; Artificial intelligence","authors":[{"name":"Lei Chen","is_ca":false},{"name":"Raymond T. Ng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01030675719209186,"gpt":0.1938552282452536,"spread":0.1835484710531617,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005692653,0.001266251,0.001904246,0.003362203,0.001366037,0.00480304,0.002758274,0.002602445,0.005836028],"category_scores_gemma":[0.03601568,0.0006213111,0.001009177,0.005672764,0.005053224,0.01349049,0.003660081,0.00408168,0.001395712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244287,"about_ca_system_score_gemma":0.0008161927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00221123,"about_ca_topic_score_gemma":0.001927656,"domain_scores_codex":[0.9951453,0.002752381,0.0003364745,0.0006832306,0.0009461991,0.0001364646],"domain_scores_gemma":[0.966473,0.02750421,0.0009868342,0.002174495,0.002361558,0.0004999473],"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":[0.00003051226,0.00002555455,0.0003361815,0.0001021163,0.000030035,0.00006186123,0.0001865614,0.009417481,0.0003040697,0.9146441,0.002343934,0.07251769],"study_design_scores_gemma":[0.00000523542,0.00002431985,0.00009332295,0.00002006478,0.000006839543,0.00005541507,0.00003340429,0.03961503,0.0001964813,0.9564309,0.003503879,0.00001522218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006479601,0.002833695,0.9782345,0.001275927,0.0003372275,0.00001587134,0.0001083964,0.0001511073,0.01056363],"genre_scores_gemma":[0.2694233,0.008467543,0.6987038,0.0008423181,0.003145663,0.0002559546,0.0006936131,0.0005199453,0.01794794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005836028,"threshold_uncertainty_score":0.03010595,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2930650313","doi":"10.3390/app9071345","title":"Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review","year":2019,"lang":"en","type":"review","venue":"Applied Sciences","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":586,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Youth Innovation Promotion Association; National Key Research and Development Program of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Wavelet transform; Wavelet; Series (stratigraphy); Discrete wavelet transform; Computer science; Selection (genetic algorithm); Interpretation (philosophy); Explosive material; Mathematics; Algorithm; Pattern recognition (psychology); Artificial intelligence; Geology; Geography; Archaeology","authors":[{"name":"Manel Rhif","is_ca":false},{"name":"Ali Ben Abbes","is_ca":true},{"name":"Imed Riadh Farah","is_ca":false},{"name":"Beatriz Martínez","is_ca":false},{"name":"Yan‐Fang Sang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03455869314376013,"gpt":0.3090370746206598,"spread":0.2744783814768996,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005827,0.0009969026,0.001168894,0.003501217,0.0002677941,0.001216711,0.0008749787,0.001088876,0.003670747],"category_scores_gemma":[0.002169117,0.0003420538,0.0008176367,0.004887267,0.0005959881,0.001913366,0.0006713124,0.001355937,0.001922963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004862426,"about_ca_system_score_gemma":0.001086792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265957,"about_ca_topic_score_gemma":0.001071644,"domain_scores_codex":[0.999709,0.00004517722,0.00005818129,0.00006278634,0.0001067434,0.00001815492],"domain_scores_gemma":[0.9989244,0.0006854113,0.0001028293,0.00003160761,0.0002257416,0.00002986476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003764408,0.00005504185,0.0003728118,0.01345843,0.0000833854,0.0001890477,0.00007084538,0.001051117,0.001362469,0.006602865,0.01553812,0.9611782],"study_design_scores_gemma":[0.00001887973,0.0001481894,0.002712262,0.008445219,0.0002584856,0.002552108,0.0001569752,0.0021086,0.001914049,0.01252248,0.9690881,0.00007459037],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0004144302,0.9930049,0.003118867,0.0006771635,0.0004894854,0.0000149857,0.00005072458,0.00002664289,0.002202788],"genre_scores_gemma":[0.001912655,0.9954581,0.00148048,0.0001567061,0.0003523545,0.00001398098,0.00005829736,0.000007073217,0.0005603642],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003670747,"threshold_uncertainty_score":0.01227987,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2949449669","doi":"10.1109/jsen.2019.2923982","title":"A Review of Deep Learning Models for Time Series Prediction","year":2019,"lang":"en","type":"review","venue":"IEEE Sensors Journal","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":488,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Deep learning; Artificial intelligence; Computer science; Machine learning; Discriminative model; Curse of dimensionality; Time series; Categorization; Generative grammar; Abstraction; Process (computing); Series (stratigraphy); Generative model","authors":[{"name":"Zhongyang Han","is_ca":false},{"name":"Jun Zhao","is_ca":false},{"name":"Henry Leung","is_ca":true},{"name":"King Ma","is_ca":true},{"name":"Wei Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05910418582005508,"gpt":0.2897347488595188,"spread":0.2306305630394637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309573,0.001269641,0.0009834812,0.002093981,0.0002392387,0.001131835,0.001510789,0.001363881,0.003248581],"category_scores_gemma":[0.003381922,0.0005161648,0.0007706041,0.003857574,0.0004259806,0.002122767,0.0007112788,0.001989667,0.002500515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101996,"about_ca_system_score_gemma":0.001466777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003285342,"about_ca_topic_score_gemma":0.002820234,"domain_scores_codex":[0.9995741,0.00007958415,0.00006429169,0.00009327508,0.000161283,0.00002737015],"domain_scores_gemma":[0.9985513,0.0008544584,0.00008204518,0.00006016428,0.0004103669,0.00004172699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000047708,0.00007543828,0.0007000805,0.006817365,0.0001214225,0.0001093258,0.00005366564,0.00995999,0.0006726794,0.02974122,0.05888046,0.8928207],"study_design_scores_gemma":[0.00001334438,0.000105259,0.00119922,0.004454929,0.0001962023,0.0004864033,0.00005152251,0.01837711,0.001351671,0.03221214,0.9414773,0.00007482738],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006110487,0.9576879,0.03140699,0.002849395,0.001282057,0.00002459109,0.0003148822,0.000150892,0.0056723],"genre_scores_gemma":[0.007179475,0.9754199,0.01186806,0.0007963798,0.001427033,0.00003906837,0.0004444839,0.00003789328,0.002787694],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003285342,"threshold_uncertainty_score":0.01086754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4319335604","doi":"10.1016/j.eswa.2023.119619","title":"SAITS: Self-attention-based imputation for time series","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":442,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ciena (Canada); Concordia University","funders":"Beijing Jiaotong University; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Imputation (statistics); Missing data; Artificial intelligence; Data mining; Multivariate statistics; Series (stratigraphy); Time series; Machine learning; Pattern recognition (psychology)","authors":[{"name":"Wenjie Du","is_ca":true},{"name":"David Côté","is_ca":true},{"name":"Yan Liu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009208493889557967,"gpt":0.2370432656503537,"spread":0.2278347717607958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007958738,0.001224393,0.002111919,0.001586427,0.0009834793,0.00212605,0.004348394,0.002669006,0.01881425],"category_scores_gemma":[0.0389766,0.001143815,0.002126356,0.002089601,0.0006711509,0.00263227,0.003309466,0.004170025,0.01093233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005889043,"about_ca_system_score_gemma":0.002116097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004678273,"about_ca_topic_score_gemma":0.008351805,"domain_scores_codex":[0.9965253,0.001867648,0.0002347757,0.000600362,0.0005574768,0.0002144117],"domain_scores_gemma":[0.9877493,0.007217033,0.0005117565,0.002861236,0.001378075,0.0002826185],"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":[0.002066973,0.0006768066,0.01072894,0.0003794767,0.001341431,0.000276288,0.0002729306,0.1176199,0.003366306,0.02432902,0.07894026,0.7600017],"study_design_scores_gemma":[0.0001236869,0.0001041119,0.001031206,0.00004354214,0.0000775303,0.00007828636,0.00002680083,0.9643422,0.002674368,0.02325323,0.008205839,0.00003918054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004955954,0.0002084874,0.9763168,0.0002295334,0.0001947763,0.0000937213,0.001440504,0.0158927,0.0006674745],"genre_scores_gemma":[0.1417042,0.0003077267,0.8301973,0.0006999563,0.000411518,0.0007589821,0.01128996,0.003158464,0.01147187],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01881425,"threshold_uncertainty_score":0.06293988,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3039352388","doi":"","title":"Tslearn, A Machine Learning Toolkit for Time Series Data","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":422,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Python (programming language); MIT License; Cluster analysis; Header; Feature selection; Machine learning; Data mining; Artificial intelligence; Programming language; Software","authors":[{"name":"Romain Tavenard","is_ca":false},{"name":"Johann Faouzi","is_ca":false},{"name":"Gilles Vandewiele","is_ca":false},{"name":"Felix Divo","is_ca":false},{"name":"Guillaume Androz","is_ca":false},{"name":"Chester Holtz","is_ca":false},{"name":"Marie C. Payne","is_ca":true},{"name":"Roman Yurchak","is_ca":false},{"name":"Marc Rußwurm","is_ca":false},{"name":"Kushal Kolar","is_ca":false},{"name":"Eli Woods","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02845174199172006,"gpt":0.2351703904673543,"spread":0.2067186484756342,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00251012,0.00242764,0.001517806,0.002455193,0.0007321425,0.002457918,0.00358404,0.001867425,0.03159998],"category_scores_gemma":[0.01806728,0.001665262,0.003093658,0.00291092,0.0004164476,0.004081296,0.003207831,0.004984974,0.02812261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006839064,"about_ca_system_score_gemma":0.002380247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007557795,"about_ca_topic_score_gemma":0.0153793,"domain_scores_codex":[0.9984842,0.0003763635,0.0003049315,0.0003394489,0.0004083253,0.00008685128],"domain_scores_gemma":[0.9946101,0.003479735,0.0001982305,0.0007993379,0.0006575697,0.0002549997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001042868,0.0004071857,0.002959432,0.003623191,0.000784826,0.0007063724,0.0004292156,0.0429544,0.006332092,0.01191318,0.4415392,0.487308],"study_design_scores_gemma":[0.0005298157,0.0001938644,0.001844739,0.0005104239,0.000302776,0.0004679218,0.0001180722,0.6605285,0.01591586,0.07256695,0.2467916,0.0002294077],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.003151564,0.001121803,0.6041752,0.0004168435,0.0005317866,0.0002913316,0.03268284,0.3542345,0.003394169],"genre_scores_gemma":[0.03945283,0.00196029,0.8050199,0.0006679633,0.0001975046,0.001448016,0.106377,0.02911525,0.01576123],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03159998,"threshold_uncertainty_score":0.1057124,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2294710185","doi":"10.1109/icmla.2015.152","title":"MLaaS: Machine Learning as a Service","year":2015,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":383,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Computer science; Scalability; Context (archaeology); Architecture; Service (business); Information extraction; Data science; Social media; Electricity; World Wide Web; The Internet; Big data; Machine learning; Artificial intelligence; Multimedia; Data mining; Database; Engineering","authors":[{"name":"Mauro C. C. Ribeiro","is_ca":true},{"name":"Katarina Grolinger","is_ca":true},{"name":"Miriam A. M. Capretz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03180556607227813,"gpt":0.2346453364751164,"spread":0.2028397704028382,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002967929,0.001650412,0.001928679,0.002553736,0.001033766,0.005445105,0.005286095,0.004228629,0.06333657],"category_scores_gemma":[0.01568071,0.0009861534,0.001346807,0.003500467,0.0008667021,0.006918375,0.005626676,0.004189815,0.05847438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001847286,"about_ca_system_score_gemma":0.003312046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004614673,"about_ca_topic_score_gemma":0.002609382,"domain_scores_codex":[0.9970096,0.0005576353,0.0002723132,0.0004356532,0.001359969,0.0003647845],"domain_scores_gemma":[0.9921538,0.002180091,0.0003592309,0.002663416,0.001603432,0.001039974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00160661,0.0005520786,0.001658227,0.0006130406,0.0002260587,0.0008225653,0.0002572027,0.01202579,0.007848082,0.02061335,0.6469688,0.3068081],"study_design_scores_gemma":[0.0005608649,0.0002279066,0.001241511,0.0002003952,0.00005802018,0.0005164703,0.0001404362,0.415579,0.01308726,0.071817,0.4963327,0.000238482],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.003639146,0.001092035,0.268985,0.002504871,0.0009045763,0.0005641847,0.009530806,0.6894932,0.02328623],"genre_scores_gemma":[0.2909951,0.004726418,0.4259682,0.0113621,0.002872595,0.002911781,0.09719197,0.07048617,0.09348571],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06333657,"threshold_uncertainty_score":0.2118819,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993855803","doi":"10.1145/1007568.1007636","title":"Indexing spatio-temporal trajectories with Chebyshev polynomials","year":2004,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":314,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Mathematics; Chebyshev nodes; Chebyshev polynomials; Polynomial; Approximation theory; Minimax approximation algorithm; Minimax; Combinatorics; Discrete mathematics; Applied mathematics; Mathematical optimization; Mathematical analysis","authors":[{"name":"Yuhan