{"meta":{"query_hash":"c1777bc12b53","filters":{"venue":"Expert Systems with Applications"},"cohort_total":505,"direct_labels_cover":0,"predictions_cover":505,"exported":505,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/c1777bc12b53","api":"https://metacan.xera.ac/api/v1/cohort?venue=Expert+Systems+with+Applications"},"results":[{"id":"W1279489136","doi":"10.1016/j.eswa.2015.08.040","title":"Hierarchy aware distributed plan execution monitoring","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Defence Research and Development Canada; Minnesota Department of Agriculture","keywords":"Computer science; Gossip; Cluster analysis; Hierarchy; Probabilistic logic; Distributed computing; Context (archaeology); Information flow; Markov decision process; Data mining; Machine learning; Markov process; Artificial intelligence","score_opus":0.046043135072479965,"score_gpt":0.255937682422771,"score_spread":0.209894547350291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1279489136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100281194,0.0005927903,0.87972105,0.0003763438,0.000089021894,0.00019362675,0.0004876967,0.011287182,0.0069711846],"genre_scores_gemma":[0.813575,0.000105878884,0.18337306,0.00007106833,0.000038931168,0.00006434587,0.0004353546,0.00019125895,0.0021450017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843687,0.00030224337,0.00011369104,0.0003305159,0.0006391783,0.00017756892],"domain_scores_gemma":[0.9963897,0.0014690034,0.0004289061,0.0007713028,0.0006998956,0.00024122345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015467671,0.00072555244,0.0007142669,0.0013651547,0.00071137334,0.0013599402,0.0014628642,0.0005150342,0.0028825335],"category_scores_gemma":[0.005553761,0.00044055778,0.00029952923,0.00095198443,0.0003335639,0.0015699323,0.0015929722,0.00091584405,0.000517617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018776741,0.0006286959,0.018072134,0.0002801727,0.00019949622,0.00037893272,0.00055481924,0.18164425,0.048806,0.012501412,0.012579885,0.72247654],"study_design_scores_gemma":[0.000040681876,0.00006257982,0.0019707268,0.000010672057,0.0000387182,0.00005894932,0.00004679018,0.9817625,0.009907352,0.0044539613,0.001634961,0.000011979479],"about_ca_topic_score_codex":0.008125347,"about_ca_topic_score_gemma":0.013091605,"teacher_disagreement_score":0.008125347,"about_ca_system_score_codex":0.000925959,"about_ca_system_score_gemma":0.0015897374,"threshold_uncertainty_score":0.016156077},"labels":[],"label_agreement":null},{"id":"W1675171940","doi":"10.1016/j.eswa.2015.08.019","title":"On the soundness, completeness and applicability of the logic of knowledge and communicative commitments in multi-agent systems","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Soundness; Modal logic; Axiom; Completeness (order theory); Computer science; Modal operator; Mathematical proof; Multimodal logic; Dynamic logic (digital electronics); Mathematics; Modal; Artificial intelligence; Theoretical computer science; Calculus (dental); Description logic; Programming language","score_opus":0.09074671861996333,"score_gpt":0.30645385999881797,"score_spread":0.21570714137885463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1675171940","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07463603,0.004138946,0.8539754,0.02231564,0.0005628164,0.00047549405,0.0006958423,0.00044479826,0.042755038],"genre_scores_gemma":[0.80155253,0.0028686249,0.18399347,0.0024292953,0.0013115917,0.0006294616,0.0006730037,0.00024302465,0.0062989676],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9710391,0.014807783,0.002426387,0.0030661041,0.006420655,0.0022399316],"domain_scores_gemma":[0.7429807,0.21972919,0.0049568308,0.014749516,0.014892042,0.0026917458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03349949,0.0016028457,0.0034170013,0.004313062,0.005403529,0.0097688595,0.0068868985,0.0070567885,0.006856744],"category_scores_gemma":[0.13651317,0.0024782773,0.0043010726,0.004072398,0.025072146,0.033702318,0.011304588,0.015034196,0.0009950242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017841427,0.00009473839,0.00053720473,0.00024315555,0.00007788583,0.00016799259,0.0010960131,0.016758123,0.00045886892,0.9661184,0.0012708332,0.012998255],"study_design_scores_gemma":[0.00003907938,0.0000247758,0.00010072271,0.00003674387,0.000026994709,0.000026786754,0.00011622016,0.023351833,0.00022064599,0.9752686,0.00077095063,0.000016706083],"about_ca_topic_score_codex":0.01156796,"about_ca_topic_score_gemma":0.0056876563,"teacher_disagreement_score":0.03349949,"about_ca_system_score_codex":0.0058668763,"about_ca_system_score_gemma":0.009056932,"threshold_uncertainty_score":0.17716438},"labels":[],"label_agreement":null},{"id":"W1733363676","doi":"10.1016/j.eswa.2015.04.068","title":"An automatic mobile-health based approach for EEG epileptic seizures detection","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Menofia University; United Arab Emirates University; National Research Foundation; Utah Agricultural Experiment Station","keywords":"Computer science; Electroencephalography; Artificial intelligence; Epileptic seizure; Feature selection; Classifier (UML); Scalability; Machine learning; Pattern recognition (psychology); Feature extraction; Data mining; Database","score_opus":0.044939078440736184,"score_gpt":0.32054726090288344,"score_spread":0.27560818246214724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1733363676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11693186,0.0012800432,0.8706537,0.00028800603,0.00034427363,0.00027962116,0.0008522077,0.0039822706,0.0053879293],"genre_scores_gemma":[0.7179967,0.0007442534,0.27129993,0.0003374478,0.00024365236,0.00022069983,0.0010357186,0.00008453543,0.008037003],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988675,0.000015515365,0.0000071890076,0.000033632645,0.000035275694,0.00002164523],"domain_scores_gemma":[0.999879,0.000021798442,0.0000110265555,0.000009334639,0.000066804445,0.000011959971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013426553,0.0006235333,0.00044875662,0.001202623,0.00020560142,0.0003641253,0.0003696356,0.0006472878,0.0020446104],"category_scores_gemma":[0.00031067536,0.00011482601,0.00034447844,0.0005499321,0.00009444946,0.00027071728,0.00038481448,0.00024964276,0.0014589987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057294173,0.00021810348,0.0058059036,0.00017183057,0.000066932436,0.0006568218,0.00007108327,0.0033553063,0.16260846,0.0007460447,0.004552673,0.82117385],"study_design_scores_gemma":[0.00021045409,0.0015144576,0.08754619,0.00008942185,0.0003677601,0.005932656,0.00038365854,0.6948386,0.18608835,0.0038780526,0.019045262,0.00010519069],"about_ca_topic_score_codex":0.0011661396,"about_ca_topic_score_gemma":0.0025226392,"teacher_disagreement_score":0.0020446104,"about_ca_system_score_codex":0.0001554099,"about_ca_system_score_gemma":0.00021862904,"threshold_uncertainty_score":0.006839931},"labels":[],"label_agreement":null},{"id":"W1828567212","doi":"10.1016/j.eswa.2015.09.044","title":"A latent Beta-Liouville allocation model","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Latent Dirichlet allocation; Computer science; Prior probability; Dirichlet distribution; Artificial intelligence; Consistency (knowledge bases); Topic model; Machine learning; Categorization; Latent variable; Beta distribution; Pattern recognition (psychology); Mathematics; Bayesian probability; Statistics","score_opus":0.051503094570884714,"score_gpt":0.2743410845641952,"score_spread":0.2228379899933105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1828567212","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017697508,0.00044222802,0.9738961,0.00105347,0.000110851135,0.000059551796,0.0003385229,0.0003197755,0.0060821003],"genre_scores_gemma":[0.69857496,0.0011551509,0.22111182,0.0009370363,0.0005054662,0.0007784293,0.0015453257,0.00035777586,0.07503405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979079,0.0012081221,0.00006909339,0.00038942092,0.0002174907,0.0002080193],"domain_scores_gemma":[0.9955059,0.0029396634,0.00029076118,0.00042030716,0.0005478092,0.00029549352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038986336,0.0007037967,0.0017791832,0.0015675201,0.0011776126,0.004108306,0.003393218,0.003451847,0.012019372],"category_scores_gemma":[0.010577857,0.0008415659,0.0012606814,0.0022157317,0.0019853606,0.0040109945,0.002153123,0.0030896862,0.0037167843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020535843,0.00018433595,0.0015559496,0.00012018993,0.00010527314,0.00012523343,0.00023735639,0.21697715,0.0009165197,0.7149988,0.008232394,0.056341525],"study_design_scores_gemma":[0.00003258099,0.000029203184,0.00019065474,0.000021035828,0.000019515857,0.000042732907,0.000024991816,0.8416613,0.00017600138,0.15521644,0.0025657113,0.000019916457],"about_ca_topic_score_codex":0.0041809455,"about_ca_topic_score_gemma":0.004372941,"teacher_disagreement_score":0.012019372,"about_ca_system_score_codex":0.0018374799,"about_ca_system_score_gemma":0.001867406,"threshold_uncertainty_score":0.040208757},"labels":[],"label_agreement":null},{"id":"W1964189664","doi":"10.1016/j.eswa.2014.05.043","title":"A novel approach for multimodal medical image fusion","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Image fusion; Fuse (electrical); Artificial intelligence; Computer science; Compressed sensing; Robustness (evolution); Fusion; Computer vision; Pattern recognition (psychology); Gaussian; Image (mathematics)","score_opus":0.008291136246485259,"score_gpt":0.2544536248195662,"score_spread":0.24616248857308093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964189664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011718797,0.00020584025,0.99702126,0.00009420684,0.000064481515,0.00003300235,0.000028284836,0.00023030755,0.001150649],"genre_scores_gemma":[0.051291447,0.00067642605,0.9424853,0.00024384499,0.00019500326,0.00012255169,0.00017868039,0.00013014568,0.0046766945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992212,0.000120895136,0.00004258381,0.00014040005,0.00042180135,0.0000531594],"domain_scores_gemma":[0.9996239,0.000076827346,0.000027912407,0.000088627145,0.00015734135,0.000025423311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080663105,0.0007633348,0.0008691968,0.0012896353,0.0005194571,0.0013040531,0.0011409726,0.0015590035,0.0036342028],"category_scores_gemma":[0.0014036562,0.0004082789,0.0012878192,0.0012277862,0.0004727609,0.0014284933,0.0025720873,0.0012114756,0.0020853034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020643602,0.0001387677,0.0004913677,0.0002841613,0.00024023489,0.0005217905,0.00018649334,0.021676378,0.20033276,0.053138226,0.008646642,0.71413666],"study_design_scores_gemma":[0.00003719848,0.00021503659,0.0010015139,0.000047995156,0.00020145683,0.003379903,0.00008573459,0.850306,0.07043828,0.03733305,0.036864087,0.00008973417],"about_ca_topic_score_codex":0.000658128,"about_ca_topic_score_gemma":0.0011433015,"teacher_disagreement_score":0.0036342028,"about_ca_system_score_codex":0.0003509334,"about_ca_system_score_gemma":0.0005843254,"threshold_uncertainty_score":0.012157679},"labels":[],"label_agreement":null},{"id":"W1965671311","doi":"10.1016/j.eswa.2014.12.026","title":"Risk assessment of hydropower stations through an integrated fuzzy entropy-weight multiple criteria decision making method: A case study of the Xiangxi River","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydropower; Closeness; Computer science; Entropy (arrow of time); Fuzzy logic; Risk assessment; Fuzzy set; Operations research; Data mining; Mathematical optimization; Mathematics; Artificial intelligence; Engineering","score_opus":0.01288814193261734,"score_gpt":0.3041931822560463,"score_spread":0.29130504032342897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965671311","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7538544,0.00021390604,0.2428965,0.00016850601,0.0000150679425,0.00011236712,0.00009240662,0.00007396245,0.0025729102],"genre_scores_gemma":[0.95679116,0.00008582035,0.04240675,0.000006983439,0.0000052562764,0.00004029829,0.00002905995,0.0000064986307,0.0006281977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989191,0.00057929027,0.000056321434,0.00011532496,0.00025946653,0.00007044445],"domain_scores_gemma":[0.9985379,0.0010258404,0.00012517316,0.000043816675,0.00021311585,0.00005413796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030471266,0.0009579289,0.0007919559,0.0018637353,0.0006821722,0.0013515423,0.00082220655,0.00096581894,0.0008544869],"category_scores_gemma":[0.002753301,0.00045067974,0.00093596755,0.0011490083,0.00047040204,0.0011607856,0.00078646065,0.0004985121,0.00004789648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040805832,0.000357022,0.010758684,0.00019864921,0.00022729873,0.0007030955,0.000498139,0.90482557,0.008733009,0.0056574084,0.00032288968,0.06731017],"study_design_scores_gemma":[0.000013596938,0.000078573095,0.0016030746,0.000010093244,0.00004737884,0.00003741111,0.0001144888,0.9956585,0.0014128336,0.0009117547,0.0000911829,0.000021098005],"about_ca_topic_score_codex":0.006402535,"about_ca_topic_score_gemma":0.008500115,"teacher_disagreement_score":0.006402535,"about_ca_system_score_codex":0.0013326716,"about_ca_system_score_gemma":0.0011998158,"threshold_uncertainty_score":0.01611495},"labels":[],"label_agreement":null},{"id":"W1966498183","doi":"10.1016/j.eswa.2010.08.072","title":"Model-based decision support system for water quality management under hybrid uncertainty","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Decision support system; Computer science; Quality (philosophy); Water quality; Fuzzy logic; Operations research; Risk analysis (engineering); Data mining; Artificial intelligence; Business; Engineering","score_opus":0.015108635172049129,"score_gpt":0.2475235357195694,"score_spread":0.23241490054752026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966498183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104397714,0.0002963551,0.88197935,0.00039245945,0.00014151807,0.00011625406,0.00038971158,0.007871369,0.0044151945],"genre_scores_gemma":[0.93857175,0.00009244047,0.05932158,0.00011951498,0.000033538163,0.00015687235,0.00023036741,0.000053575797,0.0014204475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978715,0.000055411714,0.00001856954,0.00004918386,0.00006516965,0.000024478224],"domain_scores_gemma":[0.99957687,0.0001875279,0.000046680005,0.0000336237,0.0001246587,0.000030641568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005485114,0.00052026677,0.0009632449,0.00047775934,0.0004005725,0.0009478664,0.0007639494,0.0008798846,0.0037910182],"category_scores_gemma":[0.0016181082,0.00024729653,0.00035539424,0.00031503147,0.00018630765,0.0007019767,0.0005875265,0.0005033416,0.0005959254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088628684,0.00043274387,0.0017620093,0.00017944306,0.00015358314,0.00029175312,0.000102926635,0.776081,0.013125061,0.0037672543,0.005843327,0.19737454],"study_design_scores_gemma":[0.000026500982,0.000034027384,0.00013009082,0.0000030316014,0.000014646094,0.000012967988,0.0000039857628,0.9977361,0.0008871762,0.0008366337,0.0003093804,0.0000055551727],"about_ca_topic_score_codex":0.003462025,"about_ca_topic_score_gemma":0.0030668934,"teacher_disagreement_score":0.0037910182,"about_ca_system_score_codex":0.0005174512,"about_ca_system_score_gemma":0.000593451,"threshold_uncertainty_score":0.0126821995},"labels":[],"label_agreement":null},{"id":"W1966924734","doi":"10.1016/j.eswa.2013.12.029","title":"A utility concession curve data fitting model for quantitative analysis of negotiation styles","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Negotiation; Computer science; Curve fitting; Quantitative analysis (chemistry); Operations research; Econometrics; Data mining; Machine learning; Mathematics","score_opus":0.10776854974267414,"score_gpt":0.3600948340487082,"score_spread":0.25232628430603404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966924734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04840647,0.00013273755,0.9424504,0.00025379047,0.000031306572,0.00037391586,0.0010227917,0.0019324117,0.0053960523],"genre_scores_gemma":[0.6891768,0.00018488901,0.29937565,0.00013617476,0.00002406173,0.0009845548,0.0021166399,0.00041435234,0.007586954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976388,0.0010398291,0.00013069615,0.0004805872,0.00057326327,0.00013689368],"domain_scores_gemma":[0.987827,0.0073428103,0.0007100493,0.0017816199,0.0020945517,0.00024394938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005573196,0.0009194069,0.0010001799,0.0027726765,0.00064079044,0.0020203711,0.0027920923,0.0018300907,0.006411687],"category_scores_gemma":[0.03423788,0.00050806836,0.0012677955,0.0046920883,0.0007114463,0.0026813757,0.0010019711,0.0023060883,0.0027636362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00091118406,0.0007911055,0.022861656,0.0003634316,0.00022338342,0.00028951332,0.0015527888,0.54584414,0.0055610607,0.06249454,0.0097862445,0.34932098],"study_design_scores_gemma":[0.0000145312915,0.000060306804,0.0024038923,0.000021515258,0.000017479308,0.00008765112,0.00008398481,0.98691595,0.00073266623,0.008055069,0.0015779405,0.000029072244],"about_ca_topic_score_codex":0.012516729,"about_ca_topic_score_gemma":0.005690311,"teacher_disagreement_score":0.012516729,"about_ca_system_score_codex":0.0017928913,"about_ca_system_score_gemma":0.0012817442,"threshold_uncertainty_score":0.029474258},"labels":[],"label_agreement":null},{"id":"W1967172632","doi":"10.1016/j.eswa.2010.09.128","title":"Analysis of the minimal privacy disclosure for web services collaborations with role mechanisms","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Access Control and Trust","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"","keywords":"Internet privacy; Computer science; World Wide Web; Web service; Computer security","score_opus":0.008132646858788626,"score_gpt":0.27227086741305445,"score_spread":0.26413822055426583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967172632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41984174,0.00045562693,0.5656772,0.0029127672,0.000057911548,0.00037569532,0.00043798832,0.00042057023,0.009820517],"genre_scores_gemma":[0.9844028,0.00011625265,0.013746122,0.000076491684,0.00006161644,0.00007249301,0.0001151632,0.000043497195,0.0013656094],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97963834,0.008674123,0.001157353,0.0022428236,0.004143917,0.004143439],"domain_scores_gemma":[0.8105197,0.14979917,0.012287834,0.014730277,0.007426908,0.0052361577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017462159,0.0009399041,0.0031104675,0.0018768668,0.002357373,0.006831188,0.0040763584,0.0028660137,0.0056132507],"category_scores_gemma":[0.09594601,0.0014141599,0.002306118,0.0016239509,0.005059715,0.014162877,0.0049033947,0.004065339,0.00034853528],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020482568,0.00052722887,0.006702896,0.00040420415,0.0002838779,0.0005589599,0.0011980389,0.24618845,0.0040243887,0.7098035,0.0021454175,0.026114685],"study_design_scores_gemma":[0.00010057457,0.00023246942,0.0014229428,0.00004230104,0.00015501658,0.000282201,0.00040795375,0.70532584,0.0020225523,0.28920174,0.00074518373,0.00006127719],"about_ca_topic_score_codex":0.0032404144,"about_ca_topic_score_gemma":0.0016477713,"teacher_disagreement_score":0.017462159,"about_ca_system_score_codex":0.0042876117,"about_ca_system_score_gemma":0.0069946945,"threshold_uncertainty_score":0.09234983},"labels":[],"label_agreement":null},{"id":"W1968509973","doi":"10.1016/j.eswa.2010.12.093","title":"An empirical evaluation of attribute control charts for monitoring defects","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Control chart; Computer science; Shewhart individuals control chart; Chart; Reliability engineering; Data mining; Statistical process control; Control limits; Process (computing); Statistics; EWMA chart; Mathematics; Engineering","score_opus":0.1698602375644312,"score_gpt":0.4993317718455054,"score_spread":0.32947153428107423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968509973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.981457,0.00051551295,0.016287386,0.000046997044,0.000022409182,0.00014663338,0.00035070075,0.00018632328,0.0009869457],"genre_scores_gemma":[0.9942675,0.000069573216,0.005171981,0.000007831825,0.000009940658,0.00003681288,0.00030076317,0.000014289751,0.00012133343],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9796805,0.011770209,0.0011003707,0.001358612,0.0056347414,0.0004556034],"domain_scores_gemma":[0.46930754,0.48553357,0.012545556,0.014418111,0.016503692,0.001691529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03367036,0.00084329495,0.000801998,0.0039712377,0.00043361328,0.0016628514,0.0016664212,0.0013526761,0.0013637422],"category_scores_gemma":[0.19580358,0.00022988647,0.00081000396,0.0019907078,0.0014410121,0.0023302287,0.00086563,0.00080863934,0.00016816956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.02419452,0.005097198,0.3800217,0.0009446,0.0009216437,0.00036410178,0.0012244603,0.2776806,0.008093482,0.008739395,0.0020023726,0.290716],"study_design_scores_gemma":[0.0006526409,0.011667226,0.15081096,0.0001384242,0.0007133297,0.00044246984,0.00065879384,0.8195501,0.0096117705,0.004374845,0.0012485536,0.0001309592],"about_ca_topic_score_codex":0.002815289,"about_ca_topic_score_gemma":0.0013995069,"teacher_disagreement_score":0.03367036,"about_ca_system_score_codex":0.0013994011,"about_ca_system_score_gemma":0.0009718409,"threshold_uncertainty_score":0.17806798},"labels":[],"label_agreement":null},{"id":"W1968963067","doi":"10.1016/j.eswa.2012.05.052","title":"An algorithm for the solution of second order fuzzy initial value problems","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fuzzy logic; Fuzzy classification; Fuzzy set operations; Fuzzy number; Defuzzification; Mathematics; Algorithm; Forcing (mathematics); Fuzzy set; Value (mathematics); Mathematical optimization; Computer science; Artificial intelligence; Mathematical analysis","score_opus":0.038369153274386815,"score_gpt":0.3184076515106089,"score_spread":0.28003849823622207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968963067","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018468366,0.00010495131,0.99586016,0.000059079266,0.00006190479,0.00003271213,0.0000147588335,0.0001473506,0.0018723168],"genre_scores_gemma":[0.05598412,0.00017442419,0.93904495,0.00006722375,0.000040317464,0.00023586879,0.00006926208,0.00009626475,0.0042874683],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99965405,0.000080721926,0.000019240653,0.00004396378,0.00016742568,0.000034593562],"domain_scores_gemma":[0.9994106,0.00030532444,0.000026837884,0.000045236076,0.00018484317,0.000027273885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010960511,0.00093699276,0.0009754461,0.0008689699,0.0007610241,0.0010257966,0.0015591697,0.001941352,0.0050142505],"category_scores_gemma":[0.0028667785,0.0005713827,0.0006570447,0.0009913306,0.00069419463,0.0010214584,0.0013820529,0.0014080609,0.0010789267],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026990403,0.000108939596,0.00039678637,0.00032405197,0.00009125094,0.00013006818,0.0002468736,0.3979415,0.008961985,0.120054066,0.0072202035,0.46425438],"study_design_scores_gemma":[0.000075888114,0.00007120345,0.00011516608,0.000028128296,0.000018122893,0.00007061371,0.000015829062,0.9690692,0.0018765068,0.021691564,0.0069506285,0.000017042654],"about_ca_topic_score_codex":0.002277944,"about_ca_topic_score_gemma":0.0030045516,"teacher_disagreement_score":0.0050142505,"about_ca_system_score_codex":0.00067590084,"about_ca_system_score_gemma":0.0014496075,"threshold_uncertainty_score":0.016774297},"labels":[],"label_agreement":null},{"id":"W1971493917","doi":"10.1016/j.eswa.2009.06.069","title":"Wavelet network-based motion control of DC motors","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Mathematical Analysis and Transform Methods","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Wavelet; DC motor; Artificial intelligence; Motion (physics); Electrical engineering; Engineering","score_opus":0.02880394080488911,"score_gpt":0.31473087030708424,"score_spread":0.2859269295021951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971493917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061660636,0.0004291181,0.9342047,0.00013712038,0.00009376409,0.00002457397,0.000027377746,0.00009957889,0.0033231939],"genre_scores_gemma":[0.9559267,0.0003402211,0.0409439,0.000029922086,0.000027178949,0.00004163996,0.000043354696,0.000020581376,0.0026264864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992085,0.000018517921,0.000005718941,0.000017886774,0.000027782553,0.000009231897],"domain_scores_gemma":[0.9998894,0.00003954372,0.000018767481,0.0000068492996,0.00003894889,0.0000065767003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030211845,0.00032567076,0.00035612716,0.00023374504,0.00016635969,0.00032005223,0.00039084876,0.00034015442,0.00082909846],"category_scores_gemma":[0.0006955267,0.00012967299,0.00014044128,0.00030834484,0.00021931845,0.00036754363,0.00028683658,0.00026604303,0.0001038459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002667311,0.00004707002,0.0002931967,0.000120208395,0.000023486435,0.000038238108,0.000048242626,0.8181749,0.019883147,0.011032343,0.000811966,0.14926048],"study_design_scores_gemma":[0.0000036053243,0.0000177957,0.000069990994,0.0000023122184,0.000002498686,0.0000032663092,0.0000015919693,0.9982368,0.0009465975,0.0005090213,0.0002049233,0.0000014898013],"about_ca_topic_score_codex":0.0017387973,"about_ca_topic_score_gemma":0.0013655871,"teacher_disagreement_score":0.0017387973,"about_ca_system_score_codex":0.00025217686,"about_ca_system_score_gemma":0.00020688945,"threshold_uncertainty_score":0.0034573078},"labels":[],"label_agreement":null},{"id":"W1971891529","doi":"10.1016/j.eswa.2012.01.105","title":"A scoring model to detect abusive billing patterns in health insurance claims","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Workplace Violence and Bullying","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Hongik University","keywords":"Psychological intervention; Health care; Decision tree; Computer science; Quarter (Canadian coin); Categorization; Actuarial science; Intervention (counseling); Data mining; Medicine; Artificial intelligence; Nursing; Business","score_opus":0.04169654788812277,"score_gpt":0.3407262214233228,"score_spread":0.29902967353520005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971891529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52397966,0.00061497855,0.46439523,0.001477781,0.00024378063,0.00045247143,0.0020547505,0.0037996669,0.002981723],"genre_scores_gemma":[0.9428146,0.00010880141,0.053291753,0.00014558781,0.000053326195,0.00018501884,0.0011481253,0.00003264114,0.002220242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916315,0.00025148413,0.00010471427,0.0002093919,0.00016005515,0.00011123605],"domain_scores_gemma":[0.9950669,0.0029224905,0.00030676438,0.00019345638,0.0013231764,0.00018716167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002835299,0.0007422277,0.0009367419,0.0017282219,0.0005065502,0.0011410796,0.0012997877,0.0011488978,0.001745541],"category_scores_gemma":[0.007865595,0.0002837651,0.00074902736,0.0010162194,0.00025480057,0.00085333793,0.00050579634,0.00093094254,0.0006210985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008961141,0.0018145601,0.13817394,0.00013612479,0.00040805398,0.00031992336,0.00017234284,0.3414606,0.0035401767,0.002230997,0.011201539,0.4996456],"study_design_scores_gemma":[0.000008434521,0.000052791376,0.004389969,0.000005840717,0.00002327536,0.0000257507,0.000013416768,0.99450713,0.00022826469,0.0006239859,0.00011456665,0.000006534609],"about_ca_topic_score_codex":0.020146744,"about_ca_topic_score_gemma":0.01779552,"teacher_disagreement_score":0.020146744,"about_ca_system_score_codex":0.0010181129,"about_ca_system_score_gemma":0.0011607872,"threshold_uncertainty_score":0.04005897},"labels":[],"label_agreement":null},{"id":"W1974304087","doi":"10.1016/j.eswa.2007.09.034","title":"A type-2 fuzzy rule-based expert system model for stock price analysis","year":2007,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":190,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fuzzy logic; Computer science; Econometrics; Stock price; Automotive industry; Stock (firearms); Expert system; Fuzzy set; Data mining; Artificial intelligence; Operations research; Mathematics; Engineering","score_opus":0.11750767337388693,"score_gpt":0.41300056033184923,"score_spread":0.2954928869579623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974304087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039421022,0.00048691948,0.9518233,0.00025942415,0.0001371513,0.00009993827,0.00032587833,0.00065055664,0.0067958147],"genre_scores_gemma":[0.80045366,0.00047940583,0.18853548,0.00017044824,0.00008893397,0.00028071538,0.000380357,0.00004939688,0.009561627],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996692,0.000078500845,0.000027055323,0.00007969782,0.00011988389,0.00002570302],"domain_scores_gemma":[0.99946696,0.00025948486,0.00004086451,0.000028573277,0.00018817924,0.000015953992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007026035,0.00049348077,0.0009954884,0.00041884577,0.00035511228,0.0012964958,0.001378503,0.0017271964,0.0037696196],"category_scores_gemma":[0.0022803436,0.00032671724,0.0006093079,0.0005230558,0.00024837523,0.0011036667,0.00034731138,0.0008438806,0.00094363815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017135672,0.00010124123,0.00059439027,0.0001169267,0.00009070049,0.00027099813,0.00007982229,0.9217629,0.003962445,0.006748875,0.0015174987,0.06458294],"study_design_scores_gemma":[0.000010589393,0.000017758048,0.0001300324,0.0000039421684,0.00001247206,0.000020243051,0.000002422636,0.99806243,0.00029097765,0.0011433186,0.00030013008,0.0000056618587],"about_ca_topic_score_codex":0.008726328,"about_ca_topic_score_gemma":0.0072111366,"teacher_disagreement_score":0.008726328,"about_ca_system_score_codex":0.0005507344,"about_ca_system_score_gemma":0.0008142704,"threshold_uncertainty_score":0.01735109},"labels":[],"label_agreement":null},{"id":"W1978107848","doi":"10.1016/j.eswa.2014.01.023","title":"Robust logo watermarking using biometrics inspired key generation","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Biometrics; Computer science; Key (lock); Digital watermarking; Logo (programming language); Artificial intelligence; Computer vision; Watermark; Image (mathematics); Computer security; Programming language","score_opus":0.050994204745416494,"score_gpt":0.2639735201946812,"score_spread":0.21297931544926468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978107848","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16887814,0.0012015449,0.8209813,0.00048707746,0.0002509659,0.00007550952,0.00012253356,0.0013415455,0.0066613634],"genre_scores_gemma":[0.8626554,0.000515619,0.1293044,0.00012902552,0.000080384234,0.000037867903,0.00009527674,0.00007832229,0.007103704],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997503,0.000042430136,0.000012120968,0.000056738067,0.00010925435,0.000029128423],"domain_scores_gemma":[0.9995963,0.000115243,0.00010460951,0.00011047407,0.00005978004,0.000013528488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002090078,0.00037843545,0.00039371217,0.000490271,0.0002442447,0.0005850167,0.00032209177,0.0007908616,0.0016947694],"category_scores_gemma":[0.0010923618,0.00016228069,0.0002434347,0.0003497767,0.0003605825,0.001160481,0.0006694117,0.000478476,0.0007889712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045929902,0.00008863497,0.0005752117,0.00016039866,0.000031614745,0.00031820723,0.00007106721,0.008928522,0.81793267,0.016293194,0.0008243709,0.1543168],"study_design_scores_gemma":[0.000054398213,0.00036539248,0.0019304759,0.000043945347,0.000060910334,0.0019241356,0.000060542352,0.3370303,0.64149857,0.008945707,0.008020872,0.00006486963],"about_ca_topic_score_codex":0.00006528503,"about_ca_topic_score_gemma":0.0001296291,"teacher_disagreement_score":0.0016947694,"about_ca_system_score_codex":0.00018726118,"about_ca_system_score_gemma":0.00017367426,"threshold_uncertainty_score":0.005669594},"labels":[],"label_agreement":null},{"id":"W1982490352","doi":"10.1016/j.eswa.2011.12.056","title":"An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":307,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Purchasing; Computer science; Supply chain network; Profit (economics); Reverse logistics; Reuse; Operations research; Selection (genetic algorithm); Supply chain management; Closed loop; Integer programming; Linear programming; Mathematical optimization; Operations management; Business; Mathematics","score_opus":0.02001503350720926,"score_gpt":0.2532873810124063,"score_spread":0.23327234750519701,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982490352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018936709,0.00033972677,0.97009116,0.00024005289,0.00005845269,0.000116729745,0.00025010278,0.0003725856,0.009594399],"genre_scores_gemma":[0.858916,0.0005598813,0.12716958,0.0001272611,0.000062603525,0.00073430646,0.00046657512,0.00011886125,0.011845077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989397,0.00035174377,0.0000527865,0.00022771876,0.00026162993,0.00016649175],"domain_scores_gemma":[0.9987531,0.0007196042,0.00013031684,0.000039547635,0.00029228546,0.00006514597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019616485,0.0018732803,0.0027239176,0.0014505514,0.00091970613,0.0032440154,0.0032234676,0.0040871925,0.0065709506],"category_scores_gemma":[0.0028885247,0.0014543884,0.0016118643,0.0019172289,0.001047225,0.0022959753,0.0017748676,0.0016721674,0.0008263427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001355544,0.000015620812,0.000060368242,0.000022522841,0.000018879871,0.000030107805,0.000015457548,0.99605066,0.00012581487,0.0015199686,0.00010114765,0.0020259048],"study_design_scores_gemma":[0.00000571628,0.000011251274,0.000030559673,0.0000034128975,0.000009015925,0.000003921824,0.000004049667,0.9990963,0.000038910533,0.0007032396,0.00009033108,0.0000034313857],"about_ca_topic_score_codex":0.024208477,"about_ca_topic_score_gemma":0.015517088,"teacher_disagreement_score":0.024208477,"about_ca_system_score_codex":0.0022647271,"about_ca_system_score_gemma":0.0024437134,"threshold_uncertainty_score":0.04813516},"labels":[],"label_agreement":null},{"id":"W1985369104","doi":"10.1016/j.eswa.2011.06.022","title":"An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Closeness; Ranking (information retrieval); Group decision-making; Aggregate (composite); Operator (biology); Computer science; Measure (data warehouse); TOPSIS; Mathematics; Group (periodic table); Mathematical optimization; Data mining; Operations research; Artificial intelligence","score_opus":0.157470969285867,"score_gpt":0.47660617450754156,"score_spread":0.31913520522167454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985369104","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010924654,0.000021020489,0.9974589,0.000022927179,0.000017458753,0.000033771423,0.000019902085,0.00017338002,0.001160226],"genre_scores_gemma":[0.059220176,0.00005336208,0.9378352,0.000059939637,0.00003239069,0.0003184594,0.00009555563,0.00012309471,0.0022617846],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977551,0.00086354464,0.000106129046,0.00031857894,0.0008175837,0.0001390296],"domain_scores_gemma":[0.99621445,0.0027027586,0.000108828506,0.00029066193,0.00054749794,0.00013591076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030451212,0.0008871863,0.0012208051,0.0012587646,0.0009412621,0.0013422039,0.0024599535,0.0011178236,0.013496169],"category_scores_gemma":[0.005883046,0.0005641378,0.0014855292,0.0014189448,0.0009214709,0.0016448668,0.0027409496,0.0017194739,0.0015708348],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060476165,0.00045182533,0.0007412603,0.0005522723,0.00030097654,0.0003941184,0.0009961657,0.15335105,0.013815739,0.15843223,0.0071563055,0.6632033],"study_design_scores_gemma":[0.00007890935,0.000118737786,0.0002655359,0.000044558703,0.00006635802,0.00013042886,0.00006128663,0.9466369,0.0029745447,0.04158086,0.007991001,0.000050956107],"about_ca_topic_score_codex":0.0020483444,"about_ca_topic_score_gemma":0.0030942352,"teacher_disagreement_score":0.013496169,"about_ca_system_score_codex":0.0007252168,"about_ca_system_score_gemma":0.0013136031,"threshold_uncertainty_score":0.045149148},"labels":[],"label_agreement":null},{"id":"W1991530504","doi":"10.1016/j.eswa.2014.12.002","title":"Nearest neighbor classification of categorical data by attributes weighting","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"National Natural Science Foundation of China","keywords":"Categorical variable; Weighting; k-nearest neighbors algorithm; Computer science; Artificial intelligence; Data mining; Pattern recognition (psychology); Subspace topology; Feature (linguistics); Feature selection; Decision tree; Machine learning","score_opus":0.04317540440687309,"score_gpt":0.270307934158104,"score_spread":0.2271325297512309,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991530504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11151594,0.0008571491,0.88407224,0.0001545027,0.00017199536,0.000104730054,0.00028292922,0.0003146263,0.0025259028],"genre_scores_gemma":[0.6406489,0.00048083515,0.3549272,0.00005824374,0.00010060793,0.00014139892,0.00096463447,0.000058801572,0.0026193832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967704,0.000940216,0.00029436286,0.00043703863,0.0014164489,0.00014160397],"domain_scores_gemma":[0.99735594,0.0010915466,0.00016877869,0.00044850906,0.00085982436,0.00007534297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022581962,0.00030870808,0.0011809535,0.0030835879,0.0006063614,0.0013541075,0.001019024,0.00075628114,0.0012017128],"category_scores_gemma":[0.009723048,0.00021029574,0.0009369535,0.0029628442,0.0004984537,0.0018894612,0.00085486914,0.0007169078,0.00051171426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006068594,0.00037781725,0.009280239,0.00027368584,0.0002078331,0.00013346782,0.00036518657,0.049691733,0.009242394,0.01804452,0.00412365,0.90765274],"study_design_scores_gemma":[0.00003837176,0.00021691367,0.0053289314,0.000057537225,0.00012435866,0.00023217175,0.0003188776,0.9281812,0.0063055567,0.055580407,0.003549849,0.00006586096],"about_ca_topic_score_codex":0.0031243747,"about_ca_topic_score_gemma":0.0029940899,"teacher_disagreement_score":0.0031243747,"about_ca_system_score_codex":0.00056895806,"about_ca_system_score_gemma":0.00067413144,"threshold_uncertainty_score":0.011942625},"labels":[],"label_agreement":null},{"id":"W1991859312","doi":"10.1016/j.eswa.2011.06.038","title":"Synergies of simulation, agents, and systems engineering","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Systems engineering; Software engineering; Artificial intelligence; Engineering","score_opus":0.12389075420502203,"score_gpt":0.36375395203475835,"score_spread":0.2398631978297363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991859312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067895584,0.03451429,0.71011764,0.020444013,0.0012538815,0.00014612844,0.0001901341,0.0006848742,0.16475356],"genre_scores_gemma":[0.8864376,0.017137863,0.084480174,0.0009493878,0.0008803443,0.00020249328,0.00012330673,0.00007652778,0.009712251],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996633,0.0023959575,0.00012489516,0.00023386689,0.00048980996,0.00012243826],"domain_scores_gemma":[0.989792,0.008415212,0.00039271935,0.00074898073,0.00033611144,0.000314961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037100243,0.00092200754,0.0013489646,0.0018948629,0.0006021084,0.004136843,0.00091002695,0.0015916327,0.007708292],"category_scores_gemma":[0.008690307,0.0006044149,0.00071159,0.0013301502,0.0031009524,0.004835285,0.0033709607,0.0018375079,0.00076799205],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013383868,0.00016056154,0.0019548445,0.00054830255,0.00023200133,0.00014139821,0.0003266588,0.06866174,0.0009266938,0.85228646,0.002194527,0.072433025],"study_design_scores_gemma":[0.000057898564,0.00015015368,0.0014253053,0.0003127016,0.000104631894,0.00019344703,0.00032867823,0.14020693,0.00058297854,0.82915634,0.02743829,0.00004257479],"about_ca_topic_score_codex":0.0012276763,"about_ca_topic_score_gemma":0.0014318819,"teacher_disagreement_score":0.007708292,"about_ca_system_score_codex":0.0009101072,"about_ca_system_score_gemma":0.0020433003,"threshold_uncertainty_score":0.025786757},"labels":[],"label_agreement":null},{"id":"W1993395332","doi":"10.1016/j.eswa.2007.12.046","title":"Image watermarking scheme using nonnegative matrix factorization and wavelet transform","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-negative matrix factorization; Digital watermarking; Discrete wavelet transform; Matrix decomposition; Lifting scheme; Wavelet; Wavelet packet decomposition; Wavelet transform; Mathematics; Image (mathematics); Stationary wavelet transform; Factorization; Second-generation wavelet transform; Artificial intelligence; Scheme (mathematics); Pattern recognition (psychology); Algorithm; Eigendecomposition of a matrix; Matrix (chemical analysis); Distortion (music); Computer science; Eigenvalues and eigenvectors","score_opus":0.01859545231268303,"score_gpt":0.26949049140335063,"score_spread":0.25089503909066757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993395332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035487417,0.0003118939,0.9618236,0.00017183254,0.00014814585,0.000036578524,0.00004379559,0.00020196698,0.0017748526],"genre_scores_gemma":[0.4605846,0.0007357248,0.53171,0.000114775656,0.00013267121,0.00008053771,0.00017216922,0.00004868563,0.0064208834],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996989,0.00006341238,0.000018569654,0.000065285065,0.0001170437,0.000036877587],"domain_scores_gemma":[0.9996525,0.00008257642,0.00004824101,0.000067278816,0.00012846921,0.000020897394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036374453,0.00051500223,0.0005743103,0.00047889966,0.000397853,0.0004577132,0.0003774813,0.0006156564,0.001066219],"category_scores_gemma":[0.00095219,0.00015599307,0.0005399889,0.0006834271,0.00037864802,0.0012980322,0.000482144,0.00060482003,0.00030811562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006925378,0.00016658135,0.0004269663,0.00022104883,0.000070429516,0.00035008596,0.00012425795,0.041182164,0.5233544,0.06828735,0.0033924729,0.36173177],"study_design_scores_gemma":[0.000055427507,0.00024793574,0.0006342858,0.000018918858,0.00006892817,0.00047924864,0.000036316636,0.8878012,0.09245915,0.01419311,0.0039450354,0.000060410806],"about_ca_topic_score_codex":0.00052195333,"about_ca_topic_score_gemma":0.00086646056,"teacher_disagreement_score":0.001066219,"about_ca_system_score_codex":0.00024030745,"about_ca_system_score_gemma":0.00038611807,"threshold_uncertainty_score":0.0035668015},"labels":[],"label_agreement":null},{"id":"W1993465263","doi":"10.1016/j.eswa.2013.09.018","title":"Pre-run-time scheduling in real-time systems: Current researches and Artificial Intelligence perspectives","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Real-Time Systems Scheduling","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Scheduling (production processes); Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","score_opus":0.03399876742595253,"score_gpt":0.3106230237101262,"score_spread":0.27662425628417364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993465263","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041949064,0.3568023,0.5606531,0.007380005,0.0016953254,0.000058125923,0.00008283488,0.00063389324,0.030745342],"genre_scores_gemma":[0.6655679,0.21759811,0.10541996,0.0007240939,0.004511149,0.00006307403,0.00020270958,0.000109917375,0.005803071],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993519,0.00015255858,0.0000471137,0.00017372896,0.00019906445,0.000075690055],"domain_scores_gemma":[0.9971765,0.0018507494,0.00028037053,0.00021116625,0.0003744534,0.00010665034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017863145,0.0006380861,0.0009259974,0.00076959614,0.0004194705,0.0024813265,0.0014691799,0.0010803975,0.0020217386],"category_scores_gemma":[0.0032674912,0.00030273115,0.00040716556,0.0020944662,0.0014915095,0.0028500056,0.00042712383,0.0013419841,0.0005832231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048852223,0.0004539917,0.0026443817,0.0028620027,0.000120540426,0.00022322824,0.0003039012,0.13899651,0.008106475,0.25471377,0.0060178754,0.5850687],"study_design_scores_gemma":[0.000080435675,0.00054906326,0.003142133,0.0005213895,0.0001605257,0.0004763668,0.0006058222,0.5587352,0.0090394495,0.36179376,0.06479448,0.00010140994],"about_ca_topic_score_codex":0.0014811243,"about_ca_topic_score_gemma":0.0009041335,"teacher_disagreement_score":0.0024813265,"about_ca_system_score_codex":0.0010871295,"about_ca_system_score_gemma":0.0017739041,"threshold_uncertainty_score":0.009447098},"labels":[],"label_agreement":null},{"id":"W1994597293","doi":"10.1016/j.eswa.2010.09.029","title":"Centralized fleet management system for cybernetic transportation","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"European Commission","keywords":"Computer science; Interactive kiosk; Scheduling (production processes); Routing (electronic design automation); Operations research; Control (management); Pooling; Computer network; Operations management; Engineering; World Wide Web; Artificial intelligence","score_opus":0.006656007188231658,"score_gpt":0.22212722157499093,"score_spread":0.21547121438675926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994597293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09361546,0.00040155186,0.8591624,0.00039192068,0.0003693194,0.00039662753,0.0008147283,0.02820666,0.016641337],"genre_scores_gemma":[0.9147088,0.00015768915,0.071972266,0.00012742818,0.00011308757,0.00023552371,0.0010681674,0.00018762625,0.011429416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995034,0.00006675981,0.00003368554,0.00017317927,0.00013856124,0.000084387786],"domain_scores_gemma":[0.99932706,0.00006161796,0.000059309656,0.00019236864,0.00026844407,0.00009128404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007164423,0.0005531845,0.000888145,0.00093816075,0.0011697337,0.0013801653,0.0015802394,0.000570435,0.0088731665],"category_scores_gemma":[0.00080169854,0.00025920867,0.00029962606,0.000854016,0.00034315587,0.0012132186,0.0010956895,0.00055209204,0.0019492778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012065921,0.0007966254,0.006279476,0.00019402578,0.00019852733,0.00048587742,0.00047252155,0.2669591,0.060736686,0.025083365,0.06735797,0.57022923],"study_design_scores_gemma":[0.0001260167,0.00015572699,0.00199872,0.000014502214,0.00007947524,0.00007998704,0.000081606035,0.9703592,0.009119158,0.0046055783,0.013334258,0.000045715457],"about_ca_topic_score_codex":0.011830735,"about_ca_topic_score_gemma":0.010855205,"teacher_disagreement_score":0.011830735,"about_ca_system_score_codex":0.0014540172,"about_ca_system_score_gemma":0.0024605503,"threshold_uncertainty_score":0.02968371},"labels":[],"label_agreement":null},{"id":"W1995934506","doi":"10.1016/j.eswa.2008.05.011","title":"Information extraction from syllabi for academic e-Advising","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Syllabus; Information extraction; Extraction (chemistry); Academic advising; Information retrieval; Mathematics education; Higher education; Psychology; Chromatography; Chemistry; Political science","score_opus":0.024377009563993756,"score_gpt":0.2810708853278222,"score_spread":0.25669387576382846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995934506","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34860775,0.008320469,0.26788294,0.0040445793,0.0016074898,0.0046124826,0.2683142,0.023902206,0.07270791],"genre_scores_gemma":[0.42791435,0.0025762455,0.3702288,0.00034519803,0.00055025425,0.0021864676,0.1795245,0.00057869306,0.01609543],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998664,0.00028101326,0.0002351759,0.00022497663,0.00040032782,0.00019447482],"domain_scores_gemma":[0.993454,0.002838053,0.00048763666,0.0004128667,0.0022570165,0.00055044907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095373346,0.001078469,0.00080053543,0.01569161,0.0011699727,0.0020420894,0.0010265914,0.0011191788,0.01463886],"category_scores_gemma":[0.0093316445,0.0004035244,0.0009586909,0.012045163,0.00024057887,0.0015583468,0.0012700557,0.0010894383,0.009758851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058989326,0.0006138042,0.026799362,0.0017333251,0.00007766278,0.00040848248,0.00043848125,0.0019555963,0.021673009,0.0029960796,0.07896143,0.86375284],"study_design_scores_gemma":[0.0003108093,0.0009361041,0.19694299,0.0018204972,0.0010121486,0.0013485389,0.00437001,0.16757357,0.14321977,0.01756831,0.4645271,0.00037011737],"about_ca_topic_score_codex":0.01818447,"about_ca_topic_score_gemma":0.024928655,"teacher_disagreement_score":0.01818447,"about_ca_system_score_codex":0.0014115322,"about_ca_system_score_gemma":0.004800928,"threshold_uncertainty_score":0.04897189},"labels":[],"label_agreement":null},{"id":"W1998274659","doi":"10.1016/j.eswa.2012.02.086","title":"An integrated multiple criteria preference ranking approach to the Canadian west coast port congestion conflict","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Windsor; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Analytic hierarchy process; Port (circuit theory); West coast; Ranking (information retrieval); Gateway (web page); Operations research; Geography; East coast; Preference; Regional science; Computer science; Business; Transport engineering; Economics; Engineering; Oceanography","score_opus":0.045361982091735885,"score_gpt":0.2547715520432101,"score_spread":0.20940956995147422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998274659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26261625,0.001981263,0.67440313,0.0016081432,0.00021823854,0.00092148234,0.0012695245,0.00032515757,0.056656856],"genre_scores_gemma":[0.8432494,0.00053151604,0.14852251,0.000086322485,0.000042695094,0.00019627459,0.0003730846,0.000052126365,0.006946064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99784386,0.0009631509,0.000088743785,0.0001631087,0.00066645624,0.0002746894],"domain_scores_gemma":[0.9982584,0.00068574055,0.00010443683,0.00006384028,0.00076895533,0.00011849659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002821908,0.0010682666,0.0010910206,0.005007126,0.0016977538,0.0038323707,0.0025710613,0.0008078233,0.005533241],"category_scores_gemma":[0.005703203,0.0004361223,0.00097096676,0.0052718585,0.0007870155,0.0014097206,0.0010105897,0.0010814164,0.00021780677],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029872142,0.00033548108,0.005607168,0.00045833117,0.00044773103,0.00042134649,0.00086152955,0.68165416,0.0016262399,0.07039974,0.0076282024,0.23026136],"study_design_scores_gemma":[0.00004206653,0.00009146648,0.0037521378,0.000058831527,0.00012709148,0.000048331738,0.0009199807,0.96984786,0.00030598408,0.021224432,0.0035101462,0.00007173482],"about_ca_topic_score_codex":0.46495143,"about_ca_topic_score_gemma":0.6426287,"teacher_disagreement_score":0.5350486,"about_ca_system_score_codex":0.011189167,"about_ca_system_score_gemma":0.009061526,"threshold_uncertainty_score":0.9244902},"labels":[],"label_agreement":null},{"id":"W1998587344","doi":"10.1016/j.eswa.2013.09.034","title":"Ontological map of service oriented architecture for shared services management","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Computer science; OASIS SOA Reference Model; Service-oriented architecture; Usability; Artifact (error); Architecture; Topic Maps; Service (business); Software engineering; Knowledge management; Web service; World Wide Web; Human–computer interaction; Artificial intelligence","score_opus":0.008233289673829148,"score_gpt":0.23014586249207755,"score_spread":0.22191257281824842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998587344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047210127,0.0005761405,0.8753041,0.002028567,0.00022582384,0.00026438088,0.0036331846,0.0020478882,0.06870979],"genre_scores_gemma":[0.5845941,0.0011408257,0.39349118,0.00025302783,0.000083084306,0.00039103662,0.005442212,0.00024894468,0.014355685],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99950767,0.00009647067,0.000046523943,0.000099247285,0.0001735123,0.000076715034],"domain_scores_gemma":[0.999645,0.0000869409,0.00002774987,0.0000785253,0.00012407686,0.00003775987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043093468,0.00037214282,0.00034619286,0.0031069282,0.0011973777,0.0023902785,0.00076397415,0.0009469223,0.0056192954],"category_scores_gemma":[0.0014950894,0.00026287415,0.0011298836,0.0022874584,0.0008141983,0.002500243,0.001479935,0.00076336577,0.0011151476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000562399,0.00005721813,0.0014340108,0.00013421234,0.000037244376,0.00048738072,0.0010951717,0.009696005,0.0032643632,0.9365389,0.005534486,0.04166477],"study_design_scores_gemma":[0.000025433312,0.000032373937,0.0025920353,0.0002361187,0.0001301074,0.00046748162,0.0013876103,0.17581859,0.0036222704,0.68041867,0.13522132,0.00004809445],"about_ca_topic_score_codex":0.019500712,"about_ca_topic_score_gemma":0.014715665,"teacher_disagreement_score":0.019500712,"about_ca_system_score_codex":0.0013146495,"about_ca_system_score_gemma":0.00193258,"threshold_uncertainty_score":0.03877437},"labels":[],"label_agreement":null},{"id":"W2001294708","doi":"10.1016/j.eswa.2009.06.091","title":"An input-oriented super-efficiency measure in stochastic data envelopment analysis: Evaluating chief executive officers of US public banks and thrifts","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data envelopment analysis; Measure (data warehouse); Computer science; Compensation (psychology); Sensitivity (control systems); Quadratic equation; Mathematical optimization; Econometrics; Operations research; Data mining; Mathematics; Engineering","score_opus":0.10237295158892173,"score_gpt":0.39692437167141653,"score_spread":0.2945514200824948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001294708","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9410582,0.00014950622,0.054910924,0.00020850428,0.000011842101,0.00007551726,0.0001285126,0.000036323567,0.0034206929],"genre_scores_gemma":[0.9899564,0.00003270599,0.009610448,0.000019406549,0.0000060552534,0.000022811351,0.000102041486,0.000006885794,0.00024330089],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99359083,0.0037398525,0.00035710825,0.00033008883,0.0015936629,0.00038843777],"domain_scores_gemma":[0.97771555,0.013518631,0.0022331595,0.00081265287,0.005027638,0.0006923932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014007915,0.0005963239,0.0009106802,0.0036075788,0.0005989467,0.002743049,0.0004897878,0.0007404277,0.0010017495],"category_scores_gemma":[0.034514055,0.00022145292,0.0006121499,0.0026733405,0.0007686437,0.0018806162,0.0009774924,0.00055953785,0.00016106653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015560071,0.0009697493,0.45018768,0.00033382073,0.00056725356,0.00026192516,0.0014352461,0.28858572,0.00604716,0.037066635,0.0030839366,0.20990482],"study_design_scores_gemma":[0.000065685264,0.0010270829,0.17172797,0.00007381447,0.00015268773,0.00005732778,0.0019711163,0.8029637,0.008801512,0.01158985,0.0015165758,0.00005269887],"about_ca_topic_score_codex":0.0038764141,"about_ca_topic_score_gemma":0.0049040653,"teacher_disagreement_score":0.014007915,"about_ca_system_score_codex":0.00211161,"about_ca_system_score_gemma":0.0028264606,"threshold_uncertainty_score":0.07408178},"labels":[],"label_agreement":null},{"id":"W2001890608","doi":"10.1016/j.eswa.2012.10.028","title":"An enhanced Customer Relationship Management classification framework with Partial Focus Feature Reduction","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Data mining; Customer relationship management; Dimensionality reduction; Preprocessor; Feature (linguistics); Curse of dimensionality; Data pre-processing; Focus (optics); Set (abstract data type); Artificial intelligence; Data set; Data classification; Pattern recognition (psychology); Database","score_opus":0.024674710250886817,"score_gpt":0.274360643891005,"score_spread":0.24968593364011818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001890608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030265493,0.00045338023,0.96286047,0.00024394874,0.000044623863,0.00014874537,0.00058540975,0.0034503937,0.0019475139],"genre_scores_gemma":[0.35362712,0.000256407,0.63727415,0.00030994476,0.00012520036,0.00029309237,0.0023253951,0.0001673574,0.005621326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939024,0.00010061838,0.0000286047,0.00013186163,0.00026128284,0.00008735451],"domain_scores_gemma":[0.99948955,0.00011379159,0.000030283032,0.00005959152,0.00027421382,0.000032594002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096976024,0.0005876491,0.0010481302,0.0016931124,0.00057877647,0.0010488324,0.0017249295,0.0009307128,0.0023615437],"category_scores_gemma":[0.0011042398,0.00028556667,0.00092870207,0.0012712949,0.00019291899,0.0010350001,0.0009452781,0.0009265507,0.0011508085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032058326,0.0006845481,0.0033502611,0.000108366105,0.00014584839,0.000116373674,0.000089803965,0.03882309,0.019749489,0.0049368097,0.010333002,0.9213418],"study_design_scores_gemma":[0.000016895125,0.00007719326,0.001779768,0.000009861456,0.000051507297,0.00006563176,0.000027175229,0.9881969,0.0044088014,0.0025858455,0.002762928,0.000017456854],"about_ca_topic_score_codex":0.012734836,"about_ca_topic_score_gemma":0.016263625,"teacher_disagreement_score":0.012734836,"about_ca_system_score_codex":0.00049848645,"about_ca_system_score_gemma":0.001149491,"threshold_uncertainty_score":0.025321424},"labels":[],"label_agreement":null},{"id":"W2003513713","doi":"10.1016/j.eswa.2008.02.036","title":"An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Decision support system; Energy planning; Variety (cybernetics); Context (archaeology); Sustainable development; Energy management; Operations research; Management science; Energy (signal processing); Risk analysis (engineering); Artificial intelligence; Renewable energy","score_opus":0.017521842733495444,"score_gpt":0.2429244503849656,"score_spread":0.22540260765147016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003513713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047060892,0.00017167901,0.9224535,0.00033393773,0.00008276408,0.0001409721,0.0005709648,0.0223139,0.006871369],"genre_scores_gemma":[0.7680628,0.00013204376,0.22630872,0.00020452274,0.000053553842,0.00041001345,0.0006969557,0.00048613004,0.0036453062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976677,0.000069104026,0.000018343953,0.000048755963,0.00007612572,0.000020899544],"domain_scores_gemma":[0.999556,0.00023897465,0.00004095164,0.00004043665,0.00008562317,0.000037904214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006314255,0.0007589271,0.00095059274,0.0004409063,0.00033728997,0.0009807704,0.0011228733,0.0008473703,0.008211914],"category_scores_gemma":[0.0017775097,0.00038867127,0.0003473147,0.00038142188,0.0002508075,0.0008707427,0.0007651815,0.0006009801,0.0010497011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008103883,0.0002509695,0.0007472435,0.00012440656,0.00010130467,0.0002026736,0.00008231473,0.85407555,0.0073357467,0.003788014,0.007851086,0.124630325],"study_design_scores_gemma":[0.00002725203,0.000016661375,0.00006554922,0.0000019315416,0.000008785196,0.0000070768915,0.0000020402117,0.9979672,0.0006721935,0.0006912478,0.00053553615,0.0000044942294],"about_ca_topic_score_codex":0.005727025,"about_ca_topic_score_gemma":0.0054845726,"teacher_disagreement_score":0.008211914,"about_ca_system_score_codex":0.00058281666,"about_ca_system_score_gemma":0.0007346373,"threshold_uncertainty_score":0.027471602},"labels":[],"label_agreement":null},{"id":"W2004532396","doi":"10.1016/j.eswa.2009.06.096","title":"Integrating spatial and color information in images using a statistical framework","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Smoothing; Histogram; Maximum a posteriori estimation; Spatial analysis; Artificial intelligence; Prior probability; Maximization; Simulated annealing; Pattern recognition (psychology); A priori and a posteriori; Statistical model; Expectation–maximization algorithm; Image (mathematics); Data mining; Computer vision; Machine learning; Mathematics; Mathematical optimization; Maximum likelihood; Statistics; Bayesian probability","score_opus":0.012040909331378724,"score_gpt":0.2844863040789366,"score_spread":0.27244539474755786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004532396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063708765,0.00028405408,0.99246186,0.00008538055,0.000020481386,0.000018038287,0.000049918577,0.00023582148,0.00047361356],"genre_scores_gemma":[0.3294307,0.0015034376,0.6652891,0.0001904256,0.0002742814,0.00012589077,0.00037010145,0.00024743317,0.002568697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991375,0.00022455028,0.000048968403,0.00017091114,0.0003536474,0.00006439642],"domain_scores_gemma":[0.9981115,0.0007788919,0.00023282107,0.00028297905,0.0005357681,0.000058014462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002124673,0.00064119213,0.0009712425,0.0025984847,0.00035861778,0.0015930663,0.0012203128,0.0008437499,0.0010358094],"category_scores_gemma":[0.0041799946,0.0005366594,0.0013969989,0.002779204,0.0011097759,0.002634033,0.0009669634,0.00087302737,0.00050155685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002041371,0.0002372623,0.005153643,0.00033045193,0.0004993841,0.00020478563,0.00014126321,0.362993,0.07435315,0.08046666,0.0023551527,0.47306114],"study_design_scores_gemma":[0.000008388305,0.00006611411,0.002343771,0.000013992187,0.00012033691,0.000105139145,0.000028077307,0.9617439,0.0071201595,0.026884666,0.001520985,0.000044467077],"about_ca_topic_score_codex":0.004521185,"about_ca_topic_score_gemma":0.007854874,"teacher_disagreement_score":0.004521185,"about_ca_system_score_codex":0.00069966447,"about_ca_system_score_gemma":0.0010350621,"threshold_uncertainty_score":0.011236429},"labels":[],"label_agreement":null},{"id":"W2006440622","doi":"10.1016/j.eswa.2009.11.088","title":"A novel dual wing harmonium model aided by 2-D wavelet transform subbands for document data mining","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Complex wavelet transform; Wavelet transform; Graph; Feature extraction; Wavelet; Inference; Discrete wavelet transform; Theoretical computer science","score_opus":0.04681548274256545,"score_gpt":0.3020397074454263,"score_spread":0.2552242247028608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006440622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008568937,0.00017223616,0.9902188,0.00006841268,0.000037187918,0.000024037496,0.000043069555,0.0002824728,0.00058482576],"genre_scores_gemma":[0.3833069,0.000726269,0.6086732,0.00022302356,0.0001001899,0.00025080304,0.00046902135,0.00013848071,0.0061120787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996221,0.00008119989,0.000027213733,0.00008568112,0.00014131548,0.000042509422],"domain_scores_gemma":[0.9997063,0.00008130251,0.000024948953,0.000043382017,0.00012586148,0.000018213537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007770031,0.00040267635,0.00086450647,0.0006708506,0.0003177132,0.000842752,0.0014116566,0.00087251223,0.0012580886],"category_scores_gemma":[0.0010998872,0.00031508433,0.0007955222,0.0008720452,0.00028047586,0.0011661263,0.00076646183,0.0006800178,0.0009356301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047254446,0.00023738298,0.0014026515,0.00014695758,0.00016712614,0.00011809803,0.00009381742,0.34802005,0.029463608,0.012250477,0.003645377,0.603982],"study_design_scores_gemma":[0.0000041893063,0.000024140163,0.0001146602,0.0000023968125,0.000009934493,0.000016664682,0.0000042344627,0.99744296,0.0012485264,0.00059072295,0.00053730345,0.0000042192896],"about_ca_topic_score_codex":0.003138441,"about_ca_topic_score_gemma":0.0033474728,"teacher_disagreement_score":0.003138441,"about_ca_system_score_codex":0.0003310024,"about_ca_system_score_gemma":0.00067453634,"threshold_uncertainty_score":0.006240368},"labels":[],"label_agreement":null},{"id":"W2006725660","doi":"10.1016/j.eswa.2008.12.039","title":"Supplier selection: A hybrid model using DEA, decision tree and neural network","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":282,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Data envelopment analysis; Computer science; Purchasing; Artificial neural network; Selection (genetic algorithm); Decision tree; Supplier evaluation; Vendor; Operations research; Machine learning; Artificial intelligence; Decision tree model; Data mining; Supply chain management; Supply chain; Mathematical optimization; Business; Mathematics; Marketing","score_opus":0.07092476119191075,"score_gpt":0.34356881338033196,"score_spread":0.2726440521884212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006725660","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06710571,0.000478587,0.9242399,0.00041330003,0.00006195585,0.00014729556,0.0002743647,0.0002346826,0.007044306],"genre_scores_gemma":[0.8252373,0.00054053956,0.16498488,0.0001535555,0.00008371286,0.00035447627,0.00034255008,0.00006392971,0.008238953],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987425,0.0006504288,0.000063028165,0.00018616945,0.00026152117,0.00009637787],"domain_scores_gemma":[0.9981336,0.0013483632,0.00014099597,0.000059098205,0.00026113103,0.00005680758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024704211,0.00096669904,0.0021918654,0.001602475,0.0006764634,0.001962205,0.002243173,0.001766797,0.0040260674],"category_scores_gemma":[0.003269206,0.00075865013,0.0010293184,0.0033496006,0.00043907444,0.0022176513,0.0007380532,0.0009500531,0.0005292895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006759253,0.000072300965,0.00052991975,0.000042919426,0.00008299116,0.000042461572,0.000017740856,0.9800541,0.00016847922,0.0041992622,0.00037054333,0.01435175],"study_design_scores_gemma":[0.0000059512417,0.000009474137,0.000084902174,0.0000025196907,0.000011207776,0.0000070870087,0.000003020879,0.998546,0.000048346836,0.0011958838,0.000081995764,0.0000035572598],"about_ca_topic_score_codex":0.012887959,"about_ca_topic_score_gemma":0.011800359,"teacher_disagreement_score":0.012887959,"about_ca_system_score_codex":0.0016139931,"about_ca_system_score_gemma":0.0016568311,"threshold_uncertainty_score":0.025625885},"labels":[],"label_agreement":null},{"id":"W2007050121","doi":"10.1016/j.eswa.2007.06.010","title":"Ontology-based knowledge management for joint venture projects","year":2007,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Ontology; Joint venture; Knowledge management; Joint (building); Process management; Business; Business administration","score_opus":0.035036693070060416,"score_gpt":0.295061728933148,"score_spread":0.2600250358630876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007050121","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0733502,0.0020499823,0.88297737,0.0046187746,0.00022704892,0.0005993529,0.0016104013,0.00439558,0.030171385],"genre_scores_gemma":[0.51309127,0.0014501484,0.4738788,0.00026946326,0.00007844207,0.00037009496,0.00409951,0.00023991714,0.00652233],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99530244,0.0016767175,0.0007650291,0.00055942126,0.0013889552,0.00030749702],"domain_scores_gemma":[0.9940795,0.0021885657,0.0006364568,0.0016921046,0.0010034551,0.00039991082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007688976,0.00040128568,0.0007431145,0.003947409,0.0021714298,0.0077850833,0.0022943977,0.0016472074,0.0025620917],"category_scores_gemma":[0.01451292,0.00051744556,0.0009959025,0.005451674,0.001218661,0.011847822,0.0044249906,0.001482596,0.0009128777],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030296698,0.0007783029,0.00577153,0.0003761757,0.00022910206,0.00055360544,0.0028781723,0.052166596,0.0032541375,0.23775102,0.023782028,0.6721563],"study_design_scores_gemma":[0.000108789485,0.00008700255,0.0037428574,0.00033004573,0.0002832961,0.00036702747,0.00260797,0.44826615,0.007971067,0.42345008,0.11265299,0.00013274745],"about_ca_topic_score_codex":0.015772594,"about_ca_topic_score_gemma":0.0154362675,"teacher_disagreement_score":0.015772594,"about_ca_system_score_codex":0.002089122,"about_ca_system_score_gemma":0.0037614556,"threshold_uncertainty_score":0.04066366},"labels":[],"label_agreement":null},{"id":"W2007164296","doi":"10.1016/j.eswa.2012.07.072","title":"Personal