Cai","is_ca":true},{"name":"Raymond T. Ng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01244357954838799,"gpt":0.2092422579717233,"spread":0.1967986784233353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00116185,0.0007564764,0.0009450147,0.002385064,0.0007665746,0.001841132,0.001288674,0.0008454621,0.002223056],"category_scores_gemma":[0.00815122,0.0003493113,0.0007750397,0.003719607,0.001045775,0.003327921,0.001354542,0.001566856,0.001056427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001552853,"about_ca_system_score_gemma":0.001460562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007895472,"about_ca_topic_score_gemma":0.005901687,"domain_scores_codex":[0.9989556,0.0001812879,0.0001016558,0.0002083309,0.0003915801,0.0001615482],"domain_scores_gemma":[0.9972684,0.001131073,0.0004450854,0.0006102068,0.0004581726,0.00008707757],"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":[0.0006852214,0.000129926,0.006664212,0.0003204448,0.00007222649,0.0003861893,0.000486206,0.4716005,0.02719931,0.1742361,0.002654213,0.3155656],"study_design_scores_gemma":[0.0000091385,0.0000606328,0.0007152344,0.00002666298,0.00000958976,0.0001187593,0.00007194934,0.9641045,0.005999133,0.02565119,0.00320885,0.00002435253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03114248,0.0003939791,0.9664547,0.0001263,0.00004787227,0.00003602144,0.0002569432,0.0003545701,0.001187258],"genre_scores_gemma":[0.5240736,0.001000664,0.4700971,0.0001004053,0.0001018971,0.0001156418,0.001069772,0.0001375705,0.003303475],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007895472,"threshold_uncertainty_score":0.01569903,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2080761477","doi":"10.1016/j.engappai.2014.12.015","title":"Fuzzy clustering of time series data using dynamic time warping distance","year":2015,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":275,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates - Technology Futures","keywords":"Dynamic time warping; Cluster analysis; Computer science; Fuzzy clustering; Data mining; Pattern recognition (psychology); Series (stratigraphy); Fuzzy logic; Time series; Artificial intelligence; Distance measures; Machine learning","authors":[{"name":"Hesam Izakian","is_ca":true},{"name":"Witold Pedrycz","is_ca":true},{"name":"Iqbal Jamal","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04550973745343292,"gpt":0.2776762159967628,"spread":0.2321664785433299,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001218902,0.0005387715,0.0008035827,0.002910852,0.0008335575,0.001236275,0.0009898324,0.0007486038,0.000980621],"category_scores_gemma":[0.003894342,0.0002574746,0.001099093,0.002532172,0.0004679501,0.001234562,0.0007922533,0.0006618347,0.0003471584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009264669,"about_ca_system_score_gemma":0.0009601626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005695097,"about_ca_topic_score_gemma":0.00288604,"domain_scores_codex":[0.9989257,0.000190304,0.0001157694,0.000308532,0.0003814849,0.00007809589],"domain_scores_gemma":[0.998844,0.0003647431,0.0001274506,0.0001405626,0.0004768872,0.00004631131],"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":[0.0003922332,0.0001723938,0.002910428,0.0002230942,0.0002959264,0.0001671302,0.0005191025,0.3735617,0.02141798,0.02110853,0.002334231,0.5768973],"study_design_scores_gemma":[0.000007014852,0.00004928193,0.001429153,0.00001153978,0.00002549146,0.00004807228,0.00008119344,0.9858611,0.004023521,0.007426277,0.001012407,0.00002498196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05659013,0.0002544781,0.941678,0.00008910747,0.00005685051,0.00006354282,0.0001047281,0.0001943105,0.0009688081],"genre_scores_gemma":[0.5504022,0.0003476807,0.4464591,0.00003405961,0.00005858675,0.0001601692,0.0005966313,0.00008934393,0.001852206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005695097,"threshold_uncertainty_score":0.01132393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138087993","doi":"10.1109/icde.2002.994711","title":"Similarity search over time-series data using wavelets","year":2003,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":275,"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":"","keywords":"Wavelet; Haar wavelet; Discrete wavelet transform; Wavelet transform; Computer science; Wavelet packet decomposition; Pattern recognition (psychology); Orthonormal basis; Gabor wavelet; Artificial intelligence; Lifting scheme; Stationary wavelet transform; Cascade algorithm; Second-generation wavelet transform","authors":[{"name":"Ivan Popivanov","is_ca":true},{"name":"R. J. Dwayne Miller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08081329866398176,"gpt":0.2918841588542381,"spread":0.2110708601902564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002025145,0.0005165556,0.001595473,0.003756779,0.0005454827,0.001553331,0.0008878197,0.001228665,0.0008735433],"category_scores_gemma":[0.009949064,0.0002738303,0.0008588,0.006162245,0.0007049795,0.002611981,0.0008378513,0.0006032567,0.0005424359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004002111,"about_ca_system_score_gemma":0.0005245971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00115218,"about_ca_topic_score_gemma":0.0006839875,"domain_scores_codex":[0.9986445,0.0002858749,0.0001761402,0.0002618087,0.0005545514,0.00007712908],"domain_scores_gemma":[0.996488,0.002001353,0.000408507,0.0005339428,0.0005011669,0.00006703527],"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":[0.0003881005,0.0002957021,0.006285674,0.0004704802,0.0002400417,0.0003839248,0.0003492862,0.1062815,0.03691206,0.03797394,0.001715285,0.808704],"study_design_scores_gemma":[0.00004622912,0.000522271,0.003486676,0.0000358531,0.00008389898,0.0006839963,0.0001847989,0.940788,0.02234825,0.02773442,0.00402243,0.00006319718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09343318,0.001523715,0.9023961,0.000236994,0.00007233467,0.0000874442,0.0001067745,0.0004307201,0.001712631],"genre_scores_gemma":[0.5923899,0.002548325,0.4027622,0.00009413579,0.0001967251,0.0001283518,0.000514677,0.00007346981,0.001292149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003756779,"threshold_uncertainty_score":0.01071012,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167263515","doi":"10.1186/1475-925x-10-90","title":"Review and classification of variability analysis techniques with clinical applications","year":2011,"lang":"en","type":"review","venue":"BioMedical Engineering OnLine","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":263,"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":"Variation (astronomy); Data science; Computer science; Field (mathematics); Vocabulary; Process (computing); Management science; Artificial intelligence; Machine learning; Mathematics; Engineering","authors":[{"name":"Andrea Bravi","is_ca":true},{"name":"André Longtin","is_ca":true},{"name":"Andrew Seely","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05833792296501373,"gpt":0.345699494596897,"spread":0.2873615716318833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004757455,0.001424776,0.002358984,0.009685096,0.0004563846,0.002048116,0.002283763,0.001928745,0.002811271],"category_scores_gemma":[0.01572879,0.0006642719,0.00169102,0.009739024,0.001060294,0.002719063,0.000969965,0.002058107,0.002175093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001281793,"about_ca_system_score_gemma":0.002894855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00186222,"about_ca_topic_score_gemma":0.001584582,"domain_scores_codex":[0.9978929,0.0005137148,0.0004779089,0.0003129606,0.0007409393,0.00006153469],"domain_scores_gemma":[0.9871695,0.009041276,0.0008321052,0.0002995085,0.002515061,0.0001425357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004933466,0.00005097236,0.0004289248,0.01942987,0.0001903983,0.0001576311,0.0001134767,0.001044626,0.0007712038,0.005309333,0.01957456,0.9528797],"study_design_scores_gemma":[0.0000319072,0.000217241,0.005073549,0.02277556,0.000621902,0.00321072,0.0001806796,0.002372885,0.002101812,0.01549042,0.9477581,0.000165278],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002136098,0.9917017,0.005233231,0.0009127121,0.0004804374,0.00003308707,0.00006916501,0.000031513,0.001324607],"genre_scores_gemma":[0.001921981,0.9898316,0.006275404,0.0004005193,0.0007966747,0.00005786214,0.0001296685,0.00001556727,0.0005707592],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009685096,"threshold_uncertainty_score":0.02516007,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2949468773","doi":"10.1145/3292500.3330662","title":"Multi-Horizon Time Series Forecasting with Temporal Attention Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":207,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Time series; Probabilistic logic; Series (stratigraphy); Artificial intelligence; Horizon; Machine learning; Probabilistic forecasting; Term (time); Artificial neural network; Deep learning; Recurrent neural network; Data mining; Mathematics","authors":[{"name":"Chenyou Fan","is_ca":false},{"name":"Yuze Zhang","is_ca":false},{"name":"Yi Pan","is_ca":false},{"name":"Xiaoyue Li","is_ca":false},{"name":"Chi Zhang","is_ca":false},{"name":"Rong Yuan","is_ca":false},{"name":"Di Wu","is_ca":false},{"name":"Wensheng Wang","is_ca":false},{"name":"Jian Pei","is_ca":true},{"name":"Heng Huang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01340317472712949,"gpt":0.1983379260939325,"spread":0.184934751366803,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009715377,0.0008050412,0.001049575,0.000639104,0.0003461312,0.000730994,0.001654724,0.001034469,0.001973679],"category_scores_gemma":[0.00271079,0.0005073106,0.0007127295,0.000942872,0.0004263938,0.0016367,0.001096389,0.001607982,0.0002807688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008272731,"about_ca_system_score_gemma":0.001080205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104733,"about_ca_topic_score_gemma":0.01167901,"domain_scores_codex":[0.9997048,0.00005657436,0.00001794236,0.0001029415,0.00006540822,0.00005229509],"domain_scores_gemma":[0.9991131,0.0005379737,0.0001210497,0.00006286573,0.0001000587,0.00006494709],"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":[0.00007971044,0.00006808683,0.001130096,0.00005795707,0.00007745653,0.00009115388,0.00005496808,0.8982529,0.001583131,0.01316397,0.002154903,0.08328555],"study_design_scores_gemma":[0.000001610084,0.000003671046,0.00004420312,0.000001400491,0.000002738731,0.000002917809,0.000001170361,0.9963845,0.00008939345,0.003363517,0.0001032159,0.000001729747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02702472,0.0005930879,0.9688121,0.000654127,0.0001025612,0.0000182124,0.000185776,0.0007456603,0.001863658],"genre_scores_gemma":[0.8779438,0.0004596666,0.1178207,0.0002878029,0.0001939099,0.00007394726,0.0004484946,0.0001016645,0.002669931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01104733,"threshold_uncertainty_score":0.02196604,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164427480","doi":"10.1155/2011/406391","title":"PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction","year":2011,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":184,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Python (programming language); Computer science; Open source; Electroencephalography; Feature extraction; Artificial intelligence; Pattern recognition (psychology); Speech recognition; Software; Programming language; Neuroscience; Psychology","authors":[{"name":"Forrest Sheng Bao","is_ca":false},{"name":"Xin Liu","is_ca":false},{"name":"Christina Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.121298983739666,"gpt":0.3281203659125222,"spread":0.2068213821728563,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007496332,0.001234884,0.0006916697,0.001056694,0.0003924,0.0007615577,0.002326144,0.0005691349,0.03057269],"category_scores_gemma":[0.0027959,0.0006162279,0.0008753964,0.0007574337,0.0003855948,0.001482317,0.001533976,0.001246352,0.01169583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002821336,"about_ca_system_score_gemma":0.001023891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001291182,"about_ca_topic_score_gemma":0.00117162,"domain_scores_codex":[0.9996533,0.00003771679,0.00004152282,0.00007423217,0.0001414646,0.0000518065],"domain_scores_gemma":[0.9992518,0.000252095,0.00008232312,0.0001216112,0.0002100543,0.00008212129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001222968,0.0004411014,0.006028248,0.001574171,0.0003623366,0.001395982,0.0006654046,0.02437469,0.07595921,0.01101251,0.3374454,0.5395181],"study_design_scores_gemma":[0.0004423183,0.0002110943,0.01300687,0.0001798381,0.0001130749,0.002285164,0.00008312849,0.4568448,0.1469436,0.02737542,0.352165,0.0003497139],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.004754538,0.00008303654,0.6726904,0.0001748264,0.00008990737,0.0003623384,0.006359996,0.3113311,0.004153829],"genre_scores_gemma":[0.104995,0.0003668566,0.7718093,0.0008516033,0.0001523925,0.002857015,0.02420291,0.07033629,0.02442862],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03057269,"threshold_uncertainty_score":0.1022758,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2403962807","doi":"10.1137/1.9781611972818.22","title":"Extracting Interpretable Features for Early Classification on Time Series","year":2011,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":176,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"École doctorale des Sciences Chimiques","keywords":"Interpretability; Computer science; Artificial intelligence; Benchmark (surveying); Machine learning; Statistical classification; Domain (mathematical analysis); Data mining; Pattern recognition (psychology); Mathematics; Geography","authors":[{"name":"Zhengzheng Xing","is_ca":true},{"name":"Jian Pei","is_ca":true},{"name":"Philip