bankruptcy prediction by mining credit card data","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Credit card; Bankruptcy; Computer science; Bankruptcy prediction; Support vector machine; Creditor; Data mining; Sequence (biology); Machine learning; Credit score; Artificial intelligence; Classifier (UML); Domain (mathematical analysis); Feature vector; Finance; Business; Payment; Mathematics","score_opus":0.036000725602709296,"score_gpt":0.2815176768203451,"score_spread":0.24551695121763584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007164296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97433156,0.0009152293,0.016432445,0.00043611295,0.00010949208,0.00007064171,0.004795969,0.0005338318,0.0023747045],"genre_scores_gemma":[0.98905075,0.00024030502,0.005679813,0.000034181063,0.00006881855,0.000016230499,0.003555628,0.000007319498,0.001346986],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999673,0.000038307982,0.000044977725,0.00008243829,0.000099533485,0.00006177099],"domain_scores_gemma":[0.99878865,0.00046043663,0.00022476535,0.00015487103,0.00025905317,0.000112232134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005344801,0.00049990707,0.00047184722,0.0026485415,0.00030977567,0.0009152187,0.00045643182,0.0006655973,0.001769854],"category_scores_gemma":[0.0026127605,0.00016738568,0.00038531038,0.00182198,0.00012702365,0.00077782804,0.0003654346,0.0005451278,0.00095966004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010641954,0.0009823199,0.640984,0.00011047107,0.00021925567,0.0010318854,0.00010765287,0.037145812,0.0047714175,0.0009562338,0.015629977,0.29699677],"study_design_scores_gemma":[0.000044496155,0.00022653112,0.18242729,0.000046191835,0.00014861347,0.0007604704,0.0001996318,0.80193454,0.006596388,0.0025805247,0.0049968823,0.000038532387],"about_ca_topic_score_codex":0.005572765,"about_ca_topic_score_gemma":0.006749569,"teacher_disagreement_score":0.005572765,"about_ca_system_score_codex":0.00028153945,"about_ca_system_score_gemma":0.00041874018,"threshold_uncertainty_score":0.011080682},"labels":[],"label_agreement":null},{"id":"W2007413993","doi":"10.1016/j.eswa.2011.12.038","title":"A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejection","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"","keywords":"Cluster analysis; Computer science; Outlier; Artificial intelligence; A priori and a posteriori; Pattern recognition (psychology); Data mining; Clustering high-dimensional data; Feature (linguistics); CURE data clustering algorithm; Set (abstract data type); Data set; Feature selection; Correlation clustering; Machine learning","score_opus":0.019937885009994123,"score_gpt":0.255815781509133,"score_spread":0.23587789649913884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007413993","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005826863,0.00006723575,0.99917656,0.000030626285,0.000008194577,0.000007664499,0.000009545975,0.000058653237,0.000058895774],"genre_scores_gemma":[0.117630266,0.00047018647,0.8775471,0.00016001424,0.0001296087,0.00041033223,0.00037819496,0.0002382144,0.0030360923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9942397,0.0029031534,0.0003151934,0.00090909103,0.001328264,0.0003046795],"domain_scores_gemma":[0.9864146,0.00963516,0.0007594354,0.0012033215,0.0016678518,0.00031959827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008105261,0.0012826218,0.0039020458,0.0020302325,0.0014646902,0.0030410849,0.007579328,0.004009122,0.0023346995],"category_scores_gemma":[0.02718361,0.0020817034,0.0034374637,0.003586631,0.0030077286,0.0042629503,0.004232337,0.0041783336,0.0014928565],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031141564,0.000115852294,0.00084716483,0.0002537065,0.0003116465,0.00017004905,0.00032737327,0.74718815,0.0032779865,0.12435837,0.0025747644,0.120263554],"study_design_scores_gemma":[0.000010595463,0.000014013649,0.00008916274,0.000007620986,0.00001780422,0.000033695283,0.000008225034,0.97858447,0.0002805768,0.020484535,0.00044872318,0.00002066756],"about_ca_topic_score_codex":0.008930815,"about_ca_topic_score_gemma":0.008862156,"teacher_disagreement_score":0.008930815,"about_ca_system_score_codex":0.0016883493,"about_ca_system_score_gemma":0.0023487268,"threshold_uncertainty_score":0.042865217},"labels":[],"label_agreement":null},{"id":"W2008010003","doi":"10.1016/j.eswa.2011.08.143","title":"An automated vision system for container-code recognition","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle License Plate Recognition","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Container (type theory); License; Code (set theory); Artificial intelligence; Optical character recognition; Character (mathematics); Computer vision; Projection (relational algebra); Isolation (microbiology); Process (computing); Pattern recognition (psychology); Line (geometry); Character recognition; Intelligent character recognition; Image (mathematics); Algorithm; Set (abstract data type); Engineering","score_opus":0.025345497825149516,"score_gpt":0.2629304091868009,"score_spread":0.23758491136165136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008010003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05893154,0.0008578999,0.8480811,0.00028542947,0.00065239816,0.0006045317,0.0015205479,0.07468791,0.014378545],"genre_scores_gemma":[0.26320967,0.0004898338,0.70048493,0.00089764164,0.00022008808,0.00061814237,0.0048756297,0.001242063,0.027962081],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996087,0.000029634979,0.000017285683,0.00012072999,0.0001720865,0.000051593903],"domain_scores_gemma":[0.99947613,0.000058428377,0.000027801865,0.00007158932,0.00031245913,0.000053761025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044921803,0.00073518633,0.00095349766,0.0014430074,0.00056210684,0.0009985946,0.001445404,0.0012348943,0.01171516],"category_scores_gemma":[0.0007692142,0.00045111385,0.0004771049,0.0007440804,0.00022305026,0.00088408473,0.00070416264,0.00073296006,0.0077001527],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056248636,0.00036096157,0.001544356,0.0001802857,0.00008216125,0.00023803285,0.000057938403,0.002689533,0.30540025,0.0013291134,0.039937794,0.6476171],"study_design_scores_gemma":[0.0003711805,0.0010238228,0.018886315,0.00011386646,0.00028641947,0.0019219687,0.00009978566,0.43414155,0.44900006,0.002054011,0.09186108,0.00023987796],"about_ca_topic_score_codex":0.0062336214,"about_ca_topic_score_gemma":0.0068381797,"teacher_disagreement_score":0.01171516,"about_ca_system_score_codex":0.00059960986,"about_ca_system_score_gemma":0.0013311222,"threshold_uncertainty_score":0.039191186},"labels":[],"label_agreement":null},{"id":"W2008032661","doi":"10.1016/j.eswa.2012.03.013","title":"Robust solver based on modified particle swarm optimization for improved solution of diffusion transport through containment facilities","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Groundwater flow and contamination studies","field":"Environmental Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Solver; Particle swarm optimization; Mathematical optimization; Computer science; Inverse; Diffusion; Diffusion equation; Inverse problem; Algorithm; Applied mathematics; Mathematics; Physics; Engineering; Mathematical analysis","score_opus":0.028755567443532824,"score_gpt":0.2318780746162875,"score_spread":0.20312250717275468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008032661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013458606,0.00015653887,0.983466,0.00014336266,0.00010419197,0.000053679592,0.000048691778,0.00024387575,0.0023251446],"genre_scores_gemma":[0.4665325,0.00028927927,0.52680874,0.00014920131,0.00014942745,0.00051800924,0.00030019987,0.00020640613,0.0050462433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996685,0.00013640747,0.00002127763,0.000049582875,0.000092964525,0.000031279582],"domain_scores_gemma":[0.9989423,0.00065378967,0.00009927746,0.000051939765,0.00021334739,0.000039308215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011518147,0.0010382754,0.0015209791,0.0005263589,0.00043668662,0.00091719197,0.001352595,0.002002911,0.0024724726],"category_scores_gemma":[0.00280512,0.0006461596,0.00096783304,0.0005712436,0.00053801533,0.0006489686,0.00095213775,0.001345317,0.00033056308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003471056,0.000022674758,0.00011642877,0.00004062905,0.000022036102,0.000026081394,0.000017372247,0.9864998,0.000755661,0.001953585,0.00042769974,0.010083348],"study_design_scores_gemma":[0.0000040605137,0.000005678441,0.000015254435,8.73333e-7,0.00000150862,0.000001210885,9.433458e-7,0.99971765,0.000069080204,0.00011102607,0.00007178432,9.638186e-7],"about_ca_topic_score_codex":0.010964436,"about_ca_topic_score_gemma":0.0054873694,"teacher_disagreement_score":0.010964436,"about_ca_system_score_codex":0.00058051624,"about_ca_system_score_gemma":0.0015055377,"threshold_uncertainty_score":0.021801233},"labels":[],"label_agreement":null},{"id":"W2008658258","doi":"10.1016/j.eswa.2009.05.097","title":"A novel anonymization algorithm: Privacy protection and knowledge preservation","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Privacy protection; Data anonymization; Data mining; Information privacy; Knowledge extraction; Computer security","score_opus":0.03340661311800753,"score_gpt":0.27916788897587547,"score_spread":0.24576127585786794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008658258","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005794867,0.0002108404,0.9908618,0.0005042541,0.00016716412,0.000088942994,0.00017853735,0.0005719797,0.0016217562],"genre_scores_gemma":[0.188678,0.00047138834,0.80190504,0.00045494127,0.00047417523,0.00020139363,0.0009516605,0.00017747299,0.006686025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945868,0.0014662908,0.00045152946,0.0012760306,0.0018126386,0.00040679701],"domain_scores_gemma":[0.99102813,0.0019851546,0.00052169414,0.0047341604,0.0015065046,0.00022435156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030066762,0.00084060093,0.001649787,0.0020455467,0.0021328705,0.0044342997,0.002540316,0.0026209697,0.0028062686],"category_scores_gemma":[0.011308337,0.0005104535,0.0012875887,0.0032981057,0.0016562334,0.0071726595,0.0039576115,0.0030039516,0.0016302593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090473815,0.00049472996,0.0019251782,0.00033202977,0.00032214037,0.00044142155,0.0006116585,0.054511406,0.027629782,0.30996764,0.026319651,0.57653964],"study_design_scores_gemma":[0.00018533028,0.00023752019,0.0007992357,0.00006808446,0.00023304758,0.0028928695,0.00025173303,0.6378317,0.05796092,0.25320894,0.04619699,0.00013370077],"about_ca_topic_score_codex":0.00053991843,"about_ca_topic_score_gemma":0.000689215,"teacher_disagreement_score":0.0044342997,"about_ca_system_score_codex":0.0010031367,"about_ca_system_score_gemma":0.0025823528,"threshold_uncertainty_score":0.01590103},"labels":[],"label_agreement":null},{"id":"W2012290523","doi":"10.1016/j.eswa.2011.09.058","title":"Gradient boosting trees for auto insurance loss cost modeling and prediction","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Computer science; Gradient boosting; Softmax function; Support vector machine; Feature selection; Data mining; Parameterized complexity; Generalized linear model; Hinge loss; Artificial neural network; Data pre-processing; Machine learning; Artificial intelligence; Algorithm; Mathematical optimization; Random forest; Mathematics","score_opus":0.04805340199553818,"score_gpt":0.2653546485366028,"score_spread":0.2173012465410646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012290523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017260134,0.0008897771,0.9803104,0.00023241957,0.000068914655,0.00002933138,0.000121899815,0.00049755594,0.0005896931],"genre_scores_gemma":[0.68591356,0.0010971894,0.305214,0.00021411946,0.00029795518,0.00020787652,0.0007745901,0.0002204207,0.0060603675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991721,0.0003718915,0.000049023223,0.00012963705,0.00019819276,0.000079178615],"domain_scores_gemma":[0.9972772,0.0018643994,0.0001430592,0.00022527715,0.00040918624,0.000080870086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003945287,0.0006988691,0.0019167938,0.001095439,0.00047068507,0.0009674121,0.0016903139,0.0014774076,0.001728938],"category_scores_gemma":[0.0074699568,0.0007188075,0.0008285326,0.0012066553,0.00042362948,0.0015185022,0.00065565354,0.0019225477,0.0007175068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107076194,0.000114160335,0.001404484,0.000055367193,0.000063470026,0.000044067703,0.000036393132,0.82718426,0.00075288647,0.012060322,0.0044492525,0.15372835],"study_design_scores_gemma":[0.0000016240044,0.0000041836497,0.0000644207,0.0000020129041,0.0000031624752,0.0000037200286,9.69007e-7,0.99654275,0.00007814548,0.0031479509,0.00014964257,0.0000014443588],"about_ca_topic_score_codex":0.004047426,"about_ca_topic_score_gemma":0.0041516162,"teacher_disagreement_score":0.004047426,"about_ca_system_score_codex":0.00077601115,"about_ca_system_score_gemma":0.0009020456,"threshold_uncertainty_score":0.020864964},"labels":[],"label_agreement":null},{"id":"W2012323948","doi":"10.1016/j.eswa.2010.06.071","title":"Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":263,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"SWOT analysis; Fuzzy logic; Computer science; Vagueness; Operations research; Selection (genetic algorithm); Context analysis; Order (exchange); Mathematical optimization; Artificial intelligence; Business; Mathematics; Marketing; Government (linguistics)","score_opus":0.0424467241313793,"score_gpt":0.3690723420025249,"score_spread":0.32662561787114563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012323948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048259765,0.00013736628,0.94543123,0.0001136681,0.000037980953,0.00020273814,0.000103532904,0.00011592018,0.0055977665],"genre_scores_gemma":[0.7470037,0.00018926109,0.24950668,0.00004369467,0.000027634756,0.00023101101,0.00014036536,0.000051089504,0.0028065713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972978,0.0011881447,0.00011502737,0.0002164387,0.00095376093,0.00022885698],"domain_scores_gemma":[0.9970475,0.00190962,0.00027149377,0.00009742572,0.00059875066,0.00007524134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032614246,0.0012004474,0.0024991694,0.003640353,0.0011491232,0.0026805764,0.001227494,0.0010751411,0.004315204],"category_scores_gemma":[0.005788657,0.0010808186,0.0019690767,0.0046618534,0.00084582315,0.0023317325,0.0008290668,0.00066046603,0.00038894833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027215484,0.00009054287,0.0014582758,0.00031253602,0.00014056383,0.00019437789,0.00018796162,0.8946857,0.002534744,0.024463378,0.0011671832,0.07449258],"study_design_scores_gemma":[0.00000859955,0.000041009847,0.00030033034,0.000011286026,0.000020510039,0.000021434134,0.000036922367,0.98982936,0.00053748454,0.008961301,0.00021962245,0.0000121713965],"about_ca_topic_score_codex":0.009382657,"about_ca_topic_score_gemma":0.00956654,"teacher_disagreement_score":0.009382657,"about_ca_system_score_codex":0.0021175323,"about_ca_system_score_gemma":0.0025360074,"threshold_uncertainty_score":0.018656075},"labels":[],"label_agreement":null},{"id":"W2012824567","doi":"10.1016/j.eswa.2011.07.103","title":"Modeling of the charging characteristic of linear-type superconducting power supply using granular-based radial basis function neural networks","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Frequency Control in Power Systems","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Cluster analysis; Magnet; Fuzzy logic; Power (physics); Basis (linear algebra); Voltage; Superconductivity; Function (biology); Superconducting magnet; Topology (electrical circuits); Artificial intelligence; Physics; Mathematics; Mechanical engineering; Electrical engineering; Engineering","score_opus":0.02784740974738042,"score_gpt":0.21787140345816522,"score_spread":0.1900239937107848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012824567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3459004,0.0005337751,0.63704044,0.00042501095,0.00009905413,0.00006426725,0.00011644435,0.00057259423,0.015248072],"genre_scores_gemma":[0.99601007,0.0000757905,0.0025716664,0.000014173519,0.000007030413,0.000010451216,0.0000201257,0.000012694934,0.0012779663],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998797,0.00003362519,0.000006331739,0.0000210179,0.00004068105,0.000018605278],"domain_scores_gemma":[0.9997496,0.00010281447,0.000044096603,0.000016451853,0.0000715487,0.000015554539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029462407,0.00034034578,0.000504335,0.00023564056,0.000282557,0.000646458,0.0007009606,0.00072264246,0.0009341345],"category_scores_gemma":[0.00084267725,0.00026815155,0.0003925779,0.00032799304,0.00051077595,0.0008013857,0.000293965,0.00048833404,0.00012540733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039854287,0.000010042617,0.00034750393,0.000018431027,0.000010878611,0.00006890716,0.00001832024,0.99346113,0.0015366361,0.0018567517,0.00012977158,0.0025017108],"study_design_scores_gemma":[7.5569625e-7,0.0000017912056,0.000050383118,4.5671885e-7,7.6184625e-7,0.0000025265492,0.0000010596482,0.9997069,0.000060852977,0.00015868356,0.00001483861,8.4765065e-7],"about_ca_topic_score_codex":0.009017957,"about_ca_topic_score_gemma":0.0062257866,"teacher_disagreement_score":0.009017957,"about_ca_system_score_codex":0.00068434776,"about_ca_system_score_gemma":0.0004451009,"threshold_uncertainty_score":0.017930925},"labels":[],"label_agreement":null},{"id":"W2013650127","doi":"10.1016/j.eswa.2010.07.004","title":"Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water Systems and Optimization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Program for New Century Excellent Talents in University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Department of Science and Technology, Ministry of Science and Technology, India; Shanghai Rising-Star Program; Natural Science Foundation of Chongqing; National Science Foundation","keywords":"Computer science; Flexibility (engineering); Intrusion; Similarity (geometry); Data mining; Process (computing); Intrusion detection system; Mathematical optimization; Artificial intelligence; Statistics; Mathematics","score_opus":0.007357480738357826,"score_gpt":0.2226710859223034,"score_spread":0.21531360518394557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013650127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056633665,0.000107631,0.99328107,0.000072982446,0.000029987948,0.000028707907,0.0000323556,0.00012291093,0.0006609818],"genre_scores_gemma":[0.46062002,0.0005161585,0.5326522,0.00023098009,0.00022717635,0.0002913338,0.00029641268,0.00024187467,0.004923888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948645,0.00014918158,0.000042605272,0.000105644336,0.00018710598,0.000028993652],"domain_scores_gemma":[0.99880266,0.0005367503,0.00014529718,0.00010333066,0.00033639942,0.000075614626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010956941,0.0007279071,0.0013862633,0.0012565264,0.0004211441,0.0009642357,0.0018754633,0.0016627476,0.0016711202],"category_scores_gemma":[0.0031256508,0.0005428528,0.0011028012,0.0012476988,0.00065323757,0.0018838546,0.0011267703,0.00095742254,0.0004373465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004716344,0.000041298834,0.0005012774,0.000058615078,0.000056561654,0.000040493473,0.00002561922,0.94676,0.002109179,0.005859429,0.000495964,0.04400437],"study_design_scores_gemma":[0.0000023469079,0.000004764485,0.000016514678,8.154361e-7,0.000003138366,0.000004498272,9.047445e-7,0.99924314,0.00013953923,0.00046083596,0.000122037425,0.0000015381507],"about_ca_topic_score_codex":0.006397183,"about_ca_topic_score_gemma":0.0032334188,"teacher_disagreement_score":0.006397183,"about_ca_system_score_codex":0.0007328228,"about_ca_system_score_gemma":0.0011444401,"threshold_uncertainty_score":0.012719929},"labels":[],"label_agreement":null},{"id":"W2017302001","doi":"10.1016/j.eswa.2010.11.044","title":"A new nonparametric EWMA Sign Control Chart","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":136,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Science Council","keywords":"EWMA chart; Control chart; Chart; Statistics; X-bar chart; Computer science; Control limits; Nonparametric statistics; Normality; Shewhart individuals control chart; Step detection; Mathematics; Process (computing)","score_opus":0.044990238681768914,"score_gpt":0.38330469986461363,"score_spread":0.3383144611828447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017302001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017642459,0.000092999464,0.99706155,0.000034467874,0.00005300365,0.000021270691,0.000029528557,0.0006500773,0.0002928367],"genre_scores_gemma":[0.1754177,0.00032833312,0.8188439,0.00016325049,0.00025863937,0.0001889997,0.00035354236,0.0003901211,0.004055419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971928,0.0007916927,0.00020331285,0.0005202317,0.0011602887,0.00013167036],"domain_scores_gemma":[0.9958417,0.0014802308,0.000469357,0.00055167655,0.0015073859,0.00014962073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035391608,0.00085528643,0.0015834852,0.0011842889,0.00046444786,0.0018318614,0.0015299247,0.0012884093,0.003036068],"category_scores_gemma":[0.011172314,0.00043737193,0.00069252943,0.0010927328,0.00077923795,0.001909586,0.0012700683,0.00191457,0.0010808324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007654535,0.00020487414,0.0013467963,0.00019178589,0.00010082887,0.00017158702,0.00007468566,0.1100403,0.05204287,0.035820533,0.005498624,0.7937416],"study_design_scores_gemma":[0.000041077747,0.00010127222,0.00041223012,0.000011850241,0.000030422056,0.000084371626,0.0000030512751,0.98315316,0.009109386,0.003444668,0.0035720107,0.000036448335],"about_ca_topic_score_codex":0.0017832894,"about_ca_topic_score_gemma":0.001512733,"teacher_disagreement_score":0.0035391608,"about_ca_system_score_codex":0.00054059713,"about_ca_system_score_gemma":0.0016545202,"threshold_uncertainty_score":0.01871711},"labels":[],"label_agreement":null},{"id":"W2018093209","doi":"10.1016/j.eswa.2013.09.009","title":"Automatic detection of musicians’ ancillary gestures based on video analysis","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Centre for Interdisciplinary Research in Music Media and Technology","funders":"","keywords":"Gesture; Computer science; Computer vision; Artificial intelligence; Point (geometry); Speech recognition; Mathematics","score_opus":0.009497945365193616,"score_gpt":0.22637336110067688,"score_spread":0.21687541573548327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018093209","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5314254,0.0043359445,0.43564132,0.0002762852,0.00056151784,0.00044343725,0.0025249985,0.0054141185,0.019376911],"genre_scores_gemma":[0.78606164,0.0014773799,0.20096728,0.00014899921,0.00022247,0.0001700638,0.0016733279,0.00019853088,0.009080308],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996948,0.00003473445,0.000013587556,0.00007566095,0.000121544705,0.000059717837],"domain_scores_gemma":[0.9995902,0.000088744426,0.00005146042,0.000028996194,0.00018289615,0.00005770196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027639812,0.0008321901,0.00050727086,0.002567095,0.00026419252,0.000584182,0.0004781881,0.00066421175,0.002751067],"category_scores_gemma":[0.0008697088,0.0001703717,0.0003253065,0.000850513,0.00019341445,0.00036133832,0.0003329212,0.00032505693,0.0017035909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006941107,0.00007217006,0.005508813,0.00028564592,0.000050109546,0.00035950143,0.00007567925,0.0006624514,0.54585993,0.00038122517,0.0027496405,0.44330075],"study_design_scores_gemma":[0.00016047865,0.00091630156,0.22183442,0.00020813441,0.0003316865,0.0033404496,0.0005859343,0.22671147,0.52584594,0.0013498503,0.0185646,0.00015080953],"about_ca_topic_score_codex":0.0028296113,"about_ca_topic_score_gemma":0.005456438,"teacher_disagreement_score":0.0028296113,"about_ca_system_score_codex":0.00018259246,"about_ca_system_score_gemma":0.0003501988,"threshold_uncertainty_score":0.009203196},"labels":[],"label_agreement":null},{"id":"W2018407890","doi":"10.1016/j.eswa.2013.03.037","title":"An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Bin packing problem; Mathematical optimization; Benchmark (surveying); Iterated local search; Metaheuristic; Computer science; Packing problems; Scheduling (production processes); Heuristic; Generalization; Iterated function; Upper and lower bounds; Job shop scheduling; Bin; Algorithm; Mathematics","score_opus":0.04250410525338499,"score_gpt":0.2585666167977702,"score_spread":0.21606251154438522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018407890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048683785,0.0006344042,0.94137293,0.00023064765,0.0001375845,0.0002122853,0.00005338733,0.0010032417,0.00767174],"genre_scores_gemma":[0.47179332,0.0003004557,0.52049905,0.0002078399,0.000093426235,0.00052068837,0.00016949169,0.000312912,0.006102719],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991986,0.0002933149,0.0000353388,0.00009765222,0.00024672307,0.00012837867],"domain_scores_gemma":[0.9981521,0.0011649957,0.0001505564,0.00010787265,0.00030688586,0.00011765815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017629536,0.0009493311,0.0021603166,0.0013158858,0.0007437507,0.0010386657,0.002960289,0.0020388812,0.004360852],"category_scores_gemma":[0.0038576312,0.0009281916,0.0012351783,0.0011585652,0.0009554497,0.0014243285,0.0013875245,0.0012678528,0.00062214094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001758391,0.00017631683,0.00029008166,0.0001102479,0.000052251984,0.00011918503,0.00011340559,0.9292717,0.0015485821,0.0053570466,0.0016313882,0.061154],"study_design_scores_gemma":[0.000028616481,0.00005005627,0.000043303775,0.000007801436,0.0000136988965,0.000013641299,0.000012492009,0.99839455,0.0002475597,0.00087529776,0.0003063565,0.0000065602644],"about_ca_topic_score_codex":0.006750838,"about_ca_topic_score_gemma":0.0067053433,"teacher_disagreement_score":0.006750838,"about_ca_system_score_codex":0.0012866197,"about_ca_system_score_gemma":0.00171868,"threshold_uncertainty_score":0.014588535},"labels":[],"label_agreement":null},{"id":"W2020287868","doi":"10.1016/j.eswa.2007.11.045","title":"A genetic fuzzy <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si218.gif\" overflow=\"scroll\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-Modes algorithm for clustering categorical data","year":2007,"lang":"lv","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Algorithm; Fuzzy logic; Crossover; Categorical variable; Computer science; Genetic algorithm; Operator (biology); Cluster analysis; Mathematics; Artificial intelligence; Machine learning","score_opus":0.034403805891581714,"score_gpt":0.29495871503319476,"score_spread":0.26055490914161306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020287868","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008415041,0.00007960118,0.9840109,0.00023249422,0.00007005004,0.00009829771,0.0003482506,0.0008099753,0.005935405],"genre_scores_gemma":[0.0777313,0.00010412305,0.9109325,0.00018553941,0.000040742754,0.00019030052,0.0007285099,0.00015826891,0.009928742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990734,0.00014823006,0.00004404081,0.00025891335,0.00042165854,0.000053769403],"domain_scores_gemma":[0.9991124,0.00026624068,0.000041910494,0.00013845158,0.00039444646,0.000046456742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012238625,0.00042908717,0.0006065027,0.0015195856,0.0011399243,0.00144051,0.002129828,0.0013203941,0.0068039345],"category_scores_gemma":[0.0041244444,0.00035092,0.0010874004,0.0016498184,0.00070089777,0.00070406473,0.0008425842,0.0011579462,0.0023952548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000205394,0.00018911317,0.0023041272,0.00018631626,0.0001334223,0.00017146883,0.00035146295,0.21650404,0.015859602,0.08961814,0.017643727,0.65683323],"study_design_scores_gemma":[0.000042922667,0.000074712465,0.0006493525,0.000048076243,0.0000525729,0.0001478668,0.0000673916,0.95056814,0.007548164,0.029149188,0.011610466,0.00004110689],"about_ca_topic_score_codex":0.024853459,"about_ca_topic_score_gemma":0.02555266,"teacher_disagreement_score":0.024853459,"about_ca_system_score_codex":0.0019123082,"about_ca_system_score_gemma":0.0024847584,"threshold_uncertainty_score":0.049417615},"labels":[],"label_agreement":null},{"id":"W2021468640","doi":"10.1016/j.eswa.2014.09.042","title":"A biologically inspired approach to tracking control of underactuated surface vessels subject to unknown dynamics","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Backstepping; Underactuation; Computer science; Tracking (education); Tracking error; Control theory (sociology); Artificial neural network; Dynamics (music); Control (management); Artificial intelligence; Adaptive control","score_opus":0.015355826625703605,"score_gpt":0.23348723749856115,"score_spread":0.21813141087285753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021468640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047770485,0.0004748612,0.94042134,0.0005722233,0.00015651945,0.000039305607,0.00001682831,0.00015657648,0.010391903],"genre_scores_gemma":[0.8849596,0.00044310722,0.10759146,0.00019415624,0.00007609927,0.000096058684,0.000025119369,0.000035389006,0.006578991],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992275,0.000014011347,0.0000029426012,0.000019372961,0.000031635915,0.000009327803],"domain_scores_gemma":[0.9998865,0.000047386533,0.000022400576,0.000011229921,0.000020999229,0.000011505476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020200267,0.00026361705,0.00029624844,0.00029789424,0.00032507832,0.0005407654,0.0007393633,0.0010088783,0.00088972086],"category_scores_gemma":[0.00053999404,0.00021610061,0.0004432924,0.00018280411,0.00079129887,0.0003607936,0.00076196634,0.00055686856,0.00010686565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002921549,0.000032991426,0.0003349721,0.00006851248,0.000032186515,0.00016384304,0.00016205841,0.8794746,0.03946896,0.048498306,0.0004290905,0.031305343],"study_design_scores_gemma":[0.000005018928,0.000043740423,0.00013711124,0.0000053997037,0.000006225302,0.000029488456,0.000011293645,0.9894958,0.0012605028,0.007998719,0.0010002614,0.000006540538],"about_ca_topic_score_codex":0.0017892579,"about_ca_topic_score_gemma":0.0015870384,"teacher_disagreement_score":0.0017892579,"about_ca_system_score_codex":0.00049794035,"about_ca_system_score_gemma":0.00048541187,"threshold_uncertainty_score":0.0036128163},"labels":[],"label_agreement":null},{"id":"W2021938316","doi":"10.1016/j.eswa.2011.04.222","title":"Forecasting stock indices with back propagation neural network","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":484,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education; Lanzhou University","keywords":"Artificial neural network; Stock (firearms); Computer science; Backpropagation; Stock market index; Stock price; Composite index; Econometrics; Index (typography); Stock market; Data mining; Artificial intelligence; Series (stratigraphy); Mathematics","score_opus":0.189853560597106,"score_gpt":0.3608030537518224,"score_spread":0.17094949315471641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021938316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24461363,0.0018545775,0.7485633,0.00034601163,0.00033591333,0.00007228553,0.00016793975,0.001173858,0.0028725353],"genre_scores_gemma":[0.8657986,0.00082040194,0.12910455,0.00006511412,0.00012892646,0.00006173913,0.00024727962,0.000044690998,0.0037286654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997633,0.000048425674,0.000022173808,0.000044965145,0.0000957803,0.000025330819],"domain_scores_gemma":[0.9989876,0.0005990404,0.000101597405,0.000052957548,0.00023504421,0.00002367604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009728516,0.0007283429,0.0008533912,0.0009375727,0.0002190925,0.0008073431,0.0005871444,0.00086151564,0.0009011805],"category_scores_gemma":[0.0031917046,0.0004306741,0.0004957065,0.000918054,0.00024283661,0.0011523766,0.00029766373,0.0010947343,0.0002849069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024199586,0.00018797474,0.0039635026,0.00008698916,0.00014738765,0.00007185492,0.000028360622,0.81918913,0.003956237,0.0014394439,0.0011359357,0.16955122],"study_design_scores_gemma":[0.0000037507873,0.0000059430604,0.00020647487,0.0000013862252,0.000006881196,0.0000018893483,7.9090785e-7,0.99917907,0.00029438137,0.00026804546,0.000029534403,0.0000017916296],"about_ca_topic_score_codex":0.011029647,"about_ca_topic_score_gemma":0.0086718155,"teacher_disagreement_score":0.011029647,"about_ca_system_score_codex":0.00043803157,"about_ca_system_score_gemma":0.00047246626,"threshold_uncertainty_score":0.021930933},"labels":[],"label_agreement":null},{"id":"W2022201359","doi":"10.1016/j.eswa.2013.07.002","title":"Cluster center initialization algorithm for K-modes clustering","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":120,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Computer science; Cluster analysis; Cluster (spacecraft); Center (category theory); Algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology); Computer network","score_opus":0.02231767915479643,"score_gpt":0.304274552149248,"score_spread":0.28195687299445155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022201359","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025500455,0.0001486374,0.9949216,0.00005938262,0.00008269643,0.000067016124,0.00010761667,0.000990444,0.0010725566],"genre_scores_gemma":[0.07133662,0.00016959292,0.9226873,0.000077120894,0.000052779997,0.00024573097,0.0007641893,0.0004059235,0.004260613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988613,0.00025563993,0.00006858325,0.00029303753,0.00039141582,0.00013008782],"domain_scores_gemma":[0.99860674,0.00025961504,0.00006609828,0.00023903386,0.00075892534,0.0000696179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012627749,0.0010355863,0.001137247,0.0016456352,0.0018583658,0.0014491072,0.0025928507,0.0014765321,0.006314137],"category_scores_gemma":[0.00413323,0.000676921,0.0010272211,0.002037435,0.0006528165,0.001280901,0.0016088133,0.0021921166,0.0047424845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008200851,0.00014894202,0.0015148624,0.00027587908,0.00018559818,0.000113702474,0.00041735786,0.17120343,0.018519975,0.027605856,0.030791426,0.7484029],"study_design_scores_gemma":[0.00006263667,0.00005237019,0.0008768007,0.000036911606,0.00004077988,0.00013171026,0.000092408605,0.96120375,0.013099275,0.013819383,0.010521603,0.0000623698],"about_ca_topic_score_codex":0.011170032,"about_ca_topic_score_gemma":0.013015608,"teacher_disagreement_score":0.011170032,"about_ca_system_score_codex":0.0011322491,"about_ca_system_score_gemma":0.0027797627,"threshold_uncertainty_score":0.022210002},"labels":[],"label_agreement":null},{"id":"W2025623395","doi":"10.1016/j.eswa.2015.01.060","title":"Description and prediction of time series: A general framework of Granular Computing","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Series (stratigraphy); Granularity; Granular computing; Time series; Representation (politics); Computer science; Particle swarm optimization; Amplitude; Cluster analysis; Spacetime; Data mining; Algorithm; Artificial intelligence; Machine learning","score_opus":0.021876079914666908,"score_gpt":0.23602296445092671,"score_spread":0.2141468845362598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025623395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068720216,0.001300004,0.98984164,0.0004576834,0.00005682013,0.00003627244,0.00021628951,0.00013520758,0.001084056],"genre_scores_gemma":[0.4813943,0.004841749,0.5098308,0.0002472841,0.0005115914,0.00028068753,0.00076379883,0.00008311807,0.0020467394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817634,0.0005513712,0.00029224623,0.00034920883,0.0004988815,0.00013186572],"domain_scores_gemma":[0.9966813,0.0018418365,0.00046809268,0.0005523623,0.0003038284,0.00015255422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031859842,0.0009277468,0.0028644523,0.0029467063,0.0005323011,0.004914109,0.0023857267,0.0015191509,0.0014073611],"category_scores_gemma":[0.009830423,0.00060959975,0.002216221,0.004766259,0.0017899454,0.0067540715,0.0018125828,0.0018109082,0.00025172357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008698972,0.000060233957,0.001840627,0.00046345498,0.00021832007,0.00028529987,0.0002937707,0.33454204,0.001983202,0.5965339,0.0016188985,0.062073313],"study_design_scores_gemma":[0.000010201363,0.000022958213,0.00036538288,0.000051374816,0.000047861853,0.0000529396,0.000041527528,0.6144998,0.00030666,0.38291237,0.0016624166,0.000026453283],"about_ca_topic_score_codex":0.0046394956,"about_ca_topic_score_gemma":0.0020530587,"teacher_disagreement_score":0.004914109,"about_ca_system_score_codex":0.0012394636,"about_ca_system_score_gemma":0.0010727389,"threshold_uncertainty_score":0.016849339},"labels":[],"label_agreement":null},{"id":"W2025729706","doi":"10.1016/j.eswa.2008.12.038","title":"An adjustable personalization of search and delivery of learning objects to learners","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Learning object; Personalization; Computer science; Object (grammar); Ontology; Personalized search; Order (exchange); User profile; Information retrieval; Artificial intelligence; World Wide Web","score_opus":0.02738434134061168,"score_gpt":0.2654481821081586,"score_spread":0.23806384076754694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025729706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1921978,0.0010373692,0.77145797,0.00084610016,0.00022154329,0.0004830326,0.00060386246,0.02054146,0.012610956],"genre_scores_gemma":[0.81238645,0.00031916596,0.17891589,0.00021581707,0.000107579785,0.0002662795,0.00045603764,0.0009241246,0.0064086085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99677104,0.0009509472,0.0002585846,0.0011100302,0.0007199365,0.00018947668],"domain_scores_gemma":[0.985378,0.006195155,0.0007590875,0.00520003,0.0016532162,0.000814474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004680494,0.0008290949,0.0012130227,0.0016042984,0.0006820084,0.0029914249,0.003007421,0.001944285,0.005256258],"category_scores_gemma":[0.021786185,0.0008120527,0.0005938737,0.0015841947,0.0009417457,0.0049061645,0.0027676313,0.0016135405,0.002217936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003087556,0.0027902175,0.021367488,0.0005616592,0.000384008,0.00021768534,0.0018336908,0.046680342,0.10255366,0.018143706,0.0124801835,0.7898997],"study_design_scores_gemma":[0.0005835864,0.0009210473,0.024333313,0.0001349807,0.00059043994,0.0008887486,0.00082117924,0.82465804,0.07573291,0.037602365,0.033458456,0.00027496906],"about_ca_topic_score_codex":0.0021926484,"about_ca_topic_score_gemma":0.0020990018,"teacher_disagreement_score":0.005256258,"about_ca_system_score_codex":0.0010316613,"about_ca_system_score_gemma":0.001199549,"threshold_uncertainty_score":0.024753153},"labels":[],"label_agreement":null},{"id":"W2026908126","doi":"10.1016/j.eswa.2011.03.031","title":"An approach to interval programming problems with left-hand-side stochastic coefficients: An application to environmental decisions analysis","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interval (graph theory); Stochastic programming; Mathematical optimization; Linear programming; Computer science; Multivariate statistics; Interval arithmetic; Mathematics; Applied mathematics; Statistics","score_opus":0.014432550255959815,"score_gpt":0.21277961487277663,"score_spread":0.1983470646168168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026908126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007560315,0.00020658472,0.99735755,0.00015822687,0.000043899443,0.000013930905,0.000014387862,0.000025619483,0.0014237092],"genre_scores_gemma":[0.09605046,0.0019769731,0.89362955,0.00028210608,0.00051491533,0.00028171594,0.00009374958,0.00016148669,0.007009002],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985876,0.0006332564,0.000074273434,0.0001940568,0.00043387045,0.00007688777],"domain_scores_gemma":[0.99530125,0.0036999977,0.00019437954,0.00013692008,0.00051354215,0.00015397159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034279076,0.0013437369,0.0016970194,0.0012590464,0.000706942,0.0019116537,0.0027341803,0.002390407,0.0049439375],"category_scores_gemma":[0.011509283,0.0010469023,0.0023612203,0.0020040036,0.0014761378,0.0025145353,0.0025067744,0.005347728,0.00071585196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031736654,0.00015531783,0.00026151727,0.00023892485,0.000092678645,0.00021371178,0.00020820921,0.4778065,0.0016160591,0.45096865,0.0035025394,0.06490414],"study_design_scores_gemma":[0.000016159038,0.000024986562,0.00005946406,0.00002425974,0.000022843924,0.00004505734,0.00001846346,0.89146197,0.00020074425,0.10581618,0.0022931083,0.000016816768],"about_ca_topic_score_codex":0.0039444286,"about_ca_topic_score_gemma":0.00469256,"teacher_disagreement_score":0.0049439375,"about_ca_system_score_codex":0.0011172683,"about_ca_system_score_gemma":0.0019904238,"threshold_uncertainty_score":0.018128753},"labels":[],"label_agreement":null},{"id":"W2027364089","doi":"10.1016/j.eswa.2012.10.012","title":"Data summarization ontology-based query processing","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Automatic summarization; Computer science; Ontology; Information retrieval; Multi-document summarization; Query language; Ontology-based data integration; Query expansion; Upper ontology; Data mining; Semantic Web","score_opus":0.05784543019104168,"score_gpt":0.30598091195149846,"score_spread":0.24813548176045677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027364089","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024842083,0.0014130247,0.939596,0.0010326061,0.00025753828,0.00086349336,0.005166513,0.020931087,0.0058976766],"genre_scores_gemma":[0.20118642,0.0012840233,0.76035297,0.00062552496,0.00025445677,0.00086158694,0.024939308,0.0015341908,0.008961479],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99562055,0.0007999359,0.00071704626,0.0006472542,0.0019130965,0.0003021915],"domain_scores_gemma":[0.99480164,0.00130787,0.00026601632,0.0009249394,0.002585526,0.00011412711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028603994,0.0014421366,0.0024089909,0.007817276,0.001562911,0.0044722487,0.0021901617,0.0010622127,0.0068761897],"category_scores_gemma":[0.008672628,0.00049806444,0.0019908368,0.006833201,0.0005454526,0.0030372806,0.002094854,0.0012208231,0.0042694383],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074853475,0.0004072514,0.0033948426,0.001433875,0.00037233316,0.0005709141,0.0011256594,0.017771004,0.07277117,0.017118264,0.062705465,0.82158077],"study_design_scores_gemma":[0.00017866309,0.00039769945,0.006144283,0.0002730826,0.0009917433,0.0010865185,0.0022133566,0.5782475,0.1977211,0.03764454,0.17485356,0.00024795876],"about_ca_topic_score_codex":0.0078010894,"about_ca_topic_score_gemma":0.006353418,"teacher_disagreement_score":0.007817276,"about_ca_system_score_codex":0.0012745691,"about_ca_system_score_gemma":0.0022774215,"threshold_uncertainty_score":0.023003161},"labels":[],"label_agreement":null},{"id":"W2028811096","doi":"10.1016/j.eswa.2012.01.019","title":"Erratum to “DiSeg 1.0: The first system for Spanish discourse segmentation” [Expert Systems with Applications 39 (2) (2011) 1671–1678]","year":2012,"lang":"en","type":"erratum","venue":"Expert Systems with Applications","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Natural language processing; Information retrieval","score_opus":0.0163932684451305,"score_gpt":0.2655787540280244,"score_spread":0.24918548558289388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028811096","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00087485043,0.0017221029,0.00283133,0.12781428,0.8321926,0.0001037988,0.00516053,0.002020485,0.027280023],"genre_scores_gemma":[0.018664626,0.005600417,0.006918733,0.13837804,0.10990378,0.00046233812,0.013881095,0.0050047464,0.7011862],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99620795,0.0007027788,0.0006126134,0.00049817516,0.0016339378,0.00034439182],"domain_scores_gemma":[0.9835855,0.0033757777,0.0006895416,0.00070002297,0.011129517,0.00051969715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030573704,0.0021135823,0.0018885991,0.0035475416,0.0055507803,0.0035410796,0.0027458745,0.006669096,0.07456285],"category_scores_gemma":[0.032516327,0.00092710095,0.0010436365,0.0027004674,0.0018463396,0.0022634303,0.00252477,0.007493624,0.052213356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038488797,0.000005740978,0.00003643132,0.0000476759,0.0000032151925,0.00010665365,0.00004152893,0.000024633959,0.00005102065,0.0005119841,0.99592066,0.0032119332],"study_design_scores_gemma":[0.000034978155,0.000028774053,0.0006560502,0.00022892366,0.000018199667,0.00016497781,0.00019160699,0.00027931316,0.0004961667,0.00088773796,0.99697113,0.000042191343],"about_ca_topic_score_codex":0.059504498,"about_ca_topic_score_gemma":0.055613715,"teacher_disagreement_score":0.07456285,"about_ca_system_score_codex":0.005249899,"about_ca_system_score_gemma":0.0061813467,"threshold_uncertainty_score":0.24943757},"labels":[],"label_agreement":null},{"id":"W2030141181","doi":"10.1016/j.eswa.2011.12.014","title":"Dislocation detection in field environments: A belief functions contribution","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Computer science; Dislocation; Focus (optics); Position (finance); Field (mathematics); Frame (networking); Identification (biology); Function (biology); Artificial intelligence; Algorithm; Mathematics; Physics; Optics","score_opus":0.003585760983289175,"score_gpt":0.1970263367578146,"score_spread":0.19344057577452545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030141181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015186305,0.0005418586,0.9819025,0.0005160077,0.00004105132,0.000016861703,0.000042665866,0.00008434454,0.0016684278],"genre_scores_gemma":[0.8166636,0.0014080158,0.17612112,0.00030501108,0.0003688625,0.00007459934,0.00019082465,0.00013808146,0.0047298092],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990073,0.0002739025,0.000058296635,0.00025590704,0.00029675663,0.00010784796],"domain_scores_gemma":[0.98805493,0.009120626,0.00056732417,0.0006450162,0.0013643518,0.0002477761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001983186,0.0010892121,0.0013771771,0.0018134578,0.0006710837,0.0021851177,0.0027787439,0.0020027298,0.0024632975],"category_scores_gemma":[0.01522098,0.0009694809,0.0012488104,0.001541457,0.0017517179,0.0040373337,0.002089172,0.0023810577,0.00033684153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027517902,0.00026489134,0.004603148,0.00038729006,0.00031592636,0.00022087965,0.00052755827,0.6831632,0.0036783414,0.08900231,0.0030553672,0.21450591],"study_design_scores_gemma":[0.000009016416,0.000029691288,0.00046674418,0.000023746576,0.00002726396,0.000040261868,0.000032240845,0.9584102,0.0006448546,0.03989072,0.00040508612,0.000020106077],"about_ca_topic_score_codex":0.008121797,"about_ca_topic_score_gemma":0.0042659473,"teacher_disagreement_score":0.008121797,"about_ca_system_score_codex":0.00088597706,"about_ca_system_score_gemma":0.0007794697,"threshold_uncertainty_score":0.016149044},"labels":[],"label_agreement":null},{"id":"W2031450031","doi":"10.1016/j.eswa.2014.11.067","title":"A new model to quantify the impact of a topic in a location over time with Social Media","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Badan Riset dan Inovasi Nasional; McGill University","keywords":"Computer science; Social media; Data science; Set (abstract data type); Component (thermodynamics); Doors; Social network (sociolinguistics); Mobile device; World Wide Web","score_opus":0.014825304050287804,"score_gpt":0.29949969309602126,"score_spread":0.28467438904573344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031450031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054802895,0.0006684736,0.9350263,0.0020364947,0.00026991343,0.00012413335,0.0012451023,0.0002916004,0.005535149],"genre_scores_gemma":[0.88762224,0.0013268489,0.083168514,0.00049053493,0.0006926511,0.00064085535,0.0012331189,0.00015379046,0.024671506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990339,0.00029423644,0.000038271006,0.0003257158,0.00018560498,0.00012236543],"domain_scores_gemma":[0.99511623,0.0034593379,0.0005341817,0.00020553984,0.00047008664,0.00021455102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021632018,0.0010677243,0.0010036998,0.0023097543,0.00059674215,0.0020303251,0.0025534397,0.002748933,0.0045912126],"category_scores_gemma":[0.0091345105,0.00060389756,0.000942748,0.0021477698,0.0011351482,0.005331682,0.0012636062,0.0017699029,0.0010971157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001394244,0.00019604641,0.006601434,0.00015963074,0.00016770787,0.00023404729,0.00029323506,0.8922202,0.0024984074,0.06361211,0.005090771,0.028786993],"study_design_scores_gemma":[0.000007956797,0.00001928878,0.0004887243,0.000007394762,0.000028865456,0.000038326863,0.000020624431,0.98774636,0.00011979241,0.010711429,0.00080215326,0.000009055881],"about_ca_topic_score_codex":0.010340267,"about_ca_topic_score_gemma":0.0109190205,"teacher_disagreement_score":0.010340267,"about_ca_system_score_codex":0.0016150222,"about_ca_system_score_gemma":0.00094213604,"threshold_uncertainty_score":0.020560145},"labels":[],"label_agreement":null},{"id":"W2031503490","doi":"10.1016/j.eswa.2012.08.044","title":"Erratum to “Multi-attribute decision making for green electrical discharge machining” [Expert Syst. Appl. 38 (7) (2011) 8370–8374]","year":2012,"lang":"en","type":"erratum","venue":"Expert Systems with Applications","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Electrical discharge machining; Computer science; Machining; Artificial intelligence; Machine learning; Mechanical engineering; Engineering","score_opus":0.017158827897341987,"score_gpt":0.2962299665778663,"score_spread":0.27907113868052436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031503490","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005675456,0.0022985665,0.0036488927,0.056435533,0.9227115,0.00010138738,0.0021789242,0.0004924881,0.0115652615],"genre_scores_gemma":[0.023466155,0.016014853,0.023251368,0.12857792,0.21313715,0.0005811556,0.012462425,0.0017340208,0.580775],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961288,0.0007599682,0.0007290381,0.0003576099,0.0017714273,0.00025313688],"domain_scores_gemma":[0.9800673,0.005136909,0.0006958169,0.00088763615,0.01272944,0.0004828372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037086848,0.0022366012,0.0023236831,0.0029062224,0.0036098128,0.002732571,0.003106779,0.0053217453,0.057209764],"category_scores_gemma":[0.031761393,0.0008939208,0.0016450344,0.0027425168,0.001686513,0.0023078674,0.0018999929,0.0055681,0.03317636],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005309621,0.00001556511,0.000056685043,0.00012011239,0.00000961026,0.00020247416,0.000023978233,0.00013165234,0.000076029835,0.0008466524,0.99079376,0.0076703425],"study_design_scores_gemma":[0.000072587674,0.00007906551,0.0009021874,0.00046029044,0.000048125723,0.00043053247,0.00012855243,0.0011406039,0.0008167687,0.0021385578,0.99371755,0.00006517424],"about_ca_topic_score_codex":0.016447945,"about_ca_topic_score_gemma":0.02292662,"teacher_disagreement_score":0.057209764,"about_ca_system_score_codex":0.003183517,"about_ca_system_score_gemma":0.0047971793,"threshold_uncertainty_score":0.19138569},"labels":[],"label_agreement":null},{"id":"W2032920196","doi":"10.1016/j.eswa.2011.09.154","title":"Evolutionary feature selection via structure retention","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Granular computing; Curse of dimensionality; Cluster analysis; Computer science; Particle swarm optimization; Dimensionality reduction; Granulation; Fuzzy set; Artificial intelligence; Mathematics; Pattern recognition (psychology); Feature selection; Feature (linguistics); Fuzzy logic; Reduction (mathematics); Algorithm; Data mining; Rough set","score_opus":0.021056378203984503,"score_gpt":0.2632772240430977,"score_spread":0.2422208458391132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032920196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14763042,0.00027397773,0.84738654,0.00031917763,0.0000906877,0.0001365839,0.00006572745,0.0008331132,0.0032637706],"genre_scores_gemma":[0.7880882,0.00010087049,0.20646939,0.00023932822,0.00007870395,0.00016957874,0.00021584707,0.00019441277,0.0044436497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994808,0.00015162728,0.000032559168,0.0001273075,0.00013193206,0.000075910255],"domain_scores_gemma":[0.99797255,0.0010031136,0.000116611154,0.00036536317,0.00045722086,0.00008522867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013424949,0.0004783091,0.0010214006,0.0012233403,0.00065195194,0.0006918764,0.0014146132,0.0011301021,0.003253869],"category_scores_gemma":[0.004441292,0.00041066686,0.0009269892,0.0009769496,0.0006081635,0.001149381,0.0012641901,0.00087762205,0.00056776847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039013365,0.0004005087,0.0049819145,0.00011796283,0.00019646013,0.0003143013,0.00022856965,0.27085584,0.033839043,0.020213202,0.003517244,0.6649449],"study_design_scores_gemma":[0.000056925222,0.00016816676,0.00079630833,0.000008754259,0.000053116848,0.000121621626,0.000026342661,0.9856908,0.004820104,0.0074360846,0.0008073806,0.000014385568],"about_ca_topic_score_codex":0.0008738563,"about_ca_topic_score_gemma":0.0013520714,"teacher_disagreement_score":0.003253869,"about_ca_system_score_codex":0.00038559444,"about_ca_system_score_gemma":0.00056802446,"threshold_uncertainty_score":0.010885298},"labels":[],"label_agreement":null},{"id":"W2040803101","doi":"10.1016/j.eswa.2013.07.110","title":"Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Óbudai Egyetem; Autoritatea Natională pentru Cercetare Stiintifică","keywords":"Control theory (sociology); Acceleration; Sensitivity (control systems); Parametric statistics; Position (finance); Computer science; Fuzzy logic; Nonlinear system; Particle swarm optimization; Algorithm; Mathematics; Mathematical optimization; Control (management); Artificial intelligence","score_opus":0.021281586379741476,"score_gpt":0.23765061654522304,"score_spread":0.21636903016548156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040803101","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008301077,0.00017519254,0.98909456,0.00004462911,0.0000638896,0.000034351593,0.00000874683,0.0002166052,0.0020609247],"genre_scores_gemma":[0.51705295,0.00016606647,0.47765705,0.00015379042,0.00006594564,0.00027677653,0.0000722889,0.000092889146,0.004462195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997303,0.00006993232,0.000017745177,0.000046493715,0.000107392734,0.000028095228],"domain_scores_gemma":[0.99967086,0.00013627701,0.000025614336,0.000026395255,0.0001230565,0.000017792974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006919961,0.00059009006,0.00087185815,0.00063167437,0.0005357292,0.00078546605,0.0010233773,0.0010668356,0.0028049094],"category_scores_gemma":[0.0014929475,0.00036860793,0.0004054329,0.0006088943,0.00040112544,0.00056796253,0.0009256317,0.00061445555,0.0004327315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039925973,0.0001541468,0.0007354731,0.00019742777,0.00013074958,0.00013956342,0.00014408155,0.555109,0.01893207,0.026761381,0.003932921,0.39336395],"study_design_scores_gemma":[0.00003235927,0.000031707368,0.000070840484,0.0000040556292,0.0000057872244,0.000025255078,0.000003469582,0.9974462,0.0007759714,0.0009833178,0.00061574,0.0000053774393],"about_ca_topic_score_codex":0.0021608933,"about_ca_topic_score_gemma":0.0023177534,"teacher_disagreement_score":0.0028049094,"about_ca_system_score_codex":0.00044685876,"about_ca_system_score_gemma":0.00078023033,"threshold_uncertainty_score":0.009383321},"labels":[],"label_agreement":null},{"id":"W2042020602","doi":"10.1016/j.eswa.2013.12.043","title":"Multi-objective PSO algorithm for mining numerical association rules without a priori discretization","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Discretization; Computer science; Particle swarm optimization; A priori and a posteriori; Association rule learning; Algorithm; Data mining; Apriori algorithm; Numerical analysis; Measure (data warehouse); Mathematical optimization; Machine learning; Mathematics","score_opus":0.013050005289703705,"score_gpt":0.27399422278141866,"score_spread":0.260944217491715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042020602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011490691,0.00025270466,0.9867797,0.000109327724,0.00004337507,0.000042198662,0.00003770328,0.00020920785,0.0010350837],"genre_scores_gemma":[0.26269725,0.0002578059,0.7345892,0.00015390168,0.000072378265,0.0002496668,0.00021881267,0.00005056534,0.0017103897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993579,0.00017562037,0.000068875466,0.0001303075,0.00021999764,0.000047354388],"domain_scores_gemma":[0.9982003,0.0012045036,0.00013067121,0.00013101487,0.0002766555,0.000056805366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014565511,0.0006061878,0.0014027896,0.0011250875,0.00044623905,0.0009656965,0.0012542277,0.001079194,0.001973173],"category_scores_gemma":[0.0052453266,0.00059552764,0.00094624044,0.0012856164,0.00054499006,0.00090804405,0.0009455384,0.0011111863,0.0004324076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017422323,0.00015331058,0.0026134595,0.00019811919,0.00020768416,0.0001369535,0.000098614655,0.7346649,0.0027338325,0.009745214,0.0020744218,0.2471993],"study_design_scores_gemma":[0.000017164783,0.000021247874,0.00017189987,0.0000072792986,0.0000095160585,0.000025958814,0.0000046027094,0.9978897,0.00018237044,0.0014779551,0.00018901053,0.0000032971282],"about_ca_topic_score_codex":0.0032121742,"about_ca_topic_score_gemma":0.0031383962,"teacher_disagreement_score":0.0032121742,"about_ca_system_score_codex":0.00037954934,"about_ca_system_score_gemma":0.0010498461,"threshold_uncertainty_score":0.0077030063},"labels":[],"label_agreement":null},{"id":"W2042830380","doi":"10.1016/j.eswa.2011.08.153","title":"Performance based earthquake evaluation of reinforced concrete buildings using design of experiments","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Reinforced concrete; Construction engineering; Structural engineering; Engineering","score_opus":0.06368206143791458,"score_gpt":0.26894112052549435,"score_spread":0.20525905908757977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042830380","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98340476,0.00006267249,0.0153835565,0.00002015291,0.000010627841,0.000054238415,0.00007271239,0.00005365025,0.00093752093],"genre_scores_gemma":[0.9945701,0.000041643863,0.0049588764,0.0000031646725,0.0000030568513,0.000039764604,0.000055466066,0.000007493255,0.00032035375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989066,0.0006296606,0.00006265948,0.000082266124,0.00021673575,0.00010207893],"domain_scores_gemma":[0.9957504,0.0029491712,0.00036817123,0.0002902485,0.00050860783,0.00013326337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022987197,0.001081313,0.0007250271,0.0007632192,0.0003841948,0.00043627992,0.000732281,0.00097429997,0.0008671411],"category_scores_gemma":[0.0031527786,0.0003404123,0.00062261283,0.00034525467,0.00059837924,0.0005349143,0.00051307253,0.00041229508,0.00011547771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021116303,0.0014792386,0.004598892,0.00023174817,0.00008937909,0.00011087408,0.00023299381,0.88363427,0.08611697,0.0008242886,0.00014860397,0.020421013],"study_design_scores_gemma":[0.0001385556,0.009523609,0.009092142,0.0000161202,0.00011666161,0.000050183087,0.00015837542,0.8838609,0.096101545,0.0004716345,0.00042166474,0.000048569917],"about_ca_topic_score_codex":0.0014780085,"about_ca_topic_score_gemma":0.0016245138,"teacher_disagreement_score":0.0022987197,"about_ca_system_score_codex":0.0007977521,"about_ca_system_score_gemma":0.00033302122,"threshold_uncertainty_score":0.012156904},"labels":[],"label_agreement":null},{"id":"W2045584434","doi":"10.1016/j.eswa.2012.12.084","title":"Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Sentiment analysis; Unsupervised learning; Polarity (international relations); Supervised learning; Natural language processing; Pattern recognition (psychology); Artificial neural network","score_opus":0.05847855461083074,"score_gpt":0.2670254764919242,"score_spread":0.20854692188109347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045584434","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91353935,0.0035054036,0.0557332,0.00065915857,0.00050587277,0.00043850671,0.0045419894,0.0010274043,0.020049134],"genre_scores_gemma":[0.95399004,0.001091492,0.032935116,0.00011513527,0.00045018425,0.00023592345,0.0049456772,0.00012310276,0.0061132144],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984249,0.0006694747,0.00013909081,0.00022035558,0.00042506136,0.00012118037],"domain_scores_gemma":[0.9927845,0.0016462826,0.0007368058,0.00022361241,0.004409327,0.00019947063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018111074,0.0006298225,0.0005755359,0.002768425,0.00042043193,0.001167509,0.00025399725,0.00036162333,0.0010681984],"category_scores_gemma":[0.0073311883,0.00013538968,0.00048249267,0.0013053152,0.00015038461,0.0005745712,0.00038319224,0.00030835276,0.0010106156],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001513151,0.00045192163,0.26203334,0.0017713673,0.00048559144,0.0008073703,0.002143784,0.0020181024,0.09973361,0.0011761029,0.030879684,0.596986],"study_design_scores_gemma":[0.00016992935,0.0008119774,0.7358437,0.00050283334,0.00096027215,0.0012526507,0.0047635348,0.12652245,0.061867677,0.0025417574,0.0646139,0.00014930684],"about_ca_topic_score_codex":0.0030000315,"about_ca_topic_score_gemma":0.005756351,"teacher_disagreement_score":0.0030000315,"about_ca_system_score_codex":0.00039432404,"about_ca_system_score_gemma":0.00074222207,"threshold_uncertainty_score":0.009578109},"labels":[],"label_agreement":null},{"id":"W2046459100","doi":"10.1016/j.eswa.2012.01.148","title":"Integrated classifier hyperplane placement and feature selection","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Hyperplane; Feature selection; Computer science; Process (computing); Classifier (UML); Linear programming; Mathematics; Mathematical optimization; Algorithm; Pattern recognition (psychology); Artificial intelligence; Combinatorics","score_opus":0.015432903261025154,"score_gpt":0.24420810583319041,"score_spread":0.22877520257216527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046459100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007390769,0.00014705428,0.98877037,0.000058286834,0.000105734536,0.00008429007,0.00009264131,0.002383959,0.0009669493],"genre_scores_gemma":[0.19991624,0.0001622468,0.7875242,0.00008471329,0.00013526177,0.00026907658,0.0010538095,0.00049616233,0.010358213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813855,0.00025138896,0.0001130627,0.00046116972,0.00080325635,0.00023259835],"domain_scores_gemma":[0.9984388,0.00019871286,0.000077569166,0.00028687608,0.0009358715,0.00006221307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001531388,0.0013453997,0.002225977,0.0015966773,0.00088383676,0.0023539392,0.0021664598,0.0014528104,0.010407396],"category_scores_gemma":[0.0035090416,0.0009067363,0.0012375831,0.0017001291,0.00035989715,0.0015490693,0.0015241033,0.001453541,0.006063755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046268996,0.00025183885,0.0011857668,0.0000859523,0.00013474487,0.000062430445,0.00005241007,0.034563493,0.02426228,0.0024434272,0.008266075,0.9282288],"study_design_scores_gemma":[0.000071955816,0.000253385,0.003200749,0.00002089069,0.000100318925,0.00019152038,0.00006155147,0.93540007,0.0488617,0.0039975457,0.0077987695,0.0000414199],"about_ca_topic_score_codex":0.0038868317,"about_ca_topic_score_gemma":0.0053173,"teacher_disagreement_score":0.010407396,"about_ca_system_score_codex":0.00075232086,"about_ca_system_score_gemma":0.0016780755,"threshold_uncertainty_score":0.034816206},"labels":[],"label_agreement":null},{"id":"W2047930155","doi":"10.1016/j.eswa.2012.11.004","title":"Evaluation of the OQuaRE framework for ontology quality","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Ontology; Computer science; Upper ontology; Process ontology; IDEF5; Ontology components; Quality (philosophy); Completeness (order theory); Ontology alignment; Ontology-based data integration; Reuse; Software engineering; Data mining; Information retrieval; Semantic Web","score_opus":0.10878307838211021,"score_gpt":0.38987472621091995,"score_spread":0.28109164782880974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047930155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3803307,0.0027044094,0.5712275,0.0037088254,0.00038836864,0.0021936356,0.0032654633,0.011569121,0.024611928],"genre_scores_gemma":[0.5229828,0.00040300493,0.47048524,0.00030384364,0.00003886151,0.0002844298,0.002845674,0.0009060911,0.0017500509],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9619105,0.014348464,0.0029969902,0.0023128444,0.017180227,0.0012510114],"domain_scores_gemma":[0.8981707,0.054416567,0.0039545503,0.017460711,0.024221126,0.0017763785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.051547557,0.0010593806,0.0016569669,0.00602802,0.0020606243,0.006582369,0.0033248034,0.0023402127,0.003667085],"category_scores_gemma":[0.12041341,0.00076005684,0.0017677064,0.0037018887,0.0021907173,0.0077365595,0.0070707146,0.0019365988,0.00056775304],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007805435,0.0023074625,0.04252561,0.0023036543,0.0016075735,0.00032127363,0.002268212,0.14141387,0.009662778,0.14272921,0.019707855,0.6273471],"study_design_scores_gemma":[0.00085865956,0.0015377831,0.013991362,0.00058139773,0.00053225004,0.00031202563,0.0020891023,0.90827,0.018991463,0.033046197,0.019570427,0.00021943447],"about_ca_topic_score_codex":0.03617299,"about_ca_topic_score_gemma":0.04014248,"teacher_disagreement_score":0.051547557,"about_ca_system_score_codex":0.0049803625,"about_ca_system_score_gemma":0.009377518,"threshold_uncertainty_score":0.2726128},"labels":[],"label_agreement":null},{"id":"W2048367757","doi":"10.1016/j.eswa.2014.11.050","title":"Segmentation of Terahertz imaging using k-means clustering based on ranked set sampling","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Terahertz technology and applications","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"University of California, Irvine","keywords":"Cluster analysis; Computer science; Initialization; Sampling (signal processing); Segmentation; Artificial intelligence; Pattern recognition (psychology); Sample (material); Fuzzy clustering; Set (abstract data type); Terahertz radiation; Data mining; Computer