S. Yu","is_ca":false},{"name":"Ke Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03985050477947773,"gpt":0.2341109639271254,"spread":0.1942604591476477,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468562,0.001018603,0.0009157027,0.002013408,0.0002978051,0.001104306,0.0005001391,0.00116333,0.001240362],"category_scores_gemma":[0.005859524,0.0002518669,0.0006595631,0.001466752,0.0005875214,0.001380445,0.0006137936,0.001592213,0.0009963575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002475015,"about_ca_system_score_gemma":0.0003269463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003486603,"about_ca_topic_score_gemma":0.0004783175,"domain_scores_codex":[0.9994972,0.0001421991,0.00005346831,0.0001009808,0.0001529669,0.00005317834],"domain_scores_gemma":[0.996036,0.002230773,0.0006089684,0.0005712723,0.0004514694,0.0001015702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002977281,0.0002019358,0.004830022,0.0002093721,0.00005777039,0.0004470116,0.0002805842,0.06224307,0.1012402,0.005300081,0.002540365,0.8223518],"study_design_scores_gemma":[0.00002839942,0.0002255345,0.009331371,0.00004451958,0.0000583033,0.0003447634,0.0001691511,0.9267394,0.04407853,0.01505582,0.003864286,0.00005982525],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06897188,0.000481066,0.9280404,0.0003630922,0.0000691782,0.00005710673,0.0002122216,0.0009889017,0.0008162631],"genre_scores_gemma":[0.4953364,0.0007352388,0.5010713,0.0001188102,0.000233366,0.0001032516,0.0009004675,0.0001728033,0.001328402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002013408,"threshold_uncertainty_score":0.007766604,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2958796454","doi":"10.48550/arxiv.1907.03907","title":"HMSPC: A Hybrid Mechanistic-Stochastic Physical-Continuous Model for Battery Dynamics","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":155,"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":"","keywords":"Series (stratigraphy); Ode; Time series; Mathematics; Computer science; Applied mathematics; Statistics; Geology","authors":[{"name":"Yulia Rubanova","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04842926736457927,"gpt":0.1785606763534551,"spread":0.1301314089888758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006538037,0.000842768,0.0007611441,0.0005236422,0.0003123953,0.001228842,0.002582893,0.001501116,0.002609753],"category_scores_gemma":[0.002604255,0.0005703578,0.001115376,0.0006644343,0.0005827106,0.001446314,0.001155595,0.002157665,0.0007982266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008077353,"about_ca_system_score_gemma":0.001302576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0136489,"about_ca_topic_score_gemma":0.01940486,"domain_scores_codex":[0.9997622,0.00005151568,0.00001218995,0.00009937183,0.00004974857,0.00002499355],"domain_scores_gemma":[0.9994572,0.0002743464,0.00005589245,0.00008702774,0.00009586188,0.00002961424],"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":[0.00005521253,0.00005671051,0.002775207,0.00007276908,0.00007112746,0.00005502603,0.00004853738,0.9554458,0.001247967,0.009635889,0.003718856,0.02681683],"study_design_scores_gemma":[0.000005303117,0.00001095434,0.0002820111,0.00000422089,0.000005925269,0.00001369957,0.000002765386,0.9949897,0.0001742498,0.003792523,0.0007136345,0.000005033998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08485154,0.001121248,0.8990685,0.001482032,0.0002549194,0.00009259056,0.005672146,0.003346048,0.004110958],"genre_scores_gemma":[0.8767079,0.0008714973,0.105303,0.000532336,0.0002020749,0.0003239012,0.0074949,0.000360702,0.008203575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0136489,"threshold_uncertainty_score":0.02713889,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2162756694","doi":"10.1109/icde.2007.367924","title":"SpADe: On Shape-based Pattern Detection in Streaming Time Series","year":2007,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":155,"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":"Dynamic time warping; Subsequence; Computer science; Euclidean distance; Longest common subsequence problem; Series (stratigraphy); Scaling; Matching (statistics); Dimension (graph theory); Pattern matching; Image warping; Time series; Euclidean geometry; Sequence (biology); Pattern recognition (psychology); Algorithm; Artificial intelligence; Mathematics; Machine learning; Geometry","authors":[{"name":"Yueguo Chen","is_ca":false},{"name":"Mário A. Nascimento","is_ca":true},{"name":"Beng Chin Ooi","is_ca":false},{"name":"Anthony K. H. Tung","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009636681276533455,"gpt":0.2105350676590516,"spread":0.2008983863825182,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001446683,0.0008464952,0.001179876,0.002882317,0.0004782567,0.0009322766,0.001546121,0.0009968071,0.001742209],"category_scores_gemma":[0.009449814,0.0003806609,0.0008780407,0.003442326,0.001031403,0.002641037,0.0015411,0.001100872,0.001096582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004004897,"about_ca_system_score_gemma":0.0006525385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384936,"about_ca_topic_score_gemma":0.001091613,"domain_scores_codex":[0.9986778,0.000231645,0.0001520358,0.0003164681,0.0005599619,0.00006201391],"domain_scores_gemma":[0.9955628,0.002160469,0.0004621505,0.0007280431,0.0008911795,0.0001953674],"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":[0.0004112068,0.0001541671,0.004484038,0.0002545527,0.0001154357,0.0003860448,0.0002303041,0.06242235,0.02091181,0.01337394,0.003013525,0.8942427],"study_design_scores_gemma":[0.00002718221,0.0002369111,0.003115119,0.0000285009,0.0000255477,0.0007946113,0.00009395744,0.9558418,0.01476617,0.01737857,0.007647419,0.00004418618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01425151,0.0002681894,0.9838235,0.00009786447,0.00006341981,0.00006178572,0.0001277314,0.0008019833,0.0005040032],"genre_scores_gemma":[0.1535017,0.0006642124,0.8425957,0.0001513603,0.0001309201,0.000179877,0.0007313033,0.0001990876,0.001845818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002882317,"threshold_uncertainty_score":0.007650852,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3092267918","doi":"10.1016/j.ymssp.2020.107322","title":"Analysis of different RNN autoencoder variants for time series classification and machine prognostics","year":2020,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":152,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Hyperparameter; Recurrent neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Series (stratigraphy); Prognostics; Time series; Machine learning; Feature vector; Feature (linguistics); Artificial neural network; Data mining","authors":[{"name":"Wennian Yu","is_ca":false},{"name":"Il Yong Kim","is_ca":true},{"name":"Chris K. Mechefske","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03520481640313492,"gpt":0.2340420322300764,"spread":0.1988372158269415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001578618,0.0006088072,0.0004205768,0.0004805798,0.0002151756,0.0006034818,0.0004256741,0.0005275468,0.001547972],"category_scores_gemma":[0.003823281,0.0001464064,0.0005017483,0.0004035242,0.0002315102,0.0007224219,0.000233001,0.0005540763,0.0003145657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003609815,"about_ca_system_score_gemma":0.0004199969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005748714,"about_ca_topic_score_gemma":0.005619794,"domain_scores_codex":[0.9996445,0.0001001994,0.00002915419,0.00008081929,0.0001045472,0.00004071227],"domain_scores_gemma":[0.9979635,0.001217279,0.0000741031,0.0001419112,0.0005698724,0.0000333629],"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":[0.001255157,0.0002666503,0.006866435,0.0003137607,0.0003454513,0.000201218,0.0001052153,0.5321183,0.03277177,0.003392749,0.001926657,0.4204366],"study_design_scores_gemma":[0.00001126527,0.00009903783,0.005161565,0.00001372241,0.00006306802,0.00004723405,0.00002398364,0.9873749,0.006311504,0.0004814598,0.0003989467,0.00001336761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5922539,0.004034581,0.3982967,0.0003396747,0.0002776571,0.00006897876,0.0004906836,0.0007209996,0.00351677],"genre_scores_gemma":[0.938881,0.0008097801,0.05705252,0.00004325503,0.00004540369,0.0000417064,0.0007793494,0.0001123467,0.002234498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005748714,"threshold_uncertainty_score":0.0114305,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106981782","doi":"10.1145/2110363.2110408","title":"Unsupervised pattern discovery in electronic health care data using probabilistic clustering models","year":2012,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Pacific Institute for the Mathematical Sciences","keywords":"Cluster analysis; Computer science; Probabilistic logic; Health records; Data mining; Unsupervised learning; Machine learning; Artificial intelligence; Cluster (spacecraft); Statistical model; Process (computing); Health care","authors":[{"name":"Benjamin M. Marlin","is_ca":false},{"name":"David C. Kale","is_ca":false},{"name":"Robinder G. Khemani","is_ca":false},{"name":"Randall C. Wetzel","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07369182579551344,"gpt":0.2897209502123568,"spread":0.2160291244168434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005574734,0.0006180792,0.00106094,0.002519205,0.0005902998,0.001540675,0.002078384,0.001369064,0.0005986473],"category_scores_gemma":[0.02330127,0.0006613809,0.00128411,0.002309737,0.001126732,0.002192021,0.001142235,0.001381182,0.0002430198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478296,"about_ca_system_score_gemma":0.001091968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007715324,"about_ca_topic_score_gemma":0.009603219,"domain_scores_codex":[0.9970478,0.001470707,0.0001823245,0.0007078992,0.000434211,0.0001570877],"domain_scores_gemma":[0.978528,0.016807,0.002302984,0.001192608,0.0009718568,0.0001975983],"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":[0.0001094839,0.0001094442,0.01350955,0.00007961497,0.0001519849,0.0001349938,0.0003377208,0.9162161,0.0006503555,0.02334777,0.001175593,0.04417751],"study_design_scores_gemma":[0.00000445271,0.000005880403,0.000521898,0.00000400728,0.00000435099,0.00001470668,0.00001028249,0.9903301,0.00007551347,0.00893252,0.00009171903,0.000004659898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1116375,0.0003472801,0.8854821,0.0008481423,0.00002323815,0.000122404,0.00053468,0.0003735466,0.0006312627],"genre_scores_gemma":[0.7624791,0.0004713069,0.2336578,0.0001754278,0.00007823201,0.0003007207,0.00149438,0.00005824836,0.00128471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007715324,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2797405679","doi":"10.1109/access.2018.2825538","title":"LSTM-Based Analysis of Industrial IoT Equipment","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Government of Shandong Province; University of New South Wales","keywords":"Computer science; Hyperparameter; Feature engineering; Artificial neural network; Autoregressive model; Time series; Data mining; Internet of Things; Mean squared error; Autoregressive integrated moving average; Machine learning; Data modeling; Artificial intelligence; Deep learning; Statistics; Database","authors":[{"name":"Weishan Zhang","is_ca":false},{"name":"Wuwu Guo","is_ca":false},{"name":"Xin Liu","is_ca":false},{"name":"Yan Liu","is_ca":true},{"name":"Jiehan Zhou","is_ca":false},{"name":"Bo Li","is_ca":false},{"name":"Qinghua Lu","is_ca":false},{"name":"Su Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08593991815009144,"gpt":0.3195129177943994,"spread":0.2335729996443079,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001108791,0.0004178036,0.0001981719,0.0005877261,0.0001140431,0.0002873509,0.0003241079,0.0003610123,0.001331318],"category_scores_gemma":[0.0004191799,0.0001420234,0.0003414205,0.0006713092,0.0001156991,0.0004808167,0.0002145131,0.0003087157,0.0003296027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003105803,"about_ca_system_score_gemma":0.0002183009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005229637,"about_ca_topic_score_gemma":0.004773258,"domain_scores_codex":[0.9999439,0.000005769653,0.000003793507,0.00001888132,0.00001572459,0.00001194552],"domain_scores_gemma":[0.9999331,0.00002362484,0.00001169217,0.000007766507,0.00001943026,0.000004397015],"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":[0.0001427807,0.0001331867,0.01047927,0.0001199096,0.00007150526,0.0004252424,0.0001118099,0.7054932,0.04264782,0.001616487,0.003599478,0.2351592],"study_design_scores_gemma":[7.202108e-7,0.000005612344,0.002477742,0.00000235985,0.000002865384,0.00001153894,0.000007193992,0.995955,0.001043464,0.0003516311,0.0001390575,0.000002815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6762295,0.00055107,0.3109483,0.0003213949,0.0001311274,0.0000343327,0.001068983,0.002230606,0.008484623],"genre_scores_gemma":[0.9870526,0.000107949,0.0111106,0.00002804875,0.00001317899,0.00001183245,0.0003945443,0.00002317309,0.001258138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005229637,"threshold_uncertainty_score":0.01039845,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070847340","doi":"10.1109/tase.2012.2230627","title":"Similarity Analysis of Industrial Alarm Flood Data","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Imperial Oil (Canada); University of Alberta","funders":"","keywords":"ALARM; Flood myth; Flooding (psychology); Computer science; Operator (biology); Data mining; Process (computing); Similarity (geometry); Real-time computing; Reliability engineering; Engineering; Artificial intelligence; Geography","authors":[{"name":"Kabir Ahmed","is_ca":false},{"name":"Iman Izadi","is_ca":false},{"name":"Tongwen Chen","is_ca":true},{"name":"D.G.J.T. Tjin Wong Joe","is_ca":true},{"name":"Tim