vision; Physics","score_opus":0.016816081277647662,"score_gpt":0.2624750950093901,"score_spread":0.24565901373174243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048367757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042111494,0.0003645287,0.95315546,0.00010155036,0.00002638857,0.00013879058,0.00026582216,0.0013298184,0.0025061623],"genre_scores_gemma":[0.25074103,0.00038819853,0.74441636,0.00006439996,0.000028289041,0.00021263756,0.0008855281,0.00051263586,0.002750934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917656,0.00012715276,0.00005093433,0.0001512689,0.00035785872,0.00013615207],"domain_scores_gemma":[0.99904555,0.00029565892,0.000096338554,0.00014029548,0.0003847546,0.000037376874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009253757,0.000852936,0.0014034563,0.003189959,0.000968058,0.002462448,0.0013252962,0.0012562817,0.0019710145],"category_scores_gemma":[0.0019175839,0.00070682314,0.0011126752,0.0025028023,0.00050684484,0.0010494046,0.0008400565,0.00086767005,0.0012594201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009000693,0.00030037697,0.0031799858,0.0006327303,0.00016198056,0.00037937236,0.00097491156,0.31021914,0.2678716,0.016218033,0.004638715,0.39452308],"study_design_scores_gemma":[0.000016731465,0.000060495586,0.0021200173,0.000024845718,0.000039653623,0.00015477212,0.00013689145,0.9296719,0.059311535,0.005745384,0.0026598051,0.00005805488],"about_ca_topic_score_codex":0.007341426,"about_ca_topic_score_gemma":0.009140766,"teacher_disagreement_score":0.007341426,"about_ca_system_score_codex":0.0013508636,"about_ca_system_score_gemma":0.0017632389,"threshold_uncertainty_score":0.014597356},"labels":[],"label_agreement":null},{"id":"W2048490690","doi":"10.1016/j.eswa.2014.02.002","title":"To compete or cooperate? This is the question in communities of autonomous services","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Coopetition; Computer science; Competition (biology); Context (archaeology); Order (exchange); Game theory; Shapley value; Mechanism (biology); Cooperative game theory; Operations research; Microeconomics; Business; Economics; Mathematics","score_opus":0.05254115642723251,"score_gpt":0.3656151281304478,"score_spread":0.31307397170321527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048490690","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2754771,0.0039637373,0.16714104,0.33278662,0.0015186584,0.0003321838,0.00049079955,0.00024947285,0.2180404],"genre_scores_gemma":[0.96133155,0.0010207013,0.017115258,0.0071568014,0.00051758083,0.00020883894,0.000087372915,0.00009480859,0.012467015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99381834,0.003302474,0.00014040768,0.0007789079,0.00072604214,0.0012338795],"domain_scores_gemma":[0.9804206,0.009367424,0.0013233189,0.001227081,0.0022215317,0.005440034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007332862,0.0004376839,0.0013232159,0.0008039319,0.005085752,0.0066165933,0.002469635,0.008435395,0.013022243],"category_scores_gemma":[0.028212793,0.00045423387,0.0007459568,0.0011085105,0.008805198,0.020464512,0.0050190515,0.0032264397,0.002216147],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009273396,0.00024312487,0.0041119927,0.00027346058,0.00011606169,0.0002239846,0.003390846,0.0029989795,0.00085287535,0.9227084,0.023337172,0.041650455],"study_design_scores_gemma":[0.000042702162,0.000046212837,0.0010181471,0.00008727181,0.000019044443,0.00018841363,0.00669342,0.0061209863,0.00014340917,0.96408105,0.021518443,0.000040895786],"about_ca_topic_score_codex":0.0053824815,"about_ca_topic_score_gemma":0.0066327914,"teacher_disagreement_score":0.013022243,"about_ca_system_score_codex":0.0014503374,"about_ca_system_score_gemma":0.0042048306,"threshold_uncertainty_score":0.043563724},"labels":[],"label_agreement":null},{"id":"W2049534631","doi":"10.1016/j.eswa.2009.03.010","title":"Designing a fuzzy system for controlling the armament fire in dynamic siege","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Military Defense Systems Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Siege; Battle; Adversary; Computer science; Component (thermodynamics); Fuzzy logic; Operations research; Resource allocation; Resource (disambiguation); Order (exchange); Computer security; Engineering; Business; Artificial intelligence","score_opus":0.006947324024968044,"score_gpt":0.22057525042888254,"score_spread":0.2136279264039145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049534631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08243144,0.000306976,0.90911984,0.00009523186,0.0001074528,0.00015603456,0.00004103292,0.0007991175,0.0069428138],"genre_scores_gemma":[0.9131662,0.00014387755,0.08394585,0.00005723211,0.000021261987,0.00013780785,0.00003581197,0.000021021913,0.0024708412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984026,0.000020405792,0.000011649757,0.00005181278,0.000044187535,0.000031628508],"domain_scores_gemma":[0.99983466,0.000045976394,0.000021048823,0.000014663665,0.000067254055,0.000016382357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041930104,0.00055739295,0.00060194696,0.00047346466,0.001098427,0.00080554385,0.0009269137,0.00094617106,0.0019554445],"category_scores_gemma":[0.0006074639,0.00031948675,0.00046042592,0.00027743544,0.00041627578,0.00040721524,0.000519164,0.0004956085,0.00036423624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068175956,0.00017732648,0.0017601224,0.0005404113,0.0001476881,0.00058941427,0.0006739341,0.55085385,0.19801138,0.009511743,0.0016158809,0.23543642],"study_design_scores_gemma":[0.000056397424,0.00025511128,0.0007816447,0.000036815865,0.00008225796,0.00010100827,0.00006969947,0.97470754,0.019469822,0.0013255716,0.0030828149,0.00003129099],"about_ca_topic_score_codex":0.006634818,"about_ca_topic_score_gemma":0.0066830656,"teacher_disagreement_score":0.006634818,"about_ca_system_score_codex":0.0005061373,"about_ca_system_score_gemma":0.00065536896,"threshold_uncertainty_score":0.013192415},"labels":[],"label_agreement":null},{"id":"W2049557239","doi":"10.1016/j.eswa.2015.04.054","title":"Evolutionary fine-tuning of automated semantic annotation systems","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Annotation; Task (project management); Process (computing); Artificial intelligence; Natural language processing; Domain (mathematical analysis); Information retrieval; Programming language","score_opus":0.030865148638467458,"score_gpt":0.2729550098003744,"score_spread":0.24208986116190695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049557239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19088884,0.00078521395,0.79340655,0.000800975,0.0002530551,0.00034424124,0.00036431872,0.0060957153,0.0070610684],"genre_scores_gemma":[0.6925906,0.00019956013,0.30103222,0.00024843265,0.00009721352,0.00024339368,0.0011390548,0.0011870502,0.0032625303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995337,0.0020936285,0.00025874036,0.0011875356,0.00070471765,0.000418383],"domain_scores_gemma":[0.983567,0.010565107,0.00047858525,0.0024424475,0.002570083,0.00037671314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005662939,0.0010375615,0.0016033143,0.0025801014,0.0013236874,0.0024435252,0.0028377818,0.0022018505,0.0041840873],"category_scores_gemma":[0.026621241,0.0009193548,0.0011892788,0.0016525058,0.0010255651,0.0030570782,0.0030166267,0.0019752795,0.0014342366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064550724,0.0005697532,0.009303406,0.0003657942,0.0002658873,0.00025778793,0.0009772631,0.31638095,0.028713988,0.018710546,0.008748446,0.61506075],"study_design_scores_gemma":[0.00003455079,0.00004794835,0.00064553536,0.000016380798,0.000057750163,0.0000521402,0.00016074454,0.98166496,0.003961576,0.011568923,0.0017767196,0.000012783135],"about_ca_topic_score_codex":0.0046572424,"about_ca_topic_score_gemma":0.007932248,"teacher_disagreement_score":0.005662939,"about_ca_system_score_codex":0.0018244784,"about_ca_system_score_gemma":0.0023032024,"threshold_uncertainty_score":0.02994883},"labels":[],"label_agreement":null},{"id":"W2049920566","doi":"10.1016/j.eswa.2008.02.014","title":"Building and evaluating a location-based service recommendation system with a preference adjustment mechanism","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Recommender system; Personalization; Preference; Service (business); Measure (data warehouse); Term (time); Location-based service; Mechanism (biology); Index (typography); Variation (astronomy); Information retrieval; Data mining; World Wide Web; Telecommunications; Statistics","score_opus":0.05867532360640439,"score_gpt":0.2870652731271769,"score_spread":0.2283899495207725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049920566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1348049,0.00033450816,0.8581367,0.00037284088,0.00009807033,0.00041214778,0.00020103199,0.0031551654,0.0024846792],"genre_scores_gemma":[0.6476657,0.000116892734,0.34887996,0.00011209285,0.00004916689,0.00016806282,0.00026316976,0.000065481894,0.0026794616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980392,0.00043119045,0.00020281585,0.00046894688,0.0006925722,0.00016529905],"domain_scores_gemma":[0.9971424,0.0010178812,0.0002094175,0.0003459154,0.0010864763,0.0001978841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002751135,0.0007493183,0.0021476694,0.0013332582,0.00084852177,0.0018993564,0.0026027297,0.0029009294,0.0024815449],"category_scores_gemma":[0.0068606352,0.00073961593,0.001135629,0.0012984384,0.00042612295,0.0022187857,0.0009882047,0.0011419697,0.0011522527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012126939,0.0009681827,0.014007642,0.0003320822,0.00071746146,0.0006078544,0.00017611533,0.48803902,0.041151673,0.005437131,0.0052761384,0.44207403],"study_design_scores_gemma":[0.00002930602,0.00007863675,0.00060956005,0.0000030966833,0.000053542615,0.000048776732,0.0000139794065,0.9956872,0.002836904,0.00036820385,0.00025685216,0.000013842193],"about_ca_topic_score_codex":0.022930915,"about_ca_topic_score_gemma":0.020726373,"teacher_disagreement_score":0.022930915,"about_ca_system_score_codex":0.0014707113,"about_ca_system_score_gemma":0.001877696,"threshold_uncertainty_score":0.04559487},"labels":[],"label_agreement":null},{"id":"W2051980844","doi":"10.1016/j.eswa.2009.02.068","title":"Applications of web mining for marketing of online bookstores","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Computer science; Association rule learning; Database transaction; Field (mathematics); Web mining; Key (lock); Transaction data; Data mining; Data science; Information retrieval; Order (exchange); World Wide Web; Web page; Database; Business","score_opus":0.014802555554104969,"score_gpt":0.278200047588135,"score_spread":0.26339749203403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051980844","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.590428,0.011923628,0.27529374,0.008187526,0.00094350596,0.0010872304,0.0067299535,0.009607017,0.095799334],"genre_scores_gemma":[0.8205243,0.0027194503,0.16371126,0.00033403217,0.0005196278,0.00015976984,0.0019074872,0.0003559408,0.009768155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983241,0.00065632496,0.00011204565,0.0001690389,0.0006778223,0.0000606212],"domain_scores_gemma":[0.9892595,0.0077274204,0.0005718188,0.00077432994,0.001382149,0.0002847805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020493749,0.000711289,0.0006672728,0.006946298,0.0009819687,0.0029180187,0.0008544567,0.00096855575,0.0067864694],"category_scores_gemma":[0.013895588,0.0003237663,0.00079339184,0.0072990386,0.00027254462,0.0018687894,0.00064005156,0.0007993002,0.0020062611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045630726,0.0012049987,0.08167325,0.0004496994,0.00037308084,0.0004148271,0.00053566374,0.015831722,0.0034626885,0.009344476,0.01864003,0.86761326],"study_design_scores_gemma":[0.00014950316,0.00035190373,0.08861884,0.00030572788,0.0004895377,0.0014163264,0.0015028374,0.76645124,0.017321842,0.06894607,0.05431478,0.00013134602],"about_ca_topic_score_codex":0.004605592,"about_ca_topic_score_gemma":0.006970559,"teacher_disagreement_score":0.006946298,"about_ca_system_score_codex":0.00079547026,"about_ca_system_score_gemma":0.0008173364,"threshold_uncertainty_score":0.022702992},"labels":[],"label_agreement":null},{"id":"W2052085418","doi":"10.1016/j.eswa.2013.09.030","title":"Bayesian learning of finite generalized inverted Dirichlet mixtures: Application to object classification and forgery detection","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Dirichlet distribution; Markov chain Monte Carlo; Gibbs sampling; Dirichlet process; Mixture model; Hierarchical Dirichlet process; Bayesian inference; Computer science; Latent Dirichlet allocation; Bayesian probability; Artificial intelligence; Mathematics; Pattern recognition (psychology); Algorithm; Topic model","score_opus":0.013788590419146152,"score_gpt":0.2535862511080923,"score_spread":0.23979766068894615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052085418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048060585,0.00031059692,0.99417144,0.0001471581,0.0000234661,0.000020493102,0.0000322746,0.0001664579,0.00032201657],"genre_scores_gemma":[0.25374308,0.0011723294,0.739489,0.00028152746,0.00025491038,0.00025155465,0.0004888152,0.00027696553,0.0040417775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99683666,0.0015718337,0.00015562667,0.0005882261,0.00065871445,0.00018885735],"domain_scores_gemma":[0.98343116,0.0136072915,0.0005790913,0.0009869867,0.0011633717,0.00023211539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00800566,0.001164813,0.0030618794,0.002465105,0.001242644,0.0031458924,0.004312558,0.003189119,0.0022052515],"category_scores_gemma":[0.035708833,0.0015443747,0.0023657784,0.0024538834,0.0028624388,0.0045272326,0.003727438,0.004182649,0.00075395464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004001465,0.00016177482,0.0015255327,0.00028230582,0.00018593975,0.00012306486,0.00048590818,0.6405325,0.002803204,0.11175812,0.0029682287,0.23877329],"study_design_scores_gemma":[0.0000074943214,0.000009390882,0.00011392508,0.000012397052,0.000011955796,0.000027957229,0.000013500887,0.95852804,0.00045308925,0.040398218,0.00040795846,0.000016054624],"about_ca_topic_score_codex":0.008142743,"about_ca_topic_score_gemma":0.008862961,"teacher_disagreement_score":0.008142743,"about_ca_system_score_codex":0.0019459326,"about_ca_system_score_gemma":0.0017228846,"threshold_uncertainty_score":0.04233849},"labels":[],"label_agreement":null},{"id":"W2054219113","doi":"10.1016/j.eswa.2011.04.005","title":"Application of fuzzy TOPSIS in evaluating sustainable transportation systems","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":318,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Sustainability; TOPSIS; Computer science; Multiple-criteria decision analysis; Fuzzy logic; Risk analysis (engineering); Quality (philosophy); Work (physics); Process (computing); Sustainable transport; Operations research; Business; Engineering; Artificial intelligence","score_opus":0.16140854590010217,"score_gpt":0.4177091967761213,"score_spread":0.2563006508760191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054219113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2216764,0.0019046024,0.75854313,0.0003135668,0.00017223894,0.0004041381,0.00016780308,0.00027018055,0.016547836],"genre_scores_gemma":[0.84504724,0.0006337521,0.15299045,0.000020790416,0.000022564125,0.000094377596,0.000046148434,0.000013129745,0.0011315969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945844,0.0028799262,0.00029116904,0.00016019194,0.0019184189,0.00016601961],"domain_scores_gemma":[0.9961345,0.0024967364,0.00017273222,0.000113344315,0.0010039265,0.00007869602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054295515,0.00093831355,0.0014257262,0.0042534056,0.0011368684,0.0021569242,0.0006960592,0.00075178733,0.0018192097],"category_scores_gemma":[0.009519253,0.00034294854,0.0010503263,0.0041707195,0.000604908,0.0011487298,0.00076012063,0.0007229187,0.00018750005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007525119,0.00031646978,0.007844256,0.0010513479,0.00088598003,0.0005451966,0.00085546524,0.4290133,0.01671256,0.0209415,0.0011617469,0.5199196],"study_design_scores_gemma":[0.000049668568,0.0006461125,0.004418212,0.00008340606,0.00030397353,0.00017127005,0.00046922648,0.9714087,0.006296628,0.014513976,0.0015569596,0.000081826394],"about_ca_topic_score_codex":0.008718177,"about_ca_topic_score_gemma":0.009568144,"teacher_disagreement_score":0.008718177,"about_ca_system_score_codex":0.0014723893,"about_ca_system_score_gemma":0.0016144924,"threshold_uncertainty_score":0.028714538},"labels":[],"label_agreement":null},{"id":"W2057374520","doi":"10.1016/j.eswa.2011.07.089","title":"Global optimization of an optical chaotic system by Chaotic Multi Swarm Particle Swarm Optimization","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Particle swarm optimization; Chaotic; Swarm behaviour; Speedup; Multi-swarm optimization; Mathematical optimization; Convergence (economics); Computer science; Nonlinear system; Mathematics; Artificial intelligence; Physics","score_opus":0.022615942383770793,"score_gpt":0.24484218852661233,"score_spread":0.22222624614284153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057374520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1481625,0.00051726674,0.83220196,0.00080208684,0.00013048544,0.00009704303,0.00008361013,0.0001588769,0.0178462],"genre_scores_gemma":[0.94015306,0.00016669877,0.054452278,0.00007237636,0.000037073623,0.00013154125,0.00005950236,0.0000640438,0.0048634354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998412,0.00005938246,0.0000069143416,0.000031378808,0.000039598952,0.00002150885],"domain_scores_gemma":[0.9995511,0.00023873681,0.0000655903,0.000023558412,0.00008509388,0.00003596647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007566444,0.0006929018,0.00091002486,0.00053114985,0.00064042775,0.0011258052,0.0005764293,0.0012732774,0.0013349393],"category_scores_gemma":[0.0016618636,0.00048316558,0.00050745223,0.00044976623,0.0012736025,0.000826486,0.0009680732,0.0005477014,0.00012591828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028477196,0.000008591111,0.00014346182,0.000017305516,0.000012474161,0.000020625203,0.000020437821,0.99381554,0.00045109086,0.002932126,0.0001475611,0.0024024097],"study_design_scores_gemma":[0.000005465189,0.000008692234,0.00003254436,0.000001294284,0.0000019763706,0.000002065319,0.000004023403,0.9989986,0.000071281436,0.0008106354,0.00006140785,0.0000020581579],"about_ca_topic_score_codex":0.006307027,"about_ca_topic_score_gemma":0.004032338,"teacher_disagreement_score":0.006307027,"about_ca_system_score_codex":0.00085329404,"about_ca_system_score_gemma":0.0008925786,"threshold_uncertainty_score":0.012540638},"labels":[],"label_agreement":null},{"id":"W2058024053","doi":"10.1016/s0957-4174(00)00040-3","title":"Intelligent system architecture for process operation support","year":2000,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"CHA University; University of Alberta","keywords":"Computer science; Process (computing); Intelligent decision support system; Architecture; Expert system; Interface (matter); Embedded system; Software engineering; Human–computer interaction; Operating system; Artificial intelligence","score_opus":0.006916588422571384,"score_gpt":0.2364095300437993,"score_spread":0.2294929416212279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058024053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0155011155,0.0005228618,0.96402764,0.0003951489,0.000129351,0.000110511326,0.000097700264,0.006883995,0.012331646],"genre_scores_gemma":[0.54338807,0.00085492025,0.43423983,0.0003190767,0.00011394849,0.00025005837,0.00061497366,0.00026289924,0.019956226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996574,0.00006630775,0.000029328688,0.00007228703,0.00012005316,0.000054642707],"domain_scores_gemma":[0.9997216,0.000046818386,0.000019370596,0.000066393004,0.00012430063,0.000021426722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056477997,0.0004300999,0.00044529673,0.0004563616,0.0004326872,0.0016004652,0.0013550067,0.00093167817,0.004954594],"category_scores_gemma":[0.0007691951,0.00028147237,0.0003491614,0.00035823803,0.00047797707,0.001379373,0.0006375098,0.00083772826,0.0018343813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006207985,0.0003214247,0.001627337,0.0004919456,0.00018740386,0.0004353848,0.0006325635,0.23256421,0.07782809,0.2419353,0.02087658,0.4224789],"study_design_scores_gemma":[0.00006858103,0.00022632162,0.0005709331,0.000054036842,0.00014578112,0.0001527336,0.0000519809,0.86955893,0.026807481,0.050034773,0.052289404,0.00003900172],"about_ca_topic_score_codex":0.0029223477,"about_ca_topic_score_gemma":0.0032351958,"teacher_disagreement_score":0.004954594,"about_ca_system_score_codex":0.0006569871,"about_ca_system_score_gemma":0.0010711107,"threshold_uncertainty_score":0.0165748},"labels":[],"label_agreement":null},{"id":"W2059051942","doi":"10.1016/j.eswa.2012.01.151","title":"Nonlinear inversion-based control with adaptive neural network compensation for uncertain MIMO systems","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Control theory (sociology); Nonlinear system; MIMO; Artificial neural network; Inversion (geology); Compensation (psychology); Adaptive control; Control (management); Artificial intelligence; Telecommunications","score_opus":0.020404236557200393,"score_gpt":0.236881731836792,"score_spread":0.21647749527959162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059051942","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03602559,0.00095956796,0.95493835,0.00026224775,0.00023466095,0.000052193125,0.000026213884,0.00022459087,0.0072766785],"genre_scores_gemma":[0.96259266,0.00034856822,0.033089466,0.000099879326,0.0000978223,0.00008402401,0.000041339354,0.000016477725,0.0036297247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981254,0.00003903499,0.000016268837,0.00003899514,0.00006681205,0.00002640091],"domain_scores_gemma":[0.9997464,0.00008595917,0.000043615324,0.000016897253,0.00009921648,0.0000079209885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005371274,0.00057056226,0.00047078804,0.00022650087,0.0003913924,0.0005785917,0.0005719782,0.0006702863,0.0012400256],"category_scores_gemma":[0.0010525495,0.0002586167,0.00031841607,0.00029773414,0.00050165196,0.0005438145,0.0006568951,0.00065584603,0.00018044715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002099495,0.0000889463,0.00036137318,0.0002185275,0.00007103601,0.000102905535,0.00012520397,0.8753293,0.017081575,0.0065312595,0.001171526,0.098708406],"study_design_scores_gemma":[0.000010814079,0.0000534506,0.00016625118,0.000006818423,0.000010077187,0.000013384,0.000004564333,0.9973821,0.0011819024,0.0007603611,0.0004032586,0.00000697828],"about_ca_topic_score_codex":0.0073742536,"about_ca_topic_score_gemma":0.0076446007,"teacher_disagreement_score":0.0073742536,"about_ca_system_score_codex":0.00030844068,"about_ca_system_score_gemma":0.0004584774,"threshold_uncertainty_score":0.014662683},"labels":[],"label_agreement":null},{"id":"W2063373779","doi":"10.1016/j.eswa.2011.06.018","title":"Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":127,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Dempster–Shafer theory; Domain (mathematical analysis); Component (thermodynamics); Evidential reasoning approach; Task (project management); Fuzzy logic; Fuzzy set; Set (abstract data type); Artificial intelligence; Risk analysis (engineering); Machine learning; Data mining; Natural language processing; Decision support system; Mathematics","score_opus":0.1018287816704744,"score_gpt":0.35755053417064053,"score_spread":0.25572175250016616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063373779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049768933,0.00033786567,0.9920501,0.00035686663,0.000031224274,0.000027070848,0.00001725852,0.00002477033,0.002177894],"genre_scores_gemma":[0.4212665,0.0010078908,0.5752121,0.00020615484,0.0002510971,0.00016684216,0.000059591446,0.000033158904,0.0017965754],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485415,0.0025444627,0.00036695317,0.00043175888,0.0016323415,0.00017042285],"domain_scores_gemma":[0.99378335,0.0044195084,0.0006399428,0.0002971776,0.0006945818,0.0001653984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009380397,0.001352921,0.002093685,0.0035989883,0.001283245,0.0049059107,0.0031439401,0.002199054,0.0024798645],"category_scores_gemma":[0.015187537,0.000693578,0.002565762,0.0018726136,0.002796114,0.0053703063,0.0026159014,0.0023331793,0.00036614126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009794454,0.00028282832,0.0009752339,0.00047243186,0.00044691982,0.00062744855,0.0008306645,0.44356126,0.0027327647,0.47117096,0.0012495994,0.07755191],"study_design_scores_gemma":[0.000020738084,0.000068180285,0.00021023731,0.000088225956,0.00011557105,0.000114363465,0.00011729366,0.63705885,0.00059733086,0.36053926,0.0010216715,0.00004835384],"about_ca_topic_score_codex":0.0012799803,"about_ca_topic_score_gemma":0.0014027768,"teacher_disagreement_score":0.009380397,"about_ca_system_score_codex":0.0014342217,"about_ca_system_score_gemma":0.0016470483,"threshold_uncertainty_score":0.049608886},"labels":[],"label_agreement":null},{"id":"W2064051298","doi":"10.1016/j.eswa.2011.11.071","title":"Evaluation of carsharing network’s growth strategies through discrete event simulation","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Benchmarking; Operations research; Discrete event simulation; Event (particle physics); Popularity; Market penetration; Decision support system; Business; Simulation; Marketing; Artificial intelligence","score_opus":0.037630796849611986,"score_gpt":0.31625735329542415,"score_spread":0.27862655644581213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064051298","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94823045,0.00016763463,0.043688305,0.00028139944,0.00004262359,0.00015855746,0.00034270252,0.00021075943,0.0068775937],"genre_scores_gemma":[0.99564606,0.000036704518,0.0036650565,0.000007947528,0.000001870755,0.000035741017,0.0000956534,0.000007696231,0.00050330244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994192,0.00024210577,0.00002140064,0.000085377134,0.00009434897,0.0001374921],"domain_scores_gemma":[0.99416524,0.004521553,0.00028853503,0.00018476212,0.0005991284,0.00024086057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023660262,0.0008689571,0.0007903234,0.0012551593,0.00046712565,0.0011918725,0.0011328835,0.0011824161,0.002145],"category_scores_gemma":[0.0061383797,0.0003405347,0.00055827823,0.0007688668,0.0005220158,0.0011020835,0.0005003824,0.00075087836,0.00016199947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006932411,0.000047100508,0.0005453133,0.000010191719,0.0000071842887,0.00001015735,0.000006761055,0.9970229,0.0002132675,0.00048372982,0.00005956436,0.0015244754],"study_design_scores_gemma":[0.000006996836,0.000036137026,0.00014070378,0.0000011841792,0.000004806393,0.0000019480442,0.000012427425,0.9993831,0.00026892088,0.00010960028,0.00003204936,0.0000020692728],"about_ca_topic_score_codex":0.029563747,"about_ca_topic_score_gemma":0.012349022,"teacher_disagreement_score":0.029563747,"about_ca_system_score_codex":0.002575683,"about_ca_system_score_gemma":0.0014898216,"threshold_uncertainty_score":0.058783352},"labels":[],"label_agreement":null},{"id":"W2068720048","doi":"10.1016/j.eswa.2013.01.002","title":"A two-stage approach for discriminating melanocytic skin lesions using standard cameras","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Artificial intelligence; Skin lesion; Melanoma; Computer science; Pattern recognition (psychology); Stage (stratigraphy); Melanoma diagnosis; Nevus; Melanin; Dermatology; Computation; Melanocytic nevus; Lesion; Medicine; Pathology; Biology; Algorithm","score_opus":0.03395439172344163,"score_gpt":0.30704404682150943,"score_spread":0.2730896550980678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068720048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05201309,0.00046996857,0.9425323,0.00011090797,0.00010245423,0.0003340996,0.0002110444,0.0011192947,0.0031068742],"genre_scores_gemma":[0.18463652,0.00047306324,0.80439514,0.00012141659,0.000051388568,0.0001748082,0.00040512902,0.000082793704,0.009659771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991737,0.00009805282,0.000053138327,0.00018855422,0.00038790854,0.000098562086],"domain_scores_gemma":[0.99922204,0.0001530749,0.00003796523,0.00010076525,0.00043259788,0.000053538482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068444264,0.0009850167,0.00080687326,0.0016216555,0.0004969496,0.0012334313,0.0011223236,0.0014434844,0.0041098027],"category_scores_gemma":[0.0011583549,0.0005936971,0.0009244089,0.00083926757,0.00025474324,0.0010338717,0.0009803342,0.0007320327,0.0017187897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067458686,0.00029257135,0.006477381,0.00026025024,0.00013664464,0.0002457662,0.00011695796,0.0036891608,0.26611793,0.001408824,0.0022951204,0.7182849],"study_design_scores_gemma":[0.00010544716,0.0013730873,0.036823586,0.000074205775,0.0004744903,0.0045457226,0.0003285962,0.6149331,0.3242448,0.0028754736,0.013986537,0.00023506228],"about_ca_topic_score_codex":0.0026581972,"about_ca_topic_score_gemma":0.009166093,"teacher_disagreement_score":0.0041098027,"about_ca_system_score_codex":0.00036280515,"about_ca_system_score_gemma":0.0009523618,"threshold_uncertainty_score":0.013748646},"labels":[],"label_agreement":null},{"id":"W2070150067","doi":"10.1016/j.eswa.2012.04.070","title":"On the analysis of reputation for agent-based web services","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Access Control and Trust","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Reputation; World Wide Web; Web service","score_opus":0.02438653167166386,"score_gpt":0.323104941179674,"score_spread":0.2987184095080101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070150067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11127179,0.002479598,0.8688526,0.002450323,0.00013418093,0.00014811226,0.00010400805,0.0002174601,0.014341946],"genre_scores_gemma":[0.96672004,0.00072663266,0.027958306,0.00009758139,0.00020660738,0.00004391548,0.00006505092,0.0000729237,0.00410881],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9938259,0.0029675064,0.0003029086,0.00078333914,0.0013237015,0.0007966913],"domain_scores_gemma":[0.94836414,0.03915484,0.0036623515,0.0033147924,0.0041934387,0.0013104676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008965845,0.00085316313,0.002156961,0.0027558317,0.0014521193,0.004589507,0.0026332564,0.0020638648,0.004512765],"category_scores_gemma":[0.051182486,0.00067834277,0.0018079068,0.0019863672,0.0040332037,0.008499911,0.0024541998,0.0024929494,0.00045392913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023180095,0.00017462314,0.0047334204,0.00020218907,0.00024027519,0.00041442926,0.0006691825,0.24853398,0.0023058797,0.7105456,0.002954553,0.028994028],"study_design_scores_gemma":[0.000011176311,0.000041007057,0.0009991094,0.000020457346,0.0000518059,0.0000886519,0.000082448925,0.86253273,0.00030982355,0.1350979,0.00073291844,0.000031912703],"about_ca_topic_score_codex":0.008573959,"about_ca_topic_score_gemma":0.0045095957,"teacher_disagreement_score":0.008965845,"about_ca_system_score_codex":0.0041852556,"about_ca_system_score_gemma":0.0020634641,"threshold_uncertainty_score":0.04741651},"labels":[],"label_agreement":null},{"id":"W2071851982","doi":"10.1016/j.eswa.2007.01.037","title":"An expert system to derive carryover effect for pharmaceutical sales detailing optimization","year":2007,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Expert system; Computer science; Artificial neural network; Control (management); Quarter (Canadian coin); Operations research; Industrial engineering; Machine learning; Artificial intelligence; Mathematics; Engineering","score_opus":0.02025073662905476,"score_gpt":0.2963400074163089,"score_spread":0.27608927078725415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071851982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06829835,0.00019849134,0.9229861,0.000049281196,0.00003795448,0.00012180998,0.0002544662,0.002955351,0.0050981846],"genre_scores_gemma":[0.69158083,0.00011696738,0.304543,0.000057084013,0.000024035797,0.00021181046,0.00032208345,0.00011153114,0.0030326939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984074,0.000035333465,0.000014481255,0.00003946329,0.000054242213,0.000015810032],"domain_scores_gemma":[0.9993549,0.00043106667,0.000025058194,0.000032726744,0.00013821918,0.000018127248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047765984,0.00052473764,0.00075838744,0.000562053,0.00030060948,0.0004335166,0.0004140966,0.0009435775,0.0038908843],"category_scores_gemma":[0.0017645041,0.0003479666,0.00042884776,0.0003084239,0.00011027996,0.0003389266,0.0002954077,0.00048815671,0.00031958605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000221249,0.00017593408,0.0008380589,0.00017162618,0.000059455415,0.00016132183,0.00004826238,0.75384754,0.01255529,0.0019238652,0.0020106593,0.22798674],"study_design_scores_gemma":[0.000017560911,0.00002632572,0.00027809438,0.0000033910399,0.00001759583,0.00000962197,0.000002330474,0.99731785,0.0016873941,0.00033792455,0.00029817456,0.0000037164943],"about_ca_topic_score_codex":0.008362435,"about_ca_topic_score_gemma":0.007965235,"teacher_disagreement_score":0.008362435,"about_ca_system_score_codex":0.00041554804,"about_ca_system_score_gemma":0.00077596016,"threshold_uncertainty_score":0.01662755},"labels":[],"label_agreement":null},{"id":"W2072723323","doi":"10.1016/j.eswa.2014.03.042","title":"An interval weighed fuzzy c-means clustering by genetically guided alternating optimization","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Fuzzy clustering; Weighting; Mathematics; Correlation clustering; Data mining; CURE data clustering algorithm; Fuzzy logic; FLAME clustering; k-medians clustering; Heuristic; Interval (graph theory); Partition (number theory); Computer science; Pattern recognition (psychology); Mathematical optimization; Algorithm; Artificial intelligence; Combinatorics","score_opus":0.016637616564232523,"score_gpt":0.30557719769351444,"score_spread":0.2889395811292819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072723323","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014705873,0.00009363945,0.9822489,0.000080953374,0.000062472274,0.000056183322,0.00002283103,0.00025140482,0.0024777085],"genre_scores_gemma":[0.20804134,0.00009135456,0.78806216,0.000090670874,0.00003212114,0.00021429021,0.000101261656,0.000120333796,0.003246389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993337,0.00014466853,0.00003275793,0.00018043739,0.00025337774,0.000055091943],"domain_scores_gemma":[0.9994267,0.0001552164,0.000045925743,0.000055754354,0.00028098957,0.000035510802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001013126,0.00092778844,0.0010648546,0.0009983977,0.0010730671,0.0010061868,0.0016436826,0.0016877948,0.0020077205],"category_scores_gemma":[0.0022068657,0.0005193561,0.0010312457,0.0010717324,0.00069905765,0.0008557621,0.0013155369,0.0009394461,0.00040813492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013701343,0.000081212995,0.0005653915,0.00013235846,0.000087293505,0.0000793255,0.00015482688,0.8128554,0.012518358,0.01736503,0.0026382718,0.15338542],"study_design_scores_gemma":[0.000008080578,0.000019610641,0.00006722358,0.0000046889963,0.000009910033,0.000018024299,0.000008387476,0.996516,0.0011509574,0.0017339538,0.00045410226,0.0000090946],"about_ca_topic_score_codex":0.0075760903,"about_ca_topic_score_gemma":0.0059184595,"teacher_disagreement_score":0.0075760903,"about_ca_system_score_codex":0.0011176937,"about_ca_system_score_gemma":0.0018719274,"threshold_uncertainty_score":0.0150639415},"labels":[],"label_agreement":null},{"id":"W2074970886","doi":"10.1016/j.eswa.2011.04.027","title":"A mathematical programming approach to multi-attribute decision making with interval-valued intuitionistic fuzzy assessment information","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Fujian Province","keywords":"Closeness; Ideal solution; TOPSIS; Ranking (information retrieval); Interval (graph theory); Mathematics; Ideal (ethics); Mathematical optimization; Preference; Data mining; Quadratic programming; Computer science; Artificial intelligence; Operations research; Statistics; Combinatorics","score_opus":0.16468750489868944,"score_gpt":0.4165273526632113,"score_spread":0.25183984776452184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074970886","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00078857515,0.00020542805,0.997241,0.00023758275,0.000025825553,0.000023686183,0.00002166501,0.000020758007,0.0014355392],"genre_scores_gemma":[0.09608662,0.0009533225,0.8994261,0.00020990364,0.00018778103,0.00039302884,0.00007537091,0.00003871516,0.0026291797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99586123,0.0020364088,0.00029463897,0.0005011371,0.0011595153,0.00014706871],"domain_scores_gemma":[0.9946267,0.004282744,0.00032250833,0.00017901007,0.00047102594,0.00011795789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062913205,0.0016020393,0.0022972461,0.0022345176,0.0009790064,0.0046853214,0.003137649,0.0020899156,0.003157719],"category_scores_gemma":[0.0108156325,0.0012901212,0.0029288854,0.0030273225,0.0027482233,0.0053928406,0.002310838,0.003844238,0.00062514556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004112509,0.000111674824,0.00019690744,0.00037242981,0.00014241996,0.0001924024,0.00032343133,0.21068299,0.0010482519,0.7420839,0.0016880039,0.043116428],"study_design_scores_gemma":[0.000016744523,0.00005779746,0.00009565975,0.00008307798,0.00005312246,0.000092045804,0.000033568107,0.58481276,0.00039587956,0.41183025,0.0024844918,0.00004460733],"about_ca_topic_score_codex":0.0019217035,"about_ca_topic_score_gemma":0.0020210298,"teacher_disagreement_score":0.0062913205,"about_ca_system_score_codex":0.0024716705,"about_ca_system_score_gemma":0.0021724931,"threshold_uncertainty_score":0.033272028},"labels":[],"label_agreement":null},{"id":"W2075936298","doi":"10.1016/j.eswa.2005.07.038","title":"A probabilistic reasoning-based decision support system for selection of remediation technologies for petroleum-contaminated sites","year":2005,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Probabilistic logic; Selection (genetic algorithm); Decision support system; Process (computing); Reliability (semiconductor); Domain (mathematical analysis); Fuzzy logic; Evidential reasoning approach; Risk analysis (engineering); Data mining; Artificial intelligence; Business decision mapping; Mathematics","score_opus":0.015912932512688813,"score_gpt":0.26572949399523227,"score_spread":0.24981656148254344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075936298","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04781773,0.00029937498,0.886972,0.00077234756,0.0001500642,0.00062255067,0.0014711574,0.05852641,0.0033682806],"genre_scores_gemma":[0.40951416,0.0002546855,0.58334696,0.0005951579,0.00011240101,0.00060527236,0.0017858306,0.00043618304,0.003349456],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932706,0.00010784916,0.00010500488,0.00019207389,0.00022418158,0.000043882723],"domain_scores_gemma":[0.9976642,0.0013131936,0.00021578807,0.00019254333,0.00047602423,0.00013822004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002093209,0.0011468203,0.0015679118,0.001605917,0.00072157755,0.0019400718,0.0021417018,0.0020163495,0.008761152],"category_scores_gemma":[0.0062327594,0.0005880256,0.0007783129,0.00069964485,0.0004212106,0.0018014112,0.0009996607,0.00087222084,0.002395574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003078749,0.0013282158,0.0068109953,0.000774107,0.00047031642,0.0015001469,0.00040914127,0.17691527,0.03554731,0.005500109,0.026092691,0.7415729],"study_design_scores_gemma":[0.00028512994,0.00015465579,0.0011339127,0.000041107978,0.00017599916,0.00021246058,0.0000312952,0.9795943,0.010030756,0.0043190015,0.0039655403,0.000055761957],"about_ca_topic_score_codex":0.005620111,"about_ca_topic_score_gemma":0.005106701,"teacher_disagreement_score":0.008761152,"about_ca_system_score_codex":0.0007973029,"about_ca_system_score_gemma":0.0013241577,"threshold_uncertainty_score":0.029308915},"labels":[],"label_agreement":null},{"id":"W2077832074","doi":"10.1016/j.eswa.2011.08.086","title":"Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Content-based image retrieval; Image retrieval; Context (archaeology); Information retrieval; Process (computing); Matching (statistics); Digital image; Data mining; Feature vector; Pattern recognition (psychology); Artificial intelligence; Image (mathematics); Image processing","score_opus":0.06270980461727851,"score_gpt":0.27337900697929335,"score_spread":0.21066920236201483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077832074","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033302587,0.0009164628,0.9630057,0.00012187621,0.00014829639,0.00006275349,0.00012054365,0.0014582438,0.0008635776],"genre_scores_gemma":[0.3680151,0.00077310775,0.6246818,0.00017839923,0.00035728898,0.00015021133,0.001271904,0.00016961865,0.0044026203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884725,0.00020244303,0.00009688454,0.00019773835,0.0005236482,0.00013196279],"domain_scores_gemma":[0.9983334,0.00045278642,0.00011981872,0.00029241433,0.0007551088,0.00004646419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010948501,0.0010277354,0.0026344026,0.0021547466,0.00051230623,0.0010383931,0.0013026417,0.0012159547,0.0016893414],"category_scores_gemma":[0.0028641964,0.00046938908,0.0012436606,0.002416291,0.00028430021,0.0022381777,0.0010409912,0.0010634495,0.0016756314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004203886,0.00029300424,0.0007680303,0.0001248,0.00019501304,0.00008990885,0.000041171905,0.041151293,0.04799359,0.0011366957,0.004768772,0.9030174],"study_design_scores_gemma":[0.000019493642,0.000092639755,0.0005653574,0.0000054837296,0.00007838733,0.00009015317,0.000019778541,0.98451024,0.012691067,0.0011427996,0.0007688075,0.000015709238],"about_ca_topic_score_codex":0.0020921433,"about_ca_topic_score_gemma":0.0027541642,"teacher_disagreement_score":0.0026344026,"about_ca_system_score_codex":0.0003427908,"about_ca_system_score_gemma":0.00063117035,"threshold_uncertainty_score":0.005790174},"labels":[],"label_agreement":null},{"id":"W2078700749","doi":"10.1016/j.eswa.2014.05.049","title":"A framework for context-aware self-adaptive mobile applications SPL","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Adaptability; Computer science; Human–computer interaction; Adaptation (eye); Context (archaeology); Feature (linguistics); Context awareness; Mobile computing; Mobile device; Distributed computing; World Wide Web; Operating system","score_opus":0.028575221817526603,"score_gpt":0.3066792303428736,"score_spread":0.278104008525347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078700749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024474366,0.00038058936,0.98918265,0.00019959637,0.00007274164,0.00013813404,0.000060701048,0.0038343943,0.0036836874],"genre_scores_gemma":[0.16488855,0.0008473995,0.8241373,0.0002820424,0.00010278074,0.00041271138,0.0003505861,0.00060113927,0.008377563],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99897444,0.00021568523,0.00009981705,0.00021893765,0.00035647696,0.0001347401],"domain_scores_gemma":[0.9993488,0.00014921837,0.000046176963,0.00021473801,0.00012836492,0.000112581925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013283361,0.0008286131,0.00082331354,0.0008436996,0.00097427174,0.0032419406,0.0026596123,0.0017806158,0.0039200466],"category_scores_gemma":[0.0023961905,0.0006169763,0.0012076091,0.0006470769,0.0011220525,0.0030666601,0.004096424,0.0024040707,0.001944409],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001967861,0.00039575688,0.0017323841,0.00067171344,0.0002203327,0.0011768675,0.001857148,0.07418415,0.029664407,0.59058195,0.015074385,0.28424412],"study_design_scores_gemma":[0.000051737225,0.00011864869,0.00044634508,0.00023092014,0.00016133775,0.00066114985,0.0003324657,0.633264,0.012112992,0.19246541,0.1600737,0.00008123394],"about_ca_topic_score_codex":0.0043027163,"about_ca_topic_score_gemma":0.0055330703,"teacher_disagreement_score":0.0043027163,"about_ca_system_score_codex":0.00070670486,"about_ca_system_score_gemma":0.0014724658,"threshold_uncertainty_score":0.013113856},"labels":[],"label_agreement":null},{"id":"W2080464723","doi":"10.1016/j.eswa.2011.11.093","title":"Design of prestressed concrete flat slab using modern heuristic optimization techniques","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Heuristic; Slab; Computer science; Mathematical optimization; Constraint (computer-aided design); Optimization problem; Finite element method; Boundary (topology); Genetic algorithm; Optimal design; Algorithm; Structural engineering; Mathematics; Engineering; Machine learning; Mechanical engineering","score_opus":0.0432689488108323,"score_gpt":0.2715322232163039,"score_spread":0.2282632744054716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080464723","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08185041,0.00021953385,0.904671,0.00009518257,0.000047825353,0.00014602992,0.00011617133,0.0005461334,0.012307759],"genre_scores_gemma":[0.68476206,0.00031340835,0.30645558,0.00004992688,0.000021950227,0.00036080138,0.00021489356,0.00013493004,0.007686422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982125,0.000033438846,0.000007744464,0.000025487572,0.00007834748,0.000033794797],"domain_scores_gemma":[0.9998518,0.00004664843,0.000029125269,0.000013594694,0.00004222638,0.000016679393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024876223,0.00075686147,0.00080609706,0.000727972,0.00038990128,0.00065021607,0.0008779513,0.0009097266,0.0036762692],"category_scores_gemma":[0.0005406355,0.000585617,0.00064290664,0.0005564163,0.00040081338,0.00037063175,0.0004927224,0.0005776057,0.00051678927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034836186,0.000034879035,0.0001447285,0.00005324543,0.000014271268,0.000048640984,0.000019923802,0.9724966,0.010193614,0.002114984,0.00025434478,0.01458991],"study_design_scores_gemma":[0.00001314658,0.00007104935,0.00015223106,0.000006885324,0.000009562399,0.0000151266295,0.000019182839,0.9962521,0.0021975038,0.000660226,0.0005962845,0.000006608512],"about_ca_topic_score_codex":0.005559687,"about_ca_topic_score_gemma":0.008312763,"teacher_disagreement_score":0.005559687,"about_ca_system_score_codex":0.0004956706,"about_ca_system_score_gemma":0.0016048369,"threshold_uncertainty_score":0.012298346},"labels":[],"label_agreement":null},{"id":"W2083333579","doi":"10.1016/j.eswa.2009.07.022","title":"On simulation and optimization of one natural gas industry system under the rough environment","year":2009,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Fund for Distinguished Young Scholars; Ministry of Education, India; Ministry of Earth Sciences","keywords":"Computer science; Natural gas; Natural gas industry; Natural (archaeology); Rough set; Artificial intelligence; Industrial engineering; Chemistry; Geology","score_opus":0.016127324500572412,"score_gpt":0.256695246263357,"score_spread":0.24056792176278458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083333579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79753107,0.0006232202,0.18046206,0.0010680411,0.00014091555,0.000087068525,0.00028773444,0.00034092023,0.019458972],"genre_scores_gemma":[0.99257267,0.00011922666,0.0056999237,0.00003154456,0.000012279243,0.000028604467,0.00005614226,0.00001610707,0.0014634735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997454,0.000104342325,0.0000100127145,0.000037988844,0.000039272607,0.00006286365],"domain_scores_gemma":[0.9985846,0.0010951873,0.00008248404,0.000046395195,0.00011757742,0.00007373233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064694305,0.0005434899,0.0014517584,0.0006132948,0.00081018225,0.0008581063,0.0006751019,0.0017733206,0.0022719663],"category_scores_gemma":[0.002251544,0.0004201186,0.00093916414,0.0004970879,0.0010288763,0.0007791188,0.0007972236,0.000890404,0.000098815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013049066,0.00000886316,0.00017172683,0.000005840864,0.0000057466696,0.000017384598,0.000007909414,0.99875224,0.00006544949,0.0005234918,0.000030959964,0.00039737692],"study_design_scores_gemma":[0.0000021380185,0.000005748346,0.000054834185,3.880724e-7,0.000001819413,0.0000013168044,0.0000042819356,0.99974984,0.000028032053,0.00013224348,0.000017975775,0.0000014247639],"about_ca_topic_score_codex":0.041885626,"about_ca_topic_score_gemma":0.016581286,"teacher_disagreement_score":0.041885626,"about_ca_system_score_codex":0.00073863304,"about_ca_system_score_gemma":0.0009865265,"threshold_uncertainty_score":0.0832836},"labels":[],"label_agreement":null},{"id":"W2083584242","doi":"10.1016/j.eswa.2010.06.101","title":"Dynamic independent component analysis approach for fault detection and diagnosis","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Fault detection and isolation; Component (thermodynamics); Fault (geology); Data mining; Process (computing); Independent component analysis; Component analysis; Root cause; Artificial intelligence; Pattern recognition (psychology); Reliability engineering; Machine learning; Engineering","score_opus":0.005600101482105121,"score_gpt":0.2219482992190848,"score_spread":0.21634819773697966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083584242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010663081,0.00047369438,0.99714416,0.000041115953,0.000048635353,0.000015883541,0.000023059032,0.00026409555,0.00092305604],"genre_scores_gemma":[0.33455092,0.0025267552,0.65282357,0.000150504,0.00025774117,0.0002829931,0.00042743995,0.00021493707,0.008765268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949896,0.00009956495,0.000029311403,0.00011712793,0.00021292828,0.000042103406],"domain_scores_gemma":[0.99954224,0.00016820662,0.000028165052,0.000063975625,0.00018643262,0.0000109547245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041596498,0.0012435166,0.001192927,0.0012553021,0.00052353734,0.0009174515,0.0010614793,0.0006882456,0.002238248],"category_scores_gemma":[0.0014525212,0.0003530762,0.0007906341,0.0011109493,0.0004071353,0.00082256,0.00055352494,0.0012741175,0.0010569853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021120811,0.0000991879,0.0006178321,0.00031619176,0.00032770974,0.00016466733,0.00010058516,0.26850995,0.016188523,0.05205149,0.0051298253,0.6562828],"study_design_scores_gemma":[0.0000117073805,0.00005715021,0.0004138122,0.00001216022,0.00006986555,0.00008286293,0.00001529984,0.97171956,0.0038620804,0.018723145,0.005014042,0.000018308998],"about_ca_topic_score_codex":0.005863418,"about_ca_topic_score_gemma":0.004455022,"teacher_disagreement_score":0.005863418,"about_ca_system_score_codex":0.000521305,"about_ca_system_score_gemma":0.00082907913,"threshold_uncertainty_score":0.011658609},"labels":[],"label_agreement":null},{"id":"W2084550115","doi":"10.1016/j.eswa.2013.03.028","title":"A heterogeneous framework for real-time decoding of bioacoustic signals: Applications to assistive interfaces and prosthesis control","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Task (project management); Interface (matter); Bioacoustics; Decoding methods; Interference (communication); Artificial intelligence; Support vector machine; Human–computer interaction; Speech recognition; Channel (broadcasting); Engineering","score_opus":0.023027010191537106,"score_gpt":0.29139012435612704,"score_spread":0.2683631141645899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084550115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015862662,0.00009213016,0.99721706,0.000027452561,0.000013208451,0.00001211463,0.000019839847,0.0005928303,0.0004390647],"genre_scores_gemma":[0.1528256,0.00043636208,0.84266406,0.00012825486,0.00007935581,0.00013162066,0.0002747242,0.00050229137,0.002957676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998995,0.00023512554,0.00008858524,0.00022236624,0.000348894,0.00010992821],"domain_scores_gemma":[0.9988966,0.00031199396,0.00007435779,0.00032935943,0.0002871237,0.000100620484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019335808,0.00083815545,0.0009949252,0.000951217,0.0007384064,0.0027878243,0.00261594,0.0012947435,0.0032791593],"category_scores_gemma":[0.0030349945,0.00047327767,0.0012900505,0.0011024651,0.00096230744,0.0027270503,0.002451493,0.0014631477,0.0016510347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074267405,0.00036088075,0.0013244257,0.0003277597,0.0002749222,0.0009091812,0.0006312907,0.21987769,0.09801154,0.2557221,0.006175361,0.4156421],"study_design_scores_gemma":[0.000028497856,0.00007517621,0.0002568724,0.00002897613,0.00006430132,0.00017365727,0.000056962,0.9250495,0.015803324,0.049279567,0.009149046,0.000034154862],"about_ca_topic_score_codex":0.003476532,"about_ca_topic_score_gemma":0.0045342995,"teacher_disagreement_score":0.003476532,"about_ca_system_score_codex":0.00060136116,"about_ca_system_score_gemma":0.0013444659,"threshold_uncertainty_score":0.010969877},"labels":[],"label_agreement":null},{"id":"W2084585486","doi":"10.1016/j.eswa.2012.01.210","title":"Feature evaluation for web crawler detection with data mining techniques","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Web crawler; Denial-of-service attack; Session (web analytics); Web server; Precision and recall; The Internet; Web mining; Data mining; World Wide Web; Web page; Information retrieval","score_opus":0.04390771811223085,"score_gpt":0.3336468362735624,"score_spread":0.2897391181613316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084585486","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74908626,0.0016887381,0.23742354,0.00020108622,0.00012277579,0.00034004668,0.0023771096,0.0064365175,0.0023239846],"genre_scores_gemma":[0.8637454,0.00027733872,0.1309259,0.000031201824,0.000046598754,0.00017093922,0.0033272651,0.00014249608,0.0013327686],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99767774,0.00045039048,0.00031293675,0.00030441364,0.0010603681,0.00019417643],"domain_scores_gemma":[0.99218106,0.003845051,0.00058314035,0.0007224023,0.0024538497,0.00021446215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002437514,0.0009852744,0.0013018263,0.0056851115,0.0005371025,0.0011992606,0.00080812786,0.00081528944,0.000997281],"category_scores_gemma":[0.009734444,0.00022365933,0.0010213013,0.0027554596,0.00021373792,0.0012849297,0.0005511632,0.0005117201,0.00048368878],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012968775,0.0010326774,0.073317796,0.0004995628,0.00040264227,0.0002829072,0.00010837197,0.02691959,0.048863746,0.00080007873,0.0057065254,0.8407692],"study_design_scores_gemma":[0.00007154684,0.000898178,0.044363894,0.000043757154,0.0002857295,0.00061581284,0.000118205615,0.894799,0.055332497,0.0011956107,0.0022295841,0.000046260022],"about_ca_topic_score_codex":0.0029985954,"about_ca_topic_score_gemma":0.0037335502,"teacher_disagreement_score":0.0056851115,"about_ca_system_score_codex":0.0005768041,"about_ca_system_score_gemma":0.0007257419,"threshold_uncertainty_score":0.012890995},"labels":[],"label_agreement":null},{"id":"W2085806531","doi":"10.1016/j.eswa.2012.07.007","title":"An interval-valued intuitionistic fuzzy multiattribute group decision making framework with incomplete preference over alternatives","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Group decision-making; Preference; Ranking (information retrieval); Consistency (knowledge bases); Mathematics; Interval (graph theory); Group (periodic table); Fuzzy logic; Computer science; Mathematical optimization; Artificial intelligence; Statistics; Discrete mathematics; Combinatorics","score_opus":0.15263580729946172,"score_gpt":0.4350878121505095,"score_spread":0.2824520048510478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085806531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073583936,0.00022858042,0.98840886,0.0002560104,0.00002872365,0.000050637464,0.00005601273,0.000046109893,0.0035666714],"genre_scores_gemma":[0.3490641,0.00041481564,0.6479076,0.00013249672,0.00006711137,0.00022894688,0.00012626994,0.00002245286,0.0020361403],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9955048,0.00228834,0.00023044704,0.0004907397,0.0012522278,0.00023344203],"domain_scores_gemma":[0.9982552,0.00082536094,0.00019778649,0.00014126713,0.00040360642,0.00017677803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064759464,0.0012307925,0.0015669103,0.0022250756,0.0009264232,0.003629399,0.0026358652,0.0018305035,0.0029411593],"category_scores_gemma":[0.0054954393,0.0005946009,0.0020055014,0.0028814264,0.0018374074,0.0038861558,0.0022311108,0.0019355604,0.0004428895],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015713762,0.00017882792,0.00054703915,0.0003773935,0.0002115934,0.00039907172,0.0007454903,0.20043363,0.0025499146,0.7389264,0.0017848794,0.053688638],"study_design_scores_gemma":[0.000061646744,0.00015645228,0.00029748434,0.0001219663,0.00012110674,0.00018105908,0.0001569188,0.6092047,0.00075273844,0.38474214,0.004125215,0.00007853009],"about_ca_topic_score_codex":0.0018786131,"about_ca_topic_score_gemma":0.0024986216,"teacher_disagreement_score":0.0064759464,"about_ca_system_score_codex":0.0020319335,"about_ca_system_score_gemma":0.0021905329,"threshold_uncertainty_score":0.03424847},"labels":[],"label_agreement":null},{"id":"W2086038979","doi":"10.1016/j.eswa.2014.09.015","title":"A novel contextual topic model for multi-document summarization","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; Information overload; Multi-document summarization; Context (archaeology); Artificial intelligence; Word (group theory); Topic model; Natural language processing; Information retrieval; World Wide Web; Linguistics","score_opus":0.0467911233113684,"score_gpt":0.290877761885536,"score_spread":0.2440866385741676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086038979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052541588,0.0012772891,0.990316,0.00024334562,0.00017521712,0.00008445759,0.000512331,0.0014893711,0.0006477997],"genre_scores_gemma":[0.25603142,0.0029221221,0.7240017,0.00044884757,0.0014983652,0.0010126295,0.006421034,0.00086461415,0.006799313],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981667,0.0006163746,0.00018945521,0.00052213663,0.000355089,0.0001502247],"domain_scores_gemma":[0.99769884,0.0011544317,0.00015767732,0.000269049,0.0006256465,0.000094324096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021575296,0.0012695198,0.0018491503,0.002159778,0.0008566994,0.0020300043,0.0019183551,0.0017958892,0.0027212184],"category_scores_gemma":[0.0058393655,0.0006406002,0.0019281922,0.0030874969,0.00041076043,0.0031439385,0.0013883811,0.0019738034,0.0026854505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090526976,0.0003247783,0.0027442712,0.0010499435,0.0006306788,0.00030466542,0.0007537971,0.14267734,0.027241476,0.028503949,0.028235562,0.76662827],"study_design_scores_gemma":[0.000039331793,0.00010343354,0.0007251717,0.000038017228,0.00020340494,0.00011920987,0.00007247814,0.97542113,0.0035113413,0.01113267,0.008588348,0.000045511428],"about_ca_topic_score_codex":0.005175577,"about_ca_topic_score_gemma":0.008498368,"teacher_disagreement_score":0.005175577,"about_ca_system_score_codex":0.00071975077,"about_ca_system_score_gemma":0.0014328198,"threshold_uncertainty_score":0.011410236},"labels":[],"label_agreement":null},{"id":"W2086917595","doi":"10.1016/j.eswa.2010.10.039","title":"An OWA-TOPSIS method for multiple criteria decision analysis","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"TOPSIS; Ideal solution; Decision maker; Multiple-criteria decision analysis; Computer science; Mathematical optimization; Extreme point; Robustness (evolution); Similarity (geometry); Ideal (ethics); Data mining; Mathematics; Operations research; Artificial intelligence","score_opus":0.10219207783045593,"score_gpt":0.4924242896579465,"score_spread":0.39023221182749057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086917595","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025575238,0.00039529378,0.9924555,0.00008625167,0.00013685346,0.0002236718,0.00013081134,0.0002778148,0.0037362026],"genre_scores_gemma":[0.04111115,0.0005149024,0.95464045,0.00006329857,0.0000449057,0.0005861896,0.00020856471,0.00006320977,0.0027673664],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99190134,0.0028166103,0.0006422045,0.0004326517,0.0039590867,0.00024812893],"domain_scores_gemma":[0.99728525,0.0013068465,0.000119364166,0.00016104708,0.00106132,0.00006626544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004294031,0.001484367,0.002720865,0.005634909,0.0015995167,0.0029276714,0.001487822,0.0011462064,0.007955798],"category_scores_gemma":[0.0089194905,0.0006976284,0.0029942465,0.007703932,0.0007423738,0.002166602,0.001977792,0.0016311776,0.0018722839],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016411746,0.00024143222,0.0009054092,0.0015564973,0.00059788616,0.00020764761,0.00036254487,0.03675151,0.007852859,0.041597787,0.004961778,0.9048005],"study_design_scores_gemma":[0.00016194576,0.00065819744,0.0037543294,0.0006339398,0.0007560339,0.0008462617,0.00060068927,0.80034834,0.0113733215,0.13506857,0.045442477,0.0003558645],"about_ca_topic_score_codex":0.0040529114,"about_ca_topic_score_gemma":0.0065563126,"teacher_disagreement_score":0.007955798,"about_ca_system_score_codex":0.0010741538,"about_ca_system_score_gemma":0.0035384463,"threshold_uncertainty_score":0.026614785},"labels":[],"label_agreement":null},{"id":"W2087700017","doi":"10.1016/j.eswa.2012.02.077","title":"Forecasting model of Shanghai and CRB commodity indexes","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Error correction model; Econometrics; Commodity; Index (typography); Causality (physics); Stock (firearms); Economics; Stock market index; Regression; Fuzzy inference system; Stock market; Computer science; Fuzzy logic; Cointegration; Statistics; Adaptive neuro fuzzy inference system; Mathematics; Artificial intelligence; Fuzzy control system; Finance","score_opus":0.06198460833640794,"score_gpt":0.2411297450398045,"score_spread":0.17914513670339655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087700017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9325584,0.00052396656,0.05765234,0.0004458605,0.000115430004,0.000022304304,0.0010582341,0.00032132308,0.0073021846],"genre_scores_gemma":[0.99440503,0.00014536594,0.002285402,0.0000100024035,0.000014650736,0.000008916826,0.00043846777,0.000010771224,0.0026812803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998876,0.000019781322,0.0000065172185,0.000035593614,0.000025927771,0.000024496845],"domain_scores_gemma":[0.9997904,0.00006246785,0.000034229688,0.000013301137,0.000083033774,0.000016614898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041002023,0.0004472327,0.00042078856,0.0006623577,0.00024949881,0.0007457312,0.00052754075,0.0005295673,0.0017624444],"category_scores_gemma":[0.0008390169,0.0002030948,0.00045603697,0.0008547096,0.00017645198,0.0006187585,0.0001606498,0.00034604012,0.00021000319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000566535,0.000022540922,0.013019686,0.000030357858,0.00003452063,0.00015666928,0.00006515196,0.96883047,0.0013647935,0.005854295,0.0015229983,0.009041918],"study_design_scores_gemma":[0.00000172244,0.000004991275,0.0018175362,0.0000013136187,0.0000095764,0.0000065509225,0.00000743807,0.9973788,0.00016469795,0.000456819,0.0001465941,0.000004069201],"about_ca_topic_score_codex":0.06007942,"about_ca_topic_score_gemma":0.03114356,"teacher_disagreement_score":0.06007942,"about_ca_system_score_codex":0.00079753925,"about_ca_system_score_gemma":0.00078434707,"threshold_uncertainty_score":0.11945945},"labels":[],"label_agreement":null},{"id":"W2088380385","doi":"10.1016/j.eswa.2012.01.165","title":"Iterative performance improvement of fuzzy control systems for three tank systems","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Autoritatea Natională pentru Cercetare Stiintifică","keywords":"Control theory (sociology); Fuzzy logic; Nonlinear system; MIMO; Fuzzy control system; Stability (learning theory); Invariant (physics); Mathematics; Set (abstract data type); Computer science; Lyapunov stability; Control system; Mathematical optimization; Control (management); Artificial intelligence; Engineering","score_opus":0.01431369222544535,"score_gpt":0.2330732530402272,"score_spread":0.21875956081478184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2088380385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0880374,0.00045586098,0.9036299,0.00014903849,0.000051024756,0.00005283014,0.000013294238,0.000232428,0.0073782257],"genre_scores_gemma":[0.9672774,0.00012006052,0.031131784,0.000019210487,0.000016740638,0.000043054264,0.000015738678,0.000020454841,0.0013555846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952686,0.00015025435,0.00003091795,0.000068742665,0.00015748774,0.000065804474],"domain_scores_gemma":[0.9991617,0.00045364985,0.000092348564,0.00005573606,0.00021153499,0.000025112964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001405112,0.0006979084,0.00060033065,0.00035171176,0.00053227594,0.00090643705,0.0006398247,0.000726211,0.001460545],"category_scores_gemma":[0.002956407,0.00022916275,0.00047113953,0.0003640991,0.0006543642,0.00051743124,0.0008593897,0.0008438749,0.00018574376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032235667,0.0000875697,0.00039471828,0.00016956155,0.000058326874,0.00006849671,0.00039072995,0.8631195,0.01936215,0.014050975,0.00052247714,0.10145319],"study_design_scores_gemma":[0.000012917653,0.0001149099,0.00017705381,0.000005875304,0.000010472883,0.000010393919,0.000010541895,0.99509627,0.0026526658,0.0016026889,0.00029860812,0.000007542824],"about_ca_topic_score_codex":0.0065753907,"about_ca_topic_score_gemma":0.0036790103,"teacher_disagreement_score":0.0065753907,"about_ca_system_score_codex":0.000683261,"about_ca_system_score_gemma":0.00082791864,"threshold_uncertainty_score":0.013074279},"labels":[],"label_agreement":null},{"id":"W2089412970","doi":"10.1016/j.eswa.2015.01.009","title":"Design of a multi-disciplinary and feature-based collaborative environment for chemical process projects","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Alberta Innovates - Technology Futures","keywords":"Computer science; Interoperability; Domain (mathematical analysis); Feature-oriented domain analysis; Software engineering; Feature (linguistics); Consistency (knowledge bases); Process (computing); Domain knowledge; Semantics (computer science); Domain engineering; Data science; Knowledge management; Systems engineering; Software; Artificial intelligence; Software development; World Wide Web; Component-based software engineering; Engineering; Programming language","score_opus":0.02866531343405633,"score_gpt":0.2595053272175005,"score_spread":0.2308400137834442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089412970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097553745,0.00005291819,0.89081085,0.00011899987,0.000034603534,0.00060980505,0.000117216405,0.0034565607,0.0072453376],"genre_scores_gemma":[0.41827032,0.000056535995,0.5749559,0.00004071661,0.0000172441,0.00082419935,0.00022133387,0.00019419022,0.005419638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982514,0.0005393578,0.00013335318,0.00044597255,0.00039882743,0.0002310965],"domain_scores_gemma":[0.9982085,0.0004606368,0.00020914098,0.00028604726,0.0002844515,0.0005512217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018068512,0.0005227278,0.0005555933,0.0010351874,0.0012567418,0.002174721,0.0024764438,0.0012334578,0.0065092724],"category_scores_gemma":[0.002877508,0.0005222838,0.0005634511,0.0006924546,0.00044650005,0.0019178167,0.003789468,0.0005635714,0.0016501279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021786857,0.0032337618,0.015850348,0.0006142793,0.00020546553,0.001728127,0.0039064833,0.37108278,0.13763756,0.025718078,0.008587095,0.4292574],"study_design_scores_gemma":[0.00034758166,0.001053651,0.004099055,0.00006614747,0.000107925385,0.00040623467,0.0012677648,0.91686344,0.03432664,0.006900759,0.034437504,0.00012325747],"about_ca_topic_score_codex":0.0011475857,"about_ca_topic_score_gemma":0.001604562,"teacher_disagreement_score":0.0065092724,"about_ca_system_score_codex":0.0006329115,"about_ca_system_score_gemma":0.001788288,"threshold_uncertainty_score":0.021775663},"labels":[],"label_agreement":null},{"id":"W2089870669","doi":"10.1016/j.eswa.2011.09.160","title":"Comparison of term frequency and document frequency based feature selection metrics in text categorization","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":152,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Faculty of Graduate Studies and Research, University of Alberta; Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Feature selection; Discriminative model; Computer science; Term (time); Text categorization; Categorization; Word lists by frequency; Feature (linguistics); Frequency; Artificial intelligence; Selection (genetic algorithm); Pattern recognition (psychology); tf–idf; Data mining; Mathematics; Statistics","score_opus":0.031047475942438254,"score_gpt":0.28638712605760935,"score_spread":0.2553396501151711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089870669","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8060538,0.019004213,0.16512007,0.00082113635,0.0005060783,0.0003177764,0.0023924846,0.0019093723,0.0038748714],"genre_scores_gemma":[0.8899647,0.0022261976,0.1014041,0.00009452019,0.00031522717,0.00022253304,0.0039830194,0.00015741252,0.0016323052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952807,0.0013815538,0.0006418823,0.000368515,0.0020991517,0.00022827652],"domain_scores_gemma":[0.9680778,0.024027443,0.0013362637,0.0008269638,0.0051425,0.0005889791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073694726,0.00073620223,0.0017147885,0.008694715,0.0006183662,0.0022877357,0.0008298984,0.001058735,0.0009872641],"category_scores_gemma":[0.021530889,0.00016018402,0.0009252518,0.0060653593,0.00038910387,0.003035834,0.0007324978,0.00073282485,0.00041179056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0037412317,0.0007666025,0.041271944,0.0012111277,0.0008361935,0.00011324833,0.00038352542,0.012963916,0.017147318,0.0025033206,0.006852606,0.9122091],"study_design_scores_gemma":[0.0006886059,0.006505141,0.2174399,0.00032482677,0.0014314353,0.0013437187,0.0015172681,0.72197956,0.029113404,0.009271628,0.009999031,0.0003855258],"about_ca_topic_score_codex":0.002635662,"about_ca_topic_score_gemma":0.0030270736,"teacher_disagreement_score":0.008694715,"about_ca_system_score_codex":0.0009292995,"about_ca_system_score_gemma":0.0010072835,"threshold_uncertainty_score":0.038973987},"labels":[],"label_agreement":null},{"id":"W2092890259","doi":"10.1016/j.eswa.2010.08.054","title":"Design and implementation of GEmA: A generic emotional agent","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Java; Rationality; Morality; Intelligent agent; Artificial intelligence; Software; Emotional behavior; Software agent; Software engineering; Programming language; Human–computer interaction; Machine learning; Psychology","score_opus":0.04554601365028967,"score_gpt":0.38633903429028166,"score_spread":0.340793020639992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092890259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03179463,0.00016279845,0.93124247,0.00028771156,0.00021395962,0.0015347857,0.0003014346,0.025727289,0.008734936],"genre_scores_gemma":[0.25213274,0.0001416857,0.72901857,0.00044512292,0.000036042526,0.0015668699,0.0007654031,0.0022046303,0.013688988],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949646,0.00010600104,0.000050189334,0.000118936616,0.00014131967,0.000087064116],"domain_scores_gemma":[0.9994784,0.00010494229,0.000046088007,0.00012406318,0.00012603732,0.00012040164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094059564,0.0006388449,0.00055235944,0.00033883986,0.00038167686,0.0012993612,0.0028559556,0.0013207966,0.008426075],"category_scores_gemma":[0.0020969433,0.00054278865,0.00050877786,0.00012686267,0.0005687645,0.0009363979,0.0016876985,0.0012952954,0.0035994155],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021215791,0.0019582028,0.0075399573,0.0020851842,0.00042788562,0.0010823541,0.0021868907,0.06649672,0.3136783,0.05198017,0.029573286,0.5208695],"study_design_scores_gemma":[0.0006886435,0.0013718749,0.0037432313,0.00015698008,0.00033562494,0.0010821874,0.00038591636,0.57201576,0.20048776,0.009982755,0.20956965,0.0001796039],"about_ca_topic_score_codex":0.00071366987,"about_ca_topic_score_gemma":0.0007374602,"teacher_disagreement_score":0.008426075,"about_ca_system_score_codex":0.00039220235,"about_ca_system_score_gemma":0.00084393687,"threshold_uncertainty_score":0.02818799},"labels":[],"label_agreement":null},{"id":"W2098174433","doi":"10.1016/j.eswa.2013.06.043","title":"Securing high resolution grayscale facial captures using a blockwise coevolutionary GA","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grayscale; Digital watermarking; Computer science; Artificial intelligence; Pixel; Crossover; Block (permutation group theory); Embedding; Pattern recognition (psychology); Biometrics; Computer vision; Evolutionary computation; Image (mathematics); Mathematics","score_opus":0.012138668859171366,"score_gpt":0.23699916956747052,"score_spread":0.22486050070829916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098174433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10965087,0.0001785162,0.8873475,0.00006627355,0.00003936649,0.00004712199,0.00001690234,0.00028626964,0.0023671556],"genre_scores_gemma":[0.6137685,0.00022128971,0.3795962,0.00008430266,0.000017170742,0.000070886184,0.00004486321,0.00006153391,0.0061352323],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99987304,0.000020710111,0.0000071734044,0.000029759773,0.000053262203,0.000015994989],"domain_scores_gemma":[0.99983394,0.000059724895,0.00002164029,0.00003101754,0.000044681827,0.0000088955685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027995487,0.00037281838,0.0003889307,0.00030433116,0.00016331016,0.00028003764,0.00038214453,0.00054934697,0.0009942316],"category_scores_gemma":[0.0006482827,0.00019131617,0.00037477436,0.0002610315,0.00030022083,0.0003319818,0.0004910921,0.00034644263,0.000260919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001675373,0.000076370525,0.0010919248,0.00008343702,0.00008806102,0.00020213626,0.00015867484,0.321688,0.38708317,0.006687176,0.0006705808,0.28200293],"study_design_scores_gemma":[0.000006270335,0.00008086671,0.00044982563,0.0000051691327,0.00001715615,0.000103377926,0.000009215469,0.9826376,0.01567016,0.00045688322,0.0005563719,0.0000071016975],"about_ca_topic_score_codex":0.0013791695,"about_ca_topic_score_gemma":0.0022093125,"teacher_disagreement_score":0.0013791695,"about_ca_system_score_codex":0.00023031341,"about_ca_system_score_gemma":0.00024869654,"threshold_uncertainty_score":0.0033260584},"labels":[],"label_agreement":null},{"id":"W2101267054","doi":"10.1016/j.eswa.2012.12.096","title":"Feature representation selection based on Classifier Projection Space and Oracle analysis","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Governo Brasil","keywords":"Pattern recognition (psychology); Computer science; Artificial intelligence; Classifier (UML); MNIST database; Feature extraction; Digit recognition; Feature vector; Data mining; Artificial neural network","score_opus":0.019705313984950915,"score_gpt":0.2906274716716035,"score_spread":0.2709221576866526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101267054","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009349648,0.00015817146,0.98930514,0.00006127989,0.00001995241,0.00003899811,0.000043672517,0.000663027,0.00036022614],"genre_scores_gemma":[0.4452018,0.00048933644,0.54981,0.00009787428,0.00014066946,0.00023662312,0.00086664397,0.00029481383,0.0028622297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986676,0.00034853394,0.000102550424,0.0002607388,0.00050182664,0.00011886417],"domain_scores_gemma":[0.998447,0.00058255246,0.000075704345,0.00018167096,0.0006573693,0.00005576438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015650451,0.0008178046,0.0019803161,0.0015221577,0.0005317874,0.0015114933,0.00094984315,0.000716793,0.0028553363],"category_scores_gemma":[0.0049316725,0.00031585761,0.001016908,0.0015615618,0.0005071053,0.0016401963,0.00097104907,0.0011200209,0.00090674066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044018924,0.00014816692,0.0012200188,0.00014931448,0.00011000167,0.000086016655,0.00006665363,0.036098737,0.020559723,0.01044557,0.0038242212,0.92685145],"study_design_scores_gemma":[0.000032844262,0.00014677919,0.0012878096,0.0000109763505,0.00007770031,0.00014708201,0.000026462985,0.98320436,0.008532822,0.0054094857,0.00109863,0.000025162764],"about_ca_topic_score_codex":0.0020674772,"about_ca_topic_score_gemma":0.0015758083,"teacher_disagreement_score":0.0028553363,"about_ca_system_score_codex":0.00033597287,"about_ca_system_score_gemma":0.0010141883,"threshold_uncertainty_score":0.009552062},"labels":[],"label_agreement":null},{"id":"W2107469189","doi":"10.1016/j.eswa.2011.04.197","title":"Secret sharing approaches for 3D object encryption","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Encryption; Object (grammar); Secret sharing; Computer security; Artificial intelligence; Theoretical computer science; Cryptography","score_opus":0.07957755797098086,"score_gpt":0.2553521368963065,"score_spread":0.1757745789253256,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107469189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014383702,0.00040405404,0.98103255,0.00015539552,0.00003445048,0.000030187952,0.000023503988,0.000087554574,0.0038485732],"genre_scores_gemma":[0.6958586,0.0012989844,0.29023832,0.00021075783,0.000103292674,0.00014347496,0.000090371665,0.000086198146,0.011970048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956685,0.00009785072,0.000025420697,0.000049311697,0.00020851681,0.00005208016],"domain_scores_gemma":[0.99947697,0.00021240563,0.000051941457,0.00017931403,0.000060876704,0.00001848437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005253652,0.0003535018,0.00056853256,0.00046016573,0.0005853826,0.00085249636,0.0007923337,0.00089096464,0.0029996904],"category_scores_gemma":[0.0009478845,0.00028221967,0.0006866447,0.0005334281,0.0010294218,0.0018061959,0.002034061,0.0010328469,0.00055377616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024393498,0.00007187985,0.00035295342,0.00018849887,0.00007989503,0.00028123934,0.00046029084,0.20445363,0.055004954,0.6084097,0.0020529195,0.12840013],"study_design_scores_gemma":[0.00002370707,0.00007433964,0.00014789702,0.000018929424,0.000021662743,0.00031973582,0.000068895155,0.8250329,0.016377883,0.15280814,0.005077131,0.00002867024],"about_ca_topic_score_codex":0.00034765192,"about_ca_topic_score_gemma":0.00039905502,"teacher_disagreement_score":0.0029996904,"about_ca_system_score_codex":0.0005545082,"about_ca_system_score_gemma":0.00035617486,"threshold_uncertainty_score":0.010034978},"labels":[],"label_agreement":null},{"id":"W2137180616","doi":"10.1016/j.eswa.2014.07.018","title":"OWA operator based link prediction ensemble for social network","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Badan Riset dan Inovasi Nasional; National Natural Science Foundation of China; McGill University","keywords":"Computer science; Benchmark (surveying); Link (geometry); Stability (learning theory); Operator (biology); Data mining; Variance (accounting); Social network (sociolinguistics); Ensemble learning; Algorithm; Artificial intelligence; Machine learning; Social media","score_opus":0.012930514568681406,"score_gpt":0.2658279071817388,"score_spread":0.25289739261305744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137180616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07459673,0.0006839275,0.9194549,0.00040100617,0.00027624116,0.0000911915,0.0008384822,0.0014979469,0.0021595228],"genre_scores_gemma":[0.7983769,0.00051123917,0.18910834,0.00021854973,0.00041020385,0.00021740557,0.003325203,0.00018473227,0.007647387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920374,0.00019551723,0.0000479649,0.0001877309,0.00025329622,0.00011180255],"domain_scores_gemma":[0.99750704,0.0012047701,0.00013535669,0.00035324675,0.0006687745,0.00013093613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019174672,0.0009136961,0.0014915002,0.0025321636,0.0008261045,0.0009262935,0.0013978247,0.0012228313,0.0026487089],"category_scores_gemma":[0.005114797,0.00032930335,0.00093871105,0.0020665948,0.0003537418,0.002120062,0.0012092319,0.0013686625,0.00091771956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036123008,0.0004277505,0.006170066,0.000095427145,0.00028754422,0.00012374276,0.00008948066,0.49303797,0.0039375625,0.005829894,0.011039118,0.4786002],"study_design_scores_gemma":[0.0000025111171,0.000009703759,0.0002039391,0.0000021644705,0.000009869608,0.000005720737,0.00000605527,0.9977102,0.0002647275,0.0015755084,0.00020704817,0.0000026445293],"about_ca_topic_score_codex":0.011691026,"about_ca_topic_score_gemma":0.016303694,"teacher_disagreement_score":0.011691026,"about_ca_system_score_codex":0.0004961946,"about_ca_system_score_gemma":0.0011022793,"threshold_uncertainty_score":0.02324599},"labels":[],"label_agreement":null},{"id":"W2138720691","doi":"10.1016/j.eswa.2013.01.035","title":"Performance of distributed multi-agent multi-state reinforcement spectrum management using different exploration schemes","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Reinforcement learning; Computer science; Cognitive radio; Context (archaeology); Q-learning; Reinforcement; State (computer science); Distributed computing; Scheme (mathematics); Spectrum management; Artificial intelligence; Wireless; Telecommunications; Engineering; Algorithm","score_opus":0.031108081602841873,"score_gpt":0.25405777615677094,"score_spread":0.22294969455392907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138720691","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95603395,0.00036452033,0.037641365,0.00031362745,0.00008202399,0.000045872224,0.00005356239,0.00044688123,0.0050181192],"genre_scores_gemma":[0.998069,0.000016798394,0.0016262251,0.0000128247175,0.0000024280714,0.000010282862,0.000015466258,0.000006818292,0.00024021216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922013,0.00020711566,0.000047124693,0.00011759369,0.00012049745,0.00028742166],"domain_scores_gemma":[0.9932401,0.0044677155,0.0004286128,0.00040549293,0.00097069296,0.00048739542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020059347,0.00082378276,0.0009089482,0.00049913215,0.00064392213,0.0009780485,0.0009885131,0.0012177842,0.0015909685],"category_scores_gemma":[0.0061147865,0.00023765657,0.0002912623,0.00032684935,0.0009888083,0.0010579837,0.0011326397,0.0007569462,0.0001423493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002251261,0.00033261089,0.0014580069,0.00009064963,0.00007135366,0.000075165866,0.00009709957,0.97237474,0.0044839014,0.0012505038,0.00040107412,0.017113654],"study_design_scores_gemma":[0.00006679662,0.00022482629,0.0005834688,0.000005213691,0.000017038881,0.000015936605,0.000040066683,0.9966624,0.0019669945,0.00035934526,0.000048374146,0.000009652682],"about_ca_topic_score_codex":0.0075283633,"about_ca_topic_score_gemma":0.0035521649,"teacher_disagreement_score":0.0075283633,"about_ca_system_score_codex":0.0010500957,"about_ca_system_score_gemma":0.0012615822,"threshold_uncertainty_score":0.0149691105},"labels":[],"label_agreement":null},{"id":"W2144911699","doi":"10.1016/j.eswa.2005.01.019","title":"An ANN-based element extraction method for automatic mesh generation","year":2005,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadrilateral; Hexahedron; Computer science; Polygon mesh; Finite element method; Discretization; Artificial neural network; Tetrahedron; Mesh generation; Domain (mathematical analysis); Element (criminal law); Boundary (topology); Extended finite element method; Boundary element method; Algorithm; Artificial intelligence; Mathematics; Geometry; Structural engineering; Mathematical analysis; Engineering","score_opus":0.02655501869568558,"score_gpt":0.34333901438745673,"score_spread":0.3167839956917712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144911699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003341208,0.00012628172,0.9943193,0.00004440827,0.000060599763,0.00003237318,0.00004358199,0.0010354266,0.0009966688],"genre_scores_gemma":[0.07161335,0.0001958913,0.9226173,0.00012855553,0.00005253756,0.00015999986,0.0002278583,0.00017028405,0.0048341937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996513,0.000048759262,0.000027286776,0.00007530924,0.00017743904,0.000019940086],"domain_scores_gemma":[0.99932134,0.00026749648,0.000040045703,0.000068937414,0.00028384838,0.000018379125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059325504,0.0007435722,0.000731625,0.00089951284,0.0003975446,0.00051107834,0.00095899275,0.0011742667,0.003665606],"category_scores_gemma":[0.0014410877,0.00062735786,0.0005620262,0.0008037461,0.00025259846,0.00073195243,0.00059022475,0.0008248181,0.0013360885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015007266,0.000095871525,0.00052245305,0.00016712814,0.00007626543,0.00011697773,0.000044337416,0.13395618,0.05264139,0.0023411908,0.004698781,0.8051895],"study_design_scores_gemma":[0.000008352759,0.000021574686,0.00025370513,0.000014553057,0.000017094575,0.00006838652,0.0000037333268,0.98827064,0.008997988,0.00067997916,0.0016535665,0.000010593108],"about_ca_topic_score_codex":0.002146415,"about_ca_topic_score_gemma":0.0043258606,"teacher_disagreement_score":0.003665606,"about_ca_system_score_codex":0.0002902411,"about_ca_system_score_gemma":0.0004939274,"threshold_uncertainty_score":0.012262642},"labels":[],"label_agreement":null},{"id":"W2145030452","doi":"10.1016/j.eswa.2013.06.033","title":"A classifier fusion system for bearing fault diagnosis","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Support vector machine; Robustness (evolution); Pattern recognition (psychology); Classifier (UML); Artificial intelligence; Toolbox; Fusion; Vibration; Machine learning","score_opus":0.012234558427431784,"score_gpt":0.2593144514387702,"score_spread":0.2470798930113384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145030452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024148578,0.0008441099,0.96317786,0.00018351889,0.00036299747,0.00014085528,0.0005233778,0.008693917,0.0019247752],"genre_scores_gemma":[0.4439562,0.00065690547,0.5433126,0.00039789113,0.00033299119,0.00024451056,0.0018999156,0.0002688401,0.008930115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993352,0.000060902446,0.000054474334,0.00014121566,0.00033941068,0.00006880425],"domain_scores_gemma":[0.99915755,0.0001594111,0.000053699576,0.000109902176,0.00047040364,0.000049103783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011088881,0.00060352124,0.0014665736,0.0013692264,0.0007977377,0.0007880481,0.0009029464,0.0012261145,0.00432584],"category_scores_gemma":[0.0016863011,0.00037457113,0.0005445098,0.0008407199,0.00018467712,0.0010510244,0.00091903907,0.0008417127,0.002971883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006326886,0.00021363435,0.0013579899,0.00014940901,0.00015035526,0.00019676003,0.000060982544,0.012995857,0.09628863,0.0018888799,0.011585743,0.8744791],"study_design_scores_gemma":[0.00011058801,0.00062628265,0.005036836,0.00004216822,0.00029417442,0.00068199786,0.000033777975,0.88103575,0.08962947,0.0040549343,0.01836527,0.00008884081],"about_ca_topic_score_codex":0.003544251,"about_ca_topic_score_gemma":0.004610699,"teacher_disagreement_score":0.00432584,"about_ca_system_score_codex":0.00046030458,"about_ca_system_score_gemma":0.000911527,"threshold_uncertainty_score":0.014471352},"labels":[],"label_agreement":null},{"id":"W2156096420","doi":"10.1016/j.eswa.2008.06.136","title":"Petroleum-contaminated groundwater remediation systems design: A data envelopment analysis based approach","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Environmental remediation; Groundwater; Groundwater remediation; Environmental science; Petroleum; Computer science; Data envelopment analysis; Underground storage tank; Risk analysis (engineering); Contamination; Waste management; Engineering; Business; Mathematics; Storage tank","score_opus":0.15209895542921012,"score_gpt":0.3432727566623619,"score_spread":0.19117380123315178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156096420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019549852,0.00043708127,0.9776694,0.0001682306,0.000010507556,0.00008896339,0.000053860622,0.00005144649,0.0019707393],"genre_scores_gemma":[0.73427427,0.0009772426,0.26273668,0.0000770122,0.000028920365,0.00045370084,0.000110737135,0.000049233546,0.0012921327],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956856,0.0027402078,0.000215938,0.00020905452,0.0009950575,0.00015428994],"domain_scores_gemma":[0.99609333,0.0029539664,0.00025447021,0.00014835983,0.0005010417,0.00004873087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006188229,0.0014374405,0.0030592866,0.0022849895,0.0006898183,0.0026806581,0.001035658,0.0014769054,0.0012799668],"category_scores_gemma":[0.01021614,0.0009196689,0.0015950174,0.0028785232,0.00087321916,0.0016945502,0.0013919916,0.0010954463,0.00014873478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035069632,0.000040673647,0.00027957215,0.000118432385,0.000075480915,0.000019853647,0.000033522127,0.9711892,0.00073226215,0.010134243,0.00010331787,0.017238328],"study_design_scores_gemma":[0.000009411059,0.00004926065,0.00014354388,0.000015816508,0.000029579835,0.00000788171,0.000017482937,0.99202263,0.00071572594,0.006639279,0.0003413033,0.000008019186],"about_ca_topic_score_codex":0.006562272,"about_ca_topic_score_gemma":0.0035328416,"teacher_disagreement_score":0.006562272,"about_ca_system_score_codex":0.0023641707,"about_ca_system_score_gemma":0.004087155,"threshold_uncertainty_score":0.032726884},"labels":[],"label_agreement":null},{"id":"W2171666901","doi":"10.1016/j.eswa.2006.12.027","title":"Predicting opponent’s moves in electronic negotiations using neural networks","year":2006,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; HEC Montréal","funders":"","keywords":"Computer science; Negotiation; Artificial neural network; Artificial intelligence; Machine learning; Context (archaeology); Adversary; Process (computing); Set (abstract data type); Intelligent agent; Computer security","score_opus":0.014019877024927015,"score_gpt":0.24235843668610643,"score_spread":0.2283385596611794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171666901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91418386,0.00037489855,0.080894254,0.0004844225,0.00008483093,0.0000622424,0.00007354869,0.00013096051,0.0037109545],"genre_scores_gemma":[0.9930328,0.00005532522,0.005820954,0.000020843223,0.000011819999,0.000014794116,0.000046145702,0.000008394785,0.0009889711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928504,0.00031968972,0.000051545532,0.00012524208,0.00011561414,0.00010275944],"domain_scores_gemma":[0.98905635,0.009308738,0.00060165673,0.00017729003,0.00061206345,0.000243891],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030338361,0.0005465575,0.0006722449,0.0012003885,0.00073522556,0.0013764034,0.0011388612,0.0016945743,0.0023657086],"category_scores_gemma":[0.016180228,0.0006341503,0.0003370119,0.0008485285,0.0006416322,0.002719578,0.0008254231,0.001861932,0.0003375061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013204141,0.00044909897,0.025744623,0.0000650559,0.00010553254,0.00017456315,0.00021401838,0.89949167,0.0014420337,0.0042681373,0.000669603,0.06605522],"study_design_scores_gemma":[0.000007853333,0.00001615568,0.000762146,0.0000028256286,0.000004297321,0.000004040403,0.00001915757,0.9978637,0.00022972265,0.0010596334,0.000027141909,0.0000032907537],"about_ca_topic_score_codex":0.009838804,"about_ca_topic_score_gemma":0.011720754,"teacher_disagreement_score":0.009838804,"about_ca_system_score_codex":0.00096602284,"about_ca_system_score_gemma":0.0005963319,"threshold_uncertainty_score":0.01956302},"labels":[],"label_agreement":null},{"id":"W2172715982","doi":"10.1016/j.eswa.2015.10.002","title":"A multi-criteria decision support model for evaluating the performance of partnerships","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Outsourcing and Supply Chain Management","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decision support system; Knowledge management; Artificial intelligence; Operations research; Process management; Machine learning; Management science; Business; Mathematics","score_opus":0.15143378113750794,"score_gpt":0.3461659866471319,"score_spread":0.19473220550962395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172715982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18744008,0.0004834816,0.8034425,0.00087267463,0.00011037927,0.0005893689,0.0006061373,0.00053051626,0.0059248083],"genre_scores_gemma":[0.8921471,0.00012866384,0.10524158,0.00009922519,0.00003449529,0.0004306612,0.00029921348,0.00002403087,0.0015950401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959133,0.0024661713,0.00024345194,0.00040737027,0.000590209,0.00037950266],"domain_scores_gemma":[0.988931,0.008609866,0.00053776853,0.00023922097,0.0010890244,0.00059305923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007810784,0.0016300434,0.0023224289,0.0030003723,0.0010300591,0.0035326132,0.0024110933,0.00316673,0.0048739812],"category_scores_gemma":[0.0120135965,0.0008244232,0.0014045413,0.0023782358,0.0009861769,0.002040235,0.0016000948,0.001488359,0.0004555961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020315278,0.00014802968,0.0009266067,0.000055539407,0.000074290176,0.00006717924,0.000040929153,0.98204374,0.00021240501,0.0021791726,0.00033028642,0.013718647],"study_design_scores_gemma":[0.000019720996,0.00006948414,0.00009704061,0.0000059371264,0.000013061773,0.0000068643353,0.000011142586,0.9986833,0.000053696458,0.00097528595,0.00005914485,0.00000532621],"about_ca_topic_score_codex":0.012893068,"about_ca_topic_score_gemma":0.008172253,"teacher_disagreement_score":0.012893068,"about_ca_system_score_codex":0.0031463502,"about_ca_system_score_gemma":0.00291575,"threshold_uncertainty_score":0.041307867},"labels":[],"label_agreement":null},{"id":"W2184738125","doi":"10.1016/j.eswa.2015.11.019","title":"Inference in hybrid Bayesian networks with large discrete and continuous domains","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Bayesian network; Inference; Computer science; Bounded function; Partition (number theory); Domain (mathematical analysis); Computation; Tree (set theory); Machine learning; Artificial intelligence; Algorithm; Mathematics","score_opus":0.014585747437059577,"score_gpt":0.25965684902820324,"score_spread":0.24507110159114368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184738125","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010636743,0.0011497425,0.9862598,0.0007303028,0.000041311112,0.000025740537,0.00013719982,0.00012184315,0.0008973558],"genre_scores_gemma":[0.54071414,0.0030474877,0.44799945,0.0006116415,0.0006190284,0.00047944213,0.0009032603,0.00014325003,0.0054822615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99072146,0.005378064,0.00055936666,0.0017691877,0.0012281879,0.0003437599],"domain_scores_gemma":[0.8866824,0.10545772,0.0031171264,0.002527071,0.0014073276,0.0008083992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016896503,0.001599938,0.0040226234,0.003977945,0.0014783639,0.0057829274,0.0050073066,0.005174715,0.0040583177],"category_scores_gemma":[0.07169565,0.0039729904,0.0027652371,0.004335668,0.005621345,0.011778565,0.005527778,0.0053459504,0.0004661817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012271471,0.00006884118,0.0012727971,0.00023308935,0.00024039258,0.00019960076,0.000120741366,0.80013156,0.00019704952,0.17543006,0.0007928125,0.021190442],"study_design_scores_gemma":[0.000022132608,0.0000074731497,0.00013117169,0.000016628615,0.000025514884,0.000026617268,0.000014697308,0.8439654,0.00006824517,0.15538412,0.0003254825,0.000012473931],"about_ca_topic_score_codex":0.012268727,"about_ca_topic_score_gemma":0.00975775,"teacher_disagreement_score":0.016896503,"about_ca_system_score_codex":0.003459729,"about_ca_system_score_gemma":0.0017218203,"threshold_uncertainty_score":0.08935833},"labels":[],"label_agreement":null},{"id":"W2210667604","doi":"10.1016/j.eswa.2015.12.025","title":"Bank branch operational performance: A robust multivariate and clustering approach","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Rogers Communications (Canada); University of Toronto","funders":"","keywords":"Data envelopment analysis; Cluster analysis; Computer science; Aggregate (composite); Principal component analysis; Efficient frontier; Data mining; Cluster (spacecraft); Frontier; Artificial intelligence; Statistics; Mathematics; Economics; Finance","score_opus":0.1116224531659783,"score_gpt":0.34692177683529357,"score_spread":0.23529932366931527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2210667604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06499119,0.00024023834,0.9316978,0.0001777541,0.000025116173,0.000061019295,0.00027737475,0.00032508804,0.0022044282],"genre_scores_gemma":[0.8231013,0.0001915082,0.17338671,0.000042850254,0.00010383186,0.00012054221,0.00083871087,0.00018995372,0.0020246617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976051,0.0011169433,0.00013686622,0.00044955264,0.0005408922,0.00015061359],"domain_scores_gemma":[0.9941246,0.0034909584,0.00064347207,0.0008036991,0.00084078155,0.00009641799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050323205,0.0012132047,0.0020051722,0.0030967998,0.00059567555,0.0018303834,0.0019166231,0.0012279423,0.0019872314],"category_scores_gemma":[0.013327003,0.0006178475,0.0016975721,0.0029622107,0.00071591884,0.0015703092,0.0010988782,0.0012869413,0.0004479207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020747111,0.00015972483,0.0026376992,0.00007456661,0.0003209299,0.000024751847,0.000053414504,0.9030939,0.0016316407,0.012685007,0.0010977488,0.07801316],"study_design_scores_gemma":[0.0000051589627,0.00002244065,0.0021767404,0.0000046919426,0.000022213608,0.0000079495185,0.000010764831,0.99377877,0.00031277863,0.0034764733,0.00016909516,0.000012829598],"about_ca_topic_score_codex":0.00574884,"about_ca_topic_score_gemma":0.0044733356,"teacher_disagreement_score":0.00574884,"about_ca_system_score_codex":0.0013452632,"about_ca_system_score_gemma":0.0010794094,"threshold_uncertainty_score":0.026613772},"labels":[],"label_agreement":null},{"id":"W2215029138","doi":"10.1016/j.eswa.2015.07.066","title":"An improved grey relational analysis approach for panel data clustering","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for International Governance Innovation; University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Graduate Research and Innovation Projects of Jiangsu Province; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Agra; Cluster analysis; Series (stratigraphy); Pairwise comparison; Grey relational analysis; Data mining; Mathematics; Similarity (geometry); Computer science; Mainland China; Statistics; Pattern recognition (psychology); Artificial intelligence; Geography; China","score_opus":0.3202236941888919,"score_gpt":0.41392763083218975,"score_spread":0.09370393664329785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215029138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026519496,0.000113445465,0.9961653,0.00006392174,0.000021661052,0.000033947865,0.00009888625,0.00020124987,0.0006496335],"genre_scores_gemma":[0.14153172,0.00028952444,0.85375404,0.00010220103,0.00010495525,0.00017279788,0.00070302293,0.00020553042,0.0031361757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976822,0.0009820275,0.00012652214,0.0004093725,0.0007017003,0.00009816619],"domain_scores_gemma":[0.9970727,0.0015307192,0.0001212562,0.00046535765,0.0007364734,0.000073508716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029918645,0.0006869308,0.001867837,0.0026860726,0.0007827347,0.0019386045,0.0017279158,0.0010401233,0.005450771],"category_scores_gemma":[0.008398828,0.0005158202,0.0017032801,0.003138317,0.0004832821,0.0018011369,0.001955463,0.001535358,0.0017047225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016862147,0.00012953333,0.0014739337,0.00032103332,0.000348545,0.0002334665,0.00042577687,0.41546723,0.008841312,0.066890284,0.006667401,0.49903283],"study_design_scores_gemma":[0.000006360413,0.000017851153,0.0003913762,0.000009317736,0.000033876375,0.000027536098,0.00002396401,0.98110604,0.0006025313,0.016113084,0.0016531888,0.000014851381],"about_ca_topic_score_codex":0.007570388,"about_ca_topic_score_gemma":0.0086291125,"teacher_disagreement_score":0.007570388,"about_ca_system_score_codex":0.0008094026,"about_ca_system_score_gemma":0.0014862339,"threshold_uncertainty_score":0.01823467},"labels":[],"label_agreement":null},{"id":"W2237959143","doi":"10.1016/j.eswa.2016.01.002","title":"Malicious sequential pattern mining for automatic malware detection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Malware; Computer science; Executable; Data mining; Classifier (UML); Trojan; Cryptovirology; Intrusion detection system; Sequential Pattern Mining; System call; Artificial intelligence; Machine learning; Computer security; Operating system","score_opus":0.015572173672190109,"score_gpt":0.24756622521265076,"score_spread":0.23199405154046066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237959143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16319716,0.0013346116,0.8262768,0.00025825296,0.00011949212,0.00023173765,0.001137709,0.0053652674,0.0020789863],"genre_scores_gemma":[0.61032456,0.00043479644,0.38409638,0.000084544496,0.000078033256,0.00014251878,0.0018944547,0.00015263897,0.0027919356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990802,0.00016531878,0.00011525668,0.0002519789,0.0003118892,0.00007531647],"domain_scores_gemma":[0.9977629,0.0010564546,0.00024628022,0.00034162865,0.0005023944,0.00009022326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077110284,0.000710879,0.0007063558,0.003567757,0.00074559683,0.00079345,0.00082632806,0.0005710993,0.0014960477],"category_scores_gemma":[0.0033998454,0.00034445315,0.00082173775,0.0018177667,0.00035197692,0.001003939,0.00054202066,0.0007320522,0.00079903443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047666623,0.00043369987,0.020415364,0.00034327467,0.00024330446,0.0006928627,0.00016947057,0.029026747,0.056575153,0.005295506,0.0054794825,0.8808486],"study_design_scores_gemma":[0.000018745899,0.00019034524,0.004449356,0.000026862097,0.00008970998,0.0009468088,0.000063676605,0.95452464,0.026732773,0.009229061,0.003706674,0.000021279113],"about_ca_topic_score_codex":0.002223353,"about_ca_topic_score_gemma":0.004183549,"teacher_disagreement_score":0.003567757,"about_ca_system_score_codex":0.00033820633,"about_ca_system_score_gemma":0.0009793343,"threshold_uncertainty_score":0.0050047636},"labels":[],"label_agreement":null},{"id":"W2294468862","doi":"10.1016/j.eswa.2016.02.037","title":"Erratum to “A cooperative coevolutionary algorithm for the Multi-Depot Vehicle Routing Problem [Expert Systems with Applications 43 (2015) 117–130]”","year":2016,"lang":"en","type":"erratum","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Vehicle routing problem; Depot; Routing (electronic design automation); Routing algorithm; Mathematical optimization; Artificial intelligence; Computer network; Mathematics; Routing protocol","score_opus":0.01891774664071865,"score_gpt":0.28406008841891456,"score_spread":0.2651423417781959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294468862","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00062010996,0.0022329807,0.009315983,0.062650286,0.90310544,0.00009102526,0.0020020634,0.00069095095,0.019291159],"genre_scores_gemma":[0.02502592,0.010277603,0.03968872,0.10020228,0.14744118,0.0004644309,0.009732992,0.0024055832,0.66476125],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99702865,0.0005037618,0.00051341986,0.0003692323,0.0014172692,0.00016769965],"domain_scores_gemma":[0.98733056,0.0030335966,0.00058235606,0.0007231235,0.008013486,0.00031685937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026947677,0.0017603441,0.0015730126,0.0021588616,0.0028334714,0.002473302,0.003172761,0.005868734,0.052011136],"category_scores_gemma":[0.03690613,0.00085255166,0.0013098913,0.0018932194,0.0017245698,0.0026254249,0.0018043435,0.005379529,0.02480793],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005164481,0.000012635806,0.00007082563,0.00009337959,0.000009426562,0.00013375792,0.000022059296,0.00033324317,0.000093534945,0.0025121756,0.98599094,0.010676273],"study_design_scores_gemma":[0.000058907925,0.00005671131,0.00051118125,0.00029111427,0.00003284873,0.00022473758,0.00007660932,0.0024841833,0.00085888297,0.004590529,0.99075,0.000064238375],"about_ca_topic_score_codex":0.020466737,"about_ca_topic_score_gemma":0.026683034,"teacher_disagreement_score":0.052011136,"about_ca_system_score_codex":0.0031318346,"about_ca_system_score_gemma":0.004460729,"threshold_uncertainty_score":0.1739946},"labels":[],"label_agreement":null},{"id":"W2300366791","doi":"10.1016/j.eswa.2016.03.023","title":"Subject adaptation using selective style transfer mapping for detection of facial action units","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University; University of Northern British Columbia","keywords":"Computer science; Classifier (UML); Transfer of learning; Artificial intelligence; Facial expression; Generalization; Machine learning; Training set; Test set; Test data; Action (physics); Pattern recognition (psychology); Mathematics","score_opus":0.05989822254359857,"score_gpt":0.27845274612364473,"score_spread":0.21855452358004618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300366791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23768196,0.0006109186,0.75295836,0.000083543346,0.00021756567,0.0001954812,0.00035549814,0.0021799384,0.0057167276],"genre_scores_gemma":[0.7810239,0.0006928395,0.20600952,0.00016357578,0.0001122151,0.00023059371,0.00086453307,0.0005051007,0.010397699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973375,0.000072646755,0.000009891568,0.000086155786,0.00006234992,0.000035167788],"domain_scores_gemma":[0.9997962,0.000063004954,0.000009407341,0.00003989088,0.00007525281,0.000016251824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053040055,0.0005216338,0.00042205158,0.00056493474,0.00016277861,0.00031555572,0.00029914168,0.00032721818,0.0027112192],"category_scores_gemma":[0.0008698596,0.00014843399,0.00051371526,0.0005169578,0.00016886726,0.00033073555,0.0005160767,0.00036668673,0.0011413901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005005275,0.000111219575,0.002316856,0.00008283698,0.00010433784,0.00011350062,0.00012188852,0.0053364243,0.3164403,0.00070515985,0.0021774562,0.6719895],"study_design_scores_gemma":[0.000060599945,0.00046926606,0.06159637,0.00002388727,0.00025130247,0.0009684772,0.00018659014,0.71290845,0.2136799,0.0025932568,0.0071962574,0.00006571967],"about_ca_topic_score_codex":0.0011097562,"about_ca_topic_score_gemma":0.0019201392,"teacher_disagreement_score":0.0027112192,"about_ca_system_score_codex":0.00012213684,"about_ca_system_score_gemma":0.00028971193,"threshold_uncertainty_score":0.00906992},"labels":[],"label_agreement":null},{"id":"W2301164878","doi":"10.1016/j.eswa.2016.03.016","title":"Simulating collective intelligence of bio-inspired competing agents","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evolutionary Game Theory and Cooperation","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Collective intelligence; Artificial intelligence; Machine learning","score_opus":0.03324121587427452,"score_gpt":0.31971016620704984,"score_spread":0.2864689503327753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301164878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8618399,0.00014618224,0.121629015,0.00051207957,0.00014477846,0.0000653654,0.000040261006,0.00010285716,0.015519655],"genre_scores_gemma":[0.9866514,0.000031486103,0.011339085,0.00004018594,0.00000951673,0.000039906685,0.000016172762,0.000010013742,0.0018621857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977857,0.00009290545,0.000008472293,0.000028350725,0.00004707348,0.000044618544],"domain_scores_gemma":[0.9982431,0.0012795416,0.00011688139,0.00009890898,0.000096464275,0.0001651371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007077222,0.00039872684,0.0006001679,0.00041748965,0.0005345703,0.000999696,0.0011068581,0.0014546162,0.002112001],"category_scores_gemma":[0.0033268582,0.00031052434,0.00051716523,0.00037825527,0.0010464849,0.0008772685,0.0011062851,0.0008531429,0.00013603167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006294703,0.0000776098,0.00097362325,0.000026755626,0.000038026687,0.00011874419,0.00015507468,0.9742923,0.0018771896,0.019158173,0.00021604424,0.0030035747],"study_design_scores_gemma":[0.000015947538,0.00001763448,0.00011679821,0.000001578278,0.0000043724685,0.0000067855226,0.000023303437,0.9958331,0.00016514253,0.0037160404,0.000096125535,0.000003201655],"about_ca_topic_score_codex":0.0048279297,"about_ca_topic_score_gemma":0.0032901524,"teacher_disagreement_score":0.0048279297,"about_ca_system_score_codex":0.000733741,"about_ca_system_score_gemma":0.00059821416,"threshold_uncertainty_score":0.009599626},"labels":[],"label_agreement":null},{"id":"W2398526858","doi":"10.1016/j.eswa.2016.05.027","title":"A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial neural network; Data mining; Machine learning; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Selection (genetic algorithm); Parametric statistics; Supplier evaluation; Perceptron; Process (computing); Sensitivity (control systems); Supply chain management; Supply chain; Fuzzy control system; Engineering","score_opus":0.13728538443372967,"score_gpt":0.4290438737337837,"score_spread":0.29175848930005405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398526858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045015063,0.00028466378,0.945351,0.00020535383,0.00006794116,0.00007358244,0.00014782339,0.0006230254,0.008231509],"genre_scores_gemma":[0.91378355,0.00025457513,0.081922695,0.00008104115,0.00003333941,0.00022786869,0.00016622666,0.000054077434,0.003476681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995962,0.00015472113,0.000022798373,0.000068003545,0.000118510405,0.00003979559],"domain_scores_gemma":[0.9992687,0.00047016842,0.000059155143,0.000041673193,0.00013114858,0.000029034094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009178608,0.0006480088,0.001361127,0.0010331401,0.00072631385,0.0013892342,0.0017536074,0.0019299173,0.0031191823],"category_scores_gemma":[0.0021341264,0.00064310647,0.0009723345,0.0013562475,0.00047588244,0.0014343371,0.0007747761,0.0007967557,0.0004849119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029579787,0.000024287669,0.00016689861,0.000014773985,0.00001607552,0.000029210063,0.000014325638,0.9913071,0.00021437994,0.0020276404,0.0001479259,0.0060078404],"study_design_scores_gemma":[0.0000025779857,0.000004557905,0.00001910834,0.0000011696919,0.0000028808124,0.0000025938155,0.0000011256951,0.99950755,0.000049471793,0.00035928396,0.000048089012,0.0000017476959],"about_ca_topic_score_codex":0.017082507,"about_ca_topic_score_gemma":0.009072948,"teacher_disagreement_score":0.017082507,"about_ca_system_score_codex":0.0010965457,"about_ca_system_score_gemma":0.0012666453,"threshold_uncertainty_score":0.033966184},"labels":[],"label_agreement":null},{"id":"W2523799825","doi":"10.1016/j.eswa.2016.09.018","title":"An online Bayesian filtering framework for Gaussian process regression: Application to global surface temperature analysis","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Natural Science Foundation of China","keywords":"Computer science; Gaussian process; Kriging; Bayesian probability; Regression; Process (computing); Artificial intelligence; Regression analysis; Machine learning; Data mining; Bayesian linear regression; Gaussian; Pattern recognition (psychology); Bayesian inference; Statistics; Mathematics","score_opus":0.013997732222198038,"score_gpt":0.3108659674084834,"score_spread":0.29686823518628536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523799825","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065722474,0.00007720362,0.998814,0.000060442042,0.00001757322,0.00000831804,0.000014933613,0.00014226988,0.00020805853],"genre_scores_gemma":[0.1135175,0.0009682456,0.8795954,0.0002170843,0.0002777583,0.0002086897,0.00022482811,0.00035074793,0.0046397387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882454,0.00046503788,0.00007255363,0.00021416819,0.00035794903,0.000065858345],"domain_scores_gemma":[0.9970667,0.0018198133,0.0001762274,0.00022176925,0.00062217604,0.00009321535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040673474,0.0009960415,0.0018912085,0.0009341946,0.00074178184,0.0016009094,0.0021945273,0.0023098444,0.0031045794],"category_scores_gemma":[0.010363006,0.0008079304,0.0013834278,0.001433253,0.0010558084,0.0021200487,0.0017898508,0.0024136526,0.0010372496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010509336,0.00017558366,0.0005196471,0.00015121915,0.00010572153,0.00008844618,0.00009991182,0.72837925,0.0035429336,0.0802831,0.0037826607,0.18276648],"study_design_scores_gemma":[0.0000076256892,0.000009766437,0.000053701064,0.0000047284816,0.000009261221,0.000011481717,0.0000026277137,0.9893826,0.00028085898,0.009473926,0.00075487787,0.000008630613],"about_ca_topic_score_codex":0.01565926,"about_ca_topic_score_gemma":0.017170358,"teacher_disagreement_score":0.01565926,"about_ca_system_score_codex":0.0011564653,"about_ca_system_score_gemma":0.0025990114,"threshold_uncertainty_score":0.031136274},"labels":[],"label_agreement":null},{"id":"W2533989243","doi":"10.1016/j.eswa.2016.10.028","title":"Mining erasable itemsets with subset and superset itemset constraints","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Pruning; Computer science; Data mining; Algorithm","score_opus":0.013507094472071372,"score_gpt":0.23505872936831732,"score_spread":0.22155163489624594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533989243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35044995,0.004317593,0.61490655,0.0011776718,0.00028798173,0.0009834431,0.019150449,0.0012955168,0.0074308095],"genre_scores_gemma":[0.5653013,0.0018037999,0.3999789,0.00047607423,0.00033781843,0.0007570263,0.02574601,0.00019046864,0.005408539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969151,0.00057620136,0.0006387951,0.0006551934,0.00097265793,0.00024207792],"domain_scores_gemma":[0.9822342,0.0118480325,0.0016439891,0.0017783671,0.001974481,0.00052091485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003094288,0.0013883705,0.0025130252,0.0060107964,0.00093099754,0.0030775797,0.0028600811,0.0014331478,0.004242951],"category_scores_gemma":[0.023389319,0.00092576456,0.0021907443,0.009088566,0.00074125716,0.0050800634,0.001682084,0.0018039672,0.0008793666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0036105406,0.001211918,0.08021401,0.003057699,0.0019466106,0.0071230615,0.00064526056,0.26834202,0.012622789,0.0502381,0.018973393,0.55201465],"study_design_scores_gemma":[0.00026477818,0.0007908865,0.012890513,0.000568866,0.00057220564,0.0033233196,0.0010077569,0.87448925,0.010390883,0.08205666,0.013531061,0.00011383765],"about_ca_topic_score_codex":0.002667887,"about_ca_topic_score_gemma":0.005530012,"teacher_disagreement_score":0.0060107964,"about_ca_system_score_codex":0.0005077021,"about_ca_system_score_gemma":0.00222852,"threshold_uncertainty_score":0.016364336},"labels":[],"label_agreement":null},{"id":"W2556626475","doi":"10.1016/j.eswa.2016.11.013","title":"Towards computer based lung disease diagnosis using accurate lung air segmentation of CT images in exhalation and inhalation phases","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Institutes of Health","keywords":"Voxel; Thresholding; Segmentation; Computer science; Lung volumes; Lung; Volume (thermodynamics); COPD; Artificial intelligence; Biomedical engineering; Pattern recognition (psychology); Medicine; Image (mathematics); Physics","score_opus":0.018841084999149306,"score_gpt":0.3215828325728669,"score_spread":0.3027417475737176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556626475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019489525,0.00078361796,0.97714955,0.00018031344,0.000036139878,0.00007197644,0.00010869113,0.0013088917,0.00087125436],"genre_scores_gemma":[0.16967957,0.0006506301,0.8272593,0.00014116427,0.00004492791,0.00007219709,0.00027980318,0.00015090652,0.001721542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995307,0.0001045827,0.000042355892,0.00009727127,0.00017729193,0.00004794327],"domain_scores_gemma":[0.99902916,0.0003667885,0.000084938394,0.00011956629,0.00035677725,0.00004274618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001042973,0.0006061592,0.0007348743,0.0021991765,0.00042652694,0.0019509605,0.0009360001,0.002001981,0.0014208921],"category_scores_gemma":[0.002318671,0.00062231236,0.00088371965,0.0008983085,0.00057261175,0.0008882077,0.0009620279,0.00087324745,0.0016506575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004091775,0.0001727518,0.0048844432,0.00033213338,0.0000931432,0.00026717613,0.00024294878,0.045613483,0.4105754,0.0038063487,0.0021342712,0.53146875],"study_design_scores_gemma":[0.0000308831,0.00017185863,0.0068005603,0.00006699866,0.00011941161,0.0009578075,0.00016406325,0.8325169,0.14678334,0.0056448164,0.006691375,0.000051964886],"about_ca_topic_score_codex":0.0035575223,"about_ca_topic_score_gemma":0.0042886296,"teacher_disagreement_score":0.0035575223,"about_ca_system_score_codex":0.00044317264,"about_ca_system_score_gemma":0.0010236899,"threshold_uncertainty_score":0.007073641},"labels":[],"label_agreement":null},{"id":"W2594829673","doi":"10.1016/j.eswa.2017.03.006","title":"Enhanced question understanding with dynamic memory networks for textual question answering","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Question answering; Computer science; Artificial intelligence; Dynamic random-access memory; Natural language processing; Information retrieval","score_opus":0.02361650187737214,"score_gpt":0.2928168512079417,"score_spread":0.2692003493305696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594829673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06460421,0.0011892933,0.9129167,0.0008386078,0.00017857137,0.00027146586,0.0016459797,0.011635813,0.006719325],"genre_scores_gemma":[0.62447894,0.00054995896,0.36043513,0.0004088125,0.00017523397,0.00031397492,0.003344769,0.00050051895,0.00979267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999453,0.00012522934,0.000039895556,0.00022859078,0.00008585768,0.000067358276],"domain_scores_gemma":[0.99708,0.0018904865,0.00016234102,0.00040802994,0.00035534162,0.0001036536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008309583,0.0008839921,0.0007458264,0.0015123227,0.0006609657,0.0017931218,0.0018446661,0.0015802362,0.012636325],"category_scores_gemma":[0.006743533,0.000397312,0.0006601482,0.0011819601,0.00040238528,0.0058182436,0.0023031533,0.0015634238,0.002723923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076805265,0.00048553463,0.0013817447,0.00038020275,0.000098061275,0.00020364104,0.00059205445,0.046512224,0.028236505,0.01255683,0.011175005,0.8976103],"study_design_scores_gemma":[0.000031817934,0.00011144519,0.0004379608,0.000036434518,0.00006601933,0.000089311885,0.00021356631,0.9436906,0.015639002,0.032288626,0.007369714,0.000025585843],"about_ca_topic_score_codex":0.00665113,"about_ca_topic_score_gemma":0.009801405,"teacher_disagreement_score":0.012636325,"about_ca_system_score_codex":0.0008858614,"about_ca_system_score_gemma":0.0008351735,"threshold_uncertainty_score":0.042272747},"labels":[],"label_agreement":null},{"id":"W2594856265","doi":"10.1016/j.eswa.2017.03.011","title":"Smart mobile computation offloading: Centralized selective and multi-objective approach","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Scalability; Mobile device; Distributed computing; Computation offloading; Overhead (engineering); Cloud computing; Speedup; Computation; Mobile computing; Embedded system; Computer network; Edge computing; Operating system","score_opus":0.023737246513329428,"score_gpt":0.28616601330583197,"score_spread":0.26242876679250254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2594856265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03391786,0.00042048574,0.95923525,0.00023019324,0.000096129734,0.00006096827,0.00005310044,0.000354226,0.0056318557],"genre_scores_gemma":[0.9107257,0.0002055525,0.08470519,0.00011611267,0.00015533001,0.000106165746,0.0000755636,0.000084730586,0.0038256394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949074,0.00012152545,0.000021331341,0.000110193054,0.00013421172,0.000122001555],"domain_scores_gemma":[0.9994142,0.0002748644,0.000058412275,0.000068267946,0.00013053675,0.000053663192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077918824,0.001271358,0.0017484374,0.0005610542,0.0007363071,0.0012455324,0.0014992151,0.0010042622,0.0028732943],"category_scores_gemma":[0.0010727727,0.0004863585,0.00074128195,0.0008060395,0.0006519451,0.0010466529,0.0010798085,0.0007396289,0.00032510495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021046917,0.0001395738,0.00042325194,0.000100744975,0.00009408052,0.00008731882,0.00006509276,0.9262778,0.003939872,0.00624954,0.0016154653,0.06079678],"study_design_scores_gemma":[0.000009694151,0.000024299196,0.00008957929,0.0000026058729,0.000010354564,0.00000821039,0.000010349677,0.99775714,0.00029514194,0.0016566387,0.00013313508,0.000003006154],"about_ca_topic_score_codex":0.004438394,"about_ca_topic_score_gemma":0.007689343,"teacher_disagreement_score":0.004438394,"about_ca_system_score_codex":0.0006404665,"about_ca_system_score_gemma":0.0013173951,"threshold_uncertainty_score":0.009612143},"labels":[],"label_agreement":null},{"id":"W2597133720","doi":"10.1016/j.eswa.2017.02.048","title":"A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Classifier (UML); Multiclass classification; Artificial neural network; Artificial intelligence; Constructive; Algorithm; Machine learning; Process (computing); Support vector machine","score_opus":0.15719325117653854,"score_gpt":0.35676153569637986,"score_spread":0.19956828451984132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597133720","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002737649,0.000072710616,0.99557626,0.000050302817,0.000030213785,0.000087898996,0.000017487464,0.0003408497,0.0010867435],"genre_scores_gemma":[0.0878072,0.00007270153,0.9089072,0.00012721024,0.000047723956,0.00044996216,0.00015493797,0.00012053469,0.002312618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999081,0.00022706045,0.0000689908,0.00022841593,0.00030518245,0.00008944563],"domain_scores_gemma":[0.9982869,0.0007602344,0.000121689474,0.000076771285,0.00066562823,0.00008870135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020260273,0.0012710316,0.0017797303,0.0018930004,0.0012809997,0.0014617773,0.001816824,0.002448952,0.0064572305],"category_scores_gemma":[0.0040524015,0.00092748617,0.0011825929,0.0012677604,0.0006694366,0.0012357114,0.001423458,0.0020963417,0.0021794694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030457307,0.00023076734,0.0011583068,0.00022828957,0.00009470306,0.00013613644,0.00024121937,0.34400848,0.008760286,0.015807534,0.0071182726,0.62191135],"study_design_scores_gemma":[0.000020116318,0.00005844891,0.0001377102,0.000016830976,0.000012404087,0.0000293762,0.000013345122,0.9952667,0.0011175151,0.002166222,0.0011516552,0.0000097640495],"about_ca_topic_score_codex":0.0049428353,"about_ca_topic_score_gemma":0.0049591735,"teacher_disagreement_score":0.0064572305,"about_ca_system_score_codex":0.0009579742,"about_ca_system_score_gemma":0.0018602046,"threshold_uncertainty_score":0.021601617},"labels":[],"label_agreement":null},{"id":"W2602986361","doi":"10.1016/j.eswa.2017.03.074","title":"A systematic decision making approach for product conceptual design based on fuzzy morphological matrix","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Quality Function Deployment in Product Design","field":"Business, Management and Accounting","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; National Natural Science Foundation of China","keywords":"Conceptual design; Fuzzy logic; Computer science; New product development; Product design; Pairwise comparison; Function (biology); Reliability engineering; Conceptual model; Data mining; Product (mathematics); Conceptual framework; Failure mode and effects analysis; Axiomatic design; Matrix (chemical analysis); Industrial engineering; Operations research; Management science; Artificial intelligence; Mathematics; Operations management; Engineering","score_opus":0.0767413520473259,"score_gpt":0.3153807371532216,"score_spread":0.23863938510589572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602986361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024493684,0.00005567091,0.9965634,0.00004160209,0.000006753596,0.00006777122,0.000021584361,0.000047717363,0.0007460665],"genre_scores_gemma":[0.062356226,0.00008824987,0.93691367,0.000019312334,0.000010289328,0.00019637265,0.000055600463,0.000014222163,0.00034597155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970715,0.0010477717,0.00026137286,0.00035621875,0.0011697743,0.00009329683],"domain_scores_gemma":[0.9970739,0.0017093901,0.00018354527,0.00017139755,0.00079229125,0.0000694412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031715382,0.00092372304,0.0011478531,0.0033445372,0.0011721445,0.0016512423,0.0014719624,0.0009325178,0.0031741217],"category_scores_gemma":[0.0057329643,0.0007727786,0.0019123345,0.0021095432,0.0010499156,0.0018421209,0.0014626852,0.00091397687,0.0003372882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013843422,0.00021340723,0.0015456245,0.0008919529,0.0002541942,0.000362207,0.0011210723,0.3473748,0.017070247,0.16114974,0.0022139782,0.4676643],"study_design_scores_gemma":[0.000033956934,0.00012727627,0.000490669,0.00010084007,0.00009295131,0.00012162544,0.0001492836,0.91936076,0.0025639986,0.073237814,0.0036755821,0.00004511847],"about_ca_topic_score_codex":0.005290596,"about_ca_topic_score_gemma":0.0072989585,"teacher_disagreement_score":0.005290596,"about_ca_system_score_codex":0.001357214,"about_ca_system_score_gemma":0.0031474852,"threshold_uncertainty_score":0.016772866},"labels":[],"label_agreement":null},{"id":"W2606430242","doi":"10.1016/j.eswa.2017.04.034","title":"Cooperative survival principles for underground flooding: Vitae System based multi-agent simulation","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Centre for International Governance Innovation; Balsillie School of International Affairs; University of Waterloo","funders":"","keywords":"Operationalization; Survivability; Computer science; Flooding (psychology); Vitality; Plan (archaeology); Risk analysis (engineering); Conceptual model; Operations research; Computer security; Business; Engineering; Psychology","score_opus":0.06702607769573742,"score_gpt":0.31404993555878463,"score_spread":0.2470238578630472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606430242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.241193,0.00023219497,0.72198904,0.00097189297,0.00010613875,0.00016782197,0.00014463297,0.00033478352,0.03486052],"genre_scores_gemma":[0.9690973,0.00010467202,0.02520624,0.00006758643,0.000017609798,0.0001297639,0.000041869265,0.000038303082,0.005296685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983084,0.00006016685,0.0000070811,0.000026282494,0.00003489151,0.000040685194],"domain_scores_gemma":[0.9994104,0.00031970878,0.000058179998,0.000036062134,0.000108447195,0.00006734301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049658824,0.00042179337,0.00080444344,0.0005762862,0.00086276233,0.0008874865,0.001215891,0.0014439767,0.003195343],"category_scores_gemma":[0.0020973864,0.00033770245,0.0006489086,0.0003155936,0.000986604,0.0009261977,0.0018314343,0.00087450026,0.00028652392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014842714,0.000016306756,0.0002662043,0.000013770445,0.000008109955,0.000043358676,0.00007940306,0.98611987,0.00040629983,0.010956238,0.00024644073,0.0018291865],"study_design_scores_gemma":[0.0000048708,0.0000082418655,0.000033044395,0.0000017137802,0.0000019435774,0.0000048179936,0.000013587061,0.99803907,0.000049740327,0.0016804476,0.00016020429,0.0000022790998],"about_ca_topic_score_codex":0.009536185,"about_ca_topic_score_gemma":0.0050909393,"teacher_disagreement_score":0.009536185,"about_ca_system_score_codex":0.0007575914,"about_ca_system_score_gemma":0.001020598,"threshold_uncertainty_score":0.01896137},"labels":[],"label_agreement":null},{"id":"W2606579143","doi":"10.1016/j.eswa.2017.04.038","title":"Evaluation of quality measures for contrast patterns by using unseen objects","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Consejo Nacional de Ciencia y Tecnología; Pratt and Whitney Canada","keywords":"Computer science; Quality (philosophy); Contrast (vision); Set (abstract data type); Artificial intelligence; Data mining; Machine learning; Pattern recognition (psychology)","score_opus":0.1123401987785355,"score_gpt":0.3844214697607453,"score_spread":0.2720812709822098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606579143","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6041929,0.0045892578,0.38310292,0.0003752218,0.00024614803,0.00036880857,0.00239217,0.0026772108,0.0020552666],"genre_scores_gemma":[0.79591614,0.00062619254,0.1984651,0.00006813831,0.000119218545,0.000097752854,0.0037008799,0.00034245293,0.0006640276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945416,0.0008656087,0.0008473885,0.0011894745,0.0021513412,0.00040468056],"domain_scores_gemma":[0.9652925,0.01849933,0.003021658,0.0030891122,0.009080289,0.0010170478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007324865,0.0012952745,0.0017200698,0.008335731,0.00064160593,0.003675775,0.0018226053,0.0020847647,0.0015563057],"category_scores_gemma":[0.033367492,0.00041030117,0.0015119446,0.003986501,0.00082268735,0.0037351807,0.0017805371,0.001027449,0.0004524864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008758924,0.0012838063,0.11580356,0.0019648643,0.0018655584,0.00052457175,0.00044096977,0.04361811,0.0601353,0.0035904907,0.004670929,0.75734293],"study_design_scores_gemma":[0.00055018783,0.0026321479,0.095931225,0.00021228517,0.001305997,0.0017906082,0.0006551371,0.8010915,0.08649523,0.00588574,0.0032843226,0.00016559898],"about_ca_topic_score_codex":0.0031535942,"about_ca_topic_score_gemma":0.003083075,"teacher_disagreement_score":0.008335731,"about_ca_system_score_codex":0.0010740248,"about_ca_system_score_gemma":0.0009028121,"threshold_uncertainty_score":0.038738012},"labels":[],"label_agreement":null},{"id":"W2614578299","doi":"10.1016/j.eswa.2017.05.036","title":"Lung CT image based automatic technique for COPD GOLD stage assessment","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Chronic Obstructive Pulmonary Disease (COPD) Research","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Science Research and Technology","keywords":"COPD; Naive Bayes classifier; Lung; Computer science; Medicine; Pulmonary function testing; Stage (stratigraphy); Pulmonary disease; Radiology; Gold standard (test); Image processing; Pattern recognition (psychology); Computer-aided diagnosis; Artificial intelligence; Image (mathematics); Internal medicine; Support vector machine","score_opus":0.02147292705644691,"score_gpt":0.3663812320460131,"score_spread":0.34490830498956615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614578299","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13586175,0.0040782643,0.8418492,0.0005019473,0.00038299325,0.00064380234,0.0022129945,0.006568785,0.00790037],"genre_scores_gemma":[0.5214815,0.0013269848,0.46744698,0.00040135227,0.0002111888,0.0002960189,0.0014326571,0.00030207852,0.007101345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999574,0.00008493262,0.000044220913,0.00010604456,0.0001448668,0.000045893234],"domain_scores_gemma":[0.9992023,0.00022746647,0.00004752471,0.00007246702,0.00040951933,0.000040787585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087282236,0.0005039186,0.0004623124,0.0022738478,0.00032918,0.0008566779,0.0005647285,0.0010822694,0.0040994775],"category_scores_gemma":[0.001934123,0.00027765217,0.0005353188,0.0007259771,0.00015950167,0.00039480644,0.00041097373,0.00050397695,0.0019551932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064088986,0.00015294222,0.02132816,0.0004972605,0.0001437539,0.00052834576,0.00011610666,0.0033256714,0.21672016,0.0009939624,0.007825113,0.74772763],"study_design_scores_gemma":[0.00021652531,0.0011120498,0.16682263,0.0002995362,0.0011530239,0.010598144,0.00044068607,0.43933177,0.3409977,0.0040446008,0.034739222,0.00024404115],"about_ca_topic_score_codex":0.002350854,"about_ca_topic_score_gemma":0.004205861,"teacher_disagreement_score":0.0040994775,"about_ca_system_score_codex":0.00021829388,"about_ca_system_score_gemma":0.0006056599,"threshold_uncertainty_score":0.013714135},"labels":[],"label_agreement":null},{"id":"W2618162153","doi":"10.1016/j.eswa.2017.05.057","title":"Bi-level plant selection and production allocation model under type-2 fuzzy demand","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Mathematical optimization; Selection (genetic algorithm); Production (economics); Fuzzy logic; Parametric statistics; Decision maker; Degree (music); Computer science; Type (biology); Fuzzy number; Parametric programming; Sensitivity (control systems); Order (exchange); Mathematics; Operations research; Fuzzy set; Artificial intelligence; Statistics; Economics; Engineering","score_opus":0.24455221418933454,"score_gpt":0.41841449824012295,"score_spread":0.1738622840507884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618162153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35899436,0.0007555544,0.61143893,0.0013493927,0.0002103639,0.00023289112,0.0016190211,0.00052674796,0.024872627],"genre_scores_gemma":[0.97716343,0.00019068245,0.010772363,0.00005680565,0.000027938717,0.00015688298,0.00023164894,0.00003317447,0.011367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990339,0.00029609908,0.00003860676,0.00023039247,0.00017279296,0.00022821051],"domain_scores_gemma":[0.99852306,0.0008147546,0.00018301688,0.000059558824,0.0002732809,0.0001462943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015712455,0.0012054151,0.002466441,0.0010117467,0.0010470389,0.0026032336,0.0022100916,0.00323742,0.0066797906],"category_scores_gemma":[0.001979548,0.001279703,0.0013097236,0.002103343,0.000994395,0.0017967647,0.0013608321,0.0015280101,0.0007254142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100880614,0.000043841086,0.0004199933,0.000063451065,0.000041326803,0.00016108673,0.0000347357,0.9926398,0.00068887376,0.0034097917,0.00031949516,0.0020768377],"study_design_scores_gemma":[0.0000071262357,0.000017837408,0.00015637447,0.000001965116,0.000008717834,0.000009320095,0.000010295686,0.9989164,0.000059423113,0.00075353734,0.00005268705,0.0000064167966],"about_ca_topic_score_codex":0.016978543,"about_ca_topic_score_gemma":0.009795682,"teacher_disagreement_score":0.016978543,"about_ca_system_score_codex":0.0019475095,"about_ca_system_score_gemma":0.0011491621,"threshold_uncertainty_score":0.033759415},"labels":[],"label_agreement":null},{"id":"W2712273292","doi":"10.1016/j.eswa.2019.112854","title":"Low resolution face recognition using a two-branch deep convolutional neural network architecture","year":2019,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Face (sociological concept); Pattern recognition (psychology); Low resolution; Computer vision; Resolution (logic); Facial recognition system; Transformation (genetics); Image resolution; Set (abstract data type); Image (mathematics); Network architecture; High resolution; Geography; Remote sensing","score_opus":0.026240713047599774,"score_gpt":0.2913787050287733,"score_spread":0.2651379919811735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2712273292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07224609,0.00083678326,0.9133836,0.00033581012,0.00016115159,0.00006265421,0.00046111763,0.0035083825,0.009004418],"genre_scores_gemma":[0.58084977,0.0005323747,0.3933082,0.00032303017,0.00008275197,0.00007548951,0.0011185674,0.00012564134,0.023584248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998286,0.000013607517,0.0000061174696,0.000043580985,0.00007454423,0.000033647466],"domain_scores_gemma":[0.9998659,0.000028673583,0.000009602853,0.00003754205,0.000047264373,0.000010900839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020527272,0.0004760789,0.00045304748,0.00037839738,0.0002070414,0.00062248704,0.00073565275,0.0006972997,0.0057430407],"category_scores_gemma":[0.00044911163,0.00029892588,0.00039823411,0.00035212206,0.00014298678,0.0006584379,0.00062737515,0.0007507803,0.0021312726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033857234,0.0002140185,0.0013230555,0.000114943454,0.00010400738,0.00015700932,0.000036070734,0.04635251,0.18624718,0.0049296785,0.008944442,0.75123847],"study_design_scores_gemma":[0.00001335176,0.00007443996,0.0015545366,0.000015756255,0.00003377037,0.0001625718,0.000011878778,0.9491246,0.04412015,0.0022614105,0.0026128378,0.000014642728],"about_ca_topic_score_codex":0.003933236,"about_ca_topic_score_gemma":0.008271977,"teacher_disagreement_score":0.0057430407,"about_ca_system_score_codex":0.0003425915,"about_ca_system_score_gemma":0.0004970339,"threshold_uncertainty_score":0.019212365},"labels":[],"label_agreement":null},{"id":"W2740450276","doi":"10.1016/j.eswa.2017.07.049","title":"Mathematical modeling and multi-start search simulated annealing for unequal-area facility layout problem","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Simulated annealing; Initialization; Mathematical optimization; Computer science; Benchmark (surveying); Local optimum; Integer programming; Heuristic; Nonlinear programming; Local search (optimization); Metaheuristic; Nonlinear system; Mathematics","score_opus":0.06963401338823909,"score_gpt":0.3053055582720865,"score_spread":0.2356715448838474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740450276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024471479,0.0008773086,0.9595847,0.0004803957,0.00008002381,0.000065904685,0.00012070693,0.000121195204,0.014198197],"genre_scores_gemma":[0.83018863,0.0014665315,0.1528287,0.00015829255,0.00008485832,0.00036927452,0.00023572185,0.0001231754,0.014544857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951506,0.0001898994,0.00002026252,0.000072994386,0.00012976554,0.00007204318],"domain_scores_gemma":[0.9991327,0.00054554583,0.00012233714,0.00003721491,0.00013033742,0.00003189213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085157173,0.0006997091,0.0011992222,0.0010520332,0.0007023608,0.0013272498,0.0017694868,0.00167514,0.004022558],"category_scores_gemma":[0.0021384251,0.00087800226,0.0014023689,0.0015847583,0.0009859116,0.0014239054,0.0008225075,0.0011176175,0.00036517985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000056463277,0.000006636663,0.00006834712,0.000020642066,0.000005864687,0.000014387497,0.000012402347,0.9892511,0.00017045384,0.009421767,0.000118173906,0.0009045012],"study_design_scores_gemma":[0.0000019724253,0.000004130359,0.00003528992,0.0000022535924,0.0000025453028,0.0000049871696,0.0000031533216,0.9977053,0.000044196746,0.0020556005,0.00013849557,0.0000021698418],"about_ca_topic_score_codex":0.013464767,"about_ca_topic_score_gemma":0.008486082,"teacher_disagreement_score":0.013464767,"about_ca_system_score_codex":0.0017443058,"about_ca_system_score_gemma":0.0018526723,"threshold_uncertainty_score":0.026772797},"labels":[],"label_agreement":null},{"id":"W2753170255","doi":"10.1016/j.eswa.2017.09.006","title":"Enhanced automated body feature extraction from a 2D image using anthropomorphic measures for silhouette analysis","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Silhouette; Computer science; Biometrics; Artificial intelligence; Feature extraction; Computer vision; Identification (biology); Feature (linguistics); Pattern recognition (psychology); Image (mathematics)","score_opus":0.03659363111986148,"score_gpt":0.37031999819403427,"score_spread":0.3337263670741728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753170255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036809918,0.00036379177,0.95979697,0.00005155385,0.00005761184,0.00006897036,0.00030848323,0.0013554629,0.0011872328],"genre_scores_gemma":[0.33821815,0.00074517756,0.65455514,0.000111687914,0.00011202522,0.00014197704,0.0010886069,0.00038827435,0.004639021],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996636,0.00004533953,0.000016733637,0.0000906792,0.00015301889,0.000030737272],"domain_scores_gemma":[0.9997073,0.00008084093,0.000033616794,0.0000458309,0.00011745091,0.000014959875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002702057,0.0006873528,0.0006469042,0.0014666079,0.00018760806,0.0006443258,0.0004348066,0.00054686854,0.0028028216],"category_scores_gemma":[0.00083765324,0.00035821783,0.0006110164,0.00081305084,0.00018361292,0.0005239612,0.00066118623,0.00039153473,0.0016857594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028815458,0.00009688628,0.0029654072,0.00026657173,0.00008840714,0.00026969097,0.00012968254,0.008318384,0.33707774,0.00087563944,0.0027524566,0.6468709],"study_design_scores_gemma":[0.000052511845,0.00036127173,0.05446781,0.00009099064,0.00018169194,0.003626928,0.00016997084,0.76485366,0.16291434,0.0022754844,0.0109058,0.00009958536],"about_ca_topic_score_codex":0.0010659681,"about_ca_topic_score_gemma":0.0023711843,"teacher_disagreement_score":0.0028028216,"about_ca_system_score_codex":0.00014130621,"about_ca_system_score_gemma":0.00030584566,"threshold_uncertainty_score":0.009376347},"labels":[],"label_agreement":null},{"id":"W2769284531","doi":"10.1016/j.eswa.2017.11.039","title":"A hybrid machine-learning and optimization method to solve bi-level problems","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Solver; Mathematical optimization; MATLAB; Optimization problem; Set (abstract data type); Nonlinear system; Integer (computer science); Integer programming; Nonlinear programming; Artificial intelligence; Machine learning; Algorithm; Mathematics","score_opus":0.0343912978799911,"score_gpt":0.33373801364444877,"score_spread":0.29934671576445765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769284531","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003407632,0.00012118595,0.9945251,0.00008052064,0.000051981096,0.000035931578,0.000034918095,0.0002006162,0.0015420192],"genre_scores_gemma":[0.11987282,0.00014398614,0.8745487,0.00015473554,0.00007536707,0.0003866994,0.0001754014,0.0001714286,0.004470944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993954,0.00021710045,0.00004091473,0.000086511565,0.00020127531,0.000058937163],"domain_scores_gemma":[0.9986846,0.00083080085,0.0000631485,0.0000734759,0.00027622833,0.00007187206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001609417,0.00085128366,0.0017348909,0.00094269635,0.00072921003,0.0010871311,0.002021376,0.0024987406,0.0053263893],"category_scores_gemma":[0.0026551373,0.00078833645,0.0011631205,0.0012579226,0.0005816521,0.0011645905,0.0016920133,0.0015993181,0.00096876797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056954952,0.00010984932,0.00031288536,0.0001251413,0.00007198017,0.000054670865,0.00003104041,0.9165241,0.001346395,0.011169669,0.0016482153,0.068549044],"study_design_scores_gemma":[0.0000047638546,0.000007751771,0.000020094485,0.00000217155,0.0000025345826,0.0000035473677,0.0000013199634,0.99885464,0.00007928309,0.00076758745,0.00025425357,0.0000020890466],"about_ca_topic_score_codex":0.00803424,"about_ca_topic_score_gemma":0.0088373795,"teacher_disagreement_score":0.00803424,"about_ca_system_score_codex":0.0006468423,"about_ca_system_score_gemma":0.001859386,"threshold_uncertainty_score":0.01781857},"labels":[],"label_agreement":null},{"id":"W2770579925","doi":"10.1016/j.eswa.2017.11.030","title":"Exact and metaheuristic algorithms to minimize the total tardiness of cutting tool sharpening operations","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Sharpening; Tardiness; Computer science; Scheduling (production processes); Metaheuristic; Algorithm; Mathematical optimization; Minification; Schedule; Job shop scheduling; Artificial intelligence; Mathematics","score_opus":0.014599479612991089,"score_gpt":0.2575661905310628,"score_spread":0.24296671091807168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770579925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0859045,0.0015943559,0.90330005,0.00041171262,0.0002868601,0.000114225266,0.00016761455,0.0003473877,0.007873322],"genre_scores_gemma":[0.5868105,0.0008455152,0.40462938,0.0001855028,0.00017298595,0.00024996148,0.00021588402,0.00016519475,0.0067250086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994356,0.00021449939,0.000030746087,0.00005636905,0.00018850293,0.00007432728],"domain_scores_gemma":[0.9985429,0.0009775454,0.00014432622,0.00007798047,0.00021131005,0.00004599128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001812613,0.0009220778,0.0010007065,0.0011671114,0.00042316533,0.00093403074,0.0012302375,0.0012515015,0.0021703711],"category_scores_gemma":[0.0040717055,0.0006493314,0.0008704379,0.0013129921,0.00070345844,0.000833911,0.00052553625,0.0010264609,0.00021957223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062713865,0.000034925404,0.00014737571,0.000047524463,0.000025699092,0.000010408673,0.0000133473095,0.9737292,0.00034125455,0.0039624027,0.0005069151,0.021118235],"study_design_scores_gemma":[0.000020989222,0.000028995802,0.00007288307,0.0000051328298,0.000007910345,0.000003564037,0.000005154379,0.99695873,0.00016983325,0.0025484913,0.00017538431,0.0000029183573],"about_ca_topic_score_codex":0.010061076,"about_ca_topic_score_gemma":0.008915828,"teacher_disagreement_score":0.010061076,"about_ca_system_score_codex":0.0015746431,"about_ca_system_score_gemma":0.001993309,"threshold_uncertainty_score":0.020005047},"labels":[],"label_agreement":null},{"id":"W2789731619","doi":"10.1016/j.eswa.2018.03.021","title":"Adapting dynamic classifier selection for concept drift","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Concept drift; Computer science; Classifier (UML); Artificial intelligence; Machine learning; A priori and a posteriori; Data mining","score_opus":0.018169300024093497,"score_gpt":0.29600069614713753,"score_spread":0.277831396123044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789731619","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07524928,0.0018122703,0.9161742,0.0008310106,0.0007437671,0.0002989649,0.00035162832,0.0028521377,0.0016867764],"genre_scores_gemma":[0.6458833,0.0007026991,0.3443685,0.0007922705,0.0007803733,0.0004226434,0.0017902214,0.0005438485,0.004716122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99632734,0.00083995267,0.0002860606,0.0011102582,0.0011502836,0.00028616624],"domain_scores_gemma":[0.9886798,0.0059133484,0.00042172257,0.0014013983,0.003207877,0.00037589177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008375608,0.0013952379,0.0027824065,0.0030336415,0.0011457938,0.0025453109,0.0039307727,0.0025177703,0.0024210599],"category_scores_gemma":[0.022727456,0.00069276616,0.0013292353,0.0025794622,0.0006170745,0.0031991075,0.0024491595,0.0028714903,0.0017211674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006725167,0.0007529039,0.009381339,0.00017803926,0.00044913444,0.0003203857,0.00021309768,0.10776421,0.013505983,0.0038550023,0.014386338,0.84852093],"study_design_scores_gemma":[0.00004111945,0.00010170087,0.00079241104,0.000015361962,0.00007271854,0.00015699056,0.000037722064,0.98989594,0.0026738185,0.004293418,0.0019030084,0.00001570682],"about_ca_topic_score_codex":0.0032430242,"about_ca_topic_score_gemma":0.0035592353,"teacher_disagreement_score":0.008375608,"about_ca_system_score_codex":0.001094838,"about_ca_system_score_gemma":0.0025108485,"threshold_uncertainty_score":0.044295013},"labels":[],"label_agreement":null},{"id":"W2790858115","doi":"10.1016/j.eswa.2018.01.056","title":"A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Image retrieval; Projection (relational algebra); Transfer of learning; Pattern recognition (psychology); Closing (real estate); Semantic gap; Similarity (geometry); Radon transform; Artificial intelligence; Image (mathematics); Linear search; Algorithm","score_opus":0.027239455216667565,"score_gpt":0.3211081028218519,"score_spread":0.2938686476051844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790858115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023500845,0.00050685654,0.973465,0.00010000706,0.00005533532,0.00010127579,0.00009159589,0.0010408182,0.0011382564],"genre_scores_gemma":[0.35624632,0.00069748453,0.6347252,0.00023060208,0.00011382127,0.00027493836,0.00067407126,0.00021202565,0.0068255714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996537,0.000054899072,0.000027338936,0.000095717565,0.00012334048,0.000045013676],"domain_scores_gemma":[0.99963295,0.00009155351,0.000026320255,0.00009773539,0.0001254562,0.000025879574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007942588,0.00064803904,0.0012964896,0.000916746,0.00038591697,0.00058076915,0.0014754913,0.0008753147,0.0037690785],"category_scores_gemma":[0.0014306873,0.00048802808,0.00094349065,0.0009967411,0.00036715317,0.0014877993,0.0015349699,0.000693059,0.000994009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036688484,0.00026420585,0.0008770577,0.00017167658,0.0001260331,0.00014451049,0.00008430525,0.07114487,0.07191863,0.0044411705,0.005485876,0.84497476],"study_design_scores_gemma":[0.00002488753,0.00018541086,0.0005122684,0.000009117273,0.000058514073,0.00022473281,0.000023183486,0.9784641,0.016519748,0.0019562151,0.0020044819,0.000017307617],"about_ca_topic_score_codex":0.0038778253,"about_ca_topic_score_gemma":0.0045651454,"teacher_disagreement_score":0.0038778253,"about_ca_system_score_codex":0.00034024293,"about_ca_system_score_gemma":0.0012274126,"threshold_uncertainty_score":0.012608767},"labels":[],"label_agreement":null},{"id":"W2791757685","doi":"10.1016/j.eswa.2018.01.023","title":"Progressive boosting for class imbalance and its application to face re-identification","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Mitacs; European Commission","keywords":"Boosting (machine learning); Computer science; Classifier (UML); Artificial intelligence; Uncorrelated; Pattern recognition (psychology); Facial recognition system; Class (philosophy); Machine learning; Oversampling; Face (sociological concept); Sampling (signal processing); Computer vision; Mathematics; Statistics","score_opus":0.020431693406293045,"score_gpt":0.3075387842174565,"score_spread":0.2871070908111635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791757685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013090954,0.0011303078,0.9837286,0.00024829287,0.00018085254,0.00008066334,0.00004939162,0.00072238536,0.0007684785],"genre_scores_gemma":[0.37013647,0.001126536,0.62250423,0.0002876487,0.00041482647,0.00018293064,0.00034730145,0.0002717716,0.004728282],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770373,0.000924703,0.00012418539,0.00038407568,0.0006607382,0.00020267576],"domain_scores_gemma":[0.99428445,0.0030330408,0.00026188605,0.00097563304,0.0012683255,0.00017669708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010223563,0.0011011919,0.0023032408,0.0016060163,0.001008358,0.0012891073,0.0021128824,0.0016648704,0.0015726696],"category_scores_gemma":[0.013940555,0.0006154176,0.0013023993,0.0013182549,0.00095214683,0.0013935401,0.0021633552,0.0024388628,0.0009121234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005535534,0.00032637373,0.0025534537,0.00023833866,0.00022554824,0.00018857862,0.00030187835,0.150977,0.017765217,0.01214818,0.007424822,0.807297],"study_design_scores_gemma":[0.000014719579,0.00006616011,0.0008044795,0.0000149977905,0.000045098684,0.000101919155,0.000025569001,0.98664063,0.0039026835,0.0065229,0.0018496165,0.000011281598],"about_ca_topic_score_codex":0.002567249,"about_ca_topic_score_gemma":0.0023727233,"teacher_disagreement_score":0.010223563,"about_ca_system_score_codex":0.0006712836,"about_ca_system_score_gemma":0.0011019843,"threshold_uncertainty_score":0.05406803},"labels":[],"label_agreement":null},{"id":"W2802383624","doi":"10.1016/j.eswa.2018.03.066","title":"Acceptable costs of minimax regret equilibrium: A Solution to security games with surveillance-driven probabilistic information","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Foundation for Distinguished Young Talents in Higher Education of Guangdong; Queen's University; South China Normal University; China Postdoctoral Science Foundation; Queen's University Belfast","keywords":"Regret; Computer science; Probabilistic logic; Minimax; Solution concept; Mathematical optimization; Scheduling (production processes); Operations research; Nash equilibrium; Artificial intelligence; Mathematics; Machine learning","score_opus":0.005740373240624811,"score_gpt":0.22781028593527008,"score_spread":0.22206991269464527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802383624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056546725,0.0003928225,0.9083655,0.0037836432,0.00012931113,0.00016833359,0.00047372462,0.00028928972,0.029850645],"genre_scores_gemma":[0.8890411,0.0004205977,0.08995885,0.000649987,0.00018854196,0.0004826918,0.00026178308,0.00023403011,0.018762395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980715,0.0009255098,0.00006190101,0.00028763877,0.00032028015,0.00033322198],"domain_scores_gemma":[0.9907409,0.007262142,0.00058440183,0.00029239675,0.00058246055,0.0005376417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037072017,0.001705418,0.0023338045,0.0015050926,0.0008003532,0.0033015013,0.003232784,0.004511951,0.007887688],"category_scores_gemma":[0.020704757,0.0013053102,0.001477566,0.0009835234,0.0027650676,0.0033897173,0.0036214597,0.0038548945,0.00047764258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001101909,0.000065059765,0.00031423938,0.000109990484,0.00006582146,0.00007454965,0.00011313605,0.74398386,0.00043339236,0.24705179,0.002583904,0.005094078],"study_design_scores_gemma":[0.000040600393,0.000032059408,0.00014260298,0.000029287241,0.000019714005,0.0000260669,0.000040398507,0.84286034,0.00012331494,0.15612407,0.0005433609,0.000018186282],"about_ca_topic_score_codex":0.0042789867,"about_ca_topic_score_gemma":0.0037898996,"teacher_disagreement_score":0.007887688,"about_ca_system_score_codex":0.0034850005,"about_ca_system_score_gemma":0.0036465707,"threshold_uncertainty_score":0.026386976},"labels":[],"label_agreement":null},{"id":"W2810060291","doi":"10.1016/j.eswa.2018.07.009","title":"Tracking objects within a smart home","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Trilateration; Random forest; Software deployment; Real-time computing; Tracking system; Ground truth; Radio-frequency identification; Classifier (UML); Data mining; Software; Artificial intelligence; Process (computing); Context (archaeology); Video tracking; Kalman filter; Object (grammar); Computer security","score_opus":0.010929263040730101,"score_gpt":0.2251402570885017,"score_spread":0.21421099404777158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810060291","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6071566,0.00036952694,0.38195202,0.00023705912,0.00011750307,0.000054097436,0.00039011083,0.0020278862,0.0076951636],"genre_scores_gemma":[0.93094134,0.00019193577,0.06302401,0.000055207536,0.000048165042,0.000014921175,0.00023451626,0.000049731225,0.0054402114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997937,0.00002499119,0.000010062151,0.00007762097,0.00006446872,0.00002924356],"domain_scores_gemma":[0.99975795,0.00006240979,0.000037491165,0.0000485162,0.00005296736,0.000040687773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001594924,0.00031391438,0.00044093168,0.00069818716,0.00035748692,0.00078393274,0.0004451223,0.00070365286,0.0013112569],"category_scores_gemma":[0.0006672554,0.00023845007,0.00019472919,0.00079079525,0.00020445779,0.00090109254,0.0008215514,0.0002925101,0.0005830528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015077239,0.00048365816,0.06848813,0.00021704653,0.0002102263,0.0032540793,0.0023448984,0.1197203,0.15226713,0.006237131,0.008074083,0.63719565],"study_design_scores_gemma":[0.00003413452,0.00037896872,0.04900411,0.000034617213,0.00014512172,0.0012808151,0.0013904527,0.89414424,0.03838391,0.00547569,0.009673509,0.000054529435],"about_ca_topic_score_codex":0.0034977216,"about_ca_topic_score_gemma":0.0050086323,"teacher_disagreement_score":0.0034977216,"about_ca_system_score_codex":0.00022131993,"about_ca_system_score_gemma":0.00022519528,"threshold_uncertainty_score":0.0069547296},"labels":[],"label_agreement":null},{"id":"W2896746300","doi":"10.1016/j.eswa.2018.10.034","title":"Optimal learning group formation: A multi-objective heuristic search strategy for enhancing inter-group homogeneity and intra-group heterogeneity","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Sorting; Heuristic; Cluster analysis; Genetic algorithm; Homogeneity (statistics); Algorithm","score_opus":0.03849707131950852,"score_gpt":0.3231924971725831,"score_spread":0.28469542585307456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896746300","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030321786,0.00015925487,0.96578336,0.00020024244,0.00005644226,0.00018338376,0.000023025848,0.00021315127,0.003059273],"genre_scores_gemma":[0.6291437,0.0000986858,0.36669996,0.00026857504,0.000063578314,0.00045522343,0.000083416526,0.00009589068,0.003090982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989396,0.00042435716,0.00004340509,0.00020107726,0.00023310135,0.000158561],"domain_scores_gemma":[0.9978599,0.0010934602,0.0003213978,0.00016390286,0.00038810086,0.00017316084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026768479,0.0013669073,0.0016091212,0.0017475978,0.0011257875,0.001035471,0.0026902861,0.0019744628,0.0029576076],"category_scores_gemma":[0.005667108,0.0005432066,0.0009558089,0.0010041384,0.0013598509,0.0018834368,0.002607893,0.0010719728,0.00038683595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024658255,0.00030114563,0.0008851944,0.00010656419,0.00011131287,0.00011860328,0.00028098963,0.8788032,0.0031084493,0.01576515,0.0021302435,0.0981426],"study_design_scores_gemma":[0.000036583817,0.000095654774,0.000070887654,0.000008108973,0.000019677209,0.000020356483,0.000032234704,0.99532616,0.00059656234,0.0034860864,0.00030000886,0.000007635931],"about_ca_topic_score_codex":0.0029316577,"about_ca_topic_score_gemma":0.0033577783,"teacher_disagreement_score":0.0029576076,"about_ca_system_score_codex":0.0012292818,"about_ca_system_score_gemma":0.0017511544,"threshold_uncertainty_score":0.014156699},"labels":[],"label_agreement":null},{"id":"W2897650261","doi":"10.1016/j.eswa.2024.124863","title":"An empirical evaluation of imbalanced data strategies from a practitioner’s point of view","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Support vector machine; Artificial intelligence; Gradient boosting; Classifier (UML); Boosting (machine learning); Binary classification; Computer science; Correlation; Random forest; Binary number; Pattern recognition (psychology); Cut-point; Correlation coefficient; Machine learning; Mathematics; Data mining; Statistics","score_opus":0.09491998972119446,"score_gpt":0.4088449582014986,"score_spread":0.3139249684803041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897650261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77281195,0.0071215336,0.20248479,0.0029961467,0.00031025085,0.0012113703,0.0010633498,0.0004930865,0.011507534],"genre_scores_gemma":[0.90905523,0.0009418353,0.086666286,0.0003847385,0.00010758143,0.00053996715,0.00095004623,0.0000988966,0.0012553295],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9476272,0.0397942,0.0019225333,0.0030389912,0.007100655,0.0005164115],"domain_scores_gemma":[0.65034455,0.3052039,0.008356834,0.017283617,0.016569067,0.0022419514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05458787,0.0010130748,0.0010080199,0.0027585903,0.00091523473,0.0025299268,0.002132154,0.0019789,0.0029356857],"category_scores_gemma":[0.23385116,0.0003281075,0.0006154106,0.002920041,0.0019159685,0.0051595178,0.0025612765,0.0017524059,0.0007788334],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0095291855,0.007796432,0.09565849,0.0037718,0.0013469394,0.0003250749,0.0034629102,0.11476013,0.00479572,0.039266024,0.017079508,0.7022078],"study_design_scores_gemma":[0.002474403,0.0156953,0.049932696,0.0014538817,0.0013953891,0.0011929575,0.007847077,0.8092385,0.012307034,0.06777181,0.030474627,0.00021624558],"about_ca_topic_score_codex":0.0017209804,"about_ca_topic_score_gemma":0.0020663233,"teacher_disagreement_score":0.05458787,"about_ca_system_score_codex":0.0017319708,"about_ca_system_score_gemma":0.0017505651,"threshold_uncertainty_score":0.2886917},"labels":[],"label_agreement":null},{"id":"W2901417794","doi":"10.1016/j.eswa.2018.11.031","title":"Using semi-independent variables to enhance optimization search","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"National Science Foundation","keywords":"Mathematical optimization; Simulated annealing; Variable neighborhood search; Computer science; Particle swarm optimization; Variable (mathematics); Convergence (economics); Population; Optimization problem; Algorithm; Metaheuristic; Mathematics","score_opus":0.03783625714219308,"score_gpt":0.35223674048402737,"score_spread":0.3144004833418343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901417794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020889087,0.00023149679,0.97474355,0.000099746285,0.000102070226,0.000040883904,0.000032868436,0.00025311843,0.0036070908],"genre_scores_gemma":[0.4270453,0.00027671806,0.56943583,0.00019544741,0.00011886171,0.0001826523,0.0001374245,0.00020826889,0.002399485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913126,0.0004473552,0.000052765266,0.000072547875,0.00025278897,0.000043292443],"domain_scores_gemma":[0.9969488,0.0018920682,0.00025297658,0.0003042452,0.00053183595,0.00007001898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220546,0.00092814566,0.0008145312,0.00079882774,0.00031669677,0.0011383621,0.0010082398,0.0010822074,0.0030092716],"category_scores_gemma":[0.006055145,0.00043422572,0.0006584119,0.0010197728,0.0005787249,0.0018183923,0.0014108293,0.0014148651,0.0005534738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038854592,0.0003445481,0.001275846,0.00024208223,0.00015530392,0.000078466175,0.000096524425,0.7717767,0.0126005225,0.028976861,0.0019284205,0.1821362],"study_design_scores_gemma":[0.000020840056,0.000061724204,0.00013872843,0.0000106730495,0.000018144667,0.000012099387,0.000005643843,0.9936,0.0019753734,0.0034834458,0.00066755246,0.0000058032565],"about_ca_topic_score_codex":0.0005876852,"about_ca_topic_score_gemma":0.0010861156,"teacher_disagreement_score":0.0030092716,"about_ca_system_score_codex":0.00025338578,"about_ca_system_score_gemma":0.0006298108,"threshold_uncertainty_score":0.011663735},"labels":[],"label_agreement":null},{"id":"W2906950933","doi":"10.1016/j.eswa.2019.01.011","title":"Deep understanding in industrial processes by complementing human expertise with interpretable patterns of machine learning","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Natural Resources Canada","funders":"Natural Resources Canada; Institut de Valorisation des Données","keywords":"Computer science; Fault tree analysis; Process (computing); Domain knowledge; Artificial intelligence; Data mining; Set (abstract data type); Machine learning; Domain (mathematical analysis); Fault (geology); Tree (set theory); Decision tree; Knowledge extraction; Engineering; Reliability engineering","score_opus":0.01908878235177313,"score_gpt":0.23443795402078493,"score_spread":0.2153491716690118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906950933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15982537,0.0005980276,0.8356768,0.00064466405,0.000033842593,0.00002748601,0.00014081973,0.0004154893,0.0026373952],"genre_scores_gemma":[0.9417105,0.00037204396,0.056615364,0.00007775266,0.000033740263,0.000014269969,0.00012279546,0.000035348232,0.0010180515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968517,0.00008956735,0.000018171757,0.00009909287,0.00007213636,0.00003593437],"domain_scores_gemma":[0.9984132,0.00095814886,0.00019869565,0.00023059962,0.00015026711,0.000049109105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079189474,0.00043497057,0.0003704332,0.0007520771,0.00020956775,0.000978957,0.0005637503,0.00093778316,0.0013863939],"category_scores_gemma":[0.0041747517,0.00035346404,0.00040032342,0.00043289285,0.00081852265,0.0029411244,0.00095382205,0.0011380147,0.00026245043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002917103,0.00025546164,0.011308144,0.00038295574,0.00015051862,0.0003433294,0.00079226145,0.4649542,0.037460793,0.035058077,0.0023616226,0.44664085],"study_design_scores_gemma":[0.0000052788328,0.000048570615,0.0021738682,0.000021924394,0.000015531994,0.000045312834,0.00005833128,0.94221646,0.006314425,0.048300777,0.0007881845,0.000011359266],"about_ca_topic_score_codex":0.0020407361,"about_ca_topic_score_gemma":0.0027077226,"teacher_disagreement_score":0.0020407361,"about_ca_system_score_codex":0.00038667317,"about_ca_system_score_gemma":0.0005222736,"threshold_uncertainty_score":0.0046379566},"labels":[],"label_agreement":null},{"id":"W2907738398","doi":"10.1016/j.eswa.2018.12.054","title":"Ranking résumés automatically using only résumés: A method free of job offers","year":2018,"lang":"fr","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Consejo Nacional de Ciencia y Tecnología; Association Nationale de la Recherche et de la Technologie","keywords":"Ranking (information retrieval); Computer science; Relevance (law); Rank (graph theory); Information retrieval; Similarity (geometry); Job analysis; Vocabulary; Selection (genetic algorithm); Process (computing); Learning to rank; Resource (disambiguation); Artificial intelligence; Machine learning; Mathematics; Linguistics; Job satisfaction","score_opus":0.05402005072200111,"score_gpt":0.37502874975065953,"score_spread":0.3210086990286584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907738398","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022790216,0.0014271816,0.7724781,0.00044267252,0.0007018218,0.00072924217,0.012933599,0.1767385,0.011758742],"genre_scores_gemma":[0.15446484,0.0005147258,0.75245017,0.00022769072,0.00065393664,0.0005541154,0.019865945,0.010601874,0.060666725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981336,0.00026652604,0.00017130503,0.00050370314,0.0007815406,0.0001433166],"domain_scores_gemma":[0.9941683,0.0022877916,0.00035400604,0.0015379536,0.001290834,0.00036115418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014843378,0.0018839148,0.0018459071,0.005625961,0.0011120156,0.0028250283,0.00220683,0.0013537935,0.028238574],"category_scores_gemma":[0.009490781,0.0008216814,0.0012005292,0.0036530907,0.0004001645,0.0036344167,0.0020121185,0.0014223028,0.024492107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001204078,0.00039513156,0.002801002,0.0005730352,0.0001572934,0.00022323197,0.00019431148,0.0019416523,0.021305298,0.0028456568,0.07307483,0.8952845],"study_design_scores_gemma":[0.0008424144,0.00094937754,0.019797863,0.00030716546,0.00072797615,0.002264124,0.0008524201,0.52820194,0.10079199,0.03699915,0.30773896,0.00052661315],"about_ca_topic_score_codex":0.00213444,"about_ca_topic_score_gemma":0.0055632233,"teacher_disagreement_score":0.028238574,"about_ca_system_score_codex":0.00032973627,"about_ca_system_score_gemma":0.0018156198,"threshold_uncertainty_score":0.0944674},"labels":[],"label_agreement":null},{"id":"W2921247158","doi":"10.1016/j.eswa.2019.04.022","title":"A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":109,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Innovate UK; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Ulster University; Interreg; European Cooperation in Science and Technology; Invest Northern Ireland; Northern California Institute for Research and Education; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; AbbVie; European Commission; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Support vector machine; Artificial intelligence; Machine learning; Computer science; Random forest; Clinical decision support system; Regression; Predictive power; Dementia; Decision support system; Kernel (algebra); Disease; Medicine; Statistics; Mathematics; Pathology","score_opus":0.03540534592713189,"score_gpt":0.36379911443299623,"score_spread":0.3283937685058643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921247158","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30874118,0.0016126761,0.5830751,0.0021261133,0.0008692537,0.003825274,0.011312864,0.07874582,0.009691799],"genre_scores_gemma":[0.61443377,0.0005604626,0.3700492,0.00082474126,0.0003265354,0.0016186233,0.00479475,0.00025863637,0.007133247],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993026,0.00017627711,0.00012647241,0.00018571297,0.00017327761,0.000035507615],"domain_scores_gemma":[0.9955368,0.0029467212,0.00018506694,0.00022111202,0.0008034699,0.00030697457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017640431,0.0009828939,0.0010861114,0.002051868,0.00048480148,0.001336469,0.0011268636,0.0012328827,0.012870525],"category_scores_gemma":[0.0066058263,0.00032474747,0.0003546557,0.0012214413,0.00023528517,0.0010132153,0.000753065,0.0005416602,0.0028509097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042579137,0.0012176497,0.026643196,0.00041609752,0.00020265537,0.0011447163,0.00030663935,0.011883018,0.01355809,0.0012975975,0.039575793,0.8994967],"study_design_scores_gemma":[0.0026683954,0.0034141326,0.045452066,0.0003622855,0.000765742,0.003897452,0.0004782379,0.8826751,0.02294767,0.007928751,0.029101918,0.00030834897],"about_ca_topic_score_codex":0.0060574673,"about_ca_topic_score_gemma":0.0050455197,"teacher_disagreement_score":0.012870525,"about_ca_system_score_codex":0.00053412974,"about_ca_system_score_gemma":0.001231024,"threshold_uncertainty_score":0.04305619},"labels":[],"label_agreement":null},{"id":"W2938956725","doi":"10.1016/j.eswa.2019.04.020","title":"Empirical evaluation of feature projection algorithms for multi-view text classification","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Feature (linguistics); Projection (relational algebra); Artificial intelligence; Pattern recognition (psychology); Algorithm","score_opus":0.13574058111207438,"score_gpt":0.39200477001377315,"score_spread":0.2562641889016988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938956725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30614197,0.005924289,0.6796139,0.0005531218,0.0003540104,0.0004865506,0.00086570176,0.0021969136,0.0038636243],"genre_scores_gemma":[0.7001795,0.00063597725,0.29535443,0.00007007726,0.0001260216,0.00025789265,0.002369666,0.00014989659,0.00085643446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98894286,0.005380802,0.00076617143,0.0013492084,0.0031529146,0.000408092],"domain_scores_gemma":[0.95742255,0.033013023,0.001414418,0.002843234,0.0045671863,0.00073956884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016324753,0.0014634012,0.0016401442,0.004698036,0.0007339641,0.0024041154,0.0016083646,0.0019850007,0.0015817712],"category_scores_gemma":[0.050016906,0.00031946693,0.0014656285,0.0031317405,0.0008275467,0.0036318034,0.0018127399,0.0021856274,0.0008206724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013927938,0.0007706114,0.019120902,0.00068651856,0.00080032315,0.000118979304,0.00032383826,0.14171933,0.0043102796,0.0037080664,0.0041655283,0.8228829],"study_design_scores_gemma":[0.000044876164,0.0007047926,0.0076036486,0.00005490655,0.00009795547,0.00017573398,0.00021219185,0.98292935,0.0046808347,0.0026934599,0.0007615023,0.00004085791],"about_ca_topic_score_codex":0.0018821536,"about_ca_topic_score_gemma":0.0011756422,"teacher_disagreement_score":0.016324753,"about_ca_system_score_codex":0.0012517321,"about_ca_system_score_gemma":0.0009518738,"threshold_uncertainty_score":0.086334586},"labels":[],"label_agreement":null},{"id":"W2951006205","doi":"10.1016/j.eswa.2019.06.030","title":"A knowledge-based expert system to assess power plant project cost overrun risks","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":79,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Queensland University of Technology","keywords":"Computer science; Cost overrun; Unavailability; Bayesian network; Probabilistic logic; Risk analysis (engineering); Fuzzy logic; Expert system; Domain (mathematical analysis); Artificial intelligence; Reliability engineering; Engineering; Construction engineering; Construction industry","score_opus":0.14676121974681092,"score_gpt":0.41744998424193996,"score_spread":0.270688764495129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951006205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13707672,0.0002770784,0.8348772,0.00035662123,0.000066468194,0.00065768504,0.0014447168,0.014232874,0.011010739],"genre_scores_gemma":[0.65194225,0.0001821709,0.34175307,0.00020212574,0.000033598746,0.00036101034,0.0012662085,0.00013047183,0.004129044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943906,0.00011630127,0.0000679248,0.00013709755,0.00020409912,0.000035534616],"domain_scores_gemma":[0.997633,0.0014103974,0.00016438162,0.00015904677,0.00054012274,0.00009308578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001506782,0.000718219,0.00092993013,0.0020297484,0.0004244739,0.0012767938,0.0010756591,0.0013530994,0.0057279156],"category_scores_gemma":[0.0063225348,0.00028918928,0.00038647896,0.0006203159,0.00019555273,0.0011575994,0.00066207495,0.0004623501,0.0011264865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010056284,0.001031784,0.008580794,0.0003398289,0.00023490598,0.0008010025,0.00025557206,0.25954032,0.0149282,0.0025847545,0.011237254,0.69945997],"study_design_scores_gemma":[0.00013276101,0.00014738645,0.002970871,0.00004477513,0.00009550004,0.00016612814,0.000049911883,0.9877251,0.00344961,0.0028258152,0.002354078,0.00003803768],"about_ca_topic_score_codex":0.007922696,"about_ca_topic_score_gemma":0.008491006,"teacher_disagreement_score":0.007922696,"about_ca_system_score_codex":0.0007352396,"about_ca_system_score_gemma":0.001173574,"threshold_uncertainty_score":0.01916182},"labels":[],"label_agreement":null},{"id":"W2966279695","doi":"10.1016/j.eswa.2019.112850","title":"A new model for evaluating subjective online ratings with uncertain intervals","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Grantová Agentura České Republiky","keywords":"Interval (graph theory); Ranking (information retrieval); Computer science; Heuristic; Quality (philosophy); Decision maker; Fuzzy logic; Data mining; Value (mathematics); Mathematical optimization; Machine learning; Artificial intelligence; Mathematics; Operations research","score_opus":0.22822361907365488,"score_gpt":0.48013948567186543,"score_spread":0.2519158665982105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966279695","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073915706,0.00024065922,0.99011946,0.00019436723,0.00005703073,0.000055555913,0.000114188195,0.00013945141,0.0016877097],"genre_scores_gemma":[0.68718743,0.000750019,0.30022538,0.0002495153,0.00028544877,0.00049620477,0.00050349376,0.0001234134,0.010179106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945564,0.0021791568,0.00034280182,0.0010618862,0.0014870212,0.00037278383],"domain_scores_gemma":[0.9881748,0.008236022,0.0009352834,0.0005550809,0.001763475,0.0003353062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065537384,0.0015200082,0.0021349327,0.0019538354,0.00047698873,0.0035485532,0.003926729,0.0025438392,0.004531526],"category_scores_gemma":[0.01844712,0.0008300371,0.0016511101,0.0022099519,0.0013085464,0.0041992855,0.001248114,0.0022001623,0.0010271716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020618513,0.00012929997,0.0012173356,0.00020833944,0.0001819515,0.00022136391,0.00020083078,0.8878619,0.0017765282,0.04966068,0.0018792333,0.056456372],"study_design_scores_gemma":[0.000007326924,0.000028209433,0.00013207262,0.000008083788,0.00001755527,0.000022747798,0.0000057148845,0.9924213,0.00010939457,0.007039001,0.00019726415,0.0000113692095],"about_ca_topic_score_codex":0.0055423505,"about_ca_topic_score_gemma":0.0045691184,"teacher_disagreement_score":0.0065537384,"about_ca_system_score_codex":0.002188613,"about_ca_system_score_gemma":0.0010081548,"threshold_uncertainty_score":0.034659922},"labels":[],"label_agreement":null},{"id":"W2967432812","doi":"10.1016/j.eswa.2019.112861","title":"A feature extraction model based on discriminative graph signals","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Classifier (UML); Graph; Machine learning; Data mining; Theoretical computer science","score_opus":0.013403235668727774,"score_gpt":0.27313873927255,"score_spread":0.25973550360382225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967432812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010722137,0.00020270141,0.9871525,0.00017578603,0.000060457347,0.000034320525,0.0002000163,0.00067972887,0.0007723072],"genre_scores_gemma":[0.5511576,0.0009784148,0.4352295,0.00038738985,0.00020048053,0.00023677638,0.0018786496,0.0002690995,0.009662006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998078,0.000027081122,0.000009181293,0.000078246296,0.0000531831,0.000024615349],"domain_scores_gemma":[0.9997565,0.000079686775,0.000031136555,0.00003775998,0.000076610566,0.000018296172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025379608,0.00061818643,0.00070305157,0.0009087849,0.00020947785,0.00062787527,0.00095637096,0.0008831317,0.0018232665],"category_scores_gemma":[0.0009969405,0.00029567396,0.0006796379,0.0012110313,0.00029014202,0.0011974359,0.0004632028,0.00087157614,0.0013176436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042640578,0.0002522916,0.0019401185,0.0002268781,0.0001312316,0.0002532744,0.000060603066,0.20292054,0.066856034,0.024527917,0.009548874,0.6928558],"study_design_scores_gemma":[0.000009306004,0.000047725982,0.0005702312,0.000006223528,0.000023179999,0.00006052071,0.0000045795246,0.9903514,0.0030814593,0.0047145495,0.001120537,0.000010359819],"about_ca_topic_score_codex":0.002709897,"about_ca_topic_score_gemma":0.003461895,"teacher_disagreement_score":0.002709897,"about_ca_system_score_codex":0.00033911312,"about_ca_system_score_gemma":0.0004829135,"threshold_uncertainty_score":0.0060994625},"labels":[],"label_agreement":null},{"id":"W2971382582","doi":"10.1016/j.eswa.2020.113408","title":"An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction","year":2019,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Queen's University; University of Pittsburgh","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Machine learning; Deep learning; Artificial neural network; Test data; Data mining; Pattern recognition (psychology)","score_opus":0.10109723894667501,"score_gpt":0.4392184916486733,"score_spread":0.33812125270199833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971382582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17668398,0.00366588,0.8036273,0.0010799567,0.0005455333,0.000112102454,0.0024076926,0.0042510354,0.0076265363],"genre_scores_gemma":[0.7720768,0.0010663887,0.19846575,0.00031191666,0.00023168563,0.00010303769,0.0039545083,0.0001371123,0.023652846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984956,0.000017930859,0.000008744071,0.000041468676,0.000044200635,0.000038147613],"domain_scores_gemma":[0.9997608,0.00008182516,0.000013797478,0.00003442851,0.00009454689,0.000014551223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041212223,0.0005960944,0.0005178252,0.00052198867,0.00023682666,0.00041778488,0.0010072538,0.00083519565,0.003239019],"category_scores_gemma":[0.00089882483,0.00039021438,0.00044403953,0.00048712132,0.00015129735,0.0005531049,0.0006569386,0.000603739,0.00096059905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004171508,0.0002470036,0.003099965,0.00010963771,0.00019824822,0.00021128081,0.000027826647,0.24025168,0.0192745,0.004522944,0.017331071,0.7143086],"study_design_scores_gemma":[0.000008995033,0.000016166126,0.00057988835,0.0000045458582,0.000023150724,0.000025015483,0.000002658744,0.99542373,0.0023073226,0.00091249734,0.00069267134,0.000003330096],"about_ca_topic_score_codex":0.018084478,"about_ca_topic_score_gemma":0.027004529,"teacher_disagreement_score":0.018084478,"about_ca_system_score_codex":0.00048708066,"about_ca_system_score_gemma":0.0009949148,"threshold_uncertainty_score":0.03595841},"labels":[],"label_agreement":null},{"id":"W2978373010","doi":"10.1016/j.eswa.2019.113003","title":"A hybrid project portfolio selection procedure with historical performance consideration","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Weighting; Portfolio; Computer science; Project portfolio management; Selection (genetic algorithm); Set (abstract data type); Operations research; Fuzzy logic; Process (computing); Mathematical optimization; Risk analysis (engineering); Project management; Machine learning; Artificial intelligence; Mathematics; Systems engineering; Economics; Engineering; Business","score_opus":0.07055262625820863,"score_gpt":0.354612697240098,"score_spread":0.28406007098188935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978373010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020802528,0.00009618899,0.9775643,0.000038216443,0.000021101603,0.000059802216,0.000042943204,0.0002612564,0.0011137255],"genre_scores_gemma":[0.37290758,0.000110963156,0.6229459,0.00005616393,0.000069845504,0.00023036492,0.00025139694,0.000093689836,0.0033341425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981002,0.0006722552,0.00012356973,0.00026764293,0.0007271793,0.000109029206],"domain_scores_gemma":[0.99761665,0.0013989797,0.00015311358,0.00018108485,0.0005666072,0.00008370974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029882852,0.00071880245,0.0013069977,0.0020852298,0.00041574278,0.0011830892,0.001335219,0.0010016408,0.0033756818],"category_scores_gemma":[0.0042932318,0.00052764965,0.00089264533,0.0021825784,0.00033137086,0.0011405422,0.0010449298,0.00066369737,0.00056245463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030584953,0.00017425162,0.0021324165,0.00018627678,0.0003178396,0.00019184191,0.00008590557,0.46227923,0.013208266,0.007822262,0.0013808996,0.511915],"study_design_scores_gemma":[0.00002362216,0.00011604924,0.00091009866,0.000009042524,0.000044225162,0.00007993103,0.000008797728,0.9943445,0.0018096666,0.001923167,0.0007110394,0.000019935154],"about_ca_topic_score_codex":0.0015758586,"about_ca_topic_score_gemma":0.0019763026,"teacher_disagreement_score":0.0033756818,"about_ca_system_score_codex":0.00039257508,"about_ca_system_score_gemma":0.0009571421,"threshold_uncertainty_score":0.015803695},"labels":[],"label_agreement":null},{"id":"W3001526121","doi":"10.1016/j.eswa.2020.113247","title":"Group-of-features relevance in multinomial kernel logistic regression and application to human interaction recognition","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multinomial logistic regression; Computer science; Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); ENCODE; Kernel (algebra); Gesture; Machine learning; Regression; Logistic regression; Mathematics; Statistics","score_opus":0.039308696707014146,"score_gpt":0.3069440948675826,"score_spread":0.26763539816056847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3001526121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081691526,0.0014496372,0.9142618,0.00039242764,0.000107608364,0.00007903602,0.000090983354,0.001114271,0.0008127514],"genre_scores_gemma":[0.8102142,0.0004598943,0.18538846,0.00012645694,0.00021651003,0.00009263124,0.0003075211,0.00028539912,0.002908808],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979498,0.001018767,0.000105761435,0.0004309724,0.00033092315,0.00016382744],"domain_scores_gemma":[0.9928317,0.004589169,0.0004012234,0.0009819196,0.0009940523,0.00020190852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004264163,0.00069421314,0.0019120013,0.0012867704,0.00068382686,0.0010775165,0.0020623715,0.0016470153,0.0017768787],"category_scores_gemma":[0.01944929,0.0004809854,0.0010104347,0.0017055715,0.00082824996,0.0021011103,0.002209939,0.0016839844,0.0007574145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011258656,0.0006940561,0.010249085,0.00038043087,0.00025550678,0.00036681877,0.00057296286,0.21222608,0.012090686,0.017639525,0.0084687825,0.7359302],"study_design_scores_gemma":[0.000016120644,0.000044204346,0.0013939048,0.000008481367,0.000021609036,0.000047556812,0.000032613083,0.9901216,0.0010030336,0.006796655,0.0005008643,0.000013303573],"about_ca_topic_score_codex":0.0039635724,"about_ca_topic_score_gemma":0.00405784,"teacher_disagreement_score":0.004264163,"about_ca_system_score_codex":0.0006370212,"about_ca_system_score_gemma":0.0009154415,"threshold_uncertainty_score":0.022551298},"labels":[],"label_agreement":null},{"id":"W3005328013","doi":"10.1016/j.eswa.2020.113267","title":"Improving patient-care services at an oncology clinic using a flexible and adaptive scheduling procedure","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; Concordia University","funders":"Mitacs","keywords":"Computer science; Scheduling (production processes); Schedule; Metropolitan area; Operations research; Operations management; Medicine; Operating system","score_opus":0.0877676667062025,"score_gpt":0.4127423758122773,"score_spread":0.3249747091060748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005328013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37189955,0.00015735495,0.62041944,0.00092171115,0.00015206168,0.00040445852,0.00020009358,0.0015104398,0.004334913],"genre_scores_gemma":[0.91345996,0.000041480587,0.0855558,0.000075050775,0.000033670807,0.00007491605,0.00007165603,0.00003140587,0.0006560088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989642,0.00041360795,0.00005976475,0.00021843385,0.00018992074,0.00015404382],"domain_scores_gemma":[0.9985531,0.0006287077,0.0002233307,0.0001475453,0.00024726373,0.00020010417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016221482,0.0005636603,0.00048647006,0.0007578333,0.0008495706,0.0008675597,0.0010928405,0.0007103968,0.0017778145],"category_scores_gemma":[0.0047281063,0.00031386412,0.00058024516,0.0008001636,0.00035949357,0.00069802813,0.00078617927,0.00073169585,0.0001891306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005256967,0.00081382913,0.008226798,0.00006180316,0.00009942125,0.00016910738,0.0002159981,0.8300853,0.009863105,0.002182413,0.0021912376,0.1455653],"study_design_scores_gemma":[0.000051367115,0.00017574767,0.0025314023,0.0000034747432,0.000029188881,0.000038069695,0.000083992294,0.99422246,0.0012629731,0.0010518685,0.00053133816,0.000018145993],"about_ca_topic_score_codex":0.014209462,"about_ca_topic_score_gemma":0.014626867,"teacher_disagreement_score":0.014209462,"about_ca_system_score_codex":0.0011292375,"about_ca_system_score_gemma":0.0033143046,"threshold_uncertainty_score":0.028253496},"labels":[],"label_agreement":null},{"id":"W3010036790","doi":"10.1016/j.eswa.2020.113362","title":"Structural optimization of fuzzy rule-based models: Towards efficient complexity management","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta","keywords":"Fuzzy logic; Particle swarm optimization; Flexibility (engineering); Fuzzy rule; Mathematics; Polynomial; Mathematical optimization; Mean squared error; Computer science; Fuzzy number; Process (computing); Fuzzy set; Algorithm; Artificial intelligence; Statistics","score_opus":0.04136333961467576,"score_gpt":0.24005172328713828,"score_spread":0.19868838367246253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3010036790","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016641976,0.00013421924,0.9807328,0.00020285489,0.000016073232,0.00002862381,0.00004674458,0.00011766097,0.0020790095],"genre_scores_gemma":[0.73764694,0.00043109627,0.25847864,0.000112363625,0.00005983281,0.00018132868,0.0002548346,0.00011937594,0.0027156847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990565,0.00040199433,0.000043217595,0.000127162,0.00030009059,0.00007099128],"domain_scores_gemma":[0.99796754,0.0013105704,0.0001782571,0.0002452176,0.00025155814,0.000046802747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012407681,0.000685138,0.0012545484,0.0005857409,0.00042317537,0.0014962694,0.0010957328,0.0010747833,0.0023535674],"category_scores_gemma":[0.0060531194,0.0005589227,0.0010994739,0.00073511916,0.00079178234,0.0017130263,0.0011444837,0.0013189417,0.00041454803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027588323,0.000030685267,0.00015333097,0.000036515077,0.00002550812,0.000017590171,0.00003323835,0.9513659,0.00074409135,0.023938978,0.0003142492,0.023312325],"study_design_scores_gemma":[0.000002802632,0.00000822878,0.00001895337,0.0000030949675,0.0000043147584,0.0000026792682,0.0000034147981,0.9854891,0.00017719321,0.014141149,0.00014738765,0.000001724157],"about_ca_topic_score_codex":0.0032358493,"about_ca_topic_score_gemma":0.0038577947,"teacher_disagreement_score":0.0032358493,"about_ca_system_score_codex":0.00085287297,"about_ca_system_score_gemma":0.0017321843,"threshold_uncertainty_score":0.007873535},"labels":[],"label_agreement":null},{"id":"W3012666521","doi":"10.1016/j.eswa.2020.113397","title":"A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco; Conselho Nacional de Desenvolvimento Científico e Tecnológico; École de technologie supérieure","keywords":"Computer science; Signature (topology); Context (archaeology); Transformation (genetics); Artificial intelligence; Class (philosophy); Scalability; Machine learning; Natural language processing; Mathematics","score_opus":0.016379702903542973,"score_gpt":0.24341964613745418,"score_spread":0.2270399432339112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012666521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040522877,0.00029968107,0.9544836,0.000102585764,0.00009708846,0.000058509882,0.000098283235,0.0007512489,0.003586085],"genre_scores_gemma":[0.7416051,0.00050287746,0.241547,0.00011020477,0.00019983234,0.00008989609,0.00046650224,0.0004463434,0.0150323035],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990922,0.00015431209,0.000044411794,0.00017829197,0.0004132789,0.0001174557],"domain_scores_gemma":[0.99882644,0.00045593278,0.000109328204,0.00023005132,0.0003434227,0.000034875546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007363748,0.00058056106,0.00069605483,0.0007748991,0.00058102683,0.0010681221,0.0005990889,0.00046893206,0.0073660063],"category_scores_gemma":[0.0024574741,0.00021747682,0.0007061515,0.00069275725,0.00067349407,0.0012038454,0.000760025,0.0009749593,0.0023289898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015452328,0.00026671606,0.0022287162,0.0003113294,0.00009965833,0.00064609427,0.00041247276,0.0642339,0.2543723,0.105286464,0.0052536014,0.56534356],"study_design_scores_gemma":[0.000021551716,0.00025491952,0.0028155688,0.00003689141,0.000048478552,0.0004150173,0.00007195891,0.8970922,0.077093475,0.016825989,0.005285766,0.00003823371],"about_ca_topic_score_codex":0.0009855189,"about_ca_topic_score_gemma":0.0009907259,"teacher_disagreement_score":0.0073660063,"about_ca_system_score_codex":0.00037199634,"about_ca_system_score_gemma":0.0010681659,"threshold_uncertainty_score":0.024641752},"labels":[],"label_agreement":null},{"id":"W3028645543","doi":"10.1016/j.eswa.2020.114486","title":"Super-app behavioral patterns in credit risk models: Financial, statistical and regulatory implications","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Boosting (machine learning); Gradient boosting; Credit risk; Contrast (vision); Finance; Econometrics; Machine learning; Artificial intelligence; Business; Random forest; Economics","score_opus":0.030479663611036652,"score_gpt":0.24497065526839562,"score_spread":0.21449099165735896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3028645543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.956405,0.00022888607,0.037224077,0.0010901636,0.000033102748,0.00006364798,0.00048612876,0.00017733526,0.0042917323],"genre_scores_gemma":[0.99443144,0.0000900291,0.0034537443,0.00010702869,0.000019148694,0.000042001706,0.00025470476,0.00003200134,0.0015697707],"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99856406,0.0007337702,0.000071832954,0.00028294543,0.00020684252,0.00014060953],"domain_scores_gemma":[0.9557417,0.034215722,0.0029563871,0.003856893,0.0020175716,0.0012118153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005037057,0.00041478738,0.0006321149,0.0008566766,0.00055675843,0.0019289119,0.0011934402,0.0013437266,0.0060836147],"category_scores_gemma":[0.043708667,0.00043186406,0.0004824458,0.00076790835,0.00076459756,0.003308046,0.0011619194,0.0021046605,0.00065444893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011815694,0.0014495929,0.7429419,0.0002138588,0.0002845956,0.0008431404,0.0021446336,0.087309055,0.0038132872,0.05697761,0.005669008,0.097171664],"study_design_scores_gemma":[0.000037951206,0.0003742281,0.17284419,0.00008351499,0.00010012876,0.00065813283,0.0010947525,0.7003301,0.0013518453,0.12145768,0.001597724,0.00006982266],"about_ca_topic_score_codex":0.0048438786,"about_ca_topic_score_gemma":0.00797684,"teacher_disagreement_score":0.0060836147,"about_ca_system_score_codex":0.00052102684,"about_ca_system_score_gemma":0.0006072767,"threshold_uncertainty_score":0.026638806},"labels":[],"label_agreement":null},{"id":"W3032090376","doi":"10.1016/j.eswa.2020.113559","title":"Advancement of the search process for digital heritage by utilizing artificial intelligence algorithms","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"A Thinking Ape (Canada)","funders":"","keywords":"Big data; Computer science; Process (computing); Cultural heritage; Data science; Artificial intelligence; Algorithm; Data mining; Archaeology","score_opus":0.04532716793122469,"score_gpt":0.3037497666552349,"score_spread":0.2584225987240102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032090376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035249725,0.0034353025,0.94260526,0.0006061013,0.00012710391,0.00011469626,0.00017224057,0.00080360373,0.016885938],"genre_scores_gemma":[0.29627603,0.0048171706,0.69127965,0.00013681292,0.00013959117,0.0000748415,0.0005639735,0.00011726382,0.0065946598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995894,0.00008604236,0.000030748888,0.00008032384,0.00017880138,0.000034638633],"domain_scores_gemma":[0.999448,0.00020194214,0.000065878776,0.00010210368,0.00014942288,0.00003266328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000728654,0.00059537386,0.0007116436,0.0031556033,0.00072027807,0.0024205775,0.00093011797,0.00093716907,0.0045078965],"category_scores_gemma":[0.0019412568,0.00024691602,0.0008119355,0.002057495,0.00068936613,0.002266344,0.0011732599,0.0007774049,0.0013596747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013630919,0.00019078107,0.0037567955,0.0009272862,0.00009531582,0.00014509416,0.0003467528,0.066652454,0.02453533,0.06597927,0.004723784,0.8325108],"study_design_scores_gemma":[0.000049204555,0.00021431451,0.0037245832,0.00025519915,0.00016127029,0.0005472676,0.0006432634,0.83684456,0.029726978,0.082427375,0.04533013,0.00007577742],"about_ca_topic_score_codex":0.0037561096,"about_ca_topic_score_gemma":0.0033439065,"teacher_disagreement_score":0.0045078965,"about_ca_system_score_codex":0.00052588724,"about_ca_system_score_gemma":0.0016466288,"threshold_uncertainty_score":0.015080452},"labels":[],"label_agreement":null},{"id":"W3034263010","doi":"10.1016/j.eswa.2020.113643","title":"Construction of partner selection criteria in sustainable supply chains: A systematic optimization model","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Selection (genetic algorithm); Supply chain; Sorting; Context (archaeology); Risk analysis (engineering); Genetic algorithm; Operations research; Management science; Process management; Business; Artificial intelligence; Marketing; Machine learning; Economics; Engineering","score_opus":0.013723862206888928,"score_gpt":0.23215338757909323,"score_spread":0.2184295253722043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034263010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040232982,0.00030181656,0.95301795,0.00023373723,0.000015724636,0.00023597856,0.00014865647,0.00014783182,0.0056653926],"genre_scores_gemma":[0.66021,0.000521609,0.3342633,0.00008975463,0.00002824842,0.0009886827,0.00039116124,0.00011025699,0.0033969497],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998175,0.0010970008,0.000075124335,0.00018796523,0.0003011328,0.0001638002],"domain_scores_gemma":[0.99418324,0.00479707,0.00027544942,0.00013935512,0.0004959092,0.000109018896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003848504,0.0013766092,0.0023430889,0.0034434814,0.00097617734,0.0021874572,0.0016396318,0.0027025586,0.0033737542],"category_scores_gemma":[0.00962723,0.0015653885,0.0017005047,0.0026598803,0.0015454597,0.002419011,0.0017391308,0.0012020756,0.00032829057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015547103,0.000027108941,0.00027089883,0.000045163582,0.000023423097,0.00002876593,0.00003251578,0.98606664,0.000111128524,0.006723498,0.00025794233,0.006397369],"study_design_scores_gemma":[0.000006395303,0.00001527814,0.0000608523,0.000012948816,0.000012062605,0.0000051121506,0.000011555708,0.9953863,0.00006494698,0.0042970083,0.00012311709,0.000004343287],"about_ca_topic_score_codex":0.010219404,"about_ca_topic_score_gemma":0.0065611717,"teacher_disagreement_score":0.010219404,"about_ca_system_score_codex":0.0016894259,"about_ca_system_score_gemma":0.0037907714,"threshold_uncertainty_score":0.020353079},"labels":[],"label_agreement":null},{"id":"W3034626566","doi":"10.1016/j.eswa.2020.113954","title":"Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry","year":2020,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Credit card; Quarter (Canadian coin); Profitability index; Debt; Government (linguistics); Balance sheet; Economics; Business; Computer science; Finance","score_opus":0.1360004079547361,"score_gpt":0.3935186606579673,"score_spread":0.2575182527032312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034626566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2164181,0.0010043054,0.7745349,0.0007771922,0.00014976834,0.000088675595,0.0006323832,0.0015324408,0.004862262],"genre_scores_gemma":[0.9057541,0.0005604681,0.08898452,0.00009681098,0.0001603962,0.000078491044,0.0005986185,0.00006515998,0.0037013586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998596,0.000044147913,0.000008603241,0.000034734214,0.000033091124,0.000019770394],"domain_scores_gemma":[0.9996623,0.00014644502,0.000032853186,0.000020410294,0.00011118479,0.000026768406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009404397,0.000414518,0.0007006731,0.0005687301,0.0003116145,0.0009615696,0.00061404955,0.0006607374,0.0021949885],"category_scores_gemma":[0.0018967161,0.00017098476,0.00038637713,0.00066122087,0.00018179178,0.0006184095,0.00039444905,0.0009095017,0.0004355461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001098891,0.00015970817,0.0036684938,0.0000517943,0.0000657551,0.00012343976,0.00004682573,0.8790333,0.0015304871,0.007028903,0.002998069,0.105183326],"study_design_scores_gemma":[0.0000030650913,0.0000031246925,0.00023378842,0.0000011092665,0.0000028061218,0.0000025398217,0.0000029006944,0.99907875,0.000068576,0.0005138195,0.000088011184,0.0000015474842],"about_ca_topic_score_codex":0.029056104,"about_ca_topic_score_gemma":0.015181426,"teacher_disagreement_score":0.029056104,"about_ca_system_score_codex":0.0005217203,"about_ca_system_score_gemma":0.001040476,"threshold_uncertainty_score":0.057773948},"labels":[],"label_agreement":null},{"id":"W3039757765","doi":"10.1016/j.eswa.2020.113672","title":"Dynamic prioritization of surveillance video data in real-time automated detection systems","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Metric (unit); Object detection; Artificial intelligence; Data mining; Object (grammar); Prioritization; Rank (graph theory); Key (lock); Sampling (signal processing); Computer vision; Machine learning; Pattern recognition (psychology)","score_opus":0.018857743318584544,"score_gpt":0.2983644058292559,"score_spread":0.27950666251067136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039757765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.479882,0.0038116835,0.5062526,0.0010843936,0.00059856067,0.0003568136,0.00058985973,0.0024261542,0.004997989],"genre_scores_gemma":[0.93996084,0.0004984584,0.05667522,0.00014373712,0.00016449597,0.0000607125,0.00038396887,0.00008415192,0.0020284762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985642,0.0002306314,0.00010827645,0.00027902168,0.00062481064,0.00019297782],"domain_scores_gemma":[0.9964309,0.0015826261,0.00036584973,0.00017658998,0.0011632742,0.00028073753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018311118,0.00083271955,0.0012311115,0.0034621072,0.0006783543,0.001657639,0.0010136102,0.00076391664,0.0014222893],"category_scores_gemma":[0.0058952533,0.00039274857,0.00028174918,0.0016690508,0.00033083296,0.0015356592,0.00073854334,0.00081053947,0.0005350556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027535746,0.000811572,0.018955655,0.0005190441,0.00013328984,0.00051236193,0.0003516928,0.049602017,0.122235425,0.0037588493,0.006948863,0.79341763],"study_design_scores_gemma":[0.00008453348,0.00069290324,0.02642658,0.00005817724,0.00013720692,0.00077813864,0.00044793138,0.9044936,0.0576688,0.0039334786,0.0052232845,0.000055321372],"about_ca_topic_score_codex":0.005058333,"about_ca_topic_score_gemma":0.007858863,"teacher_disagreement_score":0.005058333,"about_ca_system_score_codex":0.00078852626,"about_ca_system_score_gemma":0.0014545594,"threshold_uncertainty_score":0.010057747},"labels":[],"label_agreement":null},{"id":"W3040412430","doi":"10.1016/j.eswa.2020.113670","title":"Succinct contrast sets via false positive controlling with an application in clinical process redesign","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Contrast (vision); Process (computing); Machine learning; Artificial intelligence; Set (abstract data type); Decision tree; Outcome (game theory); Inference; Data mining; Big data","score_opus":0.024372856669884976,"score_gpt":0.3109844073328848,"score_spread":0.2866115506629998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040412430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00819667,0.00023137676,0.98905563,0.00030720717,0.00006815666,0.00011878426,0.00009427602,0.0014237475,0.00050411647],"genre_scores_gemma":[0.26697224,0.00021826138,0.72980523,0.00030018325,0.00016215663,0.00026486773,0.0002524861,0.00035565216,0.0016688948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9890454,0.004355091,0.0006270512,0.0016981902,0.0038921966,0.0003821502],"domain_scores_gemma":[0.87915844,0.09836655,0.0047016996,0.009758891,0.007051548,0.00096287037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01288887,0.0018760514,0.0020609049,0.0026617392,0.0010412744,0.0034690253,0.003832753,0.002423596,0.0035924963],"category_scores_gemma":[0.10047944,0.0011004442,0.0015714653,0.0017944888,0.0029330826,0.004602288,0.004241607,0.0050039934,0.00061670115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030567134,0.0008210303,0.007032389,0.0004442176,0.0003091872,0.00097482465,0.00061207736,0.29315996,0.018806474,0.10544542,0.0050811106,0.5642566],"study_design_scores_gemma":[0.00012659517,0.00023776195,0.00051915355,0.000046521905,0.00009979065,0.00020091074,0.000031561776,0.93844134,0.012839179,0.04565941,0.00174762,0.000050174065],"about_ca_topic_score_codex":0.00295118,"about_ca_topic_score_gemma":0.0031087217,"teacher_disagreement_score":0.01288887,"about_ca_system_score_codex":0.001658395,"about_ca_system_score_gemma":0.0027577863,"threshold_uncertainty_score":0.06816363},"labels":[],"label_agreement":null},{"id":"W3049199750","doi":"10.1016/j.eswa.2020.113863","title":"An