Burton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0432104627292473,"gpt":0.2436231372989517,"spread":0.2004126745697044,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001365552,0.0002921634,0.0006373965,0.00553725,0.0003587539,0.0006981156,0.0005224716,0.0006516696,0.0005267243],"category_scores_gemma":[0.007441277,0.0001087883,0.0005312235,0.004008509,0.0002976481,0.0008542149,0.0007693495,0.0003664737,0.000248938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004124585,"about_ca_system_score_gemma":0.0003714295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001469819,"about_ca_topic_score_gemma":0.00100472,"domain_scores_codex":[0.9973882,0.0004374885,0.0003687316,0.0003958728,0.001193548,0.0002162153],"domain_scores_gemma":[0.9961703,0.001480236,0.0007457723,0.0005182062,0.000943751,0.000141737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001542187,0.0008582328,0.1641867,0.0006119911,0.0004460239,0.003365412,0.001429076,0.1582822,0.06797002,0.01001857,0.007587756,0.5837019],"study_design_scores_gemma":[0.00002982501,0.0004617558,0.1768852,0.00002596659,0.00007820061,0.001373839,0.0007136682,0.7934489,0.01447404,0.006491742,0.005942898,0.00007390926],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8822964,0.0004812341,0.1122171,0.0001889756,0.00007595881,0.0001447318,0.001803082,0.0007551831,0.002037299],"genre_scores_gemma":[0.9806333,0.00009893154,0.01667905,0.00001532922,0.00004607045,0.00004856338,0.002147198,0.00001670593,0.0003148439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00553725,"threshold_uncertainty_score":0.007221818,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3210610608","doi":"10.3390/app11136141","title":"A Survey on Change Detection and Time Series Analysis with Applications","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Calgary","funders":"","keywords":"Computer science; Series (stratigraphy); Spectral analysis; Remote sensing; Time series; Spectral leakage; Data mining; Algorithm; Geography; Geology; Fast Fourier transform; Physics; Machine learning; Astronomy","authors":[{"name":"Ebrahim Ghaderpour","is_ca":true},{"name":"Spiros Pagiatakis","is_ca":true},{"name":"Quazi K. Hassan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0286433725364304,"gpt":0.230494893058026,"spread":0.2018515205215956,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003127596,0.001657871,0.001664228,0.007736278,0.0005886278,0.002937988,0.001791761,0.001807896,0.006319848],"category_scores_gemma":[0.007702303,0.0007234556,0.001648467,0.01338163,0.001052398,0.00380935,0.001187704,0.001896368,0.003884851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009270165,"about_ca_system_score_gemma":0.001578566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002569118,"about_ca_topic_score_gemma":0.001360036,"domain_scores_codex":[0.9971681,0.0007569782,0.0003647148,0.0005607423,0.00106236,0.00008714241],"domain_scores_gemma":[0.9952257,0.003256047,0.0002417002,0.0002673959,0.0009355852,0.00007354874],"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":[0.00003912202,0.00008237367,0.001462511,0.005602841,0.0001182982,0.0001688013,0.000158653,0.003754301,0.001017821,0.02848551,0.02745801,0.9316517],"study_design_scores_gemma":[0.00001977824,0.0001923328,0.004834738,0.004033075,0.0001987409,0.001740889,0.0003265898,0.03107129,0.002308283,0.06609976,0.8890126,0.0001618781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001963943,0.8081119,0.1654752,0.002786776,0.001741492,0.0001567077,0.0005824689,0.0005759147,0.01860567],"genre_scores_gemma":[0.01928203,0.8636725,0.1049175,0.001092551,0.003850895,0.0002231698,0.001106358,0.0001495576,0.005705475],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.007736278,"threshold_uncertainty_score":0.02114201,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078765398","doi":"10.1007/s10115-011-0400-x","title":"Early classification on time series","year":2011,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Beijing Institute of Technology; University of Windsor; Simon Fraser University; National Science Foundation","keywords":"Series (stratigraphy); Classifier (UML); Time series; Computer science; k-nearest neighbors algorithm; Benchmark (surveying); Data mining; Artificial intelligence; Pattern recognition (psychology); Machine learning; Geography","authors":[{"name":"Zhengzheng Xing","is_ca":false},{"name":"Jian Pei","is_ca":true},{"name":"Philip S. Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02641227350953722,"gpt":0.2078618429014923,"spread":0.1814495693919551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002923914,0.0007860246,0.0009662483,0.004905713,0.0008344337,0.003055407,0.0009911422,0.001203533,0.004669551],"category_scores_gemma":[0.01508437,0.0003736748,0.0007932195,0.003334575,0.001048191,0.004689715,0.001179925,0.002497486,0.002316077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375491,"about_ca_system_score_gemma":0.0006484394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001755871,"about_ca_topic_score_gemma":0.001279443,"domain_scores_codex":[0.9985178,0.000380751,0.0001191975,0.0002865187,0.0005424714,0.0001533336],"domain_scores_gemma":[0.9886628,0.005780545,0.0006920673,0.001657982,0.00285152,0.0003550258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005437552,0.0001883913,0.01347488,0.0004787052,0.0001176809,0.0002621318,0.0003735698,0.02752961,0.009754282,0.1895827,0.01782446,0.7398698],"study_design_scores_gemma":[0.00003508567,0.0002412476,0.01267101,0.0002089332,0.0001245218,0.0003598573,0.0002292125,0.5777108,0.01333039,0.3599247,0.03509586,0.00006838021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.148766,0.01166999,0.8106495,0.004044154,0.002429274,0.0001132743,0.000693086,0.001010444,0.02062434],"genre_scores_gemma":[0.8127771,0.00822032,0.1259059,0.0006020581,0.004298452,0.0001272254,0.002147187,0.0002543029,0.04566752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004905713,"threshold_uncertainty_score":0.01562119,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155357014","doi":"","title":"Multiple Alignment of Continuous Time Series","year":2004,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":122,"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":"","keywords":"TRACE (psycholinguistics); Inference; Computer science; Leverage (statistics); Series (stratigraphy); Time series; Generative model; Replicate; Artificial intelligence; Noise (video); Pattern recognition (psychology); Algorithm; Data mining; Generative grammar; Machine learning; Mathematics; Statistics","authors":[{"name":"Jennifer Listgarten","is_ca":true},{"name":"Radford M. Neal","is_ca":true},{"name":"Sam T. Roweis","is_ca":true},{"name":"Andrew Emili","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007120431936030748,"gpt":0.1891770008252462,"spread":0.1820565688892155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220042,0.0007351692,0.0008208987,0.0009367839,0.0003878982,0.001365732,0.001211769,0.001211165,0.00221954],"category_scores_gemma":[0.01203865,0.0005938247,0.001009162,0.001670628,0.000906958,0.001936455,0.001150681,0.001707473,0.0008629675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008368204,"about_ca_system_score_gemma":0.0008429234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001623635,"about_ca_topic_score_gemma":0.001459666,"domain_scores_codex":[0.9979004,0.0005958594,0.0001140257,0.0008970671,0.0004033712,0.00008919954],"domain_scores_gemma":[0.9962555,0.00181715,0.0005595925,0.0008905248,0.0003706712,0.0001064927],"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":[0.0003337836,0.0001888358,0.01174598,0.0002665676,0.0002788009,0.0005573601,0.0006066825,0.5825478,0.02772816,0.1877877,0.00288488,0.1850735],"study_design_scores_gemma":[0.000007626639,0.00003864833,0.002103319,0.00001344549,0.00001402302,0.00009466994,0.00002426753,0.9497127,0.002386293,0.04378749,0.001792385,0.00002515086],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02343077,0.0001769771,0.9744947,0.0002143292,0.00005818318,0.00003288174,0.0003190385,0.0004567383,0.0008164842],"genre_scores_gemma":[0.7148546,0.0005933384,0.2781149,0.0001671419,0.0001976977,0.0002981183,0.001705805,0.0002872329,0.003781217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00221954,"threshold_uncertainty_score":0.01163709,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2092012469","doi":"10.1109/tnsre.2013.2259640","title":"Online Segmentation of Human Motion for Automated Rehabilitation Exercise Analysis","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Toronto Rehabilitation Institute","keywords":"Segmentation; Artificial intelligence; Computer science; Hidden Markov model; Identification (biology); Motion (physics); Process (computing); Computer vision; Motion analysis; Movement (music); Pattern recognition (psychology); Inertial measurement unit","authors":[{"name":"Jonathan Feng-Shun Lin","is_ca":true},{"name":"Dana Kulić","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008957431716900626,"gpt":0.2334167848521192,"spread":0.2244593531352185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002589093,0.0004493048,0.0005415441,0.001268483,0.0002161816,0.0004376203,0.000384689,0.000509757,0.001882909],"category_scores_gemma":[0.001002938,0.0002179597,0.0002777803,0.0006232974,0.0002024998,0.0003994594,0.000390182,0.0003030107,0.0009211049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002382373,"about_ca_system_score_gemma":0.0003936208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002015969,"about_ca_topic_score_gemma":0.004250014,"domain_scores_codex":[0.9997191,0.00005149401,0.00001731847,0.00008588939,0.00009945021,0.00002680892],"domain_scores_gemma":[0.9996417,0.0001520238,0.00006340312,0.00005113027,0.00007361754,0.00001815285],"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":[0.0004856153,0.0001864881,0.006473653,0.0002617288,0.00005213641,0.0002085019,0.0002804607,0.0346964,0.1712963,0.001632556,0.003966083,0.7804601],"study_design_scores_gemma":[0.00003899838,0.0002957569,0.04673076,0.00009209105,0.00004925604,0.0006589204,0.0002938185,0.8578218,0.07919026,0.005277135,0.009483212,0.00006796206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0527623,0.0003590514,0.9427139,0.00007565142,0.00003536842,0.00008589828,0.0003126333,0.002020105,0.001635079],"genre_scores_gemma":[0.6111934,0.0004706156,0.3845637,0.00009192193,0.00007308443,0.0002230622,0.000740468,0.0002025731,0.002441179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002015969,"threshold_uncertainty_score":0.006298959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3169661950","doi":"10.48550/arxiv.2106.00750","title":"Unsupervised Representation Learning for Time Series with Temporal\\n Neighborhood Coding","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":104,"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":"","keywords":"Coding (social sciences); Series (stratigraphy); Representation (politics); Computer science; Feature learning; Unsupervised learning; Artificial intelligence; Time series; Predictive coding; Theoretical computer science; Pattern recognition (psychology); Natural language processing; Machine learning; Mathematics; Statistics","authors":[{"name":"Sana Tonekaboni","is_ca":true},{"name":"Danny Eytan","is_ca":false},{"name":"Anna Goldenberg","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05763475466629842,"gpt":0.1854699109872249,"spread":0.1278351563209265,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001237281,0.0005049295,0.0007726475,0.001086602,0.0004262002,0.0008210553,0.001428671,0.0009112348,0.0008581554],"category_scores_gemma":[0.005434495,0.0002821182,0.0007810192,0.001150392,0.0007836367,0.001543123,0.001098711,0.001544495,0.0003093492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008844492,"about_ca_system_score_gemma":0.0007923873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00403126,"about_ca_topic_score_gemma":0.004849527,"domain_scores_codex":[0.9993666,0.0002301154,0.00003176499,0.000207979,0.0001079231,0.00005561707],"domain_scores_gemma":[0.9976438,0.001328282,0.0002974227,0.0004307028,0.0002256075,0.00007418192],"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":[0.0002178703,0.0002345766,0.005168911,0.0001568879,0.0001659656,0.0001385192,0.0002854698,0.5351627,0.007782317,0.06685984,0.005986163,0.3778407],"study_design_scores_gemma":[0.000003590405,0.00001270714,0.0002128426,0.000004400164,0.000004552192,0.000013138,0.000009197189,0.9867338,0.0005544893,0.01213894,0.0003077034,0.000004499139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02961243,0.0002791868,0.9684883,0.0002182139,0.00002934355,0.00003205464,0.0001802762,0.0004910238,0.0006692135],"genre_scores_gemma":[0.755849,0.0004684357,0.2380749,0.0002077832,0.0001787506,0.0002315415,0.001821898,0.0001661434,0.003001566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00403126,"threshold_uncertainty_score":0.008015633,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W634979280","doi":"10.1007/0-387-35439-5","title":"Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series","year":2006,"lang":"en","type":"book","venue":"Lecture notes in statistics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; Series (stratigraphy); Distribution (mathematics); Computer science; Geography; Mathematics; Economics; Geology; Management; Paleontology","authors":[{"name":"Estela Bee Dagum","is_ca":false},{"name":"Pierre A. Cholette","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01347607562010095,"gpt":0.2841560288380697,"spread":0.2706799532179688,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01694954,0.001310553,0.002329784,0.003042344,0.0009687168,0.001976109,0.004021496,0.002297581,0.003176144],"category_scores_gemma":[0.06477592,0.0008090467,0.001603659,0.003932332,0.001351178,0.004710558,0.00303209,0.003058644,0.0007118789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009687924,"about_ca_system_score_gemma":0.001325135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001927467,"about_ca_topic_score_gemma":0.001631545,"domain_scores_codex":[0.9906488,0.005864475,0.0005244648,0.001074161,0.001560681,0.0003273588],"domain_scores_gemma":[0.9755879,0.01658344,0.001024037,0.004109221,0.002374731,0.0003207208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004897264,0.0001932526,0.004571102,0.0002124997,0.0002597809,0.00011204,0.0002643301,0.3137687,0.001638892,0.08154893,0.00840887,0.5885319],"study_design_scores_gemma":[0.00002442231,0.0000709039,0.001070092,0.00002461365,0.00003157547,0.00008575724,0.00003575599,0.931666,0.0009662634,0.06413781,0.001864749,0.0000220654],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01098782,0.001085398,0.98551,0.0002812212,0.000130541,0.00003346709,0.0001052046,0.0008089755,0.001057414],"genre_scores_gemma":[0.3172161,0.001301183,0.6736802,0.000217258,0.0005154884,0.0003487066,0.001755911,0.001091192,0.003873965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01694954,"threshold_uncertainty_score":0.08963883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2021793765","doi":"10.1016/j.cmpb.2012.08.016","title":"KmL3D: A non-parametric algorithm for clustering joint trajectories","year":2012,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Research Unit on Children's Psychosocial Maladjustment","funders":"","keywords":"Cluster analysis; Computer science; Partition (number theory); Variable (mathematics); Joint (building); Trajectory; Cluster (spacecraft); Algorithm; Parametric statistics; Data mining; Joint probability distribution; Artificial intelligence; Mathematics; Statistics","authors":[{"name":"Christophe Genolini","is_ca":false},{"name":"Jean‐Baptiste Pingault","is_ca":false},{"name":"Tarak Driss","is_ca":false},{"name":"Sylvana M. Côté","is_ca":true},{"name":"Richard E. Tremblay","is_ca":true},{"name":"Frank Vitaro","is_ca":true},{"name":"Catherine Arnaud","is_ca":false},{"name":"Bruno Falissard","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08056085464795523,"gpt":0.3488950608248597,"spread":0.2683342061769044,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00318789,0.002656998,0.002860647,0.003447037,0.002397502,0.002793031,0.006281372,0.003812255,0.01226152],"category_scores_gemma":[0.01292554,0.002314993,0.003760818,0.003562666,0.001199428,0.002744532,0.004639758,0.004700181,0.007048627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001364368,"about_ca_system_score_gemma":0.004348383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02419825,"about_ca_topic_score_gemma":0.03289186,"domain_scores_codex":[0.998073,0.0006146444,0.0001958423,0.0004700015,0.0004715221,0.0001749564],"domain_scores_gemma":[0.997118,0.001414935,0.000195637,0.0005247718,0.0005997752,0.0001469004],"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":[0.0006567782,0.0002312515,0.002518747,0.000593819,0.0005924648,0.0002558421,0.0005922337,0.3063162,0.004331301,0.01256322,0.0426445,0.6287038],"study_design_scores_gemma":[0.00005941257,0.00002908562,0.0002683965,0.00002731747,0.00003117552,0.00008974856,0.0000575753,0.9796021,0.002511731,0.009765788,0.007503674,0.00005393273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001387858,0.00009465538,0.9881675,0.00007553995,0.00004834603,0.00006261979,0.0005581625,0.009378954,0.000226464],"genre_scores_gemma":[0.02250232,0.0000942097,0.9713298,0.00009722471,0.00003031905,0.0004885232,0.00215719,0.002273223,0.001027257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02419825,"threshold_uncertainty_score":0.04811478,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119323564","doi":"10.14778/2536206.2536208","title":"A data-adaptive and dynamic segmentation index for whole matching on time series","year":2013,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Search engine indexing; Series (stratigraphy); Segmentation; Computer science; Matching (statistics); Index (typography); Time series; Similarity (geometry); Tree (set theory); Nearest neighbor search; Data mining; Algorithm; Pattern recognition (psychology); Mathematics; Artificial intelligence; Machine learning; Statistics; Image (mathematics)","authors":[{"name":"Yang Wang","is_ca":false},{"name":"Peng Wang","is_ca":false},{"name":"Jian Pei","is_ca":true},{"name":"Wei Wang","is_ca":false},{"name":"Sheng Huang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0158004004847677,"gpt":0.2299606015603585,"spread":0.2141602010755908,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001957362,0.0006020599,0.001426683,0.004958992,0.0008965181,0.001845068,0.001673453,0.0008962239,0.001979674],"category_scores_gemma":[0.01327511,0.0003535975,0.0006598708,0.008064051,0.0007904985,0.006622171,0.002334304,0.001156188,0.001242524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001208241,"about_ca_system_score_gemma":0.001811872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002305513,"about_ca_topic_score_gemma":0.002829862,"domain_scores_codex":[0.9980932,0.0002488084,0.0003088071,0.0004701249,0.0007824068,0.00009670242],"domain_scores_gemma":[0.9959977,0.001302144,0.0004104748,0.001138153,0.0009362596,0.0002153791],"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":[0.0004787286,0.000228838,0.007331142,0.000380214,0.0001105022,0.0001806727,0.0005022344,0.07269931,0.03145682,0.07492201,0.01958707,0.7921225],"study_design_scores_gemma":[0.00005436903,0.0003454705,0.003187095,0.0000594227,0.00007048879,0.000675909,0.0002248961,0.8702545,0.01595977,0.07671402,0.03236268,0.00009134479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02600142,0.001213434,0.9669055,0.0002289495,0.000141272,0.0001355289,0.001349936,0.001766315,0.002257567],"genre_scores_gemma":[0.211367,0.001086753,0.7805803,0.0001727184,0.0002112085,0.0002905923,0.004262335,0.0002797053,0.001749454],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004958992,"threshold_uncertainty_score":0.0103516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983095027","doi":"10.1016/j.csda.2011.05.015","title":"Trend filtering via empirical mode decompositions","year":2011,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Agence Nationale de la Recherche","keywords":"Hilbert–Huang transform; Series (stratigraphy); Mode (computer interface); Empirical orthogonal functions; Nonparametric statistics; Time series; Empirical research; Key (lock); Decomposition; Econometrics; Mathematics; Computer science; Applied mathematics; Statistics; Geology; White noise","authors":[{"name":"Azadeh Moghtaderi","is_ca":true},{"name":"Patrick Flandrin","is_ca":false},{"name":"Pierre Borgnat","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1054431682024112,"gpt":0.3564648805763219,"spread":0.2510217123739106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001640033,0.0007983305,0.0006492986,0.001436656,0.0003702689,0.00120601,0.0005656123,0.0006688958,0.0049044],"category_scores_gemma":[0.00907267,0.000599389,0.001154045,0.001441378,0.0002647851,0.001970928,0.0007410965,0.001322011,0.001881279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002395799,"about_ca_system_score_gemma":0.000490997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001714349,"about_ca_topic_score_gemma":0.001802342,"domain_scores_codex":[0.9995492,0.0001454619,0.00003857241,0.0001128349,0.0001092733,0.0000445541],"domain_scores_gemma":[0.997598,0.001374382,0.0001844738,0.0003960471,0.0003991084,0.00004801229],"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":[0.0003607538,0.0001172096,0.002441058,0.0001970717,0.0002232202,0.0001428279,0.0002282129,0.1219041,0.02138842,0.0863593,0.005056408,0.7615813],"study_design_scores_gemma":[0.00001463664,0.00003199956,0.001353109,0.00001935186,0.00003539563,0.00004700778,0.00001707672,0.9689692,0.002657934,0.02422723,0.002606595,0.0000204274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006045399,0.0001294658,0.9929068,0.00005648947,0.00003845897,0.00001353257,0.00006728178,0.0002463272,0.0004962883],"genre_scores_gemma":[0.2266223,0.0009431844,0.7631068,0.00008876115,0.0002218871,0.0001650832,0.0008446986,0.0004080541,0.007599182],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0049044,"threshold_uncertainty_score":0.01640683,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1699074795","doi":"","title":"Early prediction on time series: a nearest neighbor approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"k-nearest neighbors algorithm; Time series; Computer science; Series (stratigraphy); Classifier (UML); Benchmark (surveying); Data mining; Large margin nearest neighbor; Artificial intelligence; Pattern recognition (psychology); Machine learning; Geography","authors":[{"name":"Zhengzheng Xing","is_ca":true},{"name":"Jian Pei","is_ca":true},{"name":"Philip S. Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00906017139996502,"gpt":0.1888850023619263,"spread":0.1798248309619613,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002588775,0.0006549119,0.001623057,0.002017788,0.0006692507,0.001266898,0.002652626,0.001762985,0.001442674],"category_scores_gemma":[0.007571914,0.0004086687,0.0008204792,0.001835063,0.0006730905,0.003597009,0.0009754109,0.001867659,0.0007637503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007238083,"about_ca_system_score_gemma":0.0004926329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003851266,"about_ca_topic_score_gemma":0.003242055,"domain_scores_codex":[0.9980325,0.0006158338,0.0001454935,0.0004637978,0.0006277028,0.000114645],"domain_scores_gemma":[0.996882,0.001748263,0.0002919773,0.000326478,0.0006631263,0.00008819796],"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":[0.0002607433,0.0003195863,0.007280352,0.0002934606,0.0001706592,0.0003146178,0.0003315834,0.3589536,0.003714543,0.04649328,0.004935834,0.5769318],"study_design_scores_gemma":[0.000007680158,0.00005635466,0.0007424581,0.00002293427,0.00001814479,0.00008074434,0.0000378216,0.9702917,0.0008033018,0.02635426,0.001557598,0.00002708174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01666198,0.001532523,0.9793364,0.0003534955,0.0001269005,0.00004108579,0.0001041608,0.000222753,0.001620712],"genre_scores_gemma":[0.563656,0.001923327,0.4266631,0.0002898781,0.0006265484,0.0001505509,0.0005754347,0.0001073856,0.006007746],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003851266,"threshold_uncertainty_score":0.01369095,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111996283","doi":"10.1109/tpwrd.2008.2002846","title":"An Improved Vibration Analysis Algorithm as a Diagnostic Tool for Detecting Mechanical Anomalies on Power Circuit Breakers","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hydro-Québec","funders":"","keywords":"Circuit breaker; Vibration; Power (physics); Mechanical vibration; Engineering; Mechanical system; Image warping; Condition monitoring; Algorithm; Hydraulic machinery; Computer science; Mechanical engineering; Electrical engineering; Artificial intelligence; Acoustics; Physics","authors":[{"name":"Michel Landry","is_ca":true},{"name":"François Léonard","is_ca":true},{"name":"Champlain Landry","is_ca":true},{"name":"R. Beauchemin","is_ca":true},{"name":"Olivier Turcotte","is_ca":true},{"name":"Fouad Brikci","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01387743861244681,"gpt":0.2234417859509785,"spread":0.2095643473385317,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007838958,0.0006994269,0.0006562687,0.001408558,0.0002135682,0.0005347344,0.0006612754,0.0008460364,0.001476033],"category_scores_gemma":[0.002536998,0.0002589288,0.000384182,0.0007407981,0.0002985647,0.0007956731,0.0003807588,0.0006487389,0.0006494766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002453517,"about_ca_system_score_gemma":0.0003483936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113346,"about_ca_topic_score_gemma":0.0007664699,"domain_scores_codex":[0.9994442,0.000100672,0.0000458174,0.0001433055,0.000214525,0.00005141587],"domain_scores_gemma":[0.99901,0.0005117332,0.00008597477,0.00008438523,0.0002764804,0.00003142894],"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":[0.0003679793,0.0001077843,0.002233197,0.0001165329,0.00005806968,0.000168964,0.00009021609,0.0952484,0.1058893,0.002739272,0.001476396,0.7915038],"study_design_scores_gemma":[0.00003207383,0.0001479998,0.002947555,0.00001045349,0.00003111591,0.0002023682,0.00002020725,0.9706866,0.02210881,0.001287533,0.002502476,0.00002283442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02860045,0.0001632683,0.9695681,0.00005741186,0.00002910316,0.00002784479,0.00005256845,0.001125408,0.0003758272],"genre_scores_gemma":[0.2519944,0.0001819425,0.7458123,0.00004999922,0.00005762892,0.00006467615,0.000275816,0.00009427886,0.001469001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001476033,"threshold_uncertainty_score":0.004937768,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3082281389","doi":"10.1109/tpami.2021.3076155","title":"Pay Attention to Evolution: Time Series Forecasting With Deep Graph-Evolution Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; National Institutes of Health; National Science Foundation","keywords":"Computer science; Artificial intelligence; Time series; Multivariate statistics; Machine learning; Deep learning; Hyperparameter; Graph; Artificial neural network; Series (stratigraphy); Recurrent neural network; Theoretical computer science","authors":[{"name":"Gabriel Spadon","is_ca":false},{"name":"Shenda Hong","is_ca":false},{"name":"Bruno Brandoli Machado","is_ca":true},{"name":"Stan Matwin","is_ca":true},{"name":"José F. Rodrigues","is_ca":false},{"name":"Jimeng Sun","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01271463193382739,"gpt":0.2192652471380597,"spread":0.2065506152042323,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008407948,0.0007903373,0.0006614872,0.000561566,0.0003058763,0.0006748864,0.001200658,0.0009873883,0.001898625],"category_scores_gemma":[0.003161994,0.0003510997,0.0006391829,0.0007559958,0.0003804174,0.002418625,0.0007967941,0.001818333,0.0002904681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009888833,"about_ca_system_score_gemma":0.0006664752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01538034,"about_ca_topic_score_gemma":0.01266901,"domain_scores_codex":[0.9997976,0.00004786529,0.00000993464,0.0000697627,0.0000443046,0.00003048963],"domain_scores_gemma":[0.9991187,0.0004952776,0.00007046605,0.0001275386,0.0001337236,0.00005417152],"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":[0.0001690118,0.0001393759,0.001843293,0.00007484192,0.0000902191,0.00008887181,0.00007801775,0.8216126,0.003229446,0.008374096,0.003546862,0.1607534],"study_design_scores_gemma":[0.000002429887,0.000007470415,0.00006435575,0.000001183831,0.000003059359,0.000003564848,0.000001909149,0.9975538,0.0002639789,0.001947618,0.0001492168,0.000001423539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2084888,0.001481183,0.7762454,0.001914236,0.0003246392,0.00009526487,0.0005016017,0.004274134,0.0066746],"genre_scores_gemma":[0.8891261,0.0004949814,0.1060216,0.000338821,0.00007470508,0.00004817815,0.0008470295,0.0001406201,0.002907975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01538034,"threshold_uncertainty_score":0.03058165,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1801768344","doi":"10.1137/1.9781611972771.59","title":"A