improved ELM-based and data preprocessing integrated approach for phishing detection considering comprehensive features","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Beijing Advanced Innovation Center for Imaging Technology; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Preprocessor; Extreme learning machine; Artificial intelligence; Machine learning; Data mining; Data pre-processing; Pattern recognition (psychology); Artificial neural network","score_opus":0.05343089205035661,"score_gpt":0.2929706132145915,"score_spread":0.2395397211642349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049199750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020684045,0.0007315931,0.9712785,0.000221729,0.0001371527,0.00014116915,0.0005339133,0.0045292866,0.0017426471],"genre_scores_gemma":[0.20664172,0.00055658823,0.77992755,0.0006359529,0.00023009021,0.00026553706,0.0027945396,0.00028140354,0.008666674],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988047,0.00015589749,0.00009742687,0.00026730972,0.0005079594,0.00016677419],"domain_scores_gemma":[0.9986487,0.00029955385,0.000087003216,0.00018884357,0.0007250707,0.000050726943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013971113,0.0013232739,0.0016468844,0.0034022664,0.00059028325,0.0012593865,0.0015287473,0.0014792737,0.0036550816],"category_scores_gemma":[0.0024299428,0.00044054957,0.0012856158,0.0023898147,0.00036504708,0.0018411962,0.0016285309,0.0011393131,0.003807492],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042639975,0.00039286265,0.0041495953,0.00024994015,0.00017143732,0.00025118078,0.00009892421,0.016304279,0.059983976,0.0011641794,0.006443002,0.91036415],"study_design_scores_gemma":[0.00005301525,0.0003140617,0.009924741,0.000048648,0.00023028138,0.000577852,0.0002240945,0.9154662,0.057219774,0.0039032863,0.011960271,0.000077862824],"about_ca_topic_score_codex":0.0034399522,"about_ca_topic_score_gemma":0.0050882837,"teacher_disagreement_score":0.0036550816,"about_ca_system_score_codex":0.00047758155,"about_ca_system_score_gemma":0.0011819758,"threshold_uncertainty_score":0.012227476},"labels":[],"label_agreement":null},{"id":"W3083618710","doi":"10.1016/j.eswa.2020.113959","title":"Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea","keywords":"Computer science; Genetic algorithm; Pickup; Routing (electronic design automation); Vehicle routing problem; Operations research; Point (geometry); Delivery Performance; Set (abstract data type); Decision maker; Artificial intelligence; Industrial engineering; Machine learning; Computer network","score_opus":0.029393816094489624,"score_gpt":0.259616303172509,"score_spread":0.23022248707801937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083618710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03314631,0.00039372215,0.9611732,0.00035990987,0.00008511525,0.00010386605,0.00007286629,0.00020624342,0.0044588065],"genre_scores_gemma":[0.7513312,0.0006166831,0.23195148,0.00027629462,0.00009702049,0.0003835097,0.00027616645,0.00020150543,0.014866114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939847,0.00018518462,0.000024675981,0.00009792721,0.00013642722,0.00015729491],"domain_scores_gemma":[0.99842924,0.0011347446,0.00011726979,0.00003207372,0.00018477827,0.00010193785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016672671,0.0012760819,0.00202742,0.0011432383,0.0006379365,0.0014857309,0.002533065,0.002459269,0.004666914],"category_scores_gemma":[0.0030563504,0.000978991,0.0010783687,0.0014213712,0.00087924174,0.0013337993,0.0008431355,0.0015987114,0.00042504564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000679053,0.00005064107,0.0001487832,0.000049682287,0.000026569436,0.000042021093,0.00003193432,0.98165244,0.0005594489,0.007670757,0.0007168961,0.008982969],"study_design_scores_gemma":[0.000010935947,0.000018224537,0.000026200694,0.0000029609184,0.0000054453967,0.0000037312084,0.0000045798197,0.99849534,0.00007071105,0.0012518473,0.00010691917,0.000002983666],"about_ca_topic_score_codex":0.0160911,"about_ca_topic_score_gemma":0.0070377695,"teacher_disagreement_score":0.0160911,"about_ca_system_score_codex":0.0016477266,"about_ca_system_score_gemma":0.0023909416,"threshold_uncertainty_score":0.03199488},"labels":[],"label_agreement":null},{"id":"W3090849921","doi":"10.1016/j.eswa.2020.114061","title":"Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Recommender system; Computer science; Benchmark (surveying); Class (philosophy); Collaborative filtering; Machine learning; Artificial intelligence; Focus (optics); Decision tree; Similarity (geometry); Process (computing); Outlier; Revenue; Data mining; Information retrieval; Image (mathematics)","score_opus":0.04910518600000155,"score_gpt":0.27813269796166123,"score_spread":0.22902751196165969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090849921","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71925247,0.0017755101,0.26412383,0.0024665706,0.00039292715,0.00027912535,0.00029451345,0.0012727986,0.010142243],"genre_scores_gemma":[0.96096706,0.00016694173,0.03393074,0.00030676686,0.00006417483,0.000037034297,0.0001223238,0.000036017154,0.004368921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902356,0.00041609543,0.000027673173,0.0002321076,0.00018633943,0.000114184455],"domain_scores_gemma":[0.9969302,0.0016748827,0.000159216,0.00041594586,0.00052664964,0.0002931988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020477585,0.000696017,0.0014015477,0.000573431,0.0008231339,0.0012987327,0.0011516085,0.0016976263,0.0023160437],"category_scores_gemma":[0.0072705415,0.0002887088,0.0005871623,0.00050066266,0.00048749943,0.001804406,0.0013748483,0.0014479823,0.0010956628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046910574,0.003589634,0.12739567,0.0007034409,0.00095080154,0.001577935,0.0018802015,0.076056466,0.041833915,0.019535003,0.031964194,0.6898217],"study_design_scores_gemma":[0.00008930029,0.000874104,0.01101901,0.000032456435,0.00015883526,0.00040200676,0.0005219845,0.9706241,0.0031675391,0.009207943,0.0038473255,0.00005543417],"about_ca_topic_score_codex":0.005483938,"about_ca_topic_score_gemma":0.009555169,"teacher_disagreement_score":0.005483938,"about_ca_system_score_codex":0.00046975052,"about_ca_system_score_gemma":0.0005619842,"threshold_uncertainty_score":0.010904014},"labels":[],"label_agreement":null},{"id":"W3095237147","doi":"10.1016/j.eswa.2020.114196","title":"A comprehensive comparison of end-to-end approaches for handwritten digit string recognition","year":2020,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Fundação Araucária; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fondo Nacional de Desarrollo Científico y Tecnológico; Nvidia","keywords":"Segmentation; Computer science; Heuristics; String (physics); End-to-end principle; Artificial intelligence; Pipeline (software); Sequence (biology); NIST; Pattern recognition (psychology); Benchmark (surveying); Representation (politics); Machine learning; Speech recognition; Mathematics","score_opus":0.1278187303993129,"score_gpt":0.3236232059702125,"score_spread":0.19580447557089958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095237147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040661305,0.01900807,0.8865571,0.000276259,0.0012251585,0.00073380803,0.004076107,0.03040927,0.01705299],"genre_scores_gemma":[0.1064577,0.012203484,0.80029094,0.0006872441,0.00032882183,0.00045481173,0.028921887,0.002315004,0.04834012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953852,0.00045160667,0.00028465397,0.00060544664,0.0029442292,0.00032892328],"domain_scores_gemma":[0.9955563,0.0010511839,0.00011637698,0.00081791176,0.002301337,0.00015682937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002543988,0.0027286042,0.002132482,0.0034480256,0.0009931376,0.003428615,0.0034550305,0.0031354288,0.011815625],"category_scores_gemma":[0.005622406,0.00065088633,0.0017870323,0.003144795,0.00035052313,0.0036549377,0.0020513711,0.0016900083,0.016938884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009602506,0.0003574531,0.0008076403,0.0010957463,0.0003318661,0.00014579267,0.00005778,0.0067532538,0.026508214,0.00091855286,0.014751947,0.9473116],"study_design_scores_gemma":[0.00021036719,0.0025534215,0.018479576,0.0006929071,0.00092743634,0.0031514736,0.0006483208,0.44130957,0.39849606,0.009051824,0.12408094,0.00039821054],"about_ca_topic_score_codex":0.002909849,"about_ca_topic_score_gemma":0.0066117183,"teacher_disagreement_score":0.011815625,"about_ca_system_score_codex":0.0006130324,"about_ca_system_score_gemma":0.0013251542,"threshold_uncertainty_score":0.039527237},"labels":[],"label_agreement":null},{"id":"W3098214884","doi":"10.1016/j.eswa.2020.114280","title":"Improving DEA cross-efficiency optimization in portfolio selection","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Portfolio; Selection (genetic algorithm); Data envelopment analysis; Portfolio optimization; Computer science; Stock (firearms); Modern portfolio theory; Mathematical optimization; Econometrics; Economics; Mathematics; Finance; Machine learning","score_opus":0.04463200486552345,"score_gpt":0.3516503686956607,"score_spread":0.30701836383013725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098214884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01945532,0.00075928366,0.97698736,0.000135295,0.000047177287,0.000035223733,0.000036547754,0.00013463046,0.00240917],"genre_scores_gemma":[0.5420516,0.0009224481,0.45121217,0.00024744408,0.00014017265,0.00025964362,0.00028718144,0.00021420364,0.004665124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817383,0.0010443735,0.00011740628,0.00016384656,0.00040317728,0.000097301476],"domain_scores_gemma":[0.9958674,0.0029219515,0.00020086646,0.0002968546,0.000651277,0.0000617012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055235964,0.0013530202,0.0015135442,0.0014858837,0.00048806574,0.0014327788,0.0008780729,0.0011402256,0.0019561267],"category_scores_gemma":[0.012106013,0.0006249311,0.001006228,0.0014533728,0.0004530818,0.0013285505,0.0011367769,0.0013511016,0.000493205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008191383,0.00011124726,0.0005829201,0.00007960224,0.00014917126,0.000029492323,0.000020796522,0.923339,0.0018017159,0.008988996,0.0006751955,0.06414002],"study_design_scores_gemma":[0.000006027271,0.000020161362,0.00015407204,0.0000066117373,0.00001529275,0.000008794703,0.000002357537,0.99666613,0.0007473352,0.0020720977,0.00029804392,0.0000030521194],"about_ca_topic_score_codex":0.002110225,"about_ca_topic_score_gemma":0.0017891481,"teacher_disagreement_score":0.0055235964,"about_ca_system_score_codex":0.00078537845,"about_ca_system_score_gemma":0.0009852387,"threshold_uncertainty_score":0.029211879},"labels":[],"label_agreement":null},{"id":"W3112198773","doi":"10.1016/j.eswa.2022.117230","title":"When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Fonds Wetenschappelijk Onderzoek","keywords":"Categorical variable; Computer science; Feature selection; Black box; Surrogate model; Feature engineering; Generalized linear model; Transparency (behavior); Decision tree; Machine learning; Segmentation; Gradient boosting; Artificial intelligence; Data mining; Boosting (machine learning); Variable (mathematics); Random forest; Mathematics; Deep learning","score_opus":0.040064940698052554,"score_gpt":0.26870500282512394,"score_spread":0.2286400621270714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112198773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098863676,0.000763764,0.8628996,0.017416336,0.00028798007,0.00019603898,0.00053921214,0.00075498753,0.01827829],"genre_scores_gemma":[0.93405783,0.00014221207,0.063806534,0.0005603416,0.00009626698,0.00010844872,0.0001470491,0.00014795375,0.000933299],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.92825574,0.049160667,0.002616131,0.004957727,0.012327848,0.0026819434],"domain_scores_gemma":[0.6562676,0.2652084,0.02378012,0.040411286,0.01070937,0.0036231955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.056998335,0.001127021,0.0024022998,0.0019841648,0.0013863939,0.0127496775,0.0037727025,0.0056288447,0.0033433565],"category_scores_gemma":[0.32696941,0.0013082399,0.0011875338,0.0016588302,0.0073797815,0.018825244,0.008196112,0.008655488,0.0006466124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090777606,0.0001813147,0.0075683324,0.00029177478,0.00018814337,0.00033415365,0.0015716618,0.22272705,0.0013388139,0.71177024,0.002664095,0.05045673],"study_design_scores_gemma":[0.00006189976,0.00007049509,0.00084202894,0.0001365955,0.000042408188,0.000071385744,0.00019859296,0.29704478,0.0012375058,0.6985498,0.0016954729,0.00004904039],"about_ca_topic_score_codex":0.0018255416,"about_ca_topic_score_gemma":0.0017629913,"teacher_disagreement_score":0.056998335,"about_ca_system_score_codex":0.003820852,"about_ca_system_score_gemma":0.004266196,"threshold_uncertainty_score":0.30143958},"labels":[],"label_agreement":null},{"id":"W3115103108","doi":"10.1016/j.eswa.2020.114513","title":"Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":446,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Cluster analysis; Beijing; Convolutional neural network; Artificial intelligence; Air quality index; Artificial neural network; Deep learning; Data mining; Machine learning; Pattern recognition (psychology); Meteorology","score_opus":0.11159161912431012,"score_gpt":0.3171298078202858,"score_spread":0.2055381886959757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115103108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9667359,0.00081084325,0.026637914,0.00042670837,0.00018692164,0.000016311333,0.0013505538,0.00091214955,0.0029227543],"genre_scores_gemma":[0.9952689,0.00011974028,0.002599077,0.000019747906,0.000025194637,0.0000068537643,0.00069676636,0.0000142079125,0.0012496045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998747,0.000008252883,0.0000075414587,0.000049723953,0.00002150233,0.00003832185],"domain_scores_gemma":[0.9998946,0.000020211919,0.000016682237,0.000013389811,0.00003907153,0.000015929965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025929764,0.0006567862,0.0004780493,0.0005028642,0.00027301512,0.0003800354,0.0005337361,0.0005069664,0.0011093831],"category_scores_gemma":[0.0004419043,0.00024302167,0.0005045011,0.00080786645,0.00014335566,0.00060835853,0.00033956714,0.000372975,0.000314836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065038406,0.00030117438,0.0696009,0.00016446669,0.00027863774,0.00049492886,0.00014418046,0.62025094,0.023349306,0.00085008563,0.008758505,0.27515653],"study_design_scores_gemma":[0.0000043663154,0.000014847405,0.017280044,0.0000027871808,0.000020508869,0.000013215684,0.000025332512,0.98070866,0.0014727985,0.00023118756,0.00021734783,0.000008880311],"about_ca_topic_score_codex":0.062391818,"about_ca_topic_score_gemma":0.051922135,"teacher_disagreement_score":0.062391818,"about_ca_system_score_codex":0.0008536633,"about_ca_system_score_gemma":0.00059349684,"threshold_uncertainty_score":0.12405729},"labels":[],"label_agreement":null},{"id":"W3115589305","doi":"10.1016/j.eswa.2020.114526","title":"Multi-level fleet size optimization for containers handling using double-cycling strategy","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cycling; Computer science; Automotive engineering; Operations research; Mathematics","score_opus":0.10300711648361599,"score_gpt":0.29228677783097873,"score_spread":0.18927966134736274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115589305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21612269,0.00042480472,0.76833755,0.0002767169,0.000096817785,0.00014588604,0.00019599983,0.0005550279,0.013844424],"genre_scores_gemma":[0.9391892,0.00011239355,0.05565416,0.0000616834,0.000016271782,0.00010440465,0.00015723867,0.00009448541,0.0046102325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974245,0.000056723984,0.00000977357,0.000052976546,0.000048013466,0.00009008984],"domain_scores_gemma":[0.9996892,0.00012954888,0.000036123834,0.000022595244,0.00007287841,0.000049664453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000656253,0.0011905375,0.0012626991,0.00085749244,0.0005511028,0.0012678035,0.0012568813,0.0012746814,0.0050825123],"category_scores_gemma":[0.00093446474,0.00054486515,0.0011770973,0.0006982247,0.0003579004,0.0011232375,0.0007969114,0.0006972051,0.00032603208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000077998586,0.000054601864,0.00022909937,0.000041144216,0.000023360311,0.000040966108,0.000016543407,0.98627126,0.001988413,0.001175217,0.0004261597,0.009655167],"study_design_scores_gemma":[0.0000056822237,0.00003751745,0.000117630276,0.0000028663796,0.000007705731,0.000005440548,0.000008163033,0.9989706,0.0003339757,0.0004076765,0.00009927147,0.000003460912],"about_ca_topic_score_codex":0.010910461,"about_ca_topic_score_gemma":0.007451408,"teacher_disagreement_score":0.010910461,"about_ca_system_score_codex":0.00094730535,"about_ca_system_score_gemma":0.0013830983,"threshold_uncertainty_score":0.021693885},"labels":[],"label_agreement":null},{"id":"W3118634459","doi":"10.1016/j.eswa.2020.114552","title":"Edge-centric multi-view network representation for link mining in signed social networks","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Metric (unit); Computer science; Node (physics); Link (geometry); Sign (mathematics); Inverse; Neighbourhood (mathematics); Representation (politics); Enhanced Data Rates for GSM Evolution; Theoretical computer science; Complex network; Artificial intelligence; Mathematics; Computer network; World Wide Web","score_opus":0.029012858899141302,"score_gpt":0.31807058504843005,"score_spread":0.28905772614928876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118634459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017984461,0.00029666748,0.97836256,0.00015130891,0.000036801284,0.00006617078,0.0013236984,0.0009383357,0.000839853],"genre_scores_gemma":[0.5312562,0.00068399863,0.45708948,0.00016630458,0.00012401046,0.00030275097,0.0073266234,0.00021561638,0.002835055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993082,0.0001575057,0.00004837437,0.00021698154,0.00020585643,0.00006311742],"domain_scores_gemma":[0.99824905,0.0007725049,0.00025537823,0.00032134197,0.0003054685,0.00009630527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078603806,0.000750457,0.0009819391,0.0033894547,0.0005411996,0.001568887,0.0016829192,0.0013653805,0.0021265452],"category_scores_gemma":[0.0049039815,0.0003903489,0.0010197418,0.0033343087,0.00039464748,0.0023402527,0.0014541543,0.0013768787,0.0009996516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004285331,0.00034940313,0.007981676,0.00046664433,0.00025555177,0.00040748785,0.0004187886,0.36980695,0.014569409,0.04197842,0.015460327,0.5478768],"study_design_scores_gemma":[0.0000064175497,0.000021117963,0.0005417633,0.000020242158,0.000022577648,0.00006625026,0.000048591584,0.9838471,0.0012206654,0.012793293,0.0014035283,0.000008372417],"about_ca_topic_score_codex":0.004859921,"about_ca_topic_score_gemma":0.009106074,"teacher_disagreement_score":0.004859921,"about_ca_system_score_codex":0.00069165253,"about_ca_system_score_gemma":0.00092999043,"threshold_uncertainty_score":0.009663284},"labels":[],"label_agreement":null},{"id":"W3119037501","doi":"10.1016/j.eswa.2021.114572","title":"Reliability-driven Automotive Software Deployment based on a Parametrizable Probabilistic Model Checking","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Soundness; Software deployment; Probabilistic logic; Software; Systems Modeling Language; Software system; Distributed computing; Software engineering; Programming language; Embedded system; Unified Modeling Language; Artificial intelligence","score_opus":0.03097557327469753,"score_gpt":0.2899925838896725,"score_spread":0.25901701061497495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119037501","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0404013,0.0001487737,0.9540361,0.00011260046,0.000041322583,0.00006985002,0.00006591237,0.002913591,0.0022104215],"genre_scores_gemma":[0.920299,0.00009669432,0.07804871,0.00003696616,0.000013571883,0.00009360421,0.00007992889,0.00025311296,0.0010782676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984459,0.0004732889,0.00006542505,0.0002892255,0.0005631669,0.00016284722],"domain_scores_gemma":[0.9967205,0.001760066,0.00034057707,0.000711425,0.00038969697,0.00007771665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014765784,0.00087027374,0.0010343159,0.00085480243,0.00054164696,0.0011784357,0.001489987,0.0008354564,0.0024107676],"category_scores_gemma":[0.0063379,0.00077392044,0.001088682,0.0004889854,0.0009333432,0.0013758452,0.001474706,0.0011773591,0.00047562888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019717519,0.000068449146,0.0010848484,0.00013721401,0.000059368103,0.00021491628,0.00010826185,0.9295007,0.016916601,0.023855664,0.0007057833,0.02715109],"study_design_scores_gemma":[0.000008166462,0.00001844315,0.00010682545,0.0000053687163,0.000011330855,0.000024568631,0.0000027491865,0.99463046,0.0021867305,0.0027972432,0.00020240979,0.0000057397447],"about_ca_topic_score_codex":0.0036883398,"about_ca_topic_score_gemma":0.0035806226,"teacher_disagreement_score":0.0036883398,"about_ca_system_score_codex":0.0008349506,"about_ca_system_score_gemma":0.0017947911,"threshold_uncertainty_score":0.008064806},"labels":[],"label_agreement":null},{"id":"W3128690705","doi":"10.1016/j.eswa.2022.116555","title":"Counting and locating high-density objects using convolutional neural network","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.017363725926031737,"score_gpt":0.24615818182477409,"score_spread":0.22879445589874234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128690705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10804824,0.0011847666,0.88369226,0.00022117316,0.000107565575,0.0001052413,0.00046259537,0.003164807,0.0030133412],"genre_scores_gemma":[0.6147434,0.00087438617,0.3766417,0.00013507693,0.00009104928,0.00007438614,0.0011960147,0.00018937829,0.006054628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990275,0.0000633013,0.0000558835,0.0003504012,0.00031955462,0.00018347852],"domain_scores_gemma":[0.9987784,0.00039893258,0.00019667944,0.00019045471,0.00035831568,0.000077219345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007461808,0.001454236,0.0014648213,0.0044197105,0.00072897377,0.0020559211,0.0023117044,0.0020926162,0.0018423181],"category_scores_gemma":[0.0027914073,0.0010486626,0.00079581677,0.003350349,0.00057540584,0.0020175243,0.002065711,0.0007815869,0.0014133453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053256605,0.0002015239,0.018086134,0.00028635192,0.00021493781,0.0006053278,0.0001894052,0.09032198,0.05105222,0.005169751,0.0048962138,0.8284435],"study_design_scores_gemma":[0.00000980774,0.000045929937,0.0058603464,0.000032517928,0.000058056008,0.0004660158,0.00006228381,0.9627849,0.024492044,0.0041740634,0.001980772,0.000033259083],"about_ca_topic_score_codex":0.015247068,"about_ca_topic_score_gemma":0.021078404,"teacher_disagreement_score":0.015247068,"about_ca_system_score_codex":0.0011379095,"about_ca_system_score_gemma":0.0010813382,"threshold_uncertainty_score":0.03031665},"labels":[],"label_agreement":null},{"id":"W3145694833","doi":"10.1016/j.eswa.2021.114996","title":"Modeling train timetables as images: A cost-sensitive deep learning framework for delay propagation pattern recognition","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Railway Engineering and Dynamics","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Deep learning; Component (thermodynamics); Convolutional neural network; Artificial intelligence; Backpropagation; Data modeling; Artificial neural network; Machine learning; Pattern recognition (psychology); Database","score_opus":0.012725430522021757,"score_gpt":0.234794696189414,"score_spread":0.22206926566739224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145694833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033401176,0.00048656831,0.9629164,0.00028871416,0.000098882614,0.000027594484,0.0004998555,0.0011336504,0.0011471487],"genre_scores_gemma":[0.81635207,0.0006582015,0.17235862,0.00026740308,0.000118934135,0.000090378635,0.0014216359,0.00019376462,0.00853894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998636,0.000016067826,0.000005981733,0.000052367992,0.00003077534,0.00003121077],"domain_scores_gemma":[0.99974996,0.00008825007,0.00003626535,0.000034371642,0.00006808533,0.000023169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003410428,0.0007534088,0.00066333124,0.0005119484,0.00017435121,0.0007416172,0.0016948453,0.0010248662,0.0022688955],"category_scores_gemma":[0.0011019176,0.0004911499,0.0005055867,0.0007660004,0.0003044568,0.00090692815,0.0006655371,0.0015859108,0.00070535415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015379195,0.00010660693,0.00095762324,0.000058119047,0.000049905662,0.000060542985,0.000028033543,0.78046656,0.006827901,0.0066649104,0.0039668237,0.20065932],"study_design_scores_gemma":[0.0000015012599,0.0000060673947,0.00006314236,0.0000018909127,0.0000031497316,0.0000044287717,0.0000014552389,0.99785334,0.00066611974,0.0012236123,0.00017333037,0.0000019162424],"about_ca_topic_score_codex":0.016811384,"about_ca_topic_score_gemma":0.017441414,"teacher_disagreement_score":0.016811384,"about_ca_system_score_codex":0.00085777114,"about_ca_system_score_gemma":0.00097424194,"threshold_uncertainty_score":0.03342706},"labels":[],"label_agreement":null},{"id":"W3146978883","doi":"10.1016/j.eswa.2021.114920","title":"Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":103,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Benchmark (surveying); Computer science; Coronavirus disease 2019 (COVID-19); Convergence (economics); Gaussian; Economic shortage; Field (mathematics); Mathematical optimization; Pandemic; Lévy flight; Artificial intelligence; Machine learning; Random walk; Mathematics; Statistics","score_opus":0.16035184046878445,"score_gpt":0.35906053068425103,"score_spread":0.19870869021546658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146978883","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05500094,0.0014939202,0.9388586,0.00091130805,0.00013643826,0.000093069524,0.00015425193,0.0007686629,0.0025827833],"genre_scores_gemma":[0.8254313,0.0005910716,0.1671762,0.0004421772,0.000108296495,0.00024284172,0.0003376192,0.00026861115,0.00540192],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992336,0.00043777825,0.00004371568,0.00012902806,0.00007301198,0.00008288282],"domain_scores_gemma":[0.9935976,0.0051887375,0.00021808223,0.00011307108,0.0006798164,0.00020275572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004593748,0.0013233097,0.003535616,0.0010370595,0.00074623665,0.0017373502,0.001627642,0.003924717,0.002673634],"category_scores_gemma":[0.009701057,0.0011191448,0.0012988404,0.0008286044,0.0012877015,0.0013263689,0.0016330339,0.0025137113,0.00044023493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048993606,0.000023867493,0.0002989627,0.00003437335,0.00003513819,0.00003175216,0.000020637459,0.99263614,0.00009691469,0.0020462014,0.0003382563,0.004388866],"study_design_scores_gemma":[0.000004054747,0.000006959905,0.00002723541,0.0000023555397,0.0000024281971,0.0000016739731,0.0000015526344,0.9994056,0.000015647604,0.0005027865,0.00002785316,0.0000017812384],"about_ca_topic_score_codex":0.032693584,"about_ca_topic_score_gemma":0.013513543,"teacher_disagreement_score":0.032693584,"about_ca_system_score_codex":0.0012244985,"about_ca_system_score_gemma":0.0026965584,"threshold_uncertainty_score":0.065006614},"labels":[],"label_agreement":null},{"id":"W3156936001","doi":"10.1016/j.eswa.2021.115035","title":"Multi-level interpretable logic tree analysis: A data-driven approach for hierarchical causality analysis","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Causality (physics); Tree (set theory); Artificial intelligence; Data mining; Machine learning; Natural language processing; Mathematics","score_opus":0.054414476124081367,"score_gpt":0.29905596825074626,"score_spread":0.2446414921266649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156936001","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017564179,0.000032651737,0.9967079,0.000048412352,0.000008308888,0.000062308405,0.0002878983,0.00076995627,0.00032610146],"genre_scores_gemma":[0.09630583,0.000119654105,0.90078986,0.00011896389,0.000028779026,0.0002133828,0.0012669826,0.00028323868,0.0008733393],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99770075,0.0006242486,0.00020998385,0.00038656325,0.0009302408,0.00014820173],"domain_scores_gemma":[0.99380577,0.0035894138,0.00042474756,0.000815826,0.0012166871,0.00014753995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025987085,0.0010556802,0.0010081514,0.0032269058,0.0007761036,0.0027925025,0.002311601,0.0009413119,0.0053794626],"category_scores_gemma":[0.0126348315,0.0006864814,0.0024866231,0.0018714357,0.0009910667,0.002952363,0.0018731696,0.0026624494,0.0010670053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044338722,0.00041427396,0.0071353405,0.0011854686,0.00033260338,0.00102578,0.001111007,0.25847846,0.028000811,0.26125562,0.006475998,0.4341412],"study_design_scores_gemma":[0.000026636124,0.00005662892,0.0004009739,0.00007347109,0.00008322183,0.00008677253,0.00007075774,0.836461,0.006702142,0.15197212,0.0040375763,0.000028607014],"about_ca_topic_score_codex":0.004110525,"about_ca_topic_score_gemma":0.0076341685,"teacher_disagreement_score":0.0053794626,"about_ca_system_score_codex":0.00092367776,"about_ca_system_score_gemma":0.0024414083,"threshold_uncertainty_score":0.017996132},"labels":[],"label_agreement":null},{"id":"W3158701715","doi":"10.1016/j.eswa.2021.115127","title":"Deep graph convolutional reinforcement learning for financial portfolio management – DeepPocket","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Reinforcement learning; Computer science; Exploit; Graph; Artificial intelligence; Autoencoder; Portfolio; Financial market; Project portfolio management; Investment management; Machine learning; Finance; Deep learning; Theoretical computer science; Economics; Computer security","score_opus":0.012903987086401166,"score_gpt":0.25483929259976285,"score_spread":0.24193530551336168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158701715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051717658,0.001773068,0.94012475,0.0007845815,0.00016609773,0.00004675258,0.00018534275,0.0015021923,0.0036994622],"genre_scores_gemma":[0.8624276,0.0007421888,0.12773435,0.0002794654,0.000082021834,0.00006593984,0.00035957893,0.00011532741,0.008193498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998148,0.000051721214,0.000008165959,0.000047935544,0.000046471756,0.000030871513],"domain_scores_gemma":[0.99940956,0.00030019862,0.000057399513,0.00006433365,0.000119170836,0.00004934364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067609764,0.0005270857,0.00072862336,0.0004928025,0.00025930235,0.0006249088,0.0010488044,0.0010752431,0.0024993597],"category_scores_gemma":[0.002095182,0.0003389213,0.0003095669,0.0005747991,0.00045956078,0.0010281443,0.00079422997,0.0014837742,0.00047856176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007260285,0.0001033431,0.0008087081,0.000043128934,0.000040430237,0.000052411902,0.000017862583,0.84067214,0.0013529544,0.012554601,0.0034212805,0.1408606],"study_design_scores_gemma":[0.0000016627494,0.0000072208572,0.000041951418,0.0000017659883,0.0000016608017,0.0000032635037,6.9433725e-7,0.99645233,0.00014577681,0.0031913072,0.00015123215,0.000001200703],"about_ca_topic_score_codex":0.0073029473,"about_ca_topic_score_gemma":0.007930569,"teacher_disagreement_score":0.0073029473,"about_ca_system_score_codex":0.0008397839,"about_ca_system_score_gemma":0.00086821924,"threshold_uncertainty_score":0.014520884},"labels":[],"label_agreement":null},{"id":"W3159033197","doi":"10.1016/j.eswa.2021.115083","title":"A socially motivating and environmentally friendly tour recommendation framework for tourist groups","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tourism; Maximization; Computer science; Consumption (sociology); Cover (algebra); Set (abstract data type); Environmental economics; Minification; Operations research; Microeconomics; World Wide Web; Economics; Sociology; Mathematics; Political science; Engineering","score_opus":0.01665075919318615,"score_gpt":0.2948714532704566,"score_spread":0.27822069407727046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159033197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027789157,0.00025103104,0.9564929,0.0008905962,0.00008410227,0.00033783013,0.0006863537,0.0017293232,0.011738685],"genre_scores_gemma":[0.4159769,0.0002959977,0.56705046,0.00023819927,0.00008073896,0.00046889568,0.0011385932,0.00016023344,0.014589939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924785,0.00023054138,0.000031544176,0.00016647816,0.00023236217,0.00009108012],"domain_scores_gemma":[0.99942327,0.00017874473,0.000041975098,0.00007390059,0.00015568848,0.0001264585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079446007,0.0006523861,0.0005666558,0.0009830067,0.0010989953,0.0012969101,0.0016318945,0.001135575,0.0058451765],"category_scores_gemma":[0.0016706429,0.0002629022,0.0009673283,0.00088257267,0.00044615183,0.0010933846,0.0022032997,0.001028328,0.00097372883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031669595,0.0011528106,0.0083103115,0.00043877147,0.00031724313,0.00078335777,0.0015325287,0.4783767,0.009817626,0.1750341,0.032910988,0.2910089],"study_design_scores_gemma":[0.000024411376,0.000062718325,0.0007742827,0.000022019542,0.00004994122,0.00008723561,0.00028888177,0.9546575,0.0007707394,0.029224455,0.014011065,0.000026664798],"about_ca_topic_score_codex":0.025248239,"about_ca_topic_score_gemma":0.06211016,"teacher_disagreement_score":0.025248239,"about_ca_system_score_codex":0.00092839985,"about_ca_system_score_gemma":0.0016653951,"threshold_uncertainty_score":0.05020255},"labels":[],"label_agreement":null},{"id":"W3190147417","doi":"10.1016/j.eswa.2021.115683","title":"Solving the battery swap station location-routing problem with a mixed fleet of electric and conventional vehicles using a heuristic branch-and-price algorithm with an adaptive selection scheme","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Swap (finance); Computer science; Scheme (mathematics); Selection (genetic algorithm); Heuristic; Mathematical optimization; Vehicle routing problem; Routing (electronic design automation); Algorithm; Computer network; Artificial intelligence; Mathematics; Finance","score_opus":0.015929789995386485,"score_gpt":0.2521296384471959,"score_spread":0.23619984845180939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190147417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16618732,0.00028072263,0.8254728,0.00032287446,0.00006442031,0.0001591228,0.00012412817,0.00022849049,0.007160206],"genre_scores_gemma":[0.78061104,0.00013070602,0.21249318,0.00008270007,0.000052341402,0.00025559534,0.00016627023,0.000061690895,0.0061464976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999706,0.0001233681,0.000013011808,0.0000582539,0.000045962675,0.00005343849],"domain_scores_gemma":[0.99916697,0.00064829754,0.00005590223,0.000021691456,0.0000621007,0.000045122903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011458008,0.0010139409,0.0014213313,0.00088076445,0.00061250327,0.0011209319,0.001277115,0.0017707472,0.0037692885],"category_scores_gemma":[0.0017457005,0.00094793335,0.000882183,0.0011454718,0.00058547047,0.0012609822,0.0008514451,0.0007912344,0.00022423179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006386268,0.00003945291,0.00031897557,0.00002659566,0.000031144555,0.000043356995,0.000013796424,0.98809147,0.00038646322,0.0017486599,0.0002448924,0.008991267],"study_design_scores_gemma":[0.000013162437,0.00002189433,0.00005933706,0.0000012755057,0.0000061306887,0.0000071450822,0.0000067432825,0.9990207,0.00008427917,0.000722626,0.0000548752,0.0000018177429],"about_ca_topic_score_codex":0.009604942,"about_ca_topic_score_gemma":0.009251648,"teacher_disagreement_score":0.009604942,"about_ca_system_score_codex":0.00091357384,"about_ca_system_score_gemma":0.0015710546,"threshold_uncertainty_score":0.019098043},"labels":[],"label_agreement":null},{"id":"W3197126477","doi":"10.1016/j.eswa.2021.115836","title":"A dynamic predictor selection algorithm for predicting stock market movement","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Stock market; Machine learning; Cluster analysis; Stock market prediction; Sample (material); Data mining; Algorithm","score_opus":0.04641392151082018,"score_gpt":0.37039241105327975,"score_spread":0.32397848954245956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197126477","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029268714,0.00055101863,0.96779364,0.00015670592,0.00012573772,0.0000708654,0.0001289029,0.0010112119,0.0008931978],"genre_scores_gemma":[0.3618999,0.00058571185,0.6295369,0.00021200639,0.00025772685,0.00038292538,0.00086698995,0.00017072874,0.0060870512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941695,0.0001505558,0.000053711825,0.0001491139,0.00016600847,0.00006369325],"domain_scores_gemma":[0.99806327,0.0012397771,0.000086141365,0.00011072401,0.00043607576,0.00006405776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021245577,0.00076192856,0.0016628323,0.0016095632,0.00080265134,0.00083478034,0.0014378005,0.0010497781,0.0025343373],"category_scores_gemma":[0.003928979,0.0006350109,0.0006231626,0.0015491503,0.00034714735,0.000930211,0.0008820515,0.0012710001,0.00083500234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030167773,0.00023137196,0.003355482,0.000052653686,0.00014311596,0.00009890226,0.00004866132,0.34113133,0.0033178353,0.0027555705,0.0037271276,0.64483625],"study_design_scores_gemma":[0.000022956847,0.000039905706,0.00041196393,0.0000040930954,0.000017197968,0.000021143998,0.0000043238138,0.997829,0.00055900676,0.00068325497,0.00040144986,0.000005668368],"about_ca_topic_score_codex":0.00731818,"about_ca_topic_score_gemma":0.007026762,"teacher_disagreement_score":0.00731818,"about_ca_system_score_codex":0.00047341682,"about_ca_system_score_gemma":0.0013842052,"threshold_uncertainty_score":0.014551163},"labels":[],"label_agreement":null},{"id":"W3199363485","doi":"10.1016/j.eswa.2021.115879","title":"COVID19-HPSMP: COVID-19 adopted Hybrid and Parallel deep information fusion framework for stock price movement prediction","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scalable Vector Graphics; Coronavirus disease 2019 (COVID-19); Computer science; Discrete mathematics; Mathematics; World Wide Web; Medicine","score_opus":0.07528193509344226,"score_gpt":0.3849936110216045,"score_spread":0.30971167592816223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199363485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09604657,0.0018541958,0.86157894,0.0012264823,0.001326392,0.00041875144,0.006900714,0.020054016,0.010593932],"genre_scores_gemma":[0.4930913,0.0007444638,0.4687764,0.00090782694,0.00029208013,0.0004191077,0.019238813,0.0005943092,0.015935656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995018,0.00007214109,0.000037081543,0.00012118666,0.00017274187,0.00009504487],"domain_scores_gemma":[0.999529,0.000057069254,0.000018909774,0.00007592841,0.00027751637,0.000041613665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013201187,0.0014115638,0.0010183238,0.0009861041,0.0006624123,0.0010207954,0.0019425803,0.0013308104,0.004857793],"category_scores_gemma":[0.0018474796,0.00052356155,0.001054269,0.0010728305,0.00029769383,0.0016741968,0.0017829584,0.0020693117,0.0016889796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067142455,0.0008135306,0.00706374,0.000201468,0.0005743555,0.0002779055,0.00009540638,0.26078147,0.014024215,0.0069590705,0.07203723,0.6365002],"study_design_scores_gemma":[0.000035572437,0.00006309832,0.0007698423,0.0000074485843,0.000027925627,0.00002703634,0.000009694157,0.9898448,0.004726491,0.0013262978,0.0031421562,0.000019519508],"about_ca_topic_score_codex":0.02615311,"about_ca_topic_score_gemma":0.030800791,"teacher_disagreement_score":0.02615311,"about_ca_system_score_codex":0.0007846641,"about_ca_system_score_gemma":0.0027460586,"threshold_uncertainty_score":0.052001774},"labels":[],"label_agreement":null},{"id":"W3199582773","doi":"10.1016/j.eswa.2021.115950","title":"Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Student Research Committee, Tabriz University of Medical Sciences; Deputy for Research and Technology, Kermanshah University of Medical Sciences; Kermanshah University of Medical Sciences","keywords":"Support vector machine; Artificial intelligence; Machine learning; Computer science; Sleep apnea; Algorithm; Artificial neural network; Apnea; Hypopnea; Pattern recognition (psychology); Polysomnography; Medicine; Internal medicine","score_opus":0.03408396577353544,"score_gpt":0.3232978203401495,"score_spread":0.289213854566614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199582773","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026706983,0.9961211,0.00052411744,0.000105826286,0.000069055364,0.00010229751,0.00025622753,0.000009225066,0.00014146423],"genre_scores_gemma":[0.06918724,0.92592394,0.0031376022,0.0006247271,0.00019760746,0.00022322372,0.0005941927,0.0000141629025,0.00009739161],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9942451,0.0014582274,0.002282396,0.0009074645,0.0010050284,0.000101703095],"domain_scores_gemma":[0.97218305,0.022196209,0.0035300879,0.0004413569,0.0014884345,0.00016078156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068518138,0.0015430416,0.009249511,0.005581476,0.00041679517,0.0022237583,0.0020291265,0.0016950493,0.001733835],"category_scores_gemma":[0.028574942,0.0008744256,0.01101783,0.004387799,0.00077634957,0.0022057844,0.001057421,0.0011239188,0.00022773453],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010524392,0.00010073624,0.012724792,0.7060224,0.11790778,0.00014334448,0.00020897633,0.00076157745,0.0006601302,0.00026731245,0.0017519756,0.15839851],"study_design_scores_gemma":[0.0013268248,0.0012086991,0.040648308,0.33120996,0.6077183,0.0008137858,0.00040900684,0.0013666151,0.00082366983,0.00084298715,0.013442914,0.00018903258],"about_ca_topic_score_codex":0.00439208,"about_ca_topic_score_gemma":0.012174098,"teacher_disagreement_score":0.009249511,"about_ca_system_score_codex":0.001017421,"about_ca_system_score_gemma":0.0033769095,"threshold_uncertainty_score":0.036236227},"labels":[],"label_agreement":null},{"id":"W3203593077","doi":"10.1016/j.eswa.2022.119250","title":"Player tracking and identification in ice hockey","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Ice hockey; Identification (biology); Artificial intelligence; Convolutional neural network; Panning (audio); Tracking (education); Computer vision; Zoom","score_opus":0.012813737861620818,"score_gpt":0.2399609264847559,"score_spread":0.22714718862313507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203593077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87669027,0.0013541251,0.104675256,0.00027163269,0.00035246933,0.00021106128,0.0017878482,0.0011946254,0.013462692],"genre_scores_gemma":[0.96032083,0.0005898952,0.024560176,0.00007583753,0.000076884506,0.00005210687,0.0014144738,0.00013664197,0.012773097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997204,0.00003483483,0.000011653904,0.0000902028,0.00006732484,0.00007550603],"domain_scores_gemma":[0.9996288,0.00011540314,0.00003692321,0.00001864942,0.00014955926,0.00005066451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032124782,0.0005175731,0.0004353624,0.0018306221,0.0005422108,0.0011174945,0.00056163396,0.00071761454,0.0029681611],"category_scores_gemma":[0.001097252,0.0002532276,0.0001903086,0.001008294,0.00028096003,0.00069641613,0.0005034044,0.00037218237,0.0018728077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042301393,0.0006930828,0.09866585,0.0005701747,0.00018129699,0.0021831759,0.0018188502,0.028483203,0.15084028,0.0028725727,0.019844888,0.6896165],"study_design_scores_gemma":[0.00009059037,0.0011726011,0.3027525,0.0002321994,0.00022110877,0.0024003512,0.00594164,0.5495801,0.114520155,0.0033104853,0.019649887,0.00012835888],"about_ca_topic_score_codex":0.015964162,"about_ca_topic_score_gemma":0.020524845,"teacher_disagreement_score":0.015964162,"about_ca_system_score_codex":0.00045010934,"about_ca_system_score_gemma":0.0005046235,"threshold_uncertainty_score":0.031742454},"labels":[],"label_agreement":null},{"id":"W3205429506","doi":"10.1016/j.eswa.2021.116060","title":"Anomaly detection for data accountability of Mars telemetry data","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Mitacs; Polytechnique Montréal","keywords":"Telemetry; Mars Exploration Program; Anomaly detection; Computer science; Anomaly (physics); Remote sensing; South Atlantic Anomaly; Data mining; Geology; Telecommunications; Astrobiology","score_opus":0.06414976024322895,"score_gpt":0.3322822724052223,"score_spread":0.26813251216199335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205429506","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122373536,0.00049143884,0.8653715,0.0008591629,0.00024270912,0.00014787607,0.00063829176,0.0074980906,0.0023773126],"genre_scores_gemma":[0.89798754,0.000112628346,0.10001305,0.000068206944,0.00008222936,0.00005424796,0.0005739338,0.00009896833,0.0010091198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960769,0.00094928854,0.00037486592,0.0006425585,0.0015742076,0.00038227008],"domain_scores_gemma":[0.9858564,0.0048286305,0.0024192233,0.0024787968,0.0039838385,0.00043315376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038077885,0.0006421271,0.00097306666,0.0031015938,0.0009930388,0.0018520763,0.0012551484,0.0009837708,0.0013958963],"category_scores_gemma":[0.020454757,0.00025474906,0.00045306803,0.0019397212,0.000639426,0.0019352556,0.001462195,0.0016152606,0.00053940533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008810764,0.0004806705,0.06390839,0.00031369942,0.00022149138,0.00060102326,0.0005619431,0.078759775,0.046645526,0.028753374,0.010710317,0.76816255],"study_design_scores_gemma":[0.00001862172,0.00017558565,0.010118919,0.000033856333,0.0000462917,0.00040381367,0.00017052039,0.9432078,0.024360156,0.01621763,0.005214994,0.0000318422],"about_ca_topic_score_codex":0.0028417704,"about_ca_topic_score_gemma":0.0027148628,"teacher_disagreement_score":0.0038077885,"about_ca_system_score_codex":0.0009501106,"about_ca_system_score_gemma":0.002158195,"threshold_uncertainty_score":0.020137727},"labels":[],"label_agreement":null},{"id":"W4200150901","doi":"10.1016/j.eswa.2021.116276","title":"Multi granularity based label propagation with active learning for semi-supervised classification","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Granularity; Leverage (statistics); Graph; Semi-supervised learning; Affinity propagation; Belief propagation; Artificial intelligence; Adjacency list; Machine learning; Supervised learning; Benchmark (surveying); Data mining; Pattern recognition (psychology); Algorithm; Artificial neural network; Theoretical computer science; Cluster analysis","score_opus":0.029226167248766623,"score_gpt":0.28409829131691927,"score_spread":0.25487212406815263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200150901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034451277,0.00014209506,0.9951237,0.00009238431,0.000053174677,0.00004720824,0.0000546954,0.0007678715,0.00027388547],"genre_scores_gemma":[0.24399884,0.00021352564,0.75054854,0.00029311364,0.00024542946,0.0003683665,0.00066111557,0.00043644893,0.0032346095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99423283,0.002151659,0.00041712367,0.0012686589,0.0015609072,0.0003687792],"domain_scores_gemma":[0.9786618,0.0145102125,0.0010656335,0.0027885097,0.0025589075,0.00041497924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077530346,0.0014475997,0.002769589,0.0030906834,0.0017000838,0.003081368,0.006107922,0.0045099873,0.0029949227],"category_scores_gemma":[0.01426844,0.0013141343,0.0020421029,0.003227009,0.0020932874,0.005499465,0.004586415,0.0049049566,0.0014191787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084025314,0.00074480684,0.0015903674,0.00040569436,0.00029149675,0.0001803545,0.00055410556,0.23145404,0.016042404,0.029323312,0.006745761,0.71182746],"study_design_scores_gemma":[0.000012914011,0.000027587748,0.00007394723,0.000010918405,0.000017542314,0.000018705885,0.000013899508,0.9858942,0.0022843296,0.011227235,0.00040959546,0.000009209049],"about_ca_topic_score_codex":0.003218913,"about_ca_topic_score_gemma":0.0058306996,"teacher_disagreement_score":0.0077530346,"about_ca_system_score_codex":0.001384136,"about_ca_system_score_gemma":0.0016528182,"threshold_uncertainty_score":0.041002452},"labels":[],"label_agreement":null},{"id":"W4200394996","doi":"10.1016/j.eswa.2021.116378","title":"An advanced decision-making model for evaluating manufacturing plant locations using fuzzy inference system","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Delphi method; Delphi; Fuzzy logic; Selection (genetic algorithm); Operations research; Management science; Artificial intelligence; Engineering","score_opus":0.20329879232348097,"score_gpt":0.48459461312797536,"score_spread":0.2812958208044944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200394996","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029067008,0.00019375651,0.96728677,0.00014397305,0.000048990347,0.00007291622,0.00013000384,0.00033031215,0.0027262648],"genre_scores_gemma":[0.85503584,0.000185401,0.14138234,0.000062121515,0.00003755287,0.00023951956,0.00017367468,0.000024450967,0.0028592008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992932,0.00017856984,0.00005318179,0.00021226595,0.00018025946,0.000082586506],"domain_scores_gemma":[0.9991242,0.00046511707,0.00008757203,0.000036397065,0.000250455,0.00003621294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001576359,0.0008959055,0.0017135596,0.0010374947,0.0009575165,0.0019175502,0.0018359291,0.002098779,0.0030996425],"category_scores_gemma":[0.002382574,0.00056427333,0.0009867939,0.0010867615,0.0006099985,0.0015483327,0.00079961633,0.0011273029,0.0004683015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006334312,0.000039561317,0.00031859108,0.000042381816,0.00003434935,0.00006560056,0.000039868897,0.9810337,0.00085444737,0.0035741387,0.00026838065,0.013665645],"study_design_scores_gemma":[0.0000045198753,0.000011897548,0.000055526085,0.0000030428234,0.000009330425,0.00000529456,0.0000025864058,0.9988599,0.000121934325,0.0008547143,0.00006703047,0.0000043098357],"about_ca_topic_score_codex":0.025043497,"about_ca_topic_score_gemma":0.01729425,"teacher_disagreement_score":0.025043497,"about_ca_system_score_codex":0.0017810041,"about_ca_system_score_gemma":0.001847966,"threshold_uncertainty_score":0.04979545},"labels":[],"label_agreement":null},{"id":"W4200485312","doi":"10.1016/j.eswa.2021.116392","title":"A personalized individual semantics-based multi-attribute group decision making approach with flexible linguistic expression","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar Funded Scheme; National Natural Science Foundation of China; Sichuan University; National Science Foundation","keywords":"Ranking (information retrieval); Computer science; Group decision-making; Expression (computer science); Semantics (computer science); Consistency (knowledge bases); Selection (genetic algorithm); Process (computing); Aggregate (composite); Preference; Artificial intelligence; Mathematics; Programming language; Psychology","score_opus":0.145654702168185,"score_gpt":0.4108322455413814,"score_spread":0.26517754337319643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200485312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006181281,0.00007614312,0.99189734,0.00014738434,0.000026888669,0.00006533277,0.00004388653,0.00022147747,0.0013401995],"genre_scores_gemma":[0.31082863,0.0001538941,0.68535423,0.00023645922,0.0000941795,0.0003710445,0.00026524538,0.0001278848,0.0025683506],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99624854,0.0013529576,0.00025792752,0.0007377425,0.0011787979,0.00022397723],"domain_scores_gemma":[0.99829286,0.0008183931,0.00013251805,0.00021152548,0.00042893694,0.000115715215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003348235,0.0009350325,0.0019802237,0.0017341519,0.0013260666,0.0024862979,0.0028402982,0.0015544158,0.0035373464],"category_scores_gemma":[0.005364581,0.0006260074,0.0018739955,0.0022422993,0.0009796385,0.0041060415,0.0036822157,0.0019229649,0.00077507814],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043687332,0.00070555875,0.0015879365,0.0003563635,0.0004571491,0.0004738113,0.0016959397,0.38537058,0.009800713,0.12926328,0.0059932955,0.4638585],"study_design_scores_gemma":[0.000032335996,0.00010270214,0.00019496116,0.000025486614,0.00008947518,0.0000837493,0.00014146537,0.9247511,0.001446818,0.07115991,0.0019351214,0.0000368372],"about_ca_topic_score_codex":0.0022621406,"about_ca_topic_score_gemma":0.0027562263,"teacher_disagreement_score":0.0035373464,"about_ca_system_score_codex":0.0010780159,"about_ca_system_score_gemma":0.0020432265,"threshold_uncertainty_score":0.017707348},"labels":[],"label_agreement":null},{"id":"W4205471456","doi":"10.1016/j.eswa.2021.116429","title":"Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances","year":2021,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":544,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Oil Sands Technology and Research Authority; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credit card fraud; Anomaly detection; Exploit; Computer science; Insider; Artificial intelligence; Business; Finance; Computer security; Credit card; Payment","score_opus":0.026763444414693062,"score_gpt":0.33052858593000745,"score_spread":0.30376514151531436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205471456","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002945432,0.9956808,0.0014103994,0.0006420445,0.00030305254,0.00001385818,0.000039789124,0.00003027485,0.0015851613],"genre_scores_gemma":[0.0015504325,0.9960038,0.001301924,0.0002402802,0.00041694695,0.00001149392,0.00006799899,0.000006019848,0.00040111184],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99901116,0.00015227955,0.00015031912,0.0001600224,0.00046680495,0.00005936763],"domain_scores_gemma":[0.99588877,0.0026342822,0.00031855245,0.00010369062,0.000940109,0.00011461033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001734217,0.0011806508,0.0015277053,0.0064078365,0.00050201145,0.0018639766,0.001713806,0.0015495842,0.0032334204],"category_scores_gemma":[0.00450435,0.0005300572,0.0010228987,0.007395253,0.0008427688,0.0031801683,0.00084391626,0.0017607664,0.001972456],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044003384,0.00008802259,0.0006675661,0.01282529,0.00009453592,0.00012859154,0.00010500431,0.00075069506,0.0005004781,0.0055517857,0.02496596,0.95427805],"study_design_scores_gemma":[0.000015377413,0.00016351328,0.0026973493,0.0122348415,0.00028409623,0.0017912769,0.00022355583,0.0012257891,0.0008157347,0.0085855145,0.97188437,0.000078470686],"about_ca_topic_score_codex":0.0019141893,"about_ca_topic_score_gemma":0.002048791,"teacher_disagreement_score":0.0064078365,"about_ca_system_score_codex":0.00088315737,"about_ca_system_score_gemma":0.0019558633,"threshold_uncertainty_score":0.010816932},"labels":[],"label_agreement":null},{"id":"W4213082874","doi":"10.1016/j.eswa.2022.116637","title":"A Multi-view Kernel Clustering framework for Categorical sequences","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Categorical variable; Cluster analysis; Kernel (algebra); Artificial intelligence; Data mining; Machine learning; Mathematics; Combinatorics","score_opus":0.037202189906972265,"score_gpt":0.2879341306100634,"score_spread":0.2507319407030911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213082874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002104066,0.000115077004,0.99727255,0.00004454556,0.00001883124,0.000013908664,0.000054168297,0.00022208132,0.00015473331],"genre_scores_gemma":[0.19214371,0.00052196643,0.8000869,0.00013254858,0.00017448058,0.00017949427,0.001373067,0.0003642551,0.0050236126],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793375,0.0007478031,0.00012122777,0.00048645018,0.0005132522,0.00019758313],"domain_scores_gemma":[0.9972396,0.00071911706,0.0002054932,0.00055121264,0.0010813009,0.00020320102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026129857,0.0007928356,0.0018495728,0.0020818927,0.000880947,0.0020024811,0.0036834246,0.0017781741,0.0026819878],"category_scores_gemma":[0.006011893,0.00074164296,0.0022213282,0.0025517594,0.0007943885,0.0024786948,0.0023308508,0.0025118075,0.002169411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040983982,0.00031614833,0.0020211458,0.00028009553,0.0003118486,0.00014648758,0.00033482854,0.3372453,0.013219211,0.12477225,0.009793512,0.5111494],"study_design_scores_gemma":[0.0000061085548,0.00002929331,0.00025091632,0.000007255645,0.000014695141,0.000028189348,0.000017370034,0.98285943,0.00066504185,0.015120572,0.0009818372,0.000019309713],"about_ca_topic_score_codex":0.009903206,"about_ca_topic_score_gemma":0.010025632,"teacher_disagreement_score":0.009903206,"about_ca_system_score_codex":0.0012135827,"about_ca_system_score_gemma":0.0015792024,"threshold_uncertainty_score":0.01969111},"labels":[],"label_agreement":null},{"id":"W4220851616","doi":"10.1016/j.eswa.2022.116780","title":"Feature extraction of auto insurance size of loss data using functional principal component analysis","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Principal component analysis; Interpretability; Computer science; Dimensionality reduction; Data mining; Feature (linguistics); Dimension (graph theory); Benchmark (surveying); Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.045594108710802536,"score_gpt":0.31459272490444745,"score_spread":0.2689986161936449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220851616","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5114619,0.0006462703,0.47980672,0.00026330072,0.0001475723,0.00007972599,0.002570899,0.002621073,0.0024025554],"genre_scores_gemma":[0.9361221,0.00020972986,0.059614293,0.000029801815,0.00007306959,0.00007326762,0.0026191056,0.00012375372,0.001134849],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.000042314867,0.000027086162,0.000062730374,0.00012425364,0.000049131857],"domain_scores_gemma":[0.99894637,0.00042353402,0.00011766848,0.00013988999,0.00032672758,0.0000457837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059944333,0.0005380705,0.00064373744,0.0027494729,0.00025207133,0.0007440981,0.0004580836,0.00036745818,0.001336576],"category_scores_gemma":[0.0022919835,0.00016377158,0.00067521736,0.0015362062,0.00017834594,0.000748412,0.0003566769,0.00055585016,0.0005273545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009881665,0.0003484842,0.046026524,0.00019221795,0.00014085247,0.0005863169,0.00014955024,0.06630012,0.062443968,0.0026624224,0.009501061,0.8106603],"study_design_scores_gemma":[0.000017013697,0.00010094679,0.10588932,0.00002253695,0.000057727502,0.00037216963,0.000105939136,0.8737935,0.013907884,0.0026956357,0.0029917604,0.000045449717],"about_ca_topic_score_codex":0.0023823755,"about_ca_topic_score_gemma":0.0021844816,"teacher_disagreement_score":0.0027494729,"about_ca_system_score_codex":0.00029435707,"about_ca_system_score_gemma":0.00037522282,"threshold_uncertainty_score":0.004737079},"labels":[],"label_agreement":null},{"id":"W4220873927","doi":"10.1016/j.eswa.2022.116954","title":"Filter group delays equalization for 2D discrete wavelet transform applications","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université du Québec à Rimouski","funders":"","keywords":"Computer science; Discrete wavelet transform; Filter (signal processing); MATLAB; Wavelet; Stationary wavelet transform; Second-generation wavelet transform; Algorithm; Artificial intelligence; Wavelet transform; Image (mathematics); Noise (video); Computer vision","score_opus":0.023267663394896068,"score_gpt":0.2903187228501507,"score_spread":0.26705105945525465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220873927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011686512,0.00025456503,0.9856878,0.0000839733,0.000106404375,0.000019675248,0.000044272936,0.00030441422,0.0018123718],"genre_scores_gemma":[0.22119318,0.0010829521,0.7601351,0.00020503729,0.00017669774,0.000112540125,0.00031334985,0.00027144796,0.01650971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998591,0.00002633618,0.000007285364,0.000028151475,0.00005618754,0.000022981865],"domain_scores_gemma":[0.9997969,0.000070066766,0.000015934382,0.00004236641,0.000065115426,0.000009632917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025226513,0.00057623046,0.00030917465,0.00035915806,0.00026706388,0.0005646373,0.00030416262,0.0006941917,0.0065928656],"category_scores_gemma":[0.00074856175,0.00019587594,0.00030705202,0.0004395669,0.00023354098,0.000630885,0.00035881714,0.0005771528,0.002205327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007420742,0.00011917729,0.0005011949,0.00021422966,0.00005796042,0.00015353378,0.0001109601,0.026507655,0.34868726,0.0190104,0.0041208775,0.59977466],"study_design_scores_gemma":[0.00007432527,0.00026751112,0.0022084722,0.000065159256,0.000079613084,0.00042967516,0.000084444255,0.61849785,0.31979525,0.014223098,0.044221945,0.000052738593],"about_ca_topic_score_codex":0.0007116785,"about_ca_topic_score_gemma":0.0018661568,"teacher_disagreement_score":0.0065928656,"about_ca_system_score_codex":0.0002597767,"about_ca_system_score_gemma":0.0004175828,"threshold_uncertainty_score":0.022055328},"labels":[],"label_agreement":null},{"id":"W4224140499","doi":"10.1016/j.eswa.2022.117220","title":"Sensor-based modeling of problem-solving in virtual reality manufacturing systems","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Computer science; Virtual reality; Process (computing); Human–computer interaction; Industrial engineering; Artificial intelligence; Engineering","score_opus":0.03126756088551778,"score_gpt":0.26293859782335705,"score_spread":0.23167103693783928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224140499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07977732,0.00039683306,0.90635467,0.00045643502,0.00006634459,0.000052072333,0.0001955067,0.00034245348,0.01235839],"genre_scores_gemma":[0.9596677,0.00029818312,0.03622774,0.000040659685,0.00001744913,0.0000800883,0.00009546015,0.000047806036,0.0035247963],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996253,0.00014672913,0.000020843267,0.000059715738,0.00010104973,0.000046307767],"domain_scores_gemma":[0.9993709,0.0003994028,0.000055455028,0.000043794218,0.000097434146,0.0000331163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045871525,0.0004289338,0.00075828156,0.00046013788,0.00047280526,0.0016453633,0.0013206003,0.0011576004,0.0034465624],"category_scores_gemma":[0.0019541718,0.0005526072,0.000658699,0.0005623898,0.0008703023,0.0013470806,0.00072633807,0.0006838901,0.0003585407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013075197,0.000014398555,0.00009722682,0.0000145637805,0.0000055222604,0.000017244241,0.00003473732,0.99007845,0.00030448925,0.007280537,0.00007875027,0.0020609898],"study_design_scores_gemma":[0.0000017798983,0.0000038693825,0.000038017235,0.0000011744388,0.0000012483274,0.000003267427,0.0000048317634,0.99811757,0.00010230898,0.0016112992,0.000112914204,0.0000018039817],"about_ca_topic_score_codex":0.017119175,"about_ca_topic_score_gemma":0.010063862,"teacher_disagreement_score":0.017119175,"about_ca_system_score_codex":0.0008971855,"about_ca_system_score_gemma":0.0010225377,"threshold_uncertainty_score":0.03403902},"labels":[],"label_agreement":null},{"id":"W4224209589","doi":"10.1016/j.eswa.2022.117231","title":"PictoBERT: Transformers for next pictogram prediction","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Assistive Technology in Communication and Mobility","field":"Health Professions","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Pictogram; Computer science; Sentence; Transformer; Natural language processing; Artificial intelligence; Encoder; Human–computer interaction; Linguistics","score_opus":0.06766099040290786,"score_gpt":0.3971758998581823,"score_spread":0.32951490945527445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224209589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036773088,0.00033234232,0.6445738,0.00014945595,0.000289335,0.00012072739,0.008938843,0.33508444,0.0068337168],"genre_scores_gemma":[0.31672397,0.00094144,0.54739136,0.0007921568,0.0002616855,0.0007076926,0.03577634,0.06445674,0.032948676],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991725,0.000103312064,0.000095433476,0.00020120606,0.00032937626,0.000098187455],"domain_scores_gemma":[0.9969168,0.0012644016,0.00020593962,0.0007433271,0.00073719467,0.000132386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010429219,0.0017930783,0.00071519666,0.0021198965,0.00043410488,0.0021484687,0.0022576055,0.00095997594,0.08191543],"category_scores_gemma":[0.011778843,0.0011047003,0.0011241479,0.0013359057,0.00048053166,0.003840358,0.0017902863,0.0011831489,0.021334304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026135696,0.00017102032,0.0040785223,0.001280828,0.00014188078,0.00043371032,0.00025255527,0.01592433,0.017018126,0.02512728,0.34343517,0.589523],"study_design_scores_gemma":[0.0003339302,0.00029816013,0.0025473274,0.0004407875,0.00019165651,0.0008938358,0.00013028324,0.48911747,0.16862623,0.05529409,0.281887,0.0002393174],"about_ca_topic_score_codex":0.005532541,"about_ca_topic_score_gemma":0.0061369226,"teacher_disagreement_score":0.08191543,"about_ca_system_score_codex":0.00095564,"about_ca_system_score_gemma":0.001096539,"threshold_uncertainty_score":0.27403444},"labels":[],"label_agreement":null},{"id":"W4224903249","doi":"10.1016/j.eswa.2022.117119","title":"Newbuilding ship price forecasting by parsimonious intelligent model search engine","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.03430689111796567,"score_gpt":0.23827366274781542,"score_spread":0.20396677162984975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224903249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2855833,0.0019653433,0.6966555,0.00079034094,0.0002600453,0.00013269736,0.0007965457,0.0028815607,0.010934793],"genre_scores_gemma":[0.9256763,0.00028694316,0.068855986,0.00015855409,0.00007131506,0.000070070426,0.0006354078,0.0000980438,0.0041473764],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997261,0.000063684034,0.000025534238,0.00007929916,0.000065551685,0.000039885723],"domain_scores_gemma":[0.9994729,0.00034510513,0.000035426594,0.000039464652,0.00008639551,0.000020775813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005564035,0.00072362414,0.0011822404,0.0014252777,0.0005098483,0.00093068846,0.0010529291,0.0011027125,0.003519771],"category_scores_gemma":[0.0019810372,0.0005122899,0.00081906567,0.0013028305,0.00021614677,0.0016299178,0.00050496054,0.0007327753,0.0005887704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034209748,0.00019275513,0.0027944136,0.00008475036,0.00014310064,0.00016682886,0.00003084079,0.8729034,0.0014946166,0.0024973454,0.0033081123,0.116041735],"study_design_scores_gemma":[0.0000083767445,0.0000072184785,0.000098855395,8.9072614e-7,0.000009981166,0.0000066341636,0.0000019327774,0.9993507,0.00010179264,0.00035592323,0.000055877117,0.0000017534012],"about_ca_topic_score_codex":0.015416227,"about_ca_topic_score_gemma":0.014277041,"teacher_disagreement_score":0.015416227,"about_ca_system_score_codex":0.00055084366,"about_ca_system_score_gemma":0.0010210377,"threshold_uncertainty_score":0.030653},"labels":[],"label_agreement":null},{"id":"W4225399147","doi":"10.1016/j.eswa.2022.117386","title":"A new data augmentation method for EEG features based on the hybrid model of broad-deep networks","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Electroencephalography; Artificial intelligence; Feature (linguistics); Similarity (geometry); Set (abstract data type); Pattern recognition (psychology); Measure (data warehouse); Data set; Data mining; Image (mathematics)","score_opus":0.056964347632342933,"score_gpt":0.32947086103421386,"score_spread":0.27250651340187093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225399147","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056153205,0.00044487146,0.9921543,0.0001295114,0.00012580685,0.00003077662,0.00011868054,0.00080670597,0.00057415804],"genre_scores_gemma":[0.30311632,0.0010558948,0.68377703,0.00039942842,0.0002951313,0.0002856721,0.001344985,0.0003294221,0.009396156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997807,0.00003638668,0.000014683304,0.00006551707,0.00007578741,0.000026937432],"domain_scores_gemma":[0.9997156,0.00007465654,0.000022516057,0.000041433315,0.00012290882,0.000022914866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048499144,0.0008628039,0.0007254252,0.00052288955,0.00028474157,0.0005412553,0.0011564251,0.00072473043,0.0022703197],"category_scores_gemma":[0.0010834726,0.00038380112,0.0007960477,0.0008111046,0.0002826982,0.0010625144,0.0012415035,0.0015351935,0.00092119747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022768106,0.00013426268,0.0009559949,0.00019750021,0.00017700376,0.00011986638,0.000078811165,0.121943265,0.045457337,0.0065908665,0.008529455,0.815588],"study_design_scores_gemma":[0.000008839982,0.000037501213,0.00032360223,0.000010387118,0.000026597361,0.000055995282,0.000005745937,0.9894824,0.0057647997,0.0016035705,0.0026694883,0.000011102826],"about_ca_topic_score_codex":0.0034247313,"about_ca_topic_score_gemma":0.0058347806,"teacher_disagreement_score":0.0034247313,"about_ca_system_score_codex":0.00028029914,"about_ca_system_score_gemma":0.0006664422,"threshold_uncertainty_score":0.007594943},"labels":[],"label_agreement":null},{"id":"W4225550289","doi":"10.1016/j.eswa.2022.116928","title":"Optimizing feature selection methods by removing irrelevant features using sparse least squares","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Feature selection; Computer science; Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Feature (linguistics); Selection (genetic algorithm); Singular value decomposition; Machine learning; Partial least squares regression; Data mining","score_opus":0.02191111192332507,"score_gpt":0.30416726898630836,"score_spread":0.2822561570629833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225550289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010482616,0.00013691779,0.9884899,0.0000765883,0.00003643421,0.00002647275,0.000027143135,0.00043301826,0.00029095836],"genre_scores_gemma":[0.28641403,0.0003335777,0.7071792,0.00018660049,0.00013742415,0.00018606277,0.0005674504,0.00034017087,0.00465553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993711,0.00015143867,0.00003453786,0.00011937019,0.00026036293,0.00006328648],"domain_scores_gemma":[0.99892765,0.000485476,0.00008382751,0.00011483338,0.00035528277,0.00003279553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008012837,0.0012793824,0.0012641323,0.000770874,0.00050365546,0.0006915553,0.0007493739,0.0008274348,0.0015008429],"category_scores_gemma":[0.0028486713,0.0005731797,0.00081478694,0.00075158855,0.00041312582,0.0008210935,0.00057344337,0.0009975453,0.000889656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030715755,0.0002984558,0.0015036089,0.00019459508,0.00017557363,0.00012904423,0.000065109365,0.21464947,0.068250634,0.003567536,0.00826515,0.70259374],"study_design_scores_gemma":[0.000021992093,0.000053729593,0.00061221834,0.000005747346,0.000030865172,0.000044586664,0.00001312389,0.98746336,0.009118122,0.0015908944,0.0010357025,0.000009715195],"about_ca_topic_score_codex":0.0028473726,"about_ca_topic_score_gemma":0.00408363,"teacher_disagreement_score":0.0028473726,"about_ca_system_score_codex":0.00026580621,"about_ca_system_score_gemma":0.0008507364,"threshold_uncertainty_score":0.005661607},"labels":[],"label_agreement":null},{"id":"W4225874520","doi":"10.1016/j.eswa.2022.117053","title":"GCNFusion: An efficient graph convolutional network based model for information diffusion","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Python (programming language); Feature selection; Graph; Theoretical computer science; Artificial intelligence; Machine learning; Data mining","score_opus":0.01161583337314033,"score_gpt":0.24962203986743425,"score_spread":0.23800620649429394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225874520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013043385,0.00030676858,0.98370206,0.00025783572,0.000059014204,0.0000454061,0.0003497943,0.001201316,0.0010343762],"genre_scores_gemma":[0.5554815,0.00096308894,0.42616516,0.0004278877,0.00010148675,0.0002702058,0.0016377368,0.0005898293,0.014363065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978846,0.0000443058,0.0000091027705,0.000059232556,0.0000639132,0.000035112538],"domain_scores_gemma":[0.99950206,0.0002557878,0.000042518102,0.000064100874,0.00009692208,0.000038691247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063295587,0.0009075963,0.00088392093,0.00076074054,0.00042332464,0.00074423564,0.0022951257,0.0016688156,0.0020720116],"category_scores_gemma":[0.0024065215,0.00057279656,0.0006916569,0.0008110397,0.00061002607,0.0016466303,0.0011559058,0.0019019463,0.0005957099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081293736,0.000036922018,0.00043061475,0.000038778802,0.000037869115,0.000049292667,0.000025018191,0.9273367,0.002664194,0.017341653,0.0025190471,0.049438562],"study_design_scores_gemma":[0.0000013074714,0.0000024545127,0.000016114529,9.834529e-7,0.0000014444632,0.0000034785794,4.6176186e-7,0.99789774,0.00017582344,0.001755223,0.00014368819,0.0000012266964],"about_ca_topic_score_codex":0.03839077,"about_ca_topic_score_gemma":0.038868558,"teacher_disagreement_score":0.03839077,"about_ca_system_score_codex":0.001802672,"about_ca_system_score_gemma":0.0015738335,"threshold_uncertainty_score":0.076334596},"labels":[],"label_agreement":null},{"id":"W4229082241","doi":"10.1016/j.eswa.2022.117330","title":"Seizure localisation with attention-based graph neural networks","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Western Hospital; University of Toronto; Ontario Brain Institute; University of Manitoba","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Ictal; Computer science; Artificial intelligence; Robustness (evolution); Epilepsy; Electroencephalography; Artificial neural network; Functional connectivity; Pattern recognition (psychology); Graph; Machine learning; Neuroscience; Psychology","score_opus":0.01811676481812991,"score_gpt":0.24770610007410343,"score_spread":0.22958933525597353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229082241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0569709,0.00059998874,0.9400023,0.0003137491,0.00004279651,0.00003601814,0.00012265879,0.00079795555,0.001113565],"genre_scores_gemma":[0.8803673,0.00031780824,0.11712541,0.00019022205,0.0000890607,0.000060649036,0.0002604489,0.000086797896,0.0015022603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965584,0.00011626527,0.000015038836,0.000106710046,0.00006539024,0.000040755072],"domain_scores_gemma":[0.99877757,0.0007917302,0.00017220565,0.00007734502,0.00014681427,0.000034402525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006600768,0.0008039997,0.0005128136,0.0016363045,0.00027281838,0.0005620198,0.0008763503,0.00090777106,0.00078983925],"category_scores_gemma":[0.0035696253,0.00032179191,0.0006257855,0.0009636782,0.0006172333,0.0009991156,0.0009072773,0.00073767843,0.00016393265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000114203816,0.00003898097,0.0014508375,0.00006240355,0.00007221177,0.0000853551,0.000057521753,0.8892626,0.0045650173,0.004786963,0.00064638635,0.098857634],"study_design_scores_gemma":[0.000003821787,0.000012921615,0.00038293572,0.0000035892674,0.0000065491054,0.000014596185,0.000003763483,0.9940175,0.000608432,0.004807327,0.00013461166,0.0000039071083],"about_ca_topic_score_codex":0.010057395,"about_ca_topic_score_gemma":0.008448474,"teacher_disagreement_score":0.010057395,"about_ca_system_score_codex":0.001026103,"about_ca_system_score_gemma":0.00040113233,"threshold_uncertainty_score":0.019997716},"labels":[],"label_agreement":null},{"id":"W4229333017","doi":"10.1016/j.eswa.2022.117509","title":"Stochastic optimization model for determining support system parameters of a subway station","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada); Rocscience (Canada)","funders":"","keywords":"Computer science; Stochastic modelling; Stochastic optimization; Mathematical optimization; Operations research; Mathematics; Statistics","score_opus":0.011685643643439672,"score_gpt":0.21282870360434294,"score_spread":0.20114305996090326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229333017","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1031484,0.00043486667,0.88633955,0.000701298,0.00008753969,0.00012575263,0.0007822231,0.00041380347,0.007966559],"genre_scores_gemma":[0.97093743,0.0002660722,0.018533539,0.00010006199,0.000049936876,0.0001831459,0.00047313134,0.000070385286,0.009386281],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913067,0.00023103617,0.000035117962,0.00025526373,0.00017671434,0.00017113308],"domain_scores_gemma":[0.99873024,0.0006915834,0.00023515674,0.00003596954,0.00022862073,0.000078444464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011654383,0.0012141169,0.0023526296,0.0010449333,0.0007643127,0.001838618,0.0019034605,0.002711222,0.003613226],"category_scores_gemma":[0.0026388553,0.0011546094,0.0013178241,0.001375642,0.001204038,0.0011353435,0.0010587021,0.0015201987,0.00040538536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017857292,0.000005918809,0.00014853069,0.0000102550985,0.000009440775,0.000022198998,0.0000069629036,0.9974591,0.00019668433,0.0012842221,0.000115440416,0.0007234515],"study_design_scores_gemma":[0.000003407534,0.0000063458965,0.00010178506,0.0000012659468,0.000005581125,0.0000032511507,0.00000402537,0.99924004,0.000047223846,0.00053525204,0.00004807234,0.0000036931367],"about_ca_topic_score_codex":0.040204324,"about_ca_topic_score_gemma":0.017334135,"teacher_disagreement_score":0.040204324,"about_ca_system_score_codex":0.0023440856,"about_ca_system_score_gemma":0.0022940855,"threshold_uncertainty_score":0.07994062},"labels":[],"label_agreement":null},{"id":"W4229363931","doi":"10.1016/j.eswa.2022.117292","title":"Formulation and exact algorithms for electric vehicle production routing problem","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"European Regional Development Fund; Ministerio de Ciencia e Innovación; Agencia Estatal de Investigación; Generalitat Valenciana; European Commission","keywords":"Computer science; Solver; Vehicle routing problem; Mathematical optimization; Electric vehicle; Algorithm; Production (economics); Benchmark (surveying); Supply chain; Decomposition; Routing (electronic design automation); Mathematics","score_opus":0.01764929647510787,"score_gpt":0.2651405152044407,"score_spread":0.24749121872933283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229363931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028585845,0.00034768108,0.9910528,0.00024212172,0.000074850825,0.000042472395,0.00006683184,0.00011745315,0.0051971045],"genre_scores_gemma":[0.1795075,0.0013427626,0.8059321,0.0002610149,0.0002811516,0.00046593897,0.00036114577,0.00021419943,0.0116342325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995486,0.0001523025,0.000020855327,0.00007935256,0.00014703386,0.00005185549],"domain_scores_gemma":[0.9989059,0.0007086167,0.00007573459,0.00008853325,0.00019213671,0.000029039722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010095258,0.0010055691,0.0011690747,0.0007173287,0.00046095572,0.0015609658,0.0014471352,0.0014896366,0.0061171027],"category_scores_gemma":[0.0036625217,0.0006627925,0.0008170269,0.0010651052,0.00069251435,0.0016215445,0.0011339627,0.0021090254,0.00090964994],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000316285,0.00006468022,0.00012669414,0.00011933146,0.000015410691,0.00003043064,0.000043109987,0.8761389,0.0004541299,0.0497692,0.0038480428,0.06935847],"study_design_scores_gemma":[0.000013316855,0.000010360205,0.00003817605,0.000009962304,0.0000040675745,0.000011209039,0.000011837209,0.97402805,0.00012257275,0.024369782,0.0013770575,0.0000036481501],"about_ca_topic_score_codex":0.007686568,"about_ca_topic_score_gemma":0.006871251,"teacher_disagreement_score":0.007686568,"about_ca_system_score_codex":0.0011091866,"about_ca_system_score_gemma":0.0017972566,"threshold_uncertainty_score":0.020463765},"labels":[],"label_agreement":null},{"id":"W4280515996","doi":"10.1016/j.eswa.2022.117516","title":"Multivariate bounded support asymmetric generalized Gaussian mixture model with model selection using minimum message length","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Bounded function; Selection (genetic algorithm); Computer science; Gaussian; Model selection; Mixture model; Minimum description length; Multivariate normal distribution; Mathematical optimization; Mathematics; Artificial intelligence; Statistics; Algorithm; Machine learning","score_opus":0.027425922024945582,"score_gpt":0.28789038445424375,"score_spread":0.26046446242929816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280515996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025032996,0.00014498034,0.9967244,0.00014672724,0.000019031997,0.000021860116,0.00004794537,0.00013253084,0.000259207],"genre_scores_gemma":[0.27338496,0.0008156594,0.7173223,0.00042732467,0.0003126733,0.00066205784,0.0011195805,0.00034476395,0.005610708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99552023,0.0024630676,0.00020940947,0.0006911507,0.0008771976,0.00023887024],"domain_scores_gemma":[0.99014866,0.007372657,0.0007387955,0.00063429086,0.00090753054,0.00019807172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005273819,0.0017055877,0.003943496,0.0015886417,0.0010772495,0.0022147493,0.004418359,0.00322813,0.0033543794],"category_scores_gemma":[0.018482868,0.0017390788,0.0021638996,0.0022854144,0.001561886,0.0038551986,0.003138808,0.0039056856,0.0017205578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064207555,0.00016908767,0.00096925825,0.00033469065,0.00028046162,0.0002499454,0.00017750867,0.78797036,0.0025937546,0.09049848,0.0033305422,0.11278386],"study_design_scores_gemma":[0.000019652829,0.00001644732,0.00007409046,0.000010020658,0.000020543925,0.000025099558,0.000004969763,0.98212457,0.00031794954,0.017053358,0.00031830237,0.000015025813],"about_ca_topic_score_codex":0.0043791006,"about_ca_topic_score_gemma":0.0037466518,"teacher_disagreement_score":0.005273819,"about_ca_system_score_codex":0.0012951794,"about_ca_system_score_gemma":0.002064576,"threshold_uncertainty_score":0.02789098},"labels":[],"label_agreement":null},{"id":"W4280566838","doi":"10.1016/j.eswa.2022.117585","title":"Associative reasoning-based interpretable continuous decision making in industrial production process","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Computer science; Associative property; Production (economics); Process (computing); Artificial intelligence; Machine learning; Mathematics","score_opus":0.015480408047705826,"score_gpt":0.2773489672760624,"score_spread":0.26186855922835656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280566838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31467143,0.0004843488,0.6780327,0.0005024217,0.00010416511,0.000047554244,0.000105565894,0.00039951215,0.005652317],"genre_scores_gemma":[0.969775,0.000089278175,0.029401526,0.000030479776,0.000017473709,0.000020599511,0.000038500664,0.000016825503,0.0006102418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989335,0.00038048948,0.00009264854,0.00024212684,0.00025398936,0.00009717959],"domain_scores_gemma":[0.99581146,0.0030567304,0.00036429518,0.00021914566,0.00043119534,0.00011717723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024068546,0.0004742047,0.00076757575,0.0006637391,0.00059436465,0.002180689,0.0010975971,0.00096098037,0.0022911439],"category_scores_gemma":[0.009080921,0.0003965202,0.0006893097,0.000613676,0.0012775584,0.0028192059,0.0009175421,0.0012582889,0.00018264313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046855048,0.00023083338,0.0025403507,0.00017806012,0.0001467507,0.0005359851,0.0005566285,0.821865,0.0068879924,0.08904272,0.000605295,0.07694171],"study_design_scores_gemma":[0.000008139496,0.000019811283,0.00033896006,0.0000075282196,0.000012887773,0.000017587045,0.000022663182,0.96205765,0.0010359292,0.036369313,0.00010105426,0.00000852358],"about_ca_topic_score_codex":0.004581919,"about_ca_topic_score_gemma":0.0029725176,"teacher_disagreement_score":0.004581919,"about_ca_system_score_codex":0.0010569284,"about_ca_system_score_gemma":0.0011261324,"threshold_uncertainty_score":0.01272881},"labels":[],"label_agreement":null},{"id":"W4280575693","doi":"10.1016/j.eswa.2022.117514","title":"Dual attention-based sequential auto-encoder for Covid-19 outbreak forecasting: A case study in Vietnam","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Machine learning; Deep learning; Time series; Process (computing); Inference; Artificial neural network","score_opus":0.08596837595592559,"score_gpt":0.37274368066318225,"score_spread":0.28677530470725665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280575693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80231255,0.0015967892,0.17950346,0.0026013732,0.0003557221,0.00014784589,0.0018607662,0.003121371,0.008500187],"genre_scores_gemma":[0.9721168,0.00016662205,0.024029678,0.0001198825,0.00003232372,0.000019452533,0.00059866445,0.000039177772,0.002877416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997527,0.000057740406,0.00001785476,0.00007227532,0.000039983483,0.00005947689],"domain_scores_gemma":[0.99841,0.0009801877,0.000063065076,0.000085621956,0.0003950987,0.00006599802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000785688,0.0005543174,0.00043728919,0.0006461414,0.000499711,0.00051736244,0.00077623525,0.0007320672,0.0019101979],"category_scores_gemma":[0.002744167,0.00022317196,0.00030961385,0.00052111846,0.00021976052,0.00067747827,0.00042415372,0.00082906126,0.00037693814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013173643,0.0009766791,0.048141655,0.0004193148,0.00015976313,0.005873752,0.0009778787,0.37814614,0.01999996,0.004521083,0.024580678,0.51488566],"study_design_scores_gemma":[0.000015040981,0.0000660061,0.003048019,0.000011527098,0.000032652835,0.00025051978,0.00016153994,0.989903,0.003907892,0.0013222307,0.0012674575,0.000014205438],"about_ca_topic_score_codex":0.07736066,"about_ca_topic_score_gemma":0.07105789,"teacher_disagreement_score":0.07736066,"about_ca_system_score_codex":0.0010012992,"about_ca_system_score_gemma":0.0017315059,"threshold_uncertainty_score":0.15382075},"labels":[],"label_agreement":null},{"id":"W4280600334","doi":"10.1016/j.eswa.2022.117523","title":"A rough set-based Competitive Intelligence approach for anticipating competitor’s action","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Competitor analysis; Competition (biology); Computer science; Competitive intelligence; Knowledge management; Process (computing); Competitive advantage; Information technology; Product (mathematics); Set (abstract data type); Information system; Empirical research; Strategic management; Discipline; Business intelligence; Marketing; Business","score_opus":0.0655482291394944,"score_gpt":0.3110561721831803,"score_spread":0.24550794304368592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280600334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020325506,0.0005505832,0.97004503,0.00046497677,0.00013008194,0.000087196575,0.00007642895,0.00015524366,0.0081649255],"genre_scores_gemma":[0.74163246,0.00064422004,0.2535212,0.00017099066,0.0001358794,0.00014622483,0.000115496994,0.00002903576,0.0036044915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987901,0.00029325232,0.00007549154,0.00020110834,0.0005248379,0.00011524266],"domain_scores_gemma":[0.9986278,0.0007275397,0.0001663485,0.000068638525,0.00033108928,0.00007870136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016667291,0.0006851386,0.0014656787,0.0019121296,0.00078521797,0.002556034,0.0019947193,0.0013067712,0.002578205],"category_scores_gemma":[0.0046116663,0.00037698436,0.0013491967,0.0015677059,0.00089950947,0.0028032267,0.0011345564,0.0011662663,0.00038564447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017270667,0.0002254039,0.0018771454,0.0002492152,0.0002982946,0.00045210804,0.0005489599,0.709939,0.0033544507,0.15514009,0.0028308663,0.12491175],"study_design_scores_gemma":[0.000007167213,0.000060097453,0.00029900455,0.000011543839,0.00003277966,0.000037217695,0.00005197357,0.97771806,0.00033022583,0.020747082,0.0006808573,0.000024043256],"about_ca_topic_score_codex":0.007986363,"about_ca_topic_score_gemma":0.005797783,"teacher_disagreement_score":0.007986363,"about_ca_system_score_codex":0.0011697375,"about_ca_system_score_gemma":0.0017456264,"threshold_uncertainty_score":0.01587975},"labels":[],"label_agreement":null},{"id":"W4280642091","doi":"10.1016/j.eswa.2022.117553","title":"COVID-19 malicious domain names classification","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Phishing; Computer science; Computer security; Internet privacy; Commit; Malware; Domain (mathematical analysis); The Internet; Order (exchange); Credit card; Password; Heuristics; Information sensitivity; Personally identifiable information; World Wide Web; Business; Payment","score_opus":0.025952885132583394,"score_gpt":0.27358604216846955,"score_spread":0.24763315703588615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280642091","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89520997,0.00428667,0.040842623,0.001355485,0.0011491571,0.00085893227,0.02394309,0.008406314,0.023947738],"genre_scores_gemma":[0.9232992,0.00073338783,0.024826778,0.00026314735,0.00021435125,0.00019091813,0.04291455,0.00012431885,0.0074333525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983858,0.00020581612,0.00015878778,0.00033080013,0.0006527269,0.0002661549],"domain_scores_gemma":[0.99712414,0.0007917526,0.00050875655,0.00045896886,0.00080325396,0.00031314092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010646436,0.0012620619,0.00073050964,0.0042027766,0.0010929748,0.0014160306,0.0010457081,0.0013369381,0.0024100787],"category_scores_gemma":[0.0053396686,0.00015486567,0.00081029657,0.0015541923,0.0004918618,0.0016300086,0.0011251941,0.001258722,0.004121797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017266801,0.0016763399,0.2867465,0.0013528784,0.00031563436,0.003130543,0.000531108,0.06330017,0.02810098,0.0048570707,0.121018395,0.4872437],"study_design_scores_gemma":[0.000054202825,0.000400688,0.08744817,0.00019488794,0.000112644884,0.0037640387,0.0007418953,0.82361865,0.037107825,0.0028648432,0.04358146,0.00011061816],"about_ca_topic_score_codex":0.006010537,"about_ca_topic_score_gemma":0.0072442703,"teacher_disagreement_score":0.006010537,"about_ca_system_score_codex":0.00092667964,"about_ca_system_score_gemma":0.0009985594,"threshold_uncertainty_score":0.011951089},"labels":[],"label_agreement":null},{"id":"W4283079959","doi":"10.1016/j.eswa.2022.117904","title":"Design of fuzzy rule-based models with fuzzy relational factorization","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada First Research Excellence Fund; University of Alberta","keywords":"Interpretability; Factorization; Fuzzy rule; Fuzzy logic; Computer science; Fuzzy number; Fuzzy classification; Data mining; Fuzzy set operations; Relational database; Defuzzification; Fuzzy associative matrix; Curse of dimensionality; Artificial intelligence; Fuzzy set; Mathematics; Algorithm","score_opus":0.03439008955923596,"score_gpt":0.2346934231085828,"score_spread":0.20030333354934682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283079959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005475675,0.00013696245,0.99275726,0.00008104331,0.000017495584,0.00006677832,0.00006494134,0.0002583625,0.0011415738],"genre_scores_gemma":[0.43805262,0.00037014842,0.55807745,0.000114033144,0.000039883074,0.00045200236,0.00037201482,0.000073577256,0.0024482647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989297,0.00035065738,0.00007183624,0.00025833474,0.00029393256,0.00009559991],"domain_scores_gemma":[0.99900025,0.0004766249,0.000119554046,0.000093166964,0.00026719816,0.000043162112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001618716,0.00076495286,0.0014671072,0.00064070214,0.0006125657,0.0018022816,0.0017590967,0.0014248705,0.0027151094],"category_scores_gemma":[0.004104219,0.0009142195,0.0015444498,0.0005851137,0.0006239777,0.001526236,0.0011275031,0.0011733214,0.0010385578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011674557,0.00006743724,0.00038445203,0.00015477873,0.00009863436,0.0001596231,0.00016900094,0.9063016,0.0034611756,0.029012984,0.00089347165,0.05918009],"study_design_scores_gemma":[0.000011733184,0.000023203958,0.000031089443,0.000010261463,0.00001943358,0.000017610771,0.0000090881695,0.9919463,0.00065471756,0.0067117536,0.00055884174,0.00000595552],"about_ca_topic_score_codex":0.0057267724,"about_ca_topic_score_gemma":0.006294455,"teacher_disagreement_score":0.0057267724,"about_ca_system_score_codex":0.0008524367,"about_ca_system_score_gemma":0.0016325488,"threshold_uncertainty_score":0.011386931},"labels":[],"label_agreement":null},{"id":"W4283717003","doi":"10.1016/j.eswa.2022.117955","title":"Subspace-based outlier detection using linear programming and heuristic techniques","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Subspace topology; Linear subspace; Computer science; Best bin first; Outlier; Random subspace method; k-nearest neighbors algorithm; Linear programming; Curse of dimensionality; Anomaly detection; Heuristic; Pattern recognition (psychology); Algorithm; Mathematics; Data mining; Artificial intelligence","score_opus":0.01597098468121314,"score_gpt":0.2613519894864549,"score_spread":0.24538100480524175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283717003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032452021,0.000052415053,0.9961773,0.000032776457,0.000007973297,0.0000152316425,0.000015080269,0.00024217392,0.00021182394],"genre_scores_gemma":[0.23385057,0.00016986777,0.76377845,0.000072220726,0.000056836965,0.00018105659,0.00025135733,0.00017478474,0.0014648155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989184,0.00035235737,0.000060205457,0.00019959215,0.00035632154,0.0001130766],"domain_scores_gemma":[0.9965442,0.0021836683,0.00030102924,0.00018142661,0.0007026922,0.00008696621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013044265,0.0010092013,0.0020945002,0.0017561787,0.0006756413,0.0014436686,0.0013463924,0.00090483075,0.0018246747],"category_scores_gemma":[0.0047976733,0.0006136026,0.0011447596,0.0020133578,0.00079582736,0.0015411642,0.0011250575,0.001664391,0.00054070714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015385365,0.00016668766,0.00091083185,0.00012796572,0.000104098996,0.00004912831,0.000073271025,0.71396464,0.004431139,0.008994274,0.001856138,0.26916805],"study_design_scores_gemma":[0.0000037572966,0.000019303177,0.000055820343,0.0000025995673,0.000003928626,0.000010926397,0.000008809928,0.9969349,0.00049646676,0.002336149,0.00012289561,0.000004392436],"about_ca_topic_score_codex":0.005068381,"about_ca_topic_score_gemma":0.0046090013,"teacher_disagreement_score":0.005068381,"about_ca_system_score_codex":0.00061682425,"about_ca_system_score_gemma":0.0015904276,"threshold_uncertainty_score":0.0100777745},"labels":[],"label_agreement":null},{"id":"W4283754581","doi":"10.1016/j.eswa.2022.117947","title":"ISAIR: Deep inpainted semantic aware image representation for background subtraction","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Representation (politics); Artificial intelligence; Background subtraction; Image (mathematics); Subtraction; Computer vision; Pattern recognition (psychology); Natural language processing; Mathematics; Pixel; Arithmetic","score_opus":0.037492488970715644,"score_gpt":0.33546993028753264,"score_spread":0.29797744131681697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283754581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008654096,0.000515973,0.97479475,0.00011764076,0.00014717596,0.00007791807,0.0008712643,0.013169658,0.0016514632],"genre_scores_gemma":[0.10439258,0.0006089687,0.87952363,0.00037835733,0.00012599216,0.00013798723,0.004318608,0.0010370433,0.009476835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967504,0.00003211319,0.000010076513,0.00008400407,0.00013682581,0.000061958264],"domain_scores_gemma":[0.99982244,0.000032462693,0.0000151822005,0.000051415464,0.000057379053,0.000021286307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045394388,0.0014986685,0.0011379838,0.001134559,0.0003218625,0.00093504676,0.0019745163,0.0010246802,0.006638971],"category_scores_gemma":[0.000649032,0.00050691684,0.0009912079,0.00095282606,0.00026430713,0.0009039763,0.0012285191,0.001523045,0.0033786881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055776915,0.0002962178,0.00039112088,0.00017112965,0.00013335003,0.00012578252,0.00005043534,0.025447296,0.08275933,0.0035982386,0.026889797,0.85957956],"study_design_scores_gemma":[0.000051597453,0.00019393208,0.00088010036,0.000025187728,0.00005972318,0.00025081504,0.000028699305,0.9124938,0.0674977,0.0048247925,0.013665259,0.000028356324],"about_ca_topic_score_codex":0.004488427,"about_ca_topic_score_gemma":0.008175919,"teacher_disagreement_score":0.006638971,"about_ca_system_score_codex":0.00046636906,"about_ca_system_score_gemma":0.00079815934,"threshold_uncertainty_score":0.022209585},"labels":[],"label_agreement":null},{"id":"W4289755363","doi":"10.1016/j.eswa.2022.118349","title":"Complexity analysis and forecasting of variations in cryptocurrency trading volume with support vector regression tuned by Bayesian optimization under different kernels: An empirical comparison from a large dataset","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Autoregressive integrated moving average; Mean squared error; Computer science; Lasso (programming language); Econometrics; Mathematics; Artificial intelligence; Time series; Machine learning; Statistics","score_opus":0.06317040677205281,"score_gpt":0.2851348670470139,"score_spread":0.22196446027496108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289755363","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97438264,0.00048657227,0.023170248,0.00035500326,0.000049258324,0.000018469618,0.00078246277,0.00020552686,0.0005497986],"genre_scores_gemma":[0.9913326,0.00012810614,0.0059163277,0.000023615876,0.0000434268,0.000015206519,0.0022577883,0.000027715705,0.00025530506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930036,0.00026761563,0.00006422138,0.00015881656,0.00013296831,0.00007609761],"domain_scores_gemma":[0.99186933,0.0058556236,0.0006812601,0.0007364843,0.0006647074,0.00019254179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029253324,0.0006612448,0.00078108016,0.0018765024,0.00029412843,0.0010862511,0.0007971567,0.0010954108,0.00073058833],"category_scores_gemma":[0.012151462,0.0002641721,0.001041906,0.0012873104,0.00041329127,0.0023395135,0.0005762241,0.0012559904,0.00024542518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001363412,0.000662212,0.09136899,0.00022059307,0.0006056544,0.00023597582,0.00015528241,0.8021067,0.0032366891,0.0054066395,0.0059890747,0.08864885],"study_design_scores_gemma":[0.000017355784,0.000039719944,0.013010782,0.000007252389,0.000024939993,0.000022891027,0.0000172995,0.9851007,0.00040671724,0.001176614,0.00016116812,0.000014630793],"about_ca_topic_score_codex":0.006479147,"about_ca_topic_score_gemma":0.0051496224,"teacher_disagreement_score":0.006479147,"about_ca_system_score_codex":0.0006910379,"about_ca_system_score_gemma":0.0006301482,"threshold_uncertainty_score":0.015470862},"labels":[],"label_agreement":null},{"id":"W4292157874","doi":"10.1016/j.eswa.2022.118394","title":"GPDS: A multi-agent deep reinforcement learning game for anti-jamming secure computing in MEC network","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Windsor","funders":"National Key Research and Development Program of China; State Key Laboratory of Industrial Control Technology; Zhejiang University; Hunan Provincial Innovation Foundation for Postgraduate; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Jamming; Artificial intelligence; Computer security; Distributed computing","score_opus":0.016364903858268578,"score_gpt":0.2595943896520062,"score_spread":0.24322948579373763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292157874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04931851,0.00015379858,0.9430004,0.00052889244,0.000090255264,0.00010860626,0.000085074586,0.0006679814,0.0060464526],"genre_scores_gemma":[0.9287986,0.000082368795,0.06519548,0.00019185904,0.000017273474,0.00011227363,0.000043473574,0.00004114484,0.0055175917],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970967,0.00009976341,0.000010165702,0.000049779177,0.00006153407,0.00006917346],"domain_scores_gemma":[0.9994772,0.00030755947,0.000031856158,0.000030172972,0.00008367413,0.00006957169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071351306,0.0004900096,0.0006789089,0.00020381818,0.00033163378,0.0006303153,0.0011491325,0.0011613347,0.0039995317],"category_scores_gemma":[0.0017372329,0.00024780515,0.000312156,0.00013450439,0.00065691327,0.0007332035,0.001363626,0.001343062,0.00027109025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001691644,0.00006631368,0.00042962522,0.000050715356,0.0000287317,0.00010445589,0.000046572106,0.9585852,0.0021495933,0.015208813,0.0015542791,0.021606468],"study_design_scores_gemma":[0.000009165676,0.000020177655,0.000024305451,0.0000017355509,0.0000025579257,0.0000063621274,0.0000035130147,0.99781597,0.00017448314,0.0017383862,0.00020141482,0.000001835698],"about_ca_topic_score_codex":0.0057571204,"about_ca_topic_score_gemma":0.0062013334,"teacher_disagreement_score":0.0057571204,"about_ca_system_score_codex":0.0006991706,"about_ca_system_score_gemma":0.0011103859,"threshold_uncertainty_score":0.013379753},"labels":[],"label_agreement":null},{"id":"W4293220834","doi":"10.1016/j.eswa.2022.118577","title":"A proposed multi-objective model for cellphone closed-loop supply chain optimization based on fuzzy QFD","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Faculty of Engineering and Architectural Science, Ryerson University; Tokyo Metropolitan University","keywords":"Computer science; Supply chain; Fuzzy logic; Quality function deployment; Supply chain network; Mathematical optimization; Population; Facility location problem; Operations research; Supply chain management; New product development; Mathematics; Business","score_opus":0.01558497961031365,"score_gpt":0.23177291578478418,"score_spread":0.21618793617447052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293220834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01980821,0.00031961375,0.9693829,0.00022878565,0.0000703891,0.00009611915,0.00015798207,0.00018435308,0.009751667],"genre_scores_gemma":[0.8784747,0.0004313517,0.11119579,0.0001223671,0.00004228769,0.0004128067,0.00022331925,0.000040109488,0.009057197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996407,0.0000905817,0.000016846523,0.00009104756,0.00010316971,0.000057668283],"domain_scores_gemma":[0.9997129,0.00013876539,0.000028710234,0.000009744635,0.00009478658,0.000015161237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007759331,0.0008031681,0.0012035415,0.00055077067,0.00066074997,0.0015559811,0.0015592343,0.0021256518,0.0042191464],"category_scores_gemma":[0.00094249257,0.00043432752,0.00080695166,0.0008579857,0.0004886285,0.0009790158,0.00079213135,0.000827475,0.00032425218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015632782,0.000014731005,0.00014501715,0.000034647408,0.000013768767,0.000048728703,0.000022951437,0.99018174,0.00053867535,0.0019358178,0.0002196244,0.0068286667],"study_design_scores_gemma":[0.0000043083396,0.000012993952,0.000049873364,0.0000037966684,0.0000049161226,0.0000063369084,0.0000057672573,0.9990293,0.000095951524,0.0005698477,0.00021355915,0.000003324718],"about_ca_topic_score_codex":0.027434085,"about_ca_topic_score_gemma":0.01738863,"teacher_disagreement_score":0.027434085,"about_ca_system_score_codex":0.0011259789,"about_ca_system_score_gemma":0.0016654172,"threshold_uncertainty_score":0.0545488},"labels":[],"label_agreement":null},{"id":"W4293581464","doi":"10.1016/j.eswa.2022.118692","title":"Impact of external influence on unilateral improvements in the graph model for conflict resolution","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Conflict resolution; Graph; Resolution (logic); Artificial intelligence; Theoretical computer science; Political science","score_opus":0.08924453860568086,"score_gpt":0.4029534718254169,"score_spread":0.31370893321973603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293581464","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6629073,0.000825209,0.27436703,0.0034182833,0.00021626323,0.00017742736,0.0003343292,0.00045180586,0.057302333],"genre_scores_gemma":[0.99223584,0.00013421683,0.0049618883,0.00008585061,0.000021599159,0.00002882142,0.000032784457,0.000035834477,0.0024631226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974711,0.0016038402,0.0000564246,0.0002352292,0.00022033516,0.00041307305],"domain_scores_gemma":[0.9638986,0.030443104,0.0015847706,0.0013962704,0.0011069846,0.0015702994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004458263,0.0011278033,0.0016697287,0.0012917165,0.0010871954,0.0025153356,0.0024058484,0.002602486,0.013678525],"category_scores_gemma":[0.03756248,0.00049632613,0.0008166638,0.000978681,0.0022298426,0.00499958,0.0020539262,0.0026429712,0.000517128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007305308,0.00028212392,0.001277168,0.00019122055,0.00011198316,0.00021570956,0.00018446041,0.80508757,0.0016262546,0.17227627,0.0018597932,0.016157009],"study_design_scores_gemma":[0.00010427809,0.00022141349,0.0006961722,0.00002488245,0.00009105738,0.000057096317,0.00013897759,0.87560356,0.0005388176,0.12190968,0.0005778525,0.000036208934],"about_ca_topic_score_codex":0.005366428,"about_ca_topic_score_gemma":0.006130562,"teacher_disagreement_score":0.013678525,"about_ca_system_score_codex":0.001645386,"about_ca_system_score_gemma":0.001812476,"threshold_uncertainty_score":0.0457592},"labels":[],"label_agreement":null},{"id":"W4293581755","doi":"10.1016/j.eswa.2022.118710","title":"Social media-based COVID-19 sentiment classification model using Bi-LSTM","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Misinformation; Computer science; Social media; Sentiment analysis; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Public opinion; The Internet; Machine learning; Natural language processing; Data science; World Wide Web; Computer security; Political science","score_opus":0.14366966534227193,"score_gpt":0.39191412003643455,"score_spread":0.24824445469416262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293581755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42313033,0.0039107073,0.5083974,0.0037591618,0.0037775063,0.0005680069,0.010134685,0.0109720295,0.035350163],"genre_scores_gemma":[0.8879075,0.0008667498,0.07648667,0.00065177045,0.00063457235,0.00029077206,0.009056676,0.00018922414,0.023916056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997259,0.000040210154,0.0000243231,0.00008039413,0.000062981235,0.000066233224],"domain_scores_gemma":[0.9995078,0.00011519488,0.00003606854,0.00002919967,0.00027990236,0.000031794032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066457555,0.0011399596,0.0006649753,0.0013006136,0.0004967947,0.00089583884,0.00084770063,0.0010141544,0.0043234667],"category_scores_gemma":[0.0011833913,0.00027973158,0.0007488285,0.0012071162,0.00019612616,0.0011904928,0.00075409625,0.0014887907,0.0038138872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008863549,0.001206152,0.012403677,0.00036598492,0.00033696208,0.0003602088,0.00024681445,0.042694192,0.040954217,0.0029635266,0.04254842,0.8550335],"study_design_scores_gemma":[0.000014983581,0.00008854822,0.0028038558,0.000026221976,0.0000694195,0.00005045085,0.00004953892,0.9872318,0.0057312255,0.0013188885,0.0025954733,0.000019560657],"about_ca_topic_score_codex":0.008155222,"about_ca_topic_score_gemma":0.010980793,"teacher_disagreement_score":0.008155222,"about_ca_system_score_codex":0.0006660142,"about_ca_system_score_gemma":0.0009040646,"threshold_uncertainty_score":0.016215503},"labels":[],"label_agreement":null},{"id":"W4306319365","doi":"10.1016/j.eswa.2022.119007","title":"An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Mental Health via Writing","field":"Psychology","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Computer science; Bengali; Preprocessor; Artificial intelligence; Robustness (evolution); Convolutional neural network; Social media; Machine learning; Transformer; Scarcity; Sentiment analysis; Natural language processing; World Wide Web","score_opus":0.019854284358358402,"score_gpt":0.32651466412768565,"score_spread":0.30666037976932725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306319365","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6451368,0.0034017751,0.32972324,0.002056882,0.0006051306,0.00020222034,0.0013530437,0.0056875395,0.011833356],"genre_scores_gemma":[0.9666071,0.00043359044,0.024177391,0.00026733195,0.00008248453,0.00006817956,0.0008970688,0.000043396998,0.007423527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998281,0.000027197157,0.000010953886,0.000070991366,0.000020769783,0.000042030766],"domain_scores_gemma":[0.9996853,0.00011819002,0.000032041567,0.0000244832,0.00011772947,0.000022280816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004418287,0.000822431,0.00043761884,0.00075081177,0.00035547608,0.00070248096,0.00078589475,0.0007059076,0.0020169213],"category_scores_gemma":[0.0010078403,0.000274897,0.00062188227,0.00049501023,0.00022248147,0.0009356831,0.0005989507,0.0008676854,0.00083467894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008889469,0.00069729675,0.029688416,0.00024475876,0.0003868278,0.0007539572,0.00057313475,0.14576967,0.034608956,0.0026774358,0.010659604,0.77305096],"study_design_scores_gemma":[0.00001182638,0.00009649037,0.0050483407,0.00001903234,0.00009715389,0.00008914135,0.000052820043,0.98875004,0.0036838376,0.001171484,0.0009613825,0.000018402761],"about_ca_topic_score_codex":0.015724005,"about_ca_topic_score_gemma":0.017476652,"teacher_disagreement_score":0.015724005,"about_ca_system_score_codex":0.0008236612,"about_ca_system_score_gemma":0.00062388607,"threshold_uncertainty_score":0.03126496},"labels":[],"label_agreement":null},{"id":"W4306966770","doi":"10.1016/j.eswa.2022.119060","title":"Distilling and transferring knowledge via cGAN-generated samples for image classification and regression","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Regression; Image (mathematics); Artificial intelligence; Linear regression; Machine learning; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.04174860468710112,"score_gpt":0.28772653537682796,"score_spread":0.24597793068972684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306966770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037135042,0.00059248396,0.95881695,0.0002444985,0.00009174466,0.00007446024,0.00014714264,0.0019560354,0.0009416927],"genre_scores_gemma":[0.525758,0.00036415047,0.46748483,0.0004678936,0.00014168603,0.00021202842,0.0015354711,0.0003125046,0.003723318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993765,0.00016389997,0.00002950845,0.00022903105,0.00012820025,0.000072907176],"domain_scores_gemma":[0.99814844,0.0010067888,0.000091314214,0.00029128703,0.00038987937,0.00007226773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015579885,0.001095139,0.0012612363,0.0012181057,0.0005832915,0.000996706,0.0022192663,0.0022372718,0.001923473],"category_scores_gemma":[0.005700961,0.00057676947,0.0010263093,0.0011178672,0.0009793526,0.0016291566,0.0017182727,0.0025085136,0.0008871441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045846586,0.00041556373,0.0016898451,0.0002226206,0.00014523257,0.00021283406,0.00016034486,0.39458713,0.02226427,0.0076755784,0.006593273,0.5655749],"study_design_scores_gemma":[0.000006618597,0.00002367956,0.00015308581,0.0000059723034,0.00000853923,0.000020274261,0.000009357657,0.9937878,0.0028360744,0.002784565,0.0003582149,0.0000057396205],"about_ca_topic_score_codex":0.007888628,"about_ca_topic_score_gemma":0.010736737,"teacher_disagreement_score":0.007888628,"about_ca_system_score_codex":0.0008396895,"about_ca_system_score_gemma":0.0013048154,"threshold_uncertainty_score":0.01568538},"labels":[],"label_agreement":null},{"id":"W4307816510","doi":"10.1016/j.eswa.2022.119160","title":"A reinforcement learning model for the reliability of blockchain oracles","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Blockchain; Computer science; Commit; Reinforcement learning; Reputation; Randomness; Artificial intelligence; Solidity; Machine learning; Python (programming language); Adversarial system; Profit (economics); Computer security; Database; Programming language","score_opus":0.015039705780484235,"score_gpt":0.24926875124856526,"score_spread":0.23422904546808102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307816510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09758365,0.00044414852,0.8945754,0.0012793648,0.000073986,0.00013896594,0.00040125623,0.00055459136,0.004948662],"genre_scores_gemma":[0.9695193,0.0001916924,0.024463108,0.00007113795,0.000045396133,0.000114394694,0.00016945082,0.000057885456,0.0053675245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984256,0.0006165371,0.00007993202,0.00034744403,0.00026715195,0.00026329025],"domain_scores_gemma":[0.9837892,0.012253376,0.0011645113,0.0006920906,0.0014803866,0.00062040164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039019238,0.00080669875,0.0017244477,0.00086327223,0.00048521106,0.0016738854,0.0028755988,0.0022698033,0.005773802],"category_scores_gemma":[0.02186677,0.00070771214,0.0005936372,0.0007719035,0.0017251325,0.0030291306,0.0012287333,0.0027936618,0.0006325693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012086048,0.000037575184,0.00051481905,0.0000370943,0.00001806443,0.000052081923,0.000045199704,0.9608255,0.00037864744,0.03054515,0.00061238476,0.0068125674],"study_design_scores_gemma":[0.0000110040855,0.000010163526,0.000048204885,0.0000034507411,0.0000030268768,0.0000051663724,0.0000022247557,0.9924224,0.000050959687,0.007370149,0.000069486836,0.0000037238892],"about_ca_topic_score_codex":0.011599751,"about_ca_topic_score_gemma":0.0074002566,"teacher_disagreement_score":0.011599751,"about_ca_system_score_codex":0.0022824751,"about_ca_system_score_gemma":0.0016211569,"threshold_uncertainty_score":0.023064435},"labels":[],"label_agreement":null},{"id":"W4307816715","doi":"10.1016/j.eswa.2022.119101","title":"Predicting and explaining performance and diversity of neural network architecture for semantic segmentation","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Compute Canada","keywords":"Boosting (machine learning); Computer science; Artificial neural network; Artificial intelligence; Machine learning; Segmentation; Diversity (politics); Network architecture; Ensemble learning; Architecture; Data mining","score_opus":0.020764452668868522,"score_gpt":0.24731959830124683,"score_spread":0.2265551456323783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307816715","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97817695,0.00020579698,0.019829728,0.00016178706,0.00002619993,0.000017649185,0.00022063911,0.00028073558,0.0010804691],"genre_scores_gemma":[0.99228597,0.000046243636,0.0068189115,0.000023619818,0.000009812814,0.000010296724,0.00038165326,0.000044497527,0.0003790201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950147,0.0001599285,0.000034508157,0.00018279551,0.00003947161,0.000081820624],"domain_scores_gemma":[0.99361044,0.0049667037,0.00026067952,0.00045711172,0.0005343077,0.0001707447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022108625,0.00089240895,0.00041497857,0.0012290663,0.0004548873,0.001260595,0.0006642783,0.0014226536,0.0014885815],"category_scores_gemma":[0.011289128,0.000399575,0.0007834455,0.0007409125,0.00047434398,0.0019624168,0.00071571145,0.0014359957,0.00042253023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018228337,0.00042310386,0.17498992,0.00014180418,0.0006048376,0.00032814682,0.00073478976,0.5919685,0.024320304,0.0035048057,0.0034503369,0.19771074],"study_design_scores_gemma":[0.00001713548,0.00006480284,0.008243036,0.000008902106,0.00005632306,0.000039413917,0.0000795635,0.9853195,0.0029966263,0.0030602089,0.00010574855,0.000008758894],"about_ca_topic_score_codex":0.008657996,"about_ca_topic_score_gemma":0.008595175,"teacher_disagreement_score":0.008657996,"about_ca_system_score_codex":0.001043145,"about_ca_system_score_gemma":0.00052188244,"threshold_uncertainty_score":0.017215192},"labels":[],"label_agreement":null},{"id":"W4308600751","doi":"10.1016/j.eswa.2022.119228","title":"A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Truck; Greenhouse gas; Computer science; Vehicle routing problem; Minification; Constraint (computer-aided design); Routing (electronic design automation); Mathematical optimization; Total cost; Automotive engineering; Function (biology); Operations research; Mathematics; Engineering; Business; Computer network","score_opus":0.009149907028323509,"score_gpt":0.23046276061699422,"score_spread":0.2213128535886707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308600751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3892077,0.0013211648,0.5903082,0.0017441022,0.0003646891,0.00038328485,0.001390233,0.00033397396,0.0149465855],"genre_scores_gemma":[0.89443064,0.0004107102,0.08590294,0.00024195244,0.00013343482,0.00030534767,0.00069047057,0.00013424187,0.017750308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986551,0.0005054668,0.00004521046,0.00033125753,0.0001828851,0.00028004657],"domain_scores_gemma":[0.9982691,0.0009689811,0.000223035,0.00007175944,0.00016705618,0.0003000383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027811697,0.0030276494,0.0034474193,0.0020140144,0.0011482276,0.0032224183,0.0039384607,0.0048603434,0.0042685405],"category_scores_gemma":[0.0029859333,0.0026326466,0.0023200214,0.0029251627,0.0016664356,0.0033566596,0.0021256518,0.0018044285,0.000436885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009857173,0.00006612689,0.0003985276,0.000057388952,0.00008934127,0.00019332972,0.000019558558,0.9930252,0.00047008053,0.0026047644,0.00035727612,0.0026197864],"study_design_scores_gemma":[0.000020305717,0.00004788965,0.00018993451,0.000006001431,0.000028858152,0.000028201646,0.000029776877,0.9979911,0.000101170284,0.0013780722,0.0001705401,0.0000082042825],"about_ca_topic_score_codex":0.014172927,"about_ca_topic_score_gemma":0.011948651,"teacher_disagreement_score":0.014172927,"about_ca_system_score_codex":0.0024004094,"about_ca_system_score_gemma":0.0016911711,"threshold_uncertainty_score":0.028180838},"labels":[],"label_agreement":null},{"id":"W4313478995","doi":"10.1016/j.eswa.2022.119494","title":"Dynamic blockchain adoption for freshness-keeping in the fresh agricultural product supply chain","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":97,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Blockchain; Supply chain; Agriculture; Business; Product (mathematics); Cold chain; Commerce; Environmental economics; Industrial organization; Computer science; Marketing; Economics; Food science; Computer security; Chemistry","score_opus":0.019178596038496566,"score_gpt":0.24351083598244228,"score_spread":0.22433223994394572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313478995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85284144,0.00069694326,0.104764655,0.0016445743,0.00020200934,0.00037932605,0.00053300883,0.0013119669,0.03762609],"genre_scores_gemma":[0.9899941,0.00011535511,0.0068020886,0.000039415154,0.000010496178,0.000029375058,0.00017571296,0.000025626807,0.0028079026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982158,0.000577508,0.00008523961,0.0003226834,0.00044559702,0.0003531556],"domain_scores_gemma":[0.9923334,0.003352066,0.0004350403,0.0018118445,0.0013557593,0.0007118699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032433895,0.0003635116,0.0004090507,0.00081518455,0.0010390829,0.002138949,0.0012183291,0.0011739541,0.013595138],"category_scores_gemma":[0.009217113,0.00025744765,0.00034371248,0.0012371058,0.0006681325,0.0053588087,0.002730415,0.0011067131,0.0017976287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041859797,0.0017896922,0.037224833,0.00069253583,0.00017048014,0.0027415275,0.0029333783,0.24637368,0.045459453,0.09306694,0.013246058,0.5521154],"study_design_scores_gemma":[0.00029651306,0.0010613033,0.008598,0.00020512882,0.0001287578,0.00048428963,0.002189569,0.87039036,0.020720853,0.06427003,0.03153421,0.00012098504],"about_ca_topic_score_codex":0.004496883,"about_ca_topic_score_gemma":0.005619314,"teacher_disagreement_score":0.013595138,"about_ca_system_score_codex":0.001028712,"about_ca_system_score_gemma":0.0023082923,"threshold_uncertainty_score":0.04548025},"labels":[],"label_agreement":null},{"id":"W4313831028","doi":"10.1016/j.eswa.2023.119509","title":"News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":151,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Computer science; Artificial intelligence; Stock market; Stock market prediction; Social media; Sentiment analysis; Artificial neural network; Deep learning; Stock (firearms); Financial market; Data science; Data mining; Finance; World Wide Web; Business; Engineering","score_opus":0.10973042807311956,"score_gpt":0.3816748664956033,"score_spread":0.2719444384224837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313831028","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00439369,0.9909293,0.0018544598,0.00071098574,0.00014486875,0.00016391187,0.0011594752,0.000032865268,0.00061035133],"genre_scores_gemma":[0.02901002,0.9627932,0.0055783596,0.00061264454,0.00033747277,0.00025135378,0.0011992366,0.000012997891,0.0002047561],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9982327,0.00043475282,0.0006021755,0.00027739393,0.00041228707,0.000040646937],"domain_scores_gemma":[0.96377444,0.03145677,0.0024444468,0.00034519582,0.001809159,0.00016998043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005197126,0.0009571357,0.0026365346,0.009532619,0.0002690004,0.0019255648,0.0014102679,0.0012059021,0.002572294],"category_scores_gemma":[0.024900638,0.0004728639,0.0029848062,0.006506254,0.00042433006,0.0027491446,0.00069346797,0.0009012974,0.0004907463],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038684448,0.00027513257,0.011930186,0.32502186,0.008014997,0.00025673828,0.00027475864,0.0016106813,0.0006552723,0.0010140706,0.0068488335,0.6437106],"study_design_scores_gemma":[0.00055080035,0.0014235215,0.057766113,0.6541032,0.10459761,0.00158036,0.0012070854,0.012033012,0.0035822482,0.008092107,0.15473118,0.00033272943],"about_ca_topic_score_codex":0.0028378626,"about_ca_topic_score_gemma":0.0064484966,"teacher_disagreement_score":0.009532619,"about_ca_system_score_codex":0.0005938649,"about_ca_system_score_gemma":0.0040760045,"threshold_uncertainty_score":0.027485311},"labels":[],"label_agreement":null},{"id":"W4315865203","doi":"10.1016/j.eswa.2023.119537","title":"A user-guided reduction concept lattice and its algebraic structure","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shanxi Province; National Natural Science Foundation of China","keywords":"Lattice Miner; Computer science; Lattice (music); Formal concept analysis; Algebraic structure; Theoretical computer science; Knowledge representation and reasoning; Algebraic number; Data structure; Mathematics; Artificial intelligence; Algorithm; Pure mathematics; Programming language","score_opus":0.021589412515656468,"score_gpt":0.26947758141377437,"score_spread":0.2478881688981179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315865203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021625519,0.00013855666,0.9654237,0.00039641897,0.00008378264,0.0001225032,0.00028000216,0.0006355021,0.011293964],"genre_scores_gemma":[0.21245745,0.00011514731,0.7807012,0.00010541207,0.00004831648,0.00015008764,0.00037161255,0.00013972666,0.005910968],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99710625,0.00097097683,0.00018447572,0.0004837781,0.0011146455,0.00013994037],"domain_scores_gemma":[0.9976228,0.0008986678,0.00013811969,0.00047201652,0.00066370674,0.00020472854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022137898,0.0003007835,0.00060657994,0.0012839766,0.0010538313,0.003403997,0.001123802,0.0006910408,0.007131672],"category_scores_gemma":[0.005060913,0.0004012832,0.0011992125,0.0012706359,0.0018589558,0.0030956666,0.0016736512,0.0018661674,0.0016152967],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017414727,0.00013239823,0.00029524817,0.00010390621,0.00002584307,0.00012366593,0.00047639172,0.008079092,0.00543613,0.92113775,0.002319204,0.06169619],"study_design_scores_gemma":[0.000087694454,0.0002511445,0.00023567499,0.00005403101,0.00003560981,0.0005673628,0.00031155237,0.16259137,0.010150471,0.79639876,0.029231733,0.00008474895],"about_ca_topic_score_codex":0.0011063241,"about_ca_topic_score_gemma":0.0010527124,"teacher_disagreement_score":0.007131672,"about_ca_system_score_codex":0.00070639246,"about_ca_system_score_gemma":0.0015106617,"threshold_uncertainty_score":0.023857772},"labels":[],"label_agreement":null},{"id":"W4317548775","doi":"10.1016/j.eswa.2023.119569","title":"Evidence-based decision-making: On the use of systematicity cases to check the compliance of reviews with reporting guidelines such as PRISMA 2020","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Safety Systems Engineering in Autonomy","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Systematic review; Guideline; Certification; Quality (philosophy); Quality assurance; Computer science; Trustworthiness; Risk analysis (engineering); Management science; Process management; Knowledge management; MEDLINE; Medicine; Business; Political science; Engineering; Computer security","score_opus":0.3134951760922504,"score_gpt":0.37418380873403184,"score_spread":0.060688632641781426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317548775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":"methods","domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057411273,0.031238694,0.68362033,0.17290835,0.010757661,0.06978878,0.0014335021,0.002017298,0.02249437],"genre_scores_gemma":[0.056221925,0.0069043664,0.8531799,0.019515585,0.0014086249,0.06112253,0.00053008745,0.00026470548,0.0008522539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.046441924,0.76049244,0.1337145,0.012308732,0.045294885,0.001747645],"domain_scores_gemma":[0.023354886,0.84942985,0.04532451,0.039437275,0.040108427,0.002345034],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.88363063,0.0065610567,0.016576761,0.04085476,0.009175316,0.037776366,0.019111192,0.04388937,0.008266386],"category_scores_gemma":[0.9389206,0.008550247,0.0186997,0.026123187,0.038175665,0.06366654,0.03351743,0.025747854,0.0041804747],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021770806,0.00039655733,0.0047352985,0.07969649,0.007685272,0.0025231945,0.032579415,0.006262474,0.0012434359,0.495468,0.06748246,0.29975036],"study_design_scores_gemma":[0.0034120844,0.00065173145,0.0013420623,0.15697217,0.003255449,0.0012956879,0.0032598278,0.022351779,0.00314352,0.6393433,0.16386016,0.001112212],"about_ca_topic_score_codex":0.0044380613,"about_ca_topic_score_gemma":0.0035633368,"teacher_disagreement_score":0.11636937,"about_ca_system_score_codex":0.020203415,"about_ca_system_score_gemma":0.078385584,"threshold_uncertainty_score":0.14658672},"labels":[],"label_agreement":null},{"id":"W4318016858","doi":"10.1016/j.eswa.2023.119599","title":"On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Loan; Computer science; Credit score; Predictive power; Credit risk; Value (mathematics); Credit history; Actuarial science; Portfolio; Artificial intelligence; Machine learning; Business; Finance; Computer security","score_opus":0.013982615705062188,"score_gpt":0.24351884700910564,"score_spread":0.22953623130404346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318016858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99274045,0.0004320236,0.002342045,0.0006029552,0.000014709542,0.000008320582,0.00023385676,0.000016610365,0.0036090799],"genre_scores_gemma":[0.9990263,0.00010056658,0.0002219334,0.00001526543,0.00000935563,0.0000024627643,0.00005724835,0.0000031318314,0.0005637265],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972504,0.00010329262,0.000010416666,0.00007247196,0.000043267137,0.00004550481],"domain_scores_gemma":[0.9865224,0.011435861,0.0007486175,0.00031163095,0.00046242544,0.0005190443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073313,0.00023967863,0.00025925858,0.0007977278,0.00047538843,0.0016946816,0.00033062237,0.0006620302,0.005170394],"category_scores_gemma":[0.010290691,0.0001580635,0.0002772612,0.00062373123,0.00060134364,0.0017323253,0.0006077535,0.0008166303,0.0004314133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010257878,0.00089880964,0.8049111,0.00011590773,0.0003039264,0.0005369336,0.001944822,0.062284518,0.009261823,0.01320086,0.0031955007,0.10231989],"study_design_scores_gemma":[0.000013034898,0.00019463968,0.7632399,0.000036541314,0.00010961134,0.00012914154,0.0014969149,0.2232744,0.0007486777,0.009587543,0.0011261932,0.000043339372],"about_ca_topic_score_codex":0.010682293,"about_ca_topic_score_gemma":0.012383784,"teacher_disagreement_score":0.010682293,"about_ca_system_score_codex":0.00058905355,"about_ca_system_score_gemma":0.0004438027,"threshold_uncertainty_score":0.021240234},"labels":[],"label_agreement":null},{"id":"W4318562039","doi":"10.1016/j.eswa.2023.119638","title":"Adoption and utilization of medical decision support systems in the diagnosis of febrile Diseases: A systematic literature review","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Mount Royal University","funders":"","keywords":"Autonomy; Decision support system; Inclusion (mineral); Computer science; Clinical decision support system; Systematic review; Inclusion and exclusion criteria; Cognition; Set (abstract data type); Medical literature; Health care; MEDLINE; Knowledge management; Management science; Medicine; Alternative medicine; Data mining; Psychology; Pathology; Psychiatry","score_opus":0.03013416625905388,"score_gpt":0.35064657894600065,"score_spread":0.32051241268694675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318562039","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01140458,0.9868281,0.00022748692,0.0005998122,0.000086026455,0.00009897836,0.0004660438,0.000004493155,0.00028453663],"genre_scores_gemma":[0.11140527,0.8856658,0.0013774765,0.0008132143,0.00008991539,0.00017193801,0.00042076435,0.0000056456843,0.000049906714],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.98122865,0.0061819004,0.007629749,0.0015027707,0.003117489,0.0003394861],"domain_scores_gemma":[0.8860308,0.09022829,0.016300935,0.0010486586,0.005750186,0.0006411163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016956799,0.00060643395,0.003824834,0.007652798,0.0004986317,0.003008366,0.001500264,0.0017409282,0.0013168764],"category_scores_gemma":[0.08972841,0.00078445993,0.0066136294,0.009492642,0.0010970194,0.0024507106,0.0015739055,0.001554695,0.00009921411],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009983939,0.00012216308,0.05080815,0.7431647,0.034588978,0.00026489745,0.0014810894,0.0004937937,0.0004050329,0.0007727663,0.001911149,0.16498874],"study_design_scores_gemma":[0.0004395861,0.00065008755,0.071434386,0.776982,0.12528901,0.0010555355,0.0021633194,0.0006294853,0.0005822056,0.00059592153,0.020051854,0.00012658194],"about_ca_topic_score_codex":0.011660493,"about_ca_topic_score_gemma":0.026378496,"teacher_disagreement_score":0.016956799,"about_ca_system_score_codex":0.003339528,"about_ca_system_score_gemma":0.011410978,"threshold_uncertainty_score":0.089677155},"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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)","score_opus":0.009208493889557967,"score_gpt":0.23704326565035375,"score_spread":0.22783477176079578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319335604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049559544,0.00020848736,0.9763168,0.00022953344,0.00019477632,0.0000937213,0.0014405039,0.015892701,0.0006674745],"genre_scores_gemma":[0.14170419,0.0003077267,0.83019733,0.0006999563,0.00041151803,0.00075898215,0.01128996,0.0031584636,0.011471874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99652535,0.0018676484,0.00023477568,0.000600362,0.00055747683,0.00021441169],"domain_scores_gemma":[0.9877493,0.0072170333,0.00051175646,0.0028612355,0.0013780748,0.00028261848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007958738,0.001224393,0.0021119185,0.0015864272,0.0009834793,0.0021260504,0.004348394,0.0026690061,0.018814247],"category_scores_gemma":[0.0389766,0.0011438145,0.002126356,0.0020896012,0.00067115086,0.0026322699,0.0033094662,0.004170025,0.010932325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020669731,0.0006768066,0.010728943,0.00037947673,0.0013414306,0.00027628802,0.00027293063,0.11761995,0.0033663057,0.024329022,0.07894026,0.76000166],"study_design_scores_gemma":[0.00012368693,0.00010411195,0.0010312058,0.00004354214,0.0000775303,0.00007828636,0.000026800832,0.9643422,0.002674368,0.023253229,0.0082058385,0.000039180544],"about_ca_topic_score_codex":0.0046782726,"about_ca_topic_score_gemma":0.008351805,"teacher_disagreement_score":0.018814247,"about_ca_system_score_codex":0.00058890425,"about_ca_system_score_gemma":0.0021160971,"threshold_uncertainty_score":0.06293988},"labels":[],"label_agreement":null},{"id":"W4319660509","doi":"10.1016/j.eswa.2023.119655","title":"Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Fuzzy clustering; Fuzzy logic; Centroid; Computer science; Cluster analysis; Euclidean distance; Neuro-fuzzy; Artificial neural network; Data mining; Artificial intelligence; Fuzzy classification; Fuzzy number; Defuzzification; Mathematics; Fuzzy set; Pattern recognition (psychology); Fuzzy control