Better Alternative to Piecewise Linear Time Series Segmentation","year":2007,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Piecewise; Algorithm; Time complexity; Overfitting; Time series; Scalability; Piecewise linear function; Linear model; Series (stratigraphy); Mathematical optimization; Mathematics; Artificial intelligence; Machine learning","authors":[{"name":"Daniel Lemire","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01124462453346336,"gpt":0.2472767944003175,"spread":0.2360321698668541,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001521756,0.0009190019,0.001183882,0.001346984,0.0004089233,0.002191814,0.001815621,0.001676089,0.006331969],"category_scores_gemma":[0.007844889,0.0004282697,0.001621338,0.003066925,0.0007773475,0.003363265,0.001200242,0.002544657,0.001783803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009433648,"about_ca_system_score_gemma":0.001115652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005403709,"about_ca_topic_score_gemma":0.003994896,"domain_scores_codex":[0.9986814,0.0003677404,0.00009183039,0.0004438441,0.0003296759,0.00008555266],"domain_scores_gemma":[0.9975535,0.001051741,0.0002575819,0.0006311348,0.0003946796,0.000111342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005706784,0.0001997675,0.005133451,0.0006318313,0.000366665,0.000396956,0.0004810179,0.3376011,0.02138075,0.1979171,0.01253228,0.4227885],"study_design_scores_gemma":[0.00003451603,0.0001935296,0.00154036,0.00006090458,0.00005746863,0.0002625777,0.0001002059,0.9011679,0.003764811,0.07002045,0.02273387,0.00006341342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007148106,0.0006649013,0.9875519,0.0008140048,0.0001970963,0.00003150099,0.0002903368,0.001092507,0.002209792],"genre_scores_gemma":[0.2970915,0.001380828,0.6886142,0.0008962624,0.0004076808,0.0001882596,0.001313126,0.0009264204,0.009181632],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006331969,"threshold_uncertainty_score":0.0211826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2184801043","doi":"10.1109/dsaa.2015.7344856","title":"Time series contextual anomaly detection for detecting market manipulation in stock market","year":2015,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures; Western Canada Research Grid","keywords":"Anomaly detection; Computer science; Outlier; Intrusion detection system; Data mining; Time series; Series (stratigraphy); Anomaly (physics); Stock market; Precision and recall; Artificial intelligence; Machine learning; Context (archaeology)","authors":[{"name":"Koosha Golmohammadi","is_ca":true},{"name":"Osmar R. Zai͏̈ane","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03058452054650962,"gpt":0.2369768816069279,"spread":0.2063923610604183,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005458499,0.0005368256,0.0006009392,0.002247409,0.0003004929,0.000519864,0.0005886104,0.000443225,0.0007036994],"category_scores_gemma":[0.002604581,0.0001435778,0.0005234937,0.001609412,0.0002446761,0.0008369168,0.0004647461,0.0005244259,0.0002664243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003357618,"about_ca_system_score_gemma":0.000409678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003888981,"about_ca_topic_score_gemma":0.004516783,"domain_scores_codex":[0.9994848,0.00005778172,0.00004363602,0.000152368,0.0001979597,0.00006350251],"domain_scores_gemma":[0.9989665,0.000305277,0.0002373528,0.0001431491,0.000290012,0.00005777439],"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":[0.0005063372,0.0004392197,0.08411507,0.0002167025,0.0002476584,0.0008688254,0.0002408675,0.08792722,0.03923785,0.005979855,0.005139867,0.7750806],"study_design_scores_gemma":[0.000009765704,0.00009566499,0.01909593,0.000009134988,0.00005725097,0.0003612201,0.0000630886,0.9657056,0.01011066,0.002454971,0.002015747,0.00002092304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3876646,0.001433976,0.6037841,0.0002762381,0.0002085661,0.0001029601,0.0006063749,0.003514888,0.002408331],"genre_scores_gemma":[0.9366692,0.0003253449,0.06184379,0.00004203502,0.00009744778,0.00002717199,0.0004288001,0.00003051854,0.0005356509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003888981,"threshold_uncertainty_score":0.007732689,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3004113099","doi":"10.1109/tim.2020.2967247","title":"Multiseries Featural LSTM for Partial Periodic Time-Series Prediction: A Case Study for Steel Industry","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":75,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Series (stratigraphy); Computer science; Feature (linguistics); Time series; Matching (statistics); Term (time); Multivariable calculus; Long short term memory; Artificial intelligence; Long-term prediction; Feature extraction; Exploit; Algorithm; Pattern recognition (psychology); Machine learning; Recurrent neural network; Artificial neural network; Mathematics; Statistics; Engineering","authors":[{"name":"Tianyu Wang","is_ca":false},{"name":"Henry Leung","is_ca":true},{"name":"Jun Zhao","is_ca":false},{"name":"Wei Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06689681377014364,"gpt":0.2658982076287909,"spread":0.1990013938586473,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004068526,0.0006061176,0.0004138807,0.0004685502,0.0003808356,0.0003740209,0.0005300986,0.0009224052,0.001318068],"category_scores_gemma":[0.0008007109,0.0001422161,0.0003999973,0.0009165062,0.0002443027,0.0006647342,0.0002652601,0.0005636573,0.0002475015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000472319,"about_ca_system_score_gemma":0.0004744859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01974758,"about_ca_topic_score_gemma":0.02303467,"domain_scores_codex":[0.99988,0.00002300752,0.000008695091,0.00003462954,0.00003623207,0.00001749264],"domain_scores_gemma":[0.9997937,0.00008648169,0.00001729884,0.00002576115,0.00006479354,0.00001199317],"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":[0.0006100396,0.0003991195,0.02020968,0.0004825378,0.0001212949,0.003364292,0.0003624736,0.6778051,0.02708913,0.003752849,0.007330842,0.2584726],"study_design_scores_gemma":[0.000008530865,0.00008079846,0.003223444,0.000006923133,0.00001363565,0.00009515021,0.00007586818,0.9900514,0.004368059,0.0008930888,0.001171203,0.00001186175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8694317,0.001164893,0.1204996,0.0009936622,0.0001420251,0.00008900512,0.001154462,0.001173626,0.005351092],"genre_scores_gemma":[0.9743478,0.0002462183,0.02319627,0.00003469171,0.00001667392,0.00002381547,0.0004225094,0.00001956289,0.001692395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01974758,"threshold_uncertainty_score":0.03926528,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139135490","doi":"10.1109/cvpr.1999.784983","title":"Time-series classification using mixed-state dynamic Bayesian networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":70,"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":"","keywords":"Hidden Markov model; Dynamic Bayesian network; Computer science; Inference; Artificial intelligence; Bayesian network; Trajectory; Time series; Machine learning; Gesture; Series (stratigraphy); Approximate inference; Variable-order Bayesian network; Bayesian inference; State (computer science); Bayesian probability; Algorithm","authors":[{"name":"Vladimir Pavlović","is_ca":false},{"name":"Brendan J. Frey","is_ca":true},{"name":"Thomas S. Huang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01366954455081369,"gpt":0.2234172753131299,"spread":0.2097477307623162,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002967561,0.0009619605,0.001424443,0.002237226,0.0005298735,0.001730164,0.002165256,0.001386129,0.001498497],"category_scores_gemma":[0.01046332,0.0006991559,0.001269129,0.001543585,0.0007397276,0.003081856,0.0009632747,0.001788981,0.0005014595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00129699,"about_ca_system_score_gemma":0.0008357984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007941334,"about_ca_topic_score_gemma":0.007222291,"domain_scores_codex":[0.9984992,0.0006713716,0.00009758135,0.0003694596,0.0002700203,0.00009251054],"domain_scores_gemma":[0.9958736,0.002969737,0.0003985998,0.0002401237,0.0004066278,0.0001113059],"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":[0.0002088734,0.0001455117,0.004066973,0.00009332795,0.000222984,0.0001073664,0.0001400463,0.7946124,0.001458311,0.02664913,0.001260584,0.1710345],"study_design_scores_gemma":[0.000004237347,0.000005760852,0.0001403492,0.000004993895,0.000007628759,0.00000853737,0.000003667534,0.9913149,0.0001505436,0.008189246,0.0001640493,0.000006136466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01401105,0.0003014649,0.98451,0.0002234393,0.00002370552,0.00002923053,0.0001274692,0.0002691419,0.0005045326],"genre_scores_gemma":[0.6254728,0.0006978384,0.3701091,0.0002267384,0.0001595401,0.0003032619,0.001097079,0.00008905305,0.001844466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007941334,"threshold_uncertainty_score":0.01579022,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2553567329","doi":"10.1145/3004295","title":"Smart Meter Data Analytics","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Database Systems","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":67,"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":"","keywords":"Computer science; Data analysis; Analytics; Smart meter; Big data; Data science; Data mining; Smart grid; Electrical engineering","authors":[{"name":"Xiufeng Liu","is_ca":false},{"name":"Lukasz Golab","is_ca":true},{"name":"Wojciech Golab","is_ca":true},{"name":"Ihab F. Ilyas","is_ca":true},{"name":"Shichao Jin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09289352135828077,"gpt":0.2750155793791962,"spread":0.1821220580209155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001504524,0.001547865,0.001088516,0.002840151,0.0005625002,0.002966426,0.001777546,0.0007381661,0.01081272],"category_scores_gemma":[0.008826906,0.0004278427,0.0007255081,0.005655748,0.0003680058,0.004133721,0.001936985,0.001264438,0.009049088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009387495,"about_ca_system_score_gemma":0.001276905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003514862,"about_ca_topic_score_gemma":0.002756365,"domain_scores_codex":[0.9975321,0.0003220468,0.0002283426,0.0005749078,0.001177527,0.000165071],"domain_scores_gemma":[0.9950044,0.0009157693,0.0003808374,0.001627319,0.001904489,0.000167196],"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":[0.0005341498,0.0004017917,0.02799456,0.0009732327,0.0002824021,0.0003839718,0.0003366106,0.07179943,0.01314963,0.03920014,0.257101,0.5878431],"study_design_scores_gemma":[0.00008415136,0.0001854596,0.01528653,0.000246736,0.00009614063,0.0005062041,0.0005770042,0.5123786,0.04217698,0.07801474,0.350323,0.0001244772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06512266,0.003470585,0.6509171,0.005853023,0.001510152,0.001337661,0.102508,0.0765393,0.09274152],"genre_scores_gemma":[0.5547987,0.003953628,0.2715014,0.00142777,0.0006869314,0.0005603342,0.1433324,0.002841459,0.02089739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01081272,"threshold_uncertainty_score":0.03617221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963912395","doi":"10.1016/j.patcog.2019.106973","title":"Learning representations of multivariate time series with missing data","year":2019,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"Norges Forskningsråd","keywords":"Missing data; Autoencoder; Computer science; Dimensionality reduction; Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Pairwise comparison; Time series; Machine learning; Deep learning; Algorithm; Data mining","authors":[{"name":"Filippo Maria Bianchi","is_ca":false},{"name":"Lorenzo Livi","is_ca":true},{"name":"Karl Øyvind Mikalsen","is_ca":false},{"name":"Michael Kampffmeyer","is_ca":false},{"name":"Robert Jenssen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03434366004606687,"gpt":0.2588712664701866,"spread":0.2245276064241197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00195527,0.0007111788,0.0009717071,0.0009740392,0.0002211347,0.001319209,0.001292861,0.001286232,0.001629006],"category_scores_gemma":[0.01038205,0.0004783967,0.0009671311,0.001359879,0.0005698544,0.002621834,0.001062986,0.002412148,0.0004537847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005231099,"about_ca_system_score_gemma":0.0006620264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001595904,"about_ca_topic_score_gemma":0.001595639,"domain_scores_codex":[0.9994434,0.0001808078,0.0000456798,0.0001640603,0.00009528316,0.00007083964],"domain_scores_gemma":[0.9965328,0.00210638,0.0004314203,0.0005753863,0.0002515004,0.0001024497],"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":[0.0004269289,0.0002883408,0.004197055,0.000233989,0.000227918,0.0001348961,0.0001699998,0.6443623,0.003678613,0.03012557,0.00402785,0.3121265],"study_design_scores_gemma":[0.000006009027,0.00002496696,0.0001986878,0.00000891811,0.00001081529,0.00001406382,0.000009506695,0.9883569,0.0003112009,0.01088091,0.0001724938,0.000005617039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06159569,0.0005297315,0.9359847,0.0004339484,0.0001014925,0.00002073032,0.0003554655,0.000544192,0.0004339976],"genre_scores_gemma":[0.8841031,0.0007785154,0.1110628,0.0001288562,0.0002331915,0.0001191608,0.001421025,0.00008028213,0.002072987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00195527,"threshold_uncertainty_score":0.01034057,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2292011317","doi":"10.14778/2735461.2735463","title":"Top-k nearest neighbor search in uncertain data series","year":2014,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":59,"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":"","keywords":"Series (stratigraphy); Computer science; Nearest neighbor search; k-nearest neighbors algorithm; Metric (unit); Data mining; Independence (probability theory); Time series; Uncertain data; Variety (cybernetics); Synthetic data; Algorithm; Artificial intelligence; Machine learning; Mathematics; Statistics","authors":[{"name":"Michele Dallachiesa","is_ca":false},{"name":"Themis Palpanas","is_ca":false},{"name":"Ihab F. Ilyas","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03384988583387695,"gpt":0.2508981956415918,"spread":0.2170483098077148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003401241,0.0008382721,0.002374381,0.001970493,0.001054085,0.001621994,0.002254832,0.001553972,0.0007654309],"category_scores_gemma":[0.01456119,0.0005779832,0.001000996,0.003185657,0.0009442439,0.003176922,0.001160851,0.00138293,0.0002973064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040623,"about_ca_system_score_gemma":0.0008605332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007063354,"about_ca_topic_score_gemma":0.00723175,"domain_scores_codex":[0.9980774,0.0005935715,0.0002058252,0.0005865055,0.0004142167,0.000122477],"domain_scores_gemma":[0.9925638,0.005327981,0.0007242724,0.0006062553,0.0006383634,0.0001393926],"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":[0.0001640196,0.00005806212,0.003274414,0.0001828134,0.0001164013,0.0001846918,0.0001364778,0.9117641,0.0009636269,0.007626716,0.001294847,0.07423391],"study_design_scores_gemma":[0.000004268305,0.00001463857,0.0003050714,0.000008222487,0.000008062273,0.00003716332,0.0000441171,0.9878808,0.0004174547,0.01096813,0.00030305,0.000008960709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05944632,0.001566794,0.9371082,0.0002763069,0.00007471233,0.00004986393,0.000275326,0.0003304323,0.0008720491],"genre_scores_gemma":[0.6791968,0.0007694945,0.3178506,0.0001145317,0.0001269492,0.00007404049,0.0008385943,0.00008494298,0.0009440762],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007063354,"threshold_uncertainty_score":0.01798767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1987004515","doi":"10.1109/tsmcc.2013.2261984","title":"A Framework for Periodic Outlier Pattern Detection in Time-Series Sequences","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":59,"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":"Outlier; Anomaly detection; Computer science; Data mining; Time series; Series (stratigraphy); Noise (video); Preprocessor; Pattern recognition (psychology); Artificial intelligence; Machine learning","authors":[{"name":"Faraz Rasheed","is_ca":true},{"name":"Reda Alhajj","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01575489775294127,"gpt":0.233000894512443,"spread":0.2172459967595017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003249478,0.001160223,0.001495756,0.004051024,0.0008417194,0.00157874,0.002642922,0.001477016,0.001969231],"category_scores_gemma":[0.009349409,0.0005709867,0.001788065,0.004035902,0.001109413,0.002345672,0.001959231,0.002065611,0.00105671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007110098,"about_ca_system_score_gemma":0.001565181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003079035,"about_ca_topic_score_gemma":0.002695317,"domain_scores_codex":[0.9976552,0.0005629249,0.0002264185,0.0004407186,0.000987372,0.0001273804],"domain_scores_gemma":[0.9972401,0.001240186,0.0004272802,0.000328085,0.0006342056,0.000130181],"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":[0.0004023965,0.0003331555,0.003858385,0.0006426105,0.0003088752,0.001069866,0.0005906057,0.3392553,0.01716214,0.1503745,0.007590267,0.4784119],"study_design_scores_gemma":[0.0000198681,0.0000788235,0.0003294609,0.00003254255,0.00002187963,0.0002379201,0.00004867999,0.9684369,0.001966217,0.02262202,0.006179209,0.00002644404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009607172,0.0001110248,0.9979914,0.00004446867,0.00001655094,0.00003425025,0.00006124288,0.0005976568,0.0001827198],"genre_scores_gemma":[0.06870501,0.00040821,0.9286707,0.00009902687,0.000109356,0.0002570973,0.000582917,0.0001499151,0.001017863],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004051024,"threshold_uncertainty_score":0.01718509,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1586407478","doi":"10.1007/3-540-44816-0_24","title":"Self-Supervised Chinese Word Segmentation","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Natural language processing; Artificial intelligence; Computer science; Word (group theory); Text segmentation; Segmentation; Linguistics; Philosophy","authors":[{"name":"Fuchun Peng","is_ca":true},{"name":"Dale Schuurmans","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01228191426832061,"gpt":0.2324298831829698,"spread":0.2201479689146492,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007945267,0.001448358,0.001350294,0.002526819,0.001018005,0.00141656,0.001295983,0.001086393,0.01108736],"category_scores_gemma":[0.001802241,0.0004963162,0.001055444,0.002737464,0.0006812211,0.00203031,0.001292103,0.0009520825,0.009870516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005312663,"about_ca_system_score_gemma":0.001604646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004779294,"about_ca_topic_score_gemma":0.008878561,"domain_scores_codex":[0.9990367,0.0001479437,0.00008302144,0.0004631328,0.0001455386,0.0001236991],"domain_scores_gemma":[0.9987148,0.0003944952,0.00006509106,0.0002415778,0.0005088053,0.00007521214],"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":[0.0004008185,0.0001186829,0.001516144,0.0003145926,0.00009252851,0.0002014483,0.0001748233,0.006502296,0.04463979,0.002702201,0.02303748,0.9202991],"study_design_scores_gemma":[0.00009335087,0.0003180761,0.007968628,0.00008823175,0.0002773121,0.0008582153,0.0004654666,0.8189136,0.1190528,0.01318523,0.0386874,0.00009166516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.132256,0.003796895,0.8049629,0.0006064267,0.001101396,0.000508241,0.00445264,0.02768412,0.02463151],"genre_scores_gemma":[0.4598994,0.001075199,0.4698083,0.0004344835,0.0006051924,0.0003934079,0.0205765,0.002583153,0.04462441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01108736,"threshold_uncertainty_score":0.0370909,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110855170","doi":"10.1190/int-2013-0130.1","title":"Automatic approaches for seismic to well tying","year":2014,"lang":"en","type":"article","venue":"Interpretation","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":54,"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":"Lawrence Berkeley National Laboratory","keywords":"Tying; Computer science; Metric (unit); Similarity (geometry); Process (computing); Task (project management); Dynamic time warping; TRACE (psycholinguistics); Measure (data warehouse); Matching (statistics); Artificial intelligence; Subjectivity; Data mining; Pattern recognition (psychology); Computer vision; Image (mathematics); Mathematics; Engineering; Programming language","authors":[{"name":"Roberto Henry Herrera","is_ca":true},{"name":"Sergey Fomel","is_ca":false},{"name":"Mirko van der Baan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02222948568194331,"gpt":0.2340972728863107,"spread":0.2118677872043674,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001259294,0.001208722,0.0007147092,0.002764975,0.0007545628,0.001604544,0.001622395,0.001091323,0.005804466],"category_scores_gemma":[0.005273189,0.0006525515,0.0007417232,0.001554292,0.0009818545,0.001186503,0.001517058,0.001281747,0.001329622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006130292,"about_ca_system_score_gemma":0.001176058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002951191,"about_ca_topic_score_gemma":0.004223693,"domain_scores_codex":[0.9987848,0.000375877,0.0000745881,0.0002742668,0.0003966604,0.00009391853],"domain_scores_gemma":[0.9967952,0.001462255,0.0003939684,0.0006478149,0.0006044178,0.00009631224],"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":[0.0002899574,0.000183856,0.001454847,0.0002597747,0.00008179304,0.0001916964,0.0005226023,0.1693053,0.1077681,0.01150101,0.002326123,0.706115],"study_design_scores_gemma":[0.00002059056,0.00005171531,0.001082032,0.00001057462,0.00001498239,0.000103796,0.0001258897,0.9532918,0.03534108,0.006707277,0.00321924,0.00003087871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0152219,0.00003972738,0.9812858,0.000043669,0.00001713441,0.00005292821,0.00006600926,0.002529482,0.0007433681],"genre_scores_gemma":[0.2470623,0.0000607988,0.7506506,0.00003059653,0.00003205277,0.00008662284,0.0002912311,0.0005236421,0.001262153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005804466,"threshold_uncertainty_score":0.01941794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2095602638","doi":"10.1109/tbme.2005.844029","title":"Comparison of Trend Detection Algorithms in the Analysis of Physiological Time-Series Data","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; University of Waterloo","funders":"","keywords":"Computer science; Fuzzy logic; Heartbeat; Noise (video); Wavelet transform; Cluster analysis; Wavelet; Time series; Algorithm; Data mining; Signal processing; Artificial intelligence; Pattern recognition (psychology); Machine learning","authors":[{"name":"William Melek","is_ca":true},{"name":"Zhijian Lu","is_ca":false},{"name":"A. Kapps","is_ca":false},{"name":"W.D. Fraser","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03636498373680928,"gpt":0.2820044607571753,"spread":0.245639477020366,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0124977,0.0009345707,0.000900369,0.005592847,0.0005155806,0.00158064,0.001005438,0.001342368,0.0007571685],"category_scores_gemma":[0.03948072,0.0003142674,0.0009184557,0.003607065,0.0003479611,0.002511008,0.0007049015,0.0007173267,0.0004391898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005563338,"about_ca_system_score_gemma":0.0006476041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175066,"about_ca_topic_score_gemma":0.001600771,"domain_scores_codex":[0.9953816,0.001915209,0.0004436027,0.0004989344,0.001604118,0.0001565501],"domain_scores_gemma":[0.9738699,0.0188178,0.0008541829,0.001108908,0.005172119,0.0001770762],"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":[0.001451918,0.0003439212,0.01551971,0.0007452641,0.0008165418,0.0001153411,0.0003866318,0.09422957,0.01378697,0.004956458,0.001583785,0.8660639],"study_design_scores_gemma":[0.0001017938,0.0008571146,0.0196277,0.0001083345,0.0003479413,0.0003291726,0.0002747965,0.9445708,0.0262398,0.00340895,0.004028397,0.0001051853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2019353,0.003947529,0.7885868,0.0003456014,0.0002044812,0.0002499645,0.0003123606,0.001996893,0.002421084],"genre_scores_gemma":[0.3630039,0.002035168,0.632585,0.00007016133,0.00008271718,0.0001811338,0.0008070612,0.000266256,0.0009686808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0124977,"threshold_uncertainty_score":0.06609499,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2901999103","doi":"10.1007/s10994-018-05774-y","title":"Arbitrage of forecasting experts","year":2018,"lang":"en","type":"article","venue":"Machine Learning","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"European Regional Development Fund; Fundação para a Ciência e a Tecnologia; Electronic Components and Systems for European Leadership","keywords":"Computer science; Set (abstract data type); Task (project management); Machine learning; Arbitrage; Component (thermodynamics); Data mining; Artificial intelligence; Series (stratigraphy); Econometrics; Mathematics; Finance","authors":[{"name":"Vítor Cerqueira","is_ca":false},{"name":"Luı́s Torgo","is_ca":true},{"name":"Fábio Pinto","is_ca":false},{"name":"Carlos Soares","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02022292529165426,"gpt":0.2277035892980059,"spread":0.2074806640063517,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01551037,0.0009048842,0.002498003,0.001019552,0.0007955081,0.003561938,0.002043107,0.003802547,0.009686093],"category_scores_gemma":[0.1127303,0.0006361212,0.001217083,0.0008867022,0.002314213,0.006428888,0.003045662,0.004466246,0.0009747345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047237,"about_ca_system_score_gemma":0.0009732688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004979833,"about_ca_topic_score_gemma":0.0003024072,"domain_scores_codex":[0.9923002,0.003430982,0.0004822679,0.001641152,0.001392863,0.0007525824],"domain_scores_gemma":[0.9322249,0.0512607,0.0037078,0.008872539,0.002563152,0.001370959],"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":[0.0009124328,0.0000942857,0.00456469,0.0002029762,0.0002568388,0.0007711486,0.0005180761,0.1020248,0.002913042,0.806071,0.006159835,0.07551096],"study_design_scores_gemma":[0.00006194523,0.0001082106,0.001053752,0.00003044238,0.0000498971,0.000209271,0.00005896745,0.4326112,0.001293229,0.5620944,0.002393708,0.00003497527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.184865,0.002423245,0.7623747,0.007359264,0.0007737723,0.0001090052,0.0001858481,0.0006691209,0.04124008],"genre_scores_gemma":[0.9684362,0.0004011583,0.01831868,0.0003222924,0.0004085087,0.00006428089,0.00008709829,0.00007224146,0.01188953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01551037,"threshold_uncertainty_score":0.08202773,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156543114","doi":"10.1190/geo2013-0248.1","title":"A semiautomatic method to tie well logs to seismic data","year":2014,"lang":"en","type":"article","venue":"Geophysics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":48,"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":"Computer science; TRACE (psycholinguistics); Task (project management); Constraint (computer-aided design); Dynamic time warping; Tying; Pattern recognition (psychology); Interpreter; Image warping; Data mining; Algorithm; Artificial intelligence; Contrast (vision); Programming language; Mathematics; Engineering","authors":[{"name":"Roberto Henry Herrera","is_ca":true},{"name":"Mirko van der Baan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02155234966840133,"gpt":0.2758136727370408,"spread":0.2542613230686395,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002076818,0.001423845,0.000808134,0.002407743,0.0007030559,0.001463158,0.002061104,0.0009212497,0.007136408],"category_scores_gemma":[0.00710303,0.0009434053,0.0008372687,0.001450806,0.000711108,0.001311564,0.001292945,0.001147245,0.002720922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003742511,"about_ca_system_score_gemma":0.001568463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002540649,"about_ca_topic_score_gemma":0.004495905,"domain_scores_codex":[0.9980799,0.0004580897,0.0001450979,0.0005705408,0.0006433614,0.0001030449],"domain_scores_gemma":[0.9940429,0.001989028,0.0005704392,0.001409136,0.001800553,0.0001878072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"design_other","study_design_scores_codex":[0.000437169,0.0002974186,0.003436398,0.0002832429,0.0001527644,0.0002055588,0.0005444052,0.04069886,0.1669115,0.002433892,0.004199482,0.7803993],"study_design_scores_gemma":[0.00009056763,0.0003239695,0.00613525,0.00002911848,0.00006331358,0.0004858562,0.0002108352,0.8147929,0.1628479,0.003264923,0.01160013,0.0001552027],"study_design_candidate":"design_other","study_design_consensus":"design_other","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01641174,0.00002298509,0.9739286,0.00002344892,0.0000244789,0.0001337315,0.0001592993,0.008859161,0.0004365915],"genre_scores_gemma":[0.08484457,0.00001627396,0.9124736,0.00002677833,0.00001464734,0.0001233313,0.0004541071,0.001047817,0.0009989118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007136408,"threshold_uncertainty_score":0.02387363,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003787915","doi":"10.1109/tkde.2014.2310219","title":"Discovery of Temporal Associations in Multivariate Time Series","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":47,"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":"","keywords":"Computer science; Data mining; Multivariate statistics; Time series; Pruning; Scalability; Redundancy (engineering); Series (stratigraphy); Artificial intelligence; Machine learning","authors":[{"name":"Dennis Zhuang","is_ca":true},{"name":"Gary C.L. Li","is_ca":true},{"name":"Andrew K. C. Wong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01599466251246835,"gpt":0.2367904487629129,"spread":0.2207957862504445,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001522327,0.0006840869,0.0008588765,0.003489839,0.0005214961,0.0009756734,0.00068836,0.0004605504,0.0005719177],"category_scores_gemma":[0.008611109,0.0003128063,0.0008574013,0.004889132,0.0003796103,0.001404953,0.0009800222,0.0009087472,0.0002340198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002912756,"about_ca_system_score_gemma":0.0006407764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084514,"about_ca_topic_score_gemma":0.003166917,"domain_scores_codex":[0.9987362,0.0001940975,0.0001358031,0.0003304659,0.0004902551,0.0001131444],"domain_scores_gemma":[0.9954002,0.002312486,0.00115784,0.0004523808,0.0005371135,0.0001399065],"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":[0.0005819089,0.0004342168,0.1487457,0.0004370781,0.0004451615,0.002083504,0.0008920666,0.1718499,0.02925279,0.02352619,0.003600565,0.6181509],"study_design_scores_gemma":[0.0000139481,0.00009509289,0.02467389,0.00002869554,0.00007848774,0.0005007396,0.000173095,0.9502528,0.004261914,0.01667311,0.00321637,0.00003190058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2533352,0.0005983574,0.7434038,0.000194111,0.00006083487,0.00007463346,0.0007015392,0.0005946676,0.001036861],"genre_scores_gemma":[0.8002976,0.0005281142,0.1964441,0.00005196581,0.0001372469,0.0001073781,0.001656744,0.00006108185,0.0007157385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003489839,"threshold_uncertainty_score":0.008050978,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3212409379","doi":"10.1016/j.compind.2021.103554","title":"Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case","year":2021,"lang":"en","type":"article","venue":"Computers in Industry","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"General Motors of Canada","keywords":"Predictive maintenance; Automotive industry; Model predictive control; Prognostics; Exploit; Interpretability; Machine learning; Pipeline (software); Component (thermodynamics); Engineering; Computer science; Artificial intelligence; Reliability engineering; Control (management); Computer security","authors":[{"name":"Danilo Giordano","is_ca":false},{"name":"Flavio Giobergia","is_ca":false},{"name":"Eliana Pastor","is_ca":false},{"name":"Antonio La Macchia","is_ca":false},{"name":"Tania Cerquitelli","is_ca":false},{"name":"Elena Baralis","is_ca":false},{"name":"Marco Mellia","is_ca":false},{"name":"Davide Tricarico","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.128938899315005,"gpt":0.3249527489904817,"spread":0.1960138496754766,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001916175,0.000703493,0.0004327437,0.0007099938,0.0003896768,0.001498975,0.001590133,0.001243338,0.002085349],"category_scores_gemma":[0.007213349,0.0002796556,0.0004147038,0.000518317,0.0006589598,0.002085,0.0007606982,0.001664515,0.0003584595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008920926,"about_ca_system_score_gemma":0.001012066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006544434,"about_ca_topic_score_gemma":0.008296834,"domain_scores_codex":[0.9993696,0.0002167192,0.00004115524,0.00008927476,0.0002349431,0.00004848667],"domain_scores_gemma":[0.996089,0.002697985,0.0001015605,0.0002983253,0.0007246355,0.00008846935],"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":[0.0002339356,0.0006252383,0.01178006,0.0006286547,0.0001255013,0.001217472,0.001586239,0.3302687,0.006140205,0.08372897,0.01242057,0.5512444],"study_design_scores_gemma":[0.00006992224,0.0002328835,0.002833351,0.0002834212,0.0000662668,0.0005882439,0.00104797,0.8474717,0.01058449,0.1133333,0.02341756,0.00007083213],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1294874,0.003367303,0.8206115,0.01328781,0.0002142372,0.0001950528,0.0002932118,0.0007558899,0.03178765],"genre_scores_gemma":[0.7660994,0.001558227,0.228085,0.0002911407,0.00005938616,0.00009018079,0.0001598013,0.000090765,0.003566252],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006544434,"threshold_uncertainty_score":0.01301271,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078191985","doi":"10.1109/tpwrs.2014.2312418","title":"Synchrophasor Data Baselining and Mining for Online Monitoring of Dynamic Security Limits","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"","keywords":"SCADA; Situation awareness; Computer science; Cluster analysis; Data mining; Situation analysis; Visibility; Electric power system; Key (lock); Real-time computing; Engineering; Artificial intelligence; Power (physics); Computer security","authors":[{"name":"Anissa Kaci","is_ca":true},{"name":"Innocent Kamwa","is_ca":true},{"name":"Louis‐A. Dessaint","is_ca":true},{"name":"Sébastien Guillon","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03395652125269229,"gpt":0.2789900476697973,"spread":0.245033526417105,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006769532,0.0006243867,0.0005845638,0.003073638,0.0003173233,0.0007159086,0.0004966555,0.000363691,0.001688463],"category_scores_gemma":[0.004047116,0.0002481113,0.0003544647,0.002824378,0.0001641106,0.001069412,0.0005549211,0.0005285172,0.0007297189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002330458,"about_ca_system_score_gemma":0.0005082137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001939537,"about_ca_topic_score_gemma":0.003568274,"domain_scores_codex":[0.9994898,0.0001136459,0.00006382437,0.0001361874,0.0001620835,0.0000344965],"domain_scores_gemma":[0.9983227,0.0005489564,0.0003436651,0.0003803947,0.0003452999,0.00005907007],"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":[0.0004782086,0.0003659357,0.04844241,0.0002793263,0.0001650676,0.0003014569,0.0004798889,0.2111262,0.03390929,0.005305222,0.005842973,0.693304],"study_design_scores_gemma":[0.00001805407,0.0001533243,0.02779433,0.00005563862,0.00004197361,0.0001878427,0.0002336454,0.945441,0.01272933,0.007854224,0.005455967,0.0000346558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1515665,0.000480629,0.8382756,0.0002353534,0.0000661106,0.000147042,0.002366654,0.003098741,0.003763459],"genre_scores_gemma":[0.8657247,0.0002014371,0.1311194,0.00002936033,0.00002649277,0.00008973653,0.002073499,0.00005801965,0.0006774696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003073638,"threshold_uncertainty_score":0.005648434,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2091492917","doi":"10.1007/s10291-012-0296-2","title":"GPS interactive time series analysis software","year":2012,"lang":"en","type":"article","venue":"GPS Solutions","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Software; Computer science; Spectral density; Time series; Outlier; Wavelet; Global Positioning System; Series (stratigraphy); Geodetic datum; Bivariate analysis; Autocorrelation; Data mining; Statistics; Mathematics; Geodesy; Artificial intelligence; Machine learning; Geography","authors":[{"name":"Mohammad Ali Goudarzi","is_ca":true},{"name":"Marc Cocard","is_ca":true},{"name":"Rock Santerre","is_ca":true},{"name":"T. Woldai","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01913968829346524,"gpt":0.2367447758353172,"spread":0.217605087541852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005822307,0.001012855,0.0007325145,0.002748256,0.0003991336,0.001072228,0.00130802,0.0007110626,0.1427699],"category_scores_gemma":[0.004774738,0.0007858471,0.0008782311,0.002898599,0.0002068761,0.001121364,0.0007140362,0.00103023,0.03000441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005154715,"about_ca_system_score_gemma":0.0008519511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006812905,"about_ca_topic_score_gemma":0.00589253,"domain_scores_codex":[0.9996451,0.00004793459,0.00004448721,0.00009197451,0.0001293228,0.00004121979],"domain_scores_gemma":[0.9984893,0.0007062715,0.000109274,0.0002294562,0.0004123966,0.00005327652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008133283,0.0001275449,0.004592177,0.00113295,0.0002369044,0.0003636593,0.0005294883,0.02332251,0.01313263,0.01696229,0.5178453,0.4209413],"study_design_scores_gemma":[0.0007109984,0.0001726914,0.008717655,0.00018004,0.0003470593,0.000735915,0.0002098977,0.2448299,0.03401601,0.02211375,0.6877588,0.000207355],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.009198515,0.0003015861,0.5133795,0.0002805534,0.0003982529,0.0002296642,0.04883794,0.3977746,0.02959944],"genre_scores_gemma":[0.2002556,0.001092247,0.5075111,0.0007986565,0.0004513882,0.002365628,0.1018001,0.08290337,0.1028218],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1427699,"threshold_uncertainty_score":0.4776128,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789246757","doi":"10.1155/2018/1758731","title":"Driver and Path Detection through Time-Series Classification","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"EIT Digital; H2020 Euratom; Horizon 2020 Framework Programme","keywords":"Identification (biology); Computer science; Profiling (computer programming); Path (computing); Multilayer perceptron; Advanced driver assistance systems; Data mining; Set (abstract data type); Time series; Real-time computing; Artificial intelligence; Machine learning; Artificial neural network","authors":[{"name":"Mario Luca Bernardi","is_ca":false},{"name":"Marta Cimitile","is_ca":false},{"name":"Fabio Martinelli","is_ca":false},{"name":"Francesco Mercaldo","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01061021570622594,"gpt":0.2296504319341583,"spread":0.2190402162279323,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000247649,0.0005066372,0.0002597841,0.001307319,0.0001154663,0.0002987162,0.0003524167,0.0003468729,0.000629377],"category_scores_gemma":[0.0006425155,0.00009367316,0.0003596975,0.0007610984,0.00008615382,0.0003878799,0.0002050974,0.0003082296,0.0003653473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002248546,"about_ca_system_score_gemma":0.0002323242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005143223,"about_ca_topic_score_gemma":0.004421507,"domain_scores_codex":[0.99984,0.00002083311,0.000009352595,0.00006097094,0.00004299265,0.00002576799],"domain_scores_gemma":[0.9997495,0.00006820331,0.00005459783,0.00002532668,0.00008413147,0.00001820146],"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":[0.0007072869,0.0007035458,0.1236316,0.0001946238,0.0002563797,0.0004303635,0.000198304,0.1094007,0.03029389,0.001396739,0.0053669,0.7274197],"study_design_scores_gemma":[0.000007993964,0.0001191695,0.05988539,0.000009062315,0.00003794259,0.0001270971,0.00007517119,0.9322549,0.005451886,0.0006569463,0.001357121,0.00001743189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8125052,0.0003829087,0.182012,0.0001190349,0.00008178645,0.00008566669,0.00143197,0.00156218,0.001819414],"genre_scores_gemma":[0.9768968,0.0001194215,0.02043492,0.00001038146,0.0000172506,0.00003211053,0.001433863,0.0000149914,0.001040349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005143223,"threshold_uncertainty_score":0.01022655,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}