system","score_opus":0.035932059205595356,"score_gpt":0.2865825160744831,"score_spread":0.25065045686888776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319660509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006775454,0.000079280944,0.9911945,0.00003822484,0.000025631514,0.000043537722,0.000016943659,0.00030761058,0.0015188567],"genre_scores_gemma":[0.45845714,0.00015534456,0.5388267,0.00006090201,0.000021487807,0.000199698,0.00008838605,0.000048152884,0.0021422417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997291,0.0000625257,0.00002291181,0.00006646382,0.00009894717,0.000020086009],"domain_scores_gemma":[0.9996588,0.00007895216,0.00002901359,0.000044641245,0.00017742378,0.000011180414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007501653,0.00046296732,0.0005193019,0.00028541996,0.00039259414,0.0007217907,0.0011746595,0.0008799512,0.0013575637],"category_scores_gemma":[0.0013937206,0.000308779,0.00048456283,0.00037274108,0.00033292247,0.00084595085,0.00037139343,0.00075861294,0.00048028736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023480166,0.00011830135,0.0005384976,0.00027968874,0.000095671974,0.00012078706,0.0001920541,0.6463929,0.053527128,0.03493656,0.0020767802,0.26148695],"study_design_scores_gemma":[0.0000068326485,0.000026894506,0.00007762992,0.0000075885127,0.000012539341,0.000026118683,0.0000042922265,0.99277896,0.0050309347,0.0015249619,0.00049552857,0.000007764588],"about_ca_topic_score_codex":0.0031809125,"about_ca_topic_score_gemma":0.0038266699,"teacher_disagreement_score":0.0031809125,"about_ca_system_score_codex":0.00049699435,"about_ca_system_score_gemma":0.0007205338,"threshold_uncertainty_score":0.0063248277},"labels":[],"label_agreement":null},{"id":"W4319967424","doi":"10.1016/j.eswa.2023.119671","title":"Street closure prediction based on the combined conditions of spatially collocated municipal infrastructure assets at the segment level","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Psychological intervention; Computer science; Heuristics; Heuristic; Closure (psychology); Intervention (counseling); Environmental science; Transport engineering; Artificial intelligence; Engineering","score_opus":0.013735578835777928,"score_gpt":0.2314152723985881,"score_spread":0.2176796935628102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319967424","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9858824,0.00013351267,0.009840276,0.00004470605,0.00002127777,0.000016595277,0.0026552319,0.00026278183,0.0011432273],"genre_scores_gemma":[0.9961171,0.00003493903,0.0016231136,0.0000031097127,0.000009009097,0.0000064940423,0.0019477976,0.000006496817,0.00025191437],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996737,0.000038219227,0.0000243107,0.00011773945,0.0000693892,0.000076669196],"domain_scores_gemma":[0.9992059,0.00019417121,0.00018729355,0.000075762044,0.00023871879,0.000098265205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026279473,0.00041583693,0.00059033924,0.0021039716,0.00023811315,0.00074379856,0.00043234392,0.0005196463,0.0013063125],"category_scores_gemma":[0.0012886873,0.00015367284,0.0005272972,0.0018609352,0.0001731519,0.00071877876,0.0005442576,0.00032203866,0.0005088089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008250443,0.00033643882,0.671148,0.00014769415,0.00026310454,0.0008113191,0.00024596413,0.2184135,0.013418566,0.00052485813,0.0038549327,0.09001061],"study_design_scores_gemma":[0.0000100090765,0.00009027726,0.26443025,0.0000146987595,0.00007512051,0.00009872436,0.00029328646,0.73261625,0.0014552189,0.0003053953,0.0005917844,0.000018938996],"about_ca_topic_score_codex":0.027107703,"about_ca_topic_score_gemma":0.04260545,"teacher_disagreement_score":0.027107703,"about_ca_system_score_codex":0.00040841984,"about_ca_system_score_gemma":0.0005561411,"threshold_uncertainty_score":0.053899884},"labels":[],"label_agreement":null},{"id":"W4320169013","doi":"10.1016/j.eswa.2023.119589","title":"A multi-task approach for contrastive learning of handwritten signature feature representations","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Feature (linguistics); Signature (topology); Artificial intelligence; Task (project management); Feature learning; Feature vector; Pattern recognition (psychology); Class (philosophy); Natural language processing; Mathematics","score_opus":0.02154042704118518,"score_gpt":0.2913953367677581,"score_spread":0.2698549097265729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320169013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012332414,0.00038275897,0.9848608,0.00012514585,0.000072698334,0.00008622247,0.00009929485,0.0008828541,0.0011578356],"genre_scores_gemma":[0.33667022,0.00040876272,0.65219676,0.00046506914,0.00023991533,0.0002751712,0.0007617178,0.0002966026,0.008685732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999132,0.0001847488,0.00005585828,0.00025979124,0.00023958487,0.00012801765],"domain_scores_gemma":[0.9984236,0.0006261198,0.00010627583,0.00026064727,0.00047380535,0.00010951448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018247224,0.0012050375,0.0012596864,0.0011195665,0.00060885126,0.0012843565,0.0022032189,0.0017626289,0.003401804],"category_scores_gemma":[0.003509887,0.00049751264,0.0012746898,0.0012356015,0.0004445702,0.0015216477,0.0021515398,0.0021301352,0.0012887015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004920389,0.00043194342,0.0006766716,0.00014616379,0.00016704819,0.00013703386,0.00007268692,0.05685447,0.06151548,0.004471143,0.003655151,0.8713802],"study_design_scores_gemma":[0.000022579472,0.00016113899,0.0005683047,0.000010624995,0.000041053838,0.00010351966,0.000019617459,0.97679764,0.016682167,0.0037392606,0.0018360903,0.000018023362],"about_ca_topic_score_codex":0.0030181776,"about_ca_topic_score_gemma":0.00492359,"teacher_disagreement_score":0.003401804,"about_ca_system_score_codex":0.00053290214,"about_ca_system_score_gemma":0.0010709057,"threshold_uncertainty_score":0.011380196},"labels":[],"label_agreement":null},{"id":"W4323275923","doi":"10.1016/j.eswa.2023.119782","title":"Heuristic multi-modal integration framework for liver tumor detection from multi-modal non-enhanced MRIs","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Modal; Modality (human–computer interaction); Heuristic; Contrast (vision); Magnetic resonance imaging; Artificial intelligence; Radiology; Medicine","score_opus":0.037252953840720054,"score_gpt":0.3345337502383212,"score_spread":0.2972807963976012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323275923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041975505,0.00013629529,0.99499303,0.00003579778,0.000009578919,0.000022521595,0.000022193035,0.0002363429,0.00034679036],"genre_scores_gemma":[0.3753915,0.00033574164,0.6205464,0.00019158481,0.00008882322,0.0002714892,0.000323733,0.00021139266,0.0026394126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943024,0.0001206647,0.000038137438,0.0001237344,0.00019613252,0.000091083704],"domain_scores_gemma":[0.9993691,0.00028940293,0.000064284315,0.000037412203,0.0001917055,0.000048040918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012727889,0.0009901875,0.0015880732,0.0015752569,0.0005533469,0.001271852,0.0018275793,0.0016600704,0.0030228803],"category_scores_gemma":[0.0020804447,0.0008171779,0.0015822372,0.0010023004,0.00055174244,0.00090823614,0.0017663907,0.001046303,0.0006715805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023582848,0.0001454938,0.00080114,0.0001833573,0.0001699252,0.00021337556,0.000110526154,0.7719651,0.0126503995,0.0058179023,0.0015805839,0.20612636],"study_design_scores_gemma":[0.0000036330298,0.000013869449,0.00006324504,0.000003801635,0.000011275879,0.000018198667,0.0000055148603,0.99828917,0.0005348228,0.00089701236,0.0001553523,0.0000040777813],"about_ca_topic_score_codex":0.008460103,"about_ca_topic_score_gemma":0.008994219,"teacher_disagreement_score":0.008460103,"about_ca_system_score_codex":0.0006824915,"about_ca_system_score_gemma":0.0012526524,"threshold_uncertainty_score":0.016821742},"labels":[],"label_agreement":null},{"id":"W4323567865","doi":"10.1016/j.eswa.2023.119811","title":"A novel global solar exposure forecasting model based on air temperature: Designing a new multi-processing ensemble deep learning paradigm","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Bureau of Meteorology, Australian Government; Shahid Chamran University of Ahvaz; British Medical Association","keywords":"Hilbert–Huang transform; Computer science; Random forest; Univariate; Mean squared error; Artificial intelligence; Benchmark (surveying); Residual; Deep learning; Ensemble learning; Machine learning; Energy (signal processing); Algorithm; Statistics; Mathematics; Multivariate statistics","score_opus":0.04090230660646395,"score_gpt":0.2697017970741086,"score_spread":0.22879949046764464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323567865","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037366036,0.00020498116,0.95941186,0.000306446,0.00010908281,0.000019671785,0.00010602466,0.00023783241,0.0022381004],"genre_scores_gemma":[0.82904434,0.00035308563,0.1646245,0.00026857445,0.00015923835,0.00010140858,0.00032682015,0.000060362876,0.00506174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998795,0.000018900679,0.000006887524,0.00004319293,0.000030217374,0.000021209504],"domain_scores_gemma":[0.9998604,0.000033603126,0.000015077674,0.00001542143,0.00006141219,0.000014159221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040982306,0.00043023544,0.00056436827,0.00022272088,0.00028321726,0.0004936514,0.0010092595,0.000754862,0.0010226225],"category_scores_gemma":[0.000665558,0.0003045285,0.00051224447,0.00035259305,0.00021743534,0.001112131,0.0008039155,0.0011523301,0.00023889428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039167513,0.00006808374,0.0013848167,0.000023157125,0.00007208021,0.000038321694,0.000029152163,0.91728437,0.0051318724,0.004945044,0.0013714159,0.06961244],"study_design_scores_gemma":[8.354579e-7,0.0000039852944,0.00005201813,7.3559215e-7,0.0000029796627,0.000002611938,8.1822583e-7,0.99923277,0.00018207784,0.00041899853,0.000101147794,0.0000010609219],"about_ca_topic_score_codex":0.0053270156,"about_ca_topic_score_gemma":0.007917596,"teacher_disagreement_score":0.0053270156,"about_ca_system_score_codex":0.00035612902,"about_ca_system_score_gemma":0.00072408153,"threshold_uncertainty_score":0.010592043},"labels":[],"label_agreement":null},{"id":"W4323659903","doi":"10.1016/j.eswa.2023.119824","title":"Semi-supervised machinery health assessment framework via temporal broad learning system embedding manifold regularization with unlabeled data","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Overfitting; Artificial intelligence; Nonlinear dimensionality reduction; Regularization (linguistics); Semi-supervised learning; Embedding; Machine learning; Dimensionality reduction; Deep learning; Pattern recognition (psychology); Artificial neural network","score_opus":0.017421785464517014,"score_gpt":0.3217736876973132,"score_spread":0.3043519022327962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323659903","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010028529,0.00016761398,0.9885013,0.00011010345,0.00001913594,0.000028979654,0.00007592166,0.000505492,0.00056298607],"genre_scores_gemma":[0.69567066,0.00037215385,0.2960325,0.0002595377,0.00016192079,0.0002091813,0.0010840597,0.00023627828,0.005973606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922824,0.00022025796,0.000038760485,0.0002695524,0.00017313655,0.00007013592],"domain_scores_gemma":[0.99898416,0.00034608648,0.00012542332,0.00015802009,0.0003308226,0.000055485878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001392144,0.0008303789,0.0013894181,0.0008528267,0.00042139128,0.00092448865,0.001702934,0.0012883708,0.0016715076],"category_scores_gemma":[0.002509831,0.00045584963,0.0010142984,0.000675695,0.0007329091,0.001565399,0.0014417109,0.0013182347,0.0006423842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034830844,0.0002985332,0.0025085793,0.00025325542,0.00018586071,0.00015834685,0.00019824735,0.62275624,0.013422123,0.0198257,0.0062490385,0.3337958],"study_design_scores_gemma":[0.0000024486794,0.000017873837,0.00013917191,0.000003262934,0.0000068322806,0.000013759651,0.0000047052567,0.99670607,0.00042936153,0.0024365908,0.0002352735,0.0000046471937],"about_ca_topic_score_codex":0.0038928436,"about_ca_topic_score_gemma":0.0054851007,"teacher_disagreement_score":0.0038928436,"about_ca_system_score_codex":0.00046381296,"about_ca_system_score_gemma":0.0011666473,"threshold_uncertainty_score":0.0077403784},"labels":[],"label_agreement":null},{"id":"W4324387388","doi":"10.1016/j.eswa.2023.119791","title":"Dynamic community detection including node attributes","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agenția Națională pentru Cercetare și Dezvoltare; Instituto de Sistemas Complejos de Ingeniería; Fondo Nacional de Desarrollo Científico y Tecnológico; Agencia Nacional de Investigación y Desarrollo; Canadian Bureau for International Education; McGill University","keywords":"Computer science; Joins; Node (physics); Cluster analysis; Task (project management); Data mining; Dynamic network analysis; Community structure; Social network (sociolinguistics); State (computer science); Artificial intelligence; Machine learning; Algorithm; Mathematics; Computer network","score_opus":0.03269509715805709,"score_gpt":0.3108842685189088,"score_spread":0.27818917136085175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324387388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08984905,0.00039653934,0.9043405,0.00031446587,0.000076591845,0.00012312393,0.0012198568,0.0010462771,0.0026336259],"genre_scores_gemma":[0.7171707,0.00026946436,0.27546516,0.000093000584,0.0001553964,0.00013238612,0.0022611562,0.000121102144,0.0043316246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899083,0.00024917678,0.000042574276,0.00035212477,0.0002742396,0.00009104591],"domain_scores_gemma":[0.99587554,0.0023419817,0.00032500827,0.0006510156,0.00060772925,0.00019867244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014251379,0.00056373334,0.00092051964,0.0030186654,0.0008403763,0.0012491613,0.0012048326,0.0009471118,0.0016642],"category_scores_gemma":[0.009954551,0.00036236097,0.0006622085,0.0032395797,0.00033990448,0.0025088275,0.0011278167,0.0008571894,0.00070745795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007090419,0.0007391257,0.029173577,0.00041446052,0.0003539498,0.0004930936,0.00036456183,0.31403455,0.021162985,0.028142894,0.014205943,0.5902057],"study_design_scores_gemma":[0.000008312919,0.000023800663,0.0017429304,0.000009689634,0.000019681227,0.0000958236,0.00003926162,0.98244596,0.0013177946,0.013221274,0.0010684046,0.000007037926],"about_ca_topic_score_codex":0.0037823801,"about_ca_topic_score_gemma":0.0070344377,"teacher_disagreement_score":0.0037823801,"about_ca_system_score_codex":0.0005160927,"about_ca_system_score_gemma":0.0007583393,"threshold_uncertainty_score":0.007536888},"labels":[],"label_agreement":null},{"id":"W4327920700","doi":"10.1016/j.eswa.2023.119912","title":"Value-aware meta-transfer learning and convolutional mask attention networks for reservoir identification with limited data","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Hydraulic Fracturing and Reservoir Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Institute of Automation, Chinese Academy of Sciences; State Key Laboratory for Management and Control of Complex Systems; China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Generalizability theory; Block (permutation group theory); Transfer of learning; Identification (biology); Artificial intelligence; Data mining; Sample (material); Machine learning; Pattern recognition (psychology); Statistics","score_opus":0.034520554213026175,"score_gpt":0.2691095438647428,"score_spread":0.2345889896517166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327920700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06924123,0.0019522882,0.924786,0.0005633585,0.000090970716,0.000030694373,0.00018125153,0.0014182971,0.0017358796],"genre_scores_gemma":[0.9182239,0.00038379224,0.07696929,0.0002210853,0.00008800357,0.000052335727,0.0003107108,0.00010031689,0.0036504862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997538,0.000068445865,0.00001652526,0.00007061568,0.000044257053,0.00004638617],"domain_scores_gemma":[0.99894506,0.000697207,0.00007386714,0.00010298285,0.00014638434,0.00003463427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010622563,0.0008499091,0.0010350074,0.0005433047,0.0003074554,0.0006996821,0.0017810565,0.001508787,0.0015570786],"category_scores_gemma":[0.003195606,0.0005094791,0.00065766997,0.00060793513,0.00053882186,0.0018895477,0.0013545358,0.0014218823,0.0003952753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003424103,0.00014369315,0.0012653135,0.00013499791,0.00017274616,0.00013204233,0.000101238926,0.67113745,0.008542101,0.009128769,0.003004714,0.30589455],"study_design_scores_gemma":[0.000002286814,0.000013815448,0.00007195574,0.0000032908708,0.000007669198,0.000006280171,0.0000021947383,0.99631506,0.0007747085,0.0027115738,0.00008863639,0.000002539074],"about_ca_topic_score_codex":0.0054121697,"about_ca_topic_score_gemma":0.0055867005,"teacher_disagreement_score":0.0054121697,"about_ca_system_score_codex":0.00073984964,"about_ca_system_score_gemma":0.0008764238,"threshold_uncertainty_score":0.010761321},"labels":[],"label_agreement":null},{"id":"W4353096205","doi":"10.1016/j.eswa.2023.119940","title":"Optimal design of circular concrete-filled steel tubular columns based on a combination of artificial neural network, balancing composite motion algorithm and a large experimental database","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Structural Load-Bearing Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Computer science; Composite number; Nonlinear system; Optimal design; Compressive strength; Compression (physics); Algorithm; Mean squared error; Structural engineering; Database; Mathematics; Engineering; Artificial intelligence; Materials science; Machine learning; Composite material; Statistics","score_opus":0.013327471408952444,"score_gpt":0.2343006245392267,"score_spread":0.22097315313027427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353096205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30079186,0.00045919095,0.6916704,0.00015814742,0.00006639667,0.00017275661,0.00030030837,0.0009225595,0.00545833],"genre_scores_gemma":[0.8753117,0.00011166997,0.12220734,0.000047520338,0.00001318167,0.00017451547,0.00036694613,0.00007931708,0.0016878468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982834,0.000043148604,0.000009341415,0.00005275109,0.000034134428,0.000032342494],"domain_scores_gemma":[0.99939454,0.0002754999,0.000080493286,0.000025969812,0.00017569817,0.000047719626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049189624,0.0008718863,0.0009480042,0.00094935275,0.00041547162,0.0006065016,0.00072348077,0.0011336365,0.0025283343],"category_scores_gemma":[0.0011835943,0.00076407095,0.00055576535,0.00042849916,0.00040991686,0.00055547385,0.0003455043,0.00037050102,0.00030421567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009518407,0.00006804368,0.0004124252,0.00004559705,0.000014508779,0.000029320085,0.000012818162,0.97714067,0.002817923,0.00046966033,0.00040005086,0.018493759],"study_design_scores_gemma":[0.000007292679,0.000021556532,0.000092590366,0.0000014068024,0.000004317173,0.0000027762053,0.000004786648,0.9992741,0.0004481137,0.000084015475,0.00005699156,0.0000020234436],"about_ca_topic_score_codex":0.011965195,"about_ca_topic_score_gemma":0.019706933,"teacher_disagreement_score":0.011965195,"about_ca_system_score_codex":0.0006556414,"about_ca_system_score_gemma":0.001742802,"threshold_uncertainty_score":0.023791075},"labels":[],"label_agreement":null},{"id":"W4360603965","doi":"10.1016/j.eswa.2023.119948","title":"Optimizing consistency and consensus in group decision making based on relative projection between multiplicative reciprocal matrices","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Consistency (knowledge bases); Projection (relational algebra); Multiplicative function; Mathematical optimization; Analytic hierarchy process; Computer science; Group (periodic table); Group decision-making; Reciprocal; Projection pursuit; Mathematics; Local consistency; Algorithm; Artificial intelligence; Operations research; Constraint satisfaction","score_opus":0.12458810276891401,"score_gpt":0.4171919492966053,"score_spread":0.2926038465276913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360603965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021912498,0.000091059635,0.97714555,0.00011106625,0.000017256656,0.000037093134,0.000011422608,0.000034273657,0.00063979556],"genre_scores_gemma":[0.6635724,0.00021955631,0.33453974,0.00008491152,0.000066930756,0.0002976004,0.0000800047,0.00006810767,0.0010707419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9891256,0.0071792984,0.00037664556,0.0012739489,0.001680528,0.00036404116],"domain_scores_gemma":[0.9575723,0.036164586,0.0017287182,0.0012281407,0.0028138664,0.00049239554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013610756,0.0012914527,0.0029764883,0.0015444675,0.0009407072,0.0024969722,0.0024058188,0.0021249359,0.0011097398],"category_scores_gemma":[0.049864314,0.0010441268,0.0013599674,0.0017670114,0.0033336086,0.0046015736,0.0034923526,0.0024758219,0.00020740773],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022727979,0.00010909986,0.00059469184,0.00020353438,0.00016538898,0.00006354156,0.00028411517,0.89130366,0.0017002326,0.06733282,0.00040273703,0.03761292],"study_design_scores_gemma":[0.000016970518,0.00008227537,0.000105524145,0.000010295338,0.000016858023,0.000017236678,0.00002267833,0.96528214,0.00054249517,0.033796594,0.000091313086,0.000015553876],"about_ca_topic_score_codex":0.0020606099,"about_ca_topic_score_gemma":0.0010859532,"teacher_disagreement_score":0.013610756,"about_ca_system_score_codex":0.0011427847,"about_ca_system_score_gemma":0.001921772,"threshold_uncertainty_score":0.07198137},"labels":[],"label_agreement":null},{"id":"W4361288163","doi":"10.1016/j.eswa.2023.119972","title":"Multiresolution texture analysis of histopathologic images using ecological diversity measures","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Texture (cosmology); Artificial intelligence; Pattern recognition (psychology); Computer science; Fixation (population genetics); Wavelet; Image (mathematics); Computer vision; Wavelet transform; Biology","score_opus":0.048090713525796434,"score_gpt":0.28626409642895634,"score_spread":0.2381733829031599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361288163","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.593631,0.0007153283,0.40216142,0.00014643742,0.00002271982,0.00007765182,0.00045337618,0.0004229864,0.0023690984],"genre_scores_gemma":[0.8930713,0.00039266175,0.10575584,0.000023153536,0.000028090151,0.000033819982,0.00023257584,0.000050534654,0.0004120467],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975187,0.00003974042,0.000017911367,0.00004671134,0.00008759137,0.000056198023],"domain_scores_gemma":[0.99932885,0.00023841891,0.00011089127,0.00006494382,0.00020281742,0.000054191387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064142863,0.0002628461,0.00032656494,0.0058197803,0.00024404495,0.0011157894,0.00018410361,0.00029438737,0.0008659637],"category_scores_gemma":[0.0014064582,0.00018635063,0.00058353733,0.0021792557,0.0002875737,0.00049046567,0.0004898172,0.0003452412,0.00016065102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006774408,0.00017070168,0.047250547,0.000551763,0.00029699467,0.0004812152,0.00073510606,0.044995893,0.46651056,0.004437601,0.00085319916,0.43303916],"study_design_scores_gemma":[0.000046319245,0.00024894488,0.23198818,0.000096001335,0.00043124345,0.0021071373,0.0011540739,0.665228,0.08679386,0.007662724,0.0041317586,0.000111753514],"about_ca_topic_score_codex":0.0017628077,"about_ca_topic_score_gemma":0.002545294,"teacher_disagreement_score":0.0058197803,"about_ca_system_score_codex":0.000347638,"about_ca_system_score_gemma":0.0003029148,"threshold_uncertainty_score":0.0035051107},"labels":[],"label_agreement":null},{"id":"W4362638934","doi":"10.1016/j.eswa.2023.120025","title":"A multi-task learning framework for politeness and emotion detection in dialogues for mental health counselling and legal aid","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Mental Health via Writing","field":"Psychology","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Politeness; Task (project management); Mental health; Converse; Computer science; Harassment; Scarcity; Psychology; Social psychology; Political science; Psychiatry; Law","score_opus":0.052255324375008315,"score_gpt":0.3845297240838819,"score_spread":0.3322743997088736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362638934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0240223,0.0005823743,0.963591,0.0005145718,0.00016288292,0.0005268785,0.0005242601,0.007924139,0.0021516166],"genre_scores_gemma":[0.38759965,0.00033563055,0.6008471,0.00060058694,0.00018082304,0.0011308746,0.0020036153,0.00034572816,0.0069560693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980197,0.00075354404,0.00013318167,0.000592474,0.00026913808,0.00023201462],"domain_scores_gemma":[0.9961958,0.0024290937,0.00014674275,0.00019585309,0.00071383995,0.00031862635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043271054,0.0017771221,0.0014004096,0.0017025125,0.0011265083,0.0023248938,0.0023976888,0.0027098376,0.0064483834],"category_scores_gemma":[0.007553976,0.0005977842,0.0015650219,0.00092643296,0.00057192455,0.0024722258,0.0031244238,0.003624994,0.0028515065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011293022,0.0014924428,0.0024605694,0.00027638947,0.00023267404,0.0002206227,0.00059720234,0.034426834,0.019915333,0.0031442647,0.010299471,0.9258049],"study_design_scores_gemma":[0.000049962626,0.00022099442,0.0011076948,0.000035756093,0.000055823257,0.00006598559,0.00018130444,0.98454595,0.005322394,0.005641539,0.0027333163,0.000039294584],"about_ca_topic_score_codex":0.011076792,"about_ca_topic_score_gemma":0.013691686,"teacher_disagreement_score":0.011076792,"about_ca_system_score_codex":0.0011202673,"about_ca_system_score_gemma":0.0016652913,"threshold_uncertainty_score":0.02288419},"labels":[],"label_agreement":null},{"id":"W4362701204","doi":"10.1016/j.eswa.2023.120072","title":"Spectral unmixing based random forest classifier for detecting surface water changes in multitemporal pansharpened Landsat image","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cégep de la Gaspésie et des Îles","funders":"","keywords":"Thematic Mapper; Endmember; Multispectral image; Remote sensing; Random forest; Change detection; Pixel; Image fusion; Thematic map; Multispectral pattern recognition; Computer science; Environmental science; Cohen's kappa; Artificial intelligence; Pattern recognition (psychology); Satellite imagery; Cartography; Geography; Image (mathematics)","score_opus":0.024966662935199734,"score_gpt":0.25359962704855943,"score_spread":0.2286329641133597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362701204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3988099,0.0008901897,0.59425765,0.00010832038,0.00014455277,0.00012614287,0.0005922052,0.0028364763,0.0022345288],"genre_scores_gemma":[0.7660181,0.000409109,0.22846092,0.00005606552,0.000057625097,0.0000951587,0.0016191372,0.000088645575,0.0031952807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978393,0.000022763,0.000013478415,0.000063307794,0.00008255436,0.000033980647],"domain_scores_gemma":[0.9997818,0.000057443733,0.000018062721,0.000018403947,0.00011377744,0.000010413442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052213774,0.0005036939,0.00043426378,0.0011164045,0.0003316047,0.00030111746,0.00045100783,0.00044605593,0.0008818981],"category_scores_gemma":[0.00049528474,0.00017791588,0.00060097896,0.00058596703,0.00015082922,0.0005354961,0.00018179628,0.00040208467,0.00048742784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000616923,0.00043930006,0.0070899967,0.00017517431,0.00012072367,0.0001311153,0.00007996838,0.03865907,0.14190187,0.0007445866,0.0027806086,0.80726063],"study_design_scores_gemma":[0.000024904852,0.00015558914,0.012890284,0.00001236873,0.000116275885,0.00014333407,0.000054284326,0.94495773,0.03977399,0.0005166563,0.0013313683,0.000023228535],"about_ca_topic_score_codex":0.0044618007,"about_ca_topic_score_gemma":0.007307789,"teacher_disagreement_score":0.0044618007,"about_ca_system_score_codex":0.0001756273,"about_ca_system_score_gemma":0.000397307,"threshold_uncertainty_score":0.008871675},"labels":[],"label_agreement":null},{"id":"W4364375123","doi":"10.1016/j.eswa.2023.120112","title":"Neural Network-based control using Actor-Critic Reinforcement Learning and Grey Wolf Optimizer with experimental servo system validation","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education and Research, Romania; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Corporation for National and Community Service","keywords":"Reinforcement learning; Computer science; Artificial neural network; Particle swarm optimization; Gradient descent; Convergence (economics); Process (computing); Controller (irrigation); Mathematical optimization; Servomechanism; Artificial intelligence; Machine learning; Control engineering; Mathematics; Engineering","score_opus":0.015174882540149644,"score_gpt":0.2595307435211804,"score_spread":0.24435586098103074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364375123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29356778,0.00058941124,0.6955472,0.00032888315,0.00014935873,0.0003450553,0.00008878723,0.0009478498,0.008435732],"genre_scores_gemma":[0.9722214,0.000046433302,0.026453359,0.000019273759,0.0000041130074,0.000117255484,0.000031705524,0.00003224178,0.0010742918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935335,0.0002762162,0.00004552995,0.000078234225,0.00018609544,0.000060587132],"domain_scores_gemma":[0.9968759,0.0017186818,0.00028084766,0.0002103506,0.0008568593,0.000057487894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029458655,0.00088333746,0.0009327244,0.00047183418,0.0005564335,0.00068517524,0.00083085813,0.0013077117,0.0018807926],"category_scores_gemma":[0.005399694,0.00040388523,0.00044112044,0.00029907952,0.0009616219,0.00062432385,0.000817609,0.0010955449,0.00018988572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018567697,0.000103607104,0.0004655233,0.0001422766,0.000042461714,0.000038514554,0.0000676321,0.97825694,0.004492837,0.0016905162,0.00024908627,0.0142649235],"study_design_scores_gemma":[0.000015231714,0.00004600466,0.00015852584,0.0000062049753,0.0000050916174,0.000004133199,0.000003320964,0.9979431,0.0015878519,0.00016697457,0.000059172526,0.000004389027],"about_ca_topic_score_codex":0.011828065,"about_ca_topic_score_gemma":0.0072491583,"teacher_disagreement_score":0.011828065,"about_ca_system_score_codex":0.0009873732,"about_ca_system_score_gemma":0.0011550884,"threshold_uncertainty_score":0.023518443},"labels":[],"label_agreement":null},{"id":"W4364382428","doi":"10.1016/j.eswa.2023.120017","title":"SwiftR: Cross-platform ransomware fingerprinting using hierarchical neural networks on hybrid features","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Ransomware; Computer science; Static analysis; Artificial neural network; Artificial intelligence; Code (set theory); Machine learning; Word (group theory); Data mining; Theoretical computer science; Set (abstract data type); Malware; Computer security; Programming language","score_opus":0.022298329806999983,"score_gpt":0.3073305714782253,"score_spread":0.2850322416712253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364382428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1443771,0.0007822384,0.79256845,0.00019196192,0.00030560556,0.00025149988,0.0013609388,0.05606697,0.004095309],"genre_scores_gemma":[0.5378196,0.00021884259,0.44743937,0.00019590244,0.000073247946,0.00015288679,0.003206127,0.0007810693,0.010112999],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948895,0.00005816793,0.000026479496,0.00013051066,0.00019609,0.00009973835],"domain_scores_gemma":[0.9994629,0.00014135416,0.00006938186,0.00016940029,0.000119687684,0.000037263446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062232214,0.0013087727,0.00085062446,0.0014818326,0.00033160567,0.00064614264,0.0011681975,0.0009156513,0.0037812502],"category_scores_gemma":[0.0015863865,0.00047222764,0.0005350056,0.0008450429,0.00019676037,0.0014494791,0.0014859495,0.0007967481,0.0022683179],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053137174,0.00032290793,0.0038405976,0.00010266373,0.00019425771,0.0002755834,0.000058388963,0.030093288,0.039451383,0.0011983615,0.014851711,0.90907955],"study_design_scores_gemma":[0.0000270438,0.00016529328,0.0020064786,0.000010792763,0.000028903129,0.00020189406,0.000027912401,0.96835583,0.025615444,0.0011750921,0.0023571383,0.000028209097],"about_ca_topic_score_codex":0.0033665886,"about_ca_topic_score_gemma":0.0065995585,"teacher_disagreement_score":0.0037812502,"about_ca_system_score_codex":0.00028890267,"about_ca_system_score_gemma":0.00051494135,"threshold_uncertainty_score":0.012649596},"labels":[],"label_agreement":null},{"id":"W4366992302","doi":"10.1016/j.eswa.2023.119936","title":"A multi-criteria group-based decision-making method considering linguistic neutrosophic clouds","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Group decision-making; Ambiguity; Computer science; Measure (data warehouse); Set (abstract data type); Terminology; Rule-based machine translation; Artificial intelligence; Linguistics; Data mining","score_opus":0.15088401660841,"score_gpt":0.46632951404911954,"score_spread":0.31544549744070954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366992302","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01041966,0.00009710862,0.9878473,0.00011196304,0.000052272237,0.000117210664,0.00003429019,0.00008464651,0.0012355234],"genre_scores_gemma":[0.28848255,0.000120979304,0.7090675,0.000104784274,0.00007040796,0.00050748826,0.00010873295,0.000046489364,0.0014910498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978598,0.0007991644,0.00014457507,0.00036955593,0.0006802099,0.0001467699],"domain_scores_gemma":[0.99830854,0.0009113549,0.000102017904,0.00007710585,0.00047068234,0.00013031649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032194974,0.0011080365,0.0020317885,0.0024171649,0.001530146,0.00215059,0.002075481,0.0017937945,0.0033518567],"category_scores_gemma":[0.0035542785,0.0006909098,0.0019791855,0.002271682,0.0007455627,0.0017655225,0.0018069405,0.001087722,0.0003568754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005686978,0.00041036413,0.0016381537,0.00069899746,0.00046945282,0.00046274817,0.0006845251,0.64659876,0.012346547,0.03364501,0.0034895488,0.2989872],"study_design_scores_gemma":[0.0000382716,0.00008217892,0.00015917132,0.000022492684,0.000058035264,0.000042514082,0.0000533502,0.9917636,0.0009488505,0.0061744354,0.00063061237,0.000026442369],"about_ca_topic_score_codex":0.0034037186,"about_ca_topic_score_gemma":0.003216169,"teacher_disagreement_score":0.0034037186,"about_ca_system_score_codex":0.0011582738,"about_ca_system_score_gemma":0.0019878566,"threshold_uncertainty_score":0.017026544},"labels":[],"label_agreement":null},{"id":"W4367043673","doi":"10.1016/j.eswa.2023.120276","title":"Remaining useful life prediction of bearings using multi-source adversarial online regression under online unknown conditions","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Data mining; Weighting; Machine learning; Multi-source; Domain adaptation; Classifier (UML)","score_opus":0.06376855239436252,"score_gpt":0.3184634452071968,"score_spread":0.2546948928128343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367043673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10749673,0.0005913002,0.8895006,0.0003382802,0.000109050205,0.000024910032,0.00020422749,0.00060989556,0.0011249997],"genre_scores_gemma":[0.97778887,0.00019248211,0.019432796,0.00005021105,0.000062279,0.000022776094,0.00031917926,0.00004575493,0.0020857006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996922,0.000063882486,0.000014879827,0.00008983069,0.00008665667,0.000052602954],"domain_scores_gemma":[0.99776256,0.0014296812,0.00026796758,0.00015324962,0.00028668286,0.00009984686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000940644,0.0009080548,0.0009141345,0.00049014157,0.00022711979,0.0006247051,0.000908128,0.0008898812,0.0010093426],"category_scores_gemma":[0.0045826696,0.0003419694,0.00041007163,0.00042340925,0.00074451446,0.00110681,0.00087867724,0.0013984492,0.0003476584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102698745,0.00002920175,0.0010846399,0.000031669402,0.000014904301,0.000062489555,0.000021479125,0.9755721,0.001239441,0.0015977726,0.000674902,0.01956871],"study_design_scores_gemma":[6.3733046e-7,0.000004184851,0.00008852661,0.0000010518793,9.504451e-7,0.0000041022727,0.0000011346182,0.9993293,0.00016098749,0.0003847666,0.000023134042,0.000001223423],"about_ca_topic_score_codex":0.0039081913,"about_ca_topic_score_gemma":0.0039559766,"teacher_disagreement_score":0.0039081913,"about_ca_system_score_codex":0.0005619834,"about_ca_system_score_gemma":0.0005617932,"threshold_uncertainty_score":0.007770896},"labels":[],"label_agreement":null},{"id":"W4367172240","doi":"10.1016/j.eswa.2023.120274","title":"Fault diagnosis of bearings using a two-stage transfer alignment approach with semantic consistency and entropy loss","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Domain adaptation; Data mining; Pattern recognition (psychology); Transfer of learning; Machine learning; Entropy (arrow of time); Fault (geology); Classifier (UML)","score_opus":0.01615758877259138,"score_gpt":0.2698782460268407,"score_spread":0.2537206572542493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367172240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028736265,0.00009421395,0.9698963,0.000054027347,0.000024717763,0.000037088106,0.00003076296,0.0005563139,0.0005703917],"genre_scores_gemma":[0.7090341,0.00011021118,0.28816903,0.00006588628,0.00005832799,0.000094789146,0.0002479144,0.00009394373,0.0021258527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995314,0.00008614295,0.00003432404,0.00012169799,0.00016680291,0.000059685983],"domain_scores_gemma":[0.9994555,0.00020100332,0.00006702542,0.00007958343,0.00017134027,0.000025473219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000756669,0.00072141975,0.0008679587,0.0010625407,0.00053702446,0.00070867053,0.00075958297,0.0008338026,0.0018330975],"category_scores_gemma":[0.0015401894,0.00031961288,0.0006908436,0.000729795,0.0004162264,0.0015417235,0.0010662718,0.0006851269,0.0005758999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062913843,0.0002314099,0.0021786832,0.0001553497,0.00012902448,0.00018328076,0.00018519575,0.16352595,0.07925397,0.006004375,0.0014341552,0.7460895],"study_design_scores_gemma":[0.00001781284,0.00016869514,0.0013194274,0.000005659583,0.000036815025,0.0000760196,0.00003271574,0.97725224,0.016097143,0.0044582947,0.0005198416,0.00001527493],"about_ca_topic_score_codex":0.0017022023,"about_ca_topic_score_gemma":0.001882092,"teacher_disagreement_score":0.0018330975,"about_ca_system_score_codex":0.0003186177,"about_ca_system_score_gemma":0.00080927415,"threshold_uncertainty_score":0.0061323643},"labels":[],"label_agreement":null},{"id":"W4367319399","doi":"10.1016/j.eswa.2023.120262","title":"A context-enhanced Dirichlet model for online clustering in short text streams","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Cluster analysis; Latent Dirichlet allocation; Inference; Data mining; Artificial intelligence; Topic model; Exploit; Machine learning","score_opus":0.042790149101430225,"score_gpt":0.3247153135910042,"score_spread":0.28192516448957394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367319399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046361303,0.0005837981,0.99349856,0.00019780209,0.00008257759,0.00006842954,0.00021602848,0.00040897905,0.00030780328],"genre_scores_gemma":[0.23862137,0.0019036029,0.74577063,0.0005809992,0.0008679976,0.0010396753,0.0033250656,0.00059611816,0.0072945417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956191,0.0018247227,0.00036482952,0.0011727914,0.0007148727,0.00030368555],"domain_scores_gemma":[0.99076337,0.006952446,0.0003348654,0.0007593262,0.0009229835,0.00026703833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058961622,0.0012287257,0.0035489968,0.0032989618,0.0017329743,0.0029987409,0.0050515626,0.004004266,0.003735695],"category_scores_gemma":[0.020262253,0.0015592767,0.0029816746,0.0047275685,0.0014652504,0.0051793,0.0033974065,0.0043739974,0.002797948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010791157,0.00040508498,0.0016262111,0.00051481795,0.0002952257,0.00027178228,0.0007340495,0.5625248,0.0052843574,0.09548573,0.009328437,0.32245046],"study_design_scores_gemma":[0.000019750107,0.000017606948,0.00011268301,0.000017630304,0.000018931392,0.000030525076,0.000019316454,0.97465116,0.00044765542,0.023839638,0.0008050704,0.00002007169],"about_ca_topic_score_codex":0.010330615,"about_ca_topic_score_gemma":0.016202362,"teacher_disagreement_score":0.010330615,"about_ca_system_score_codex":0.0019544854,"about_ca_system_score_gemma":0.0023630024,"threshold_uncertainty_score":0.03118229},"labels":[],"label_agreement":null},{"id":"W4367396455","doi":"10.1016/j.eswa.2023.120282","title":"Automated breast cancer detection in mammography using ensemble classifier and feature weighting algorithms","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Department of Science and Technology of Jilin Province","keywords":"Computer science; Artificial intelligence; Mammography; Weighting; Classifier (UML); Breast cancer; Pattern recognition (psychology); False positive paradox; Ensemble learning; Feature (linguistics); Machine learning; Algorithm; Cancer; Medicine; Radiology","score_opus":0.017320838011506315,"score_gpt":0.2784307853574309,"score_spread":0.2611099473459246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367396455","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21727416,0.0015799884,0.776784,0.00021347862,0.00017500154,0.00009471168,0.0003163376,0.0016723983,0.0018899228],"genre_scores_gemma":[0.7239698,0.0005952325,0.2722671,0.000107230015,0.000113996175,0.000080046164,0.00065289636,0.00009384942,0.002119801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927336,0.00015050676,0.000061904546,0.00016622523,0.00025518987,0.000092771406],"domain_scores_gemma":[0.9985764,0.00047361874,0.00008911903,0.0001551802,0.00065758557,0.00004800533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014730246,0.00051840104,0.0011857711,0.001926013,0.0003956553,0.00082266616,0.0006731238,0.0007681659,0.00094251713],"category_scores_gemma":[0.0028973815,0.00023845621,0.00093194493,0.0010576713,0.00009648495,0.0008727687,0.0006622252,0.0006385378,0.0005320155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003355745,0.00026857294,0.01959385,0.00008751876,0.00027193606,0.00010921906,0.000068497095,0.02920997,0.036311857,0.00066028774,0.002717257,0.9103656],"study_design_scores_gemma":[0.000019113078,0.0002084591,0.012232377,0.000022853883,0.00024495978,0.00041633073,0.00006274999,0.9654375,0.018118003,0.00158101,0.0016271631,0.000029498755],"about_ca_topic_score_codex":0.0027347382,"about_ca_topic_score_gemma":0.004320208,"teacher_disagreement_score":0.0027347382,"about_ca_system_score_codex":0.00028979592,"about_ca_system_score_gemma":0.00051776844,"threshold_uncertainty_score":0.007790208},"labels":[],"label_agreement":null},{"id":"W4375947964","doi":"10.1016/j.eswa.2023.120393","title":"Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Intersection (aeronautics); Graph; SIGNAL (programming language); Artificial intelligence; Signal timing; Real-time computing; Traffic flow (computer networking); Control (management); Theoretical computer science","score_opus":0.008063514962585337,"score_gpt":0.212056501173638,"score_spread":0.20399298621105266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375947964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06703252,0.000257399,0.92840564,0.00022165514,0.000074287236,0.000041653482,0.00045909965,0.0007208954,0.0027867854],"genre_scores_gemma":[0.9792662,0.00018355846,0.01711649,0.000042618893,0.000028740522,0.0000705353,0.00047046703,0.000050018592,0.002771297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977607,0.00003570023,0.000010463017,0.00008478023,0.000055160603,0.000037836053],"domain_scores_gemma":[0.9995994,0.00016609087,0.000043360793,0.000028566326,0.000141483,0.000021185251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038926228,0.0006300621,0.0008475984,0.00064732606,0.00029973063,0.0006673377,0.0012001044,0.0006745096,0.0014770299],"category_scores_gemma":[0.0014453034,0.00032515207,0.00058981054,0.0007545875,0.00034894657,0.0007375168,0.00037153185,0.00089393015,0.00025929962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013884101,0.000008892062,0.00020644392,0.0000067173514,0.000006356574,0.000012398383,0.0000055130567,0.9949759,0.00028682023,0.0009546298,0.00017295769,0.0033494814],"study_design_scores_gemma":[6.172329e-7,0.000001289823,0.000035247176,3.3719675e-7,0.0000013007863,9.042064e-7,3.142495e-7,0.9997141,0.000022610486,0.00020096044,0.000021752609,5.3597796e-7],"about_ca_topic_score_codex":0.049114183,"about_ca_topic_score_gemma":0.027692309,"teacher_disagreement_score":0.049114183,"about_ca_system_score_codex":0.0009181372,"about_ca_system_score_gemma":0.0011690469,"threshold_uncertainty_score":0.09765661},"labels":[],"label_agreement":null},{"id":"W4376274319","doi":"10.1016/j.eswa.2023.120423","title":"A robust optimization model for green supplier selection and order allocation in a closed-loop supply chain considering cap-and-trade mechanism","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Allowance (engineering); Robustness (evolution); Supply chain; Computer science; Mathematical optimization; Closeness; Closed loop; Order (exchange); Operations research; Industrial organization; Microeconomics; Environmental economics; Business; Economics; Operations management; Mathematics; Engineering; Control engineering","score_opus":0.021831088817327253,"score_gpt":0.22640864751034853,"score_spread":0.20457755869302127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376274319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040324792,0.00061745604,0.9414099,0.00075421383,0.00013162543,0.00018642082,0.0004679725,0.000492393,0.015615234],"genre_scores_gemma":[0.9589008,0.000400947,0.025902372,0.00013487582,0.00004772608,0.00027089537,0.00031260704,0.000088466884,0.013941235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854726,0.00037069368,0.00006100934,0.00044986093,0.00029348538,0.00027754487],"domain_scores_gemma":[0.99793255,0.0011032055,0.0003406521,0.000073856114,0.00043499525,0.000114610404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023126593,0.0017552568,0.0037218025,0.001032059,0.0010288091,0.0041421265,0.002769992,0.0046988786,0.006367054],"category_scores_gemma":[0.0037586456,0.0016268262,0.0015085313,0.001552824,0.0021356826,0.0020617151,0.002224626,0.0021966132,0.0007417953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039556093,0.000019243695,0.00009442901,0.000042838587,0.00002358871,0.00006540744,0.000019570634,0.994463,0.00041287008,0.0033314496,0.00020747368,0.001280477],"study_design_scores_gemma":[0.000010612123,0.000020808184,0.000052740314,0.0000040712503,0.0000099370745,0.000004932922,0.000006285922,0.99873024,0.00007429594,0.0009722322,0.00010763827,0.000006256175],"about_ca_topic_score_codex":0.026139028,"about_ca_topic_score_gemma":0.012656074,"teacher_disagreement_score":0.026139028,"about_ca_system_score_codex":0.002767671,"about_ca_system_score_gemma":0.0030242773,"threshold_uncertainty_score":0.05197376},"labels":[],"label_agreement":null},{"id":"W4377031088","doi":"10.1016/j.eswa.2023.120472","title":"Automatic detection of surface defects based on deep random chains","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Fuse (electrical); Variety (cybernetics); Generalization; Pattern recognition (psychology); Sample (material); Machine learning; Focus (optics); Pixel; Scale (ratio); Mathematics","score_opus":0.012574117385895619,"score_gpt":0.23054086774900756,"score_spread":0.21796675036311194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377031088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15212007,0.00030409364,0.84406734,0.00011306515,0.000038075505,0.000055675533,0.00018169207,0.0017385957,0.0013813932],"genre_scores_gemma":[0.8076195,0.00021000057,0.1880912,0.00009117621,0.00003650725,0.000047246955,0.000720529,0.0002366526,0.0029471843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944514,0.00009120763,0.000021627255,0.00013223648,0.00021617163,0.000093597715],"domain_scores_gemma":[0.9980514,0.0007581663,0.00030790406,0.00028018813,0.00048214,0.00012028743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067803956,0.00079287036,0.0010155332,0.0018013341,0.00023097864,0.00075599004,0.0009867309,0.0011599356,0.0016948428],"category_scores_gemma":[0.0015759036,0.00048783317,0.0006882954,0.00080430007,0.00055157836,0.0010868665,0.00097469415,0.0009965897,0.00089858123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008984096,0.00041916815,0.01208759,0.00024233751,0.000130722,0.0005830403,0.00012949633,0.26857653,0.18687072,0.0075345254,0.0037073097,0.51882017],"study_design_scores_gemma":[0.000006481961,0.000042911743,0.0006901233,0.000006367346,0.000007925847,0.000051555315,0.0000062538447,0.99044156,0.0070239143,0.0015221944,0.00019364401,0.000007072788],"about_ca_topic_score_codex":0.001698373,"about_ca_topic_score_gemma":0.0030181373,"teacher_disagreement_score":0.0018013341,"about_ca_system_score_codex":0.0003210225,"about_ca_system_score_gemma":0.0005822243,"threshold_uncertainty_score":0.0056697726},"labels":[],"label_agreement":null},{"id":"W4377695009","doi":"10.1016/j.eswa.2023.120524","title":"Optimising stochastic task allocation and scheduling plans for mission workers subject to learning-forgetting, fatigue-recovery, and stress-recovery effects","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Forgetting; Computer science; Task (project management); Scheduling (production processes); Subject (documents); Operations research; Cognitive psychology; Mathematical optimization; Psychology; World Wide Web; Mathematics; Management","score_opus":0.05532329433892541,"score_gpt":0.35535601379853987,"score_spread":0.30003271945961446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377695009","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44782352,0.0009478642,0.5463032,0.0008132025,0.00013364447,0.0002722704,0.00030144717,0.0004931243,0.0029117751],"genre_scores_gemma":[0.96223867,0.00018321356,0.035366934,0.00007880466,0.000038805716,0.00015576993,0.00015255586,0.00005140672,0.001733954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995586,0.00015102701,0.00002203065,0.000088183464,0.00006011973,0.00011989349],"domain_scores_gemma":[0.9969266,0.0021945594,0.0003445928,0.00006804181,0.00024141972,0.00022477783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018453406,0.0009320097,0.0013250281,0.0006614247,0.00039835434,0.0008349522,0.0009612468,0.0013454586,0.0017315941],"category_scores_gemma":[0.0051953583,0.0008925731,0.00068380765,0.00044302858,0.0005943567,0.00071059656,0.0006832819,0.0011037813,0.00020728141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008151402,0.000029376492,0.00021060821,0.00002227354,0.000012517819,0.000012798903,0.000016561668,0.9952272,0.000344275,0.0003089676,0.000114987844,0.0036189605],"study_design_scores_gemma":[0.000014021561,0.000040529725,0.00015827705,0.0000031628508,0.000006088536,0.000003117684,0.000009745184,0.99913543,0.000111207184,0.00047944952,0.00003641494,0.000002547333],"about_ca_topic_score_codex":0.018615633,"about_ca_topic_score_gemma":0.013333231,"teacher_disagreement_score":0.018615633,"about_ca_system_score_codex":0.0011921461,"about_ca_system_score_gemma":0.0030020885,"threshold_uncertainty_score":0.037014544},"labels":[],"label_agreement":null},{"id":"W4378839621","doi":"10.1016/j.eswa.2023.120548","title":"Efficient 2D irregular layout by vector superposition NFP and mixed-integer programming","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Polygon (computer graphics); Mathematical optimization; Integer programming; Superposition principle; Computer science; Process (computing); Integer (computer science); Algorithm; Scale (ratio); Mathematics","score_opus":0.0071729571088853545,"score_gpt":0.21363831685667295,"score_spread":0.2064653597477876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378839621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011465751,0.00022949933,0.97792065,0.00014405076,0.00007188843,0.000047843016,0.00014746161,0.0006435486,0.009329331],"genre_scores_gemma":[0.27478316,0.00021272516,0.71733403,0.00017508428,0.00005627616,0.00021162258,0.0003732108,0.00042146968,0.0064323596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999421,0.00020932392,0.000021144537,0.000068231704,0.00019899981,0.000081431375],"domain_scores_gemma":[0.9990901,0.00056607806,0.000076228804,0.000110999135,0.000118461816,0.000038127655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006088037,0.00100654,0.0012988613,0.0008092383,0.0005016587,0.0012951567,0.0013072615,0.0009762857,0.008431139],"category_scores_gemma":[0.0023246661,0.0007771123,0.0009219662,0.0018272045,0.0005191388,0.0017038283,0.0012899739,0.0010452619,0.0010343095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058746245,0.000055022087,0.00020456094,0.000088085515,0.000022145445,0.00004759327,0.000033235563,0.92034894,0.0013239752,0.017951172,0.003066621,0.056799885],"study_design_scores_gemma":[0.000003978548,0.000009073214,0.00001875213,0.0000034393988,0.000001688284,0.000008724667,0.0000050804847,0.99458426,0.00019550828,0.004708911,0.0004582735,0.0000023760078],"about_ca_topic_score_codex":0.003949226,"about_ca_topic_score_gemma":0.0069322176,"teacher_disagreement_score":0.008431139,"about_ca_system_score_codex":0.0008917422,"about_ca_system_score_gemma":0.0011007447,"threshold_uncertainty_score":0.028204978},"labels":[],"label_agreement":null},{"id":"W4379790528","doi":"10.1016/j.eswa.2023.120750","title":"A multi-stage decision framework for managing hazardous waste logistics with random release dates","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Operations research; Hazardous waste; Decision maker; Risk aversion (psychology); Plan (archaeology); Stage (stratigraphy); Decision analysis; Risk analysis (engineering); Business; Expected utility hypothesis; Mathematics; Engineering; Statistics","score_opus":0.03388972768180201,"score_gpt":0.29097618046455564,"score_spread":0.25708645278275366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379790528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010900192,0.00019952882,0.98616576,0.00019902976,0.000042246353,0.00015933029,0.0001259994,0.00022317268,0.001984691],"genre_scores_gemma":[0.49508038,0.00042813167,0.4969099,0.00014235164,0.000089216555,0.00067104964,0.00036878826,0.00011151953,0.006198638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99728954,0.0010492543,0.00018303431,0.0004891437,0.0005788377,0.00041016727],"domain_scores_gemma":[0.9971902,0.0018984057,0.00018798618,0.00006460891,0.00045317828,0.00020573243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006729339,0.0015224194,0.0029165861,0.0021176955,0.0012512447,0.004014434,0.0036774762,0.003091759,0.0055840756],"category_scores_gemma":[0.005045814,0.0017509975,0.002209732,0.0017605863,0.0011570387,0.0028706207,0.0020683184,0.001763045,0.0005078026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052239928,0.000053951735,0.00016846714,0.00004666992,0.000038024857,0.00005835927,0.000039823306,0.9833813,0.00030773523,0.007156887,0.00023939578,0.008457225],"study_design_scores_gemma":[0.000010464453,0.00002412904,0.000035740588,0.0000064570286,0.000015458925,0.0000065349427,0.000010298394,0.99700516,0.00010577162,0.002579722,0.00019181702,0.000008510338],"about_ca_topic_score_codex":0.028033344,"about_ca_topic_score_gemma":0.028598506,"teacher_disagreement_score":0.028033344,"about_ca_system_score_codex":0.00335928,"about_ca_system_score_gemma":0.005493024,"threshold_uncertainty_score":0.055740356},"labels":[],"label_agreement":null},{"id":"W4379878604","doi":"10.1016/j.eswa.2023.120702","title":"<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si144.svg\" display=\"inline\" id=\"d1e549\"><mml:mi>Δ</mml:mi></mml:math>V-learning: An adaptive reinforcement learning algorithm for the optimal stopping problem","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Reinforcement learning; Optimal stopping; Markov decision process; Computer science; Algorithm; Q-learning; Benchmark (surveying); Bellman equation; Artificial intelligence; Machine learning; Mathematical optimization; Markov process; Mathematics","score_opus":0.044898547089644414,"score_gpt":0.3107925151993854,"score_spread":0.265893968109741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379878604","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000985121,0.00035638758,0.39637202,0.003758064,0.0009881827,0.0005353941,0.11183385,0.17987224,0.3052988],"genre_scores_gemma":[0.026202686,0.0012006122,0.25181508,0.0024276495,0.0006137298,0.0015751675,0.14591782,0.1458802,0.4243671],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999271,0.00012210032,0.000078246434,0.00013817515,0.00032184305,0.000068648624],"domain_scores_gemma":[0.99734265,0.0009922053,0.00015796247,0.0005160056,0.00084392325,0.00014719974],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011674073,0.0017583886,0.0011355614,0.0015252053,0.0006452648,0.004859324,0.0038964231,0.0023952639,0.65667903],"category_scores_gemma":[0.008046649,0.0011202258,0.00092000765,0.0028027697,0.00056135835,0.0037932103,0.0019332938,0.002339617,0.53080803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080823345,0.000044860895,0.00013055633,0.00027299847,0.0000107972255,0.000044989516,0.000056830653,0.0012525796,0.00090918207,0.022003824,0.9030938,0.07209878],"study_design_scores_gemma":[0.0001019764,0.000021393118,0.00041542776,0.00009923352,0.0000074801633,0.00010265798,0.00003171612,0.013474668,0.003804966,0.02915768,0.95274013,0.000042688396],"about_ca_topic_score_codex":0.008022635,"about_ca_topic_score_gemma":0.008665277,"teacher_disagreement_score":0.65667903,"about_ca_system_score_codex":0.0018164478,"about_ca_system_score_gemma":0.0013497581,"threshold_uncertainty_score":0.48970568},"labels":[],"label_agreement":null},{"id":"W4380742475","doi":"10.1016/j.eswa.2023.120763","title":"Modelling auto insurance Size-of-Loss distributions using Exponentiated Weibull distribution and de-grouping methods","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Weibull distribution; Randomness; Computer science; Parametric statistics; Statistics; Econometrics; Aggregate (composite); Constraint (computer-aided design); Distribution (mathematics); Benchmark (surveying); Mathematics","score_opus":0.13399580414312076,"score_gpt":0.41613360066996097,"score_spread":0.28213779652684023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380742475","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.152052,0.00020677478,0.8456898,0.00014995947,0.000027569653,0.00006453823,0.00014257987,0.000159272,0.001507581],"genre_scores_gemma":[0.9440365,0.00015771668,0.054132674,0.00003636725,0.000016806403,0.000078695506,0.0001632792,0.000023909413,0.0013541989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934655,0.00026879183,0.000044783133,0.0001320416,0.000123276,0.000084464715],"domain_scores_gemma":[0.99439174,0.0039288513,0.00080999156,0.00037946636,0.0003991274,0.000090823254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003154339,0.00052136835,0.0006034945,0.00076988805,0.00022830938,0.00075787335,0.0013877314,0.0010723746,0.0011258519],"category_scores_gemma":[0.007839968,0.0002849012,0.00072720554,0.00065368443,0.00058550393,0.0010757067,0.0005780108,0.0010231272,0.00017518176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019060839,0.000015706226,0.0015295096,0.000011525897,0.000009421215,0.000031313553,0.000041802497,0.9884736,0.00042775724,0.005431023,0.00008643113,0.0039229053],"study_design_scores_gemma":[0.0000012171017,0.0000070236156,0.00024640214,0.0000021690644,0.0000014866577,0.0000071648356,0.0000057933694,0.9979931,0.0001292386,0.0015325487,0.00007031956,0.0000035333599],"about_ca_topic_score_codex":0.0056993267,"about_ca_topic_score_gemma":0.0028899864,"teacher_disagreement_score":0.0056993267,"about_ca_system_score_codex":0.0007546573,"about_ca_system_score_gemma":0.0005042626,"threshold_uncertainty_score":0.01668191},"labels":[],"label_agreement":null},{"id":"W4380742485","doi":"10.1016/j.eswa.2023.120833","title":"A decision-making framework for blockchain platform evaluation in spherical fuzzy environment","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Blockchain; Multiple-criteria decision analysis; Computer science; Ranking (information retrieval); Flexibility (engineering); Pairwise comparison; Data mining; Fuzzy logic; Profitability index; Traceability; Operations research; Machine learning; Artificial intelligence; Computer security; Mathematics; Software engineering; Business","score_opus":0.023924304903835118,"score_gpt":0.30078350182585395,"score_spread":0.2768591969220188,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380742485","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005599829,0.000097610086,0.99122286,0.00015220714,0.000018835526,0.00007613954,0.00005787681,0.000086690125,0.0026879269],"genre_scores_gemma":[0.50049555,0.00025134362,0.4956135,0.00008255978,0.00006212304,0.00027043757,0.00017442023,0.000038841365,0.0030112262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998281,0.00063560915,0.00012103252,0.00023892807,0.0005359015,0.00018759153],"domain_scores_gemma":[0.998412,0.00079935434,0.000115848394,0.00007777552,0.0004789795,0.00011609072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039750366,0.00073456025,0.0013270659,0.001584836,0.0010095143,0.0026002193,0.0015439785,0.0011080813,0.004616158],"category_scores_gemma":[0.0044427067,0.0003518709,0.0011722422,0.0013883865,0.0010769377,0.0019541264,0.0015821952,0.0011033934,0.00044277188],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000105366744,0.00009714016,0.00066731346,0.00015680761,0.00010038118,0.00024043281,0.00024205446,0.65390843,0.002474705,0.27177173,0.0018596394,0.06837598],"study_design_scores_gemma":[0.000010645086,0.000025836436,0.00007948255,0.000017831775,0.000018374616,0.000020585387,0.000028162902,0.9507402,0.0003962473,0.04781442,0.00083615154,0.0000119463675],"about_ca_topic_score_codex":0.01109284,"about_ca_topic_score_gemma":0.008969757,"teacher_disagreement_score":0.01109284,"about_ca_system_score_codex":0.002037057,"about_ca_system_score_gemma":0.0026689693,"threshold_uncertainty_score":0.02205652},"labels":[],"label_agreement":null},{"id":"W4380894276","doi":"10.1016/j.eswa.2023.120682","title":"TOPSIS-based comprehensive measure of variable importance in predictive modelling","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Interpretability; Computer science; Feature selection; Robustness (evolution); Data mining; Variable (mathematics); Machine learning; Measure (data warehouse); TOPSIS; Curse of dimensionality; Artificial intelligence; Operations research; Mathematics","score_opus":0.04060741544420712,"score_gpt":0.2660326687072231,"score_spread":0.22542525326301596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380894276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02228083,0.001096976,0.97286206,0.00028087993,0.00006189332,0.00007837588,0.00017960925,0.00017536765,0.0029839708],"genre_scores_gemma":[0.841824,0.0009295201,0.15528828,0.00010851994,0.0001317175,0.00018463137,0.0004143144,0.000029189614,0.001089736],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99061275,0.0037202605,0.00063105643,0.001072085,0.00358785,0.00037598785],"domain_scores_gemma":[0.9875729,0.008869078,0.00062431337,0.0005936803,0.0021166669,0.00022342187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00713691,0.0012569136,0.0023362616,0.00510158,0.0011102415,0.004090864,0.0015705088,0.0012372307,0.0024071054],"category_scores_gemma":[0.022474982,0.0004328256,0.0019702371,0.006006752,0.0014762052,0.0033169389,0.0016038966,0.0019713936,0.0002707751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002953223,0.0002768335,0.011401975,0.0012512499,0.002002418,0.0004883732,0.00074950303,0.4157871,0.0034390972,0.1658751,0.0034408947,0.3949921],"study_design_scores_gemma":[0.0000198372,0.00013987762,0.003932234,0.0001317855,0.00028639342,0.00020154253,0.00013661,0.8671132,0.000763407,0.12600547,0.001215886,0.000053831438],"about_ca_topic_score_codex":0.004125892,"about_ca_topic_score_gemma":0.0035710572,"teacher_disagreement_score":0.00713691,"about_ca_system_score_codex":0.0017031983,"about_ca_system_score_gemma":0.0024076996,"threshold_uncertainty_score":0.037744045},"labels":[],"label_agreement":null},{"id":"W4380986449","doi":"10.1016/j.eswa.2023.120745","title":"TDRLM: Stylometric learning for authorship verification by Topic-Debiasing","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Debiasing; Computer science; Artificial intelligence; Stylometry; Machine learning; Representation (politics); Natural language processing; Task (project management); Multi-task learning; Latent semantic analysis; Writing style; Information retrieval; Linguistics","score_opus":0.04554347233216235,"score_gpt":0.3100462451674291,"score_spread":0.2645027728352668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380986449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020174183,0.0008981007,0.8983823,0.00042511686,0.0004796718,0.0004055836,0.007953989,0.0687083,0.0025726834],"genre_scores_gemma":[0.21451691,0.00042098606,0.7550112,0.0001923487,0.00051727454,0.00070622284,0.01814436,0.0022899345,0.008200689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99383366,0.0017403497,0.000692834,0.0017502991,0.0015216474,0.00046113756],"domain_scores_gemma":[0.9864674,0.005910633,0.0011270295,0.004497906,0.0015459099,0.00045112794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075460193,0.0019156995,0.0023663728,0.01148317,0.0016013577,0.003915532,0.0030036971,0.0025773076,0.011438072],"category_scores_gemma":[0.0349763,0.0009487375,0.001837039,0.007061936,0.00088420603,0.0051103095,0.0044880714,0.0024777115,0.01680326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041428726,0.0003557044,0.008582203,0.0004118628,0.00026481153,0.00016185505,0.0003190521,0.0131488005,0.005832108,0.0063415305,0.05104347,0.9131244],"study_design_scores_gemma":[0.00012771304,0.00012453062,0.0034465988,0.000071256356,0.000072105526,0.00031754156,0.0001870159,0.93438303,0.012669657,0.02831921,0.020202331,0.000078966026],"about_ca_topic_score_codex":0.002284999,"about_ca_topic_score_gemma":0.0051645087,"teacher_disagreement_score":0.01148317,"about_ca_system_score_codex":0.00081576814,"about_ca_system_score_gemma":0.0022105763,"threshold_uncertainty_score":0.039907634},"labels":[],"label_agreement":null},{"id":"W4381885510","doi":"10.1016/j.eswa.2023.120899","title":"Developing a fuzzy optimized model for selecting a maintenance strategy in the paper industry: An integrated FGP-ANP-FMEA approach","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Failure mode and effects analysis; Risk analysis (engineering); Preventive maintenance; Fuzzy logic; Predictive maintenance; Reliability engineering; Proactive maintenance; Optimal maintenance; Operations research; Business; Engineering; Artificial intelligence","score_opus":0.05490420174599783,"score_gpt":0.29411638113663324,"score_spread":0.2392121793906354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381885510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017148077,0.00019991492,0.97623867,0.00013199892,0.000031497544,0.00007649032,0.00009417501,0.00020688641,0.0058723283],"genre_scores_gemma":[0.7233408,0.00041965215,0.2680795,0.00013367977,0.00004849078,0.00049197016,0.00024986817,0.000100838486,0.0071352194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997631,0.00006501946,0.0000127047415,0.00004850396,0.000072074516,0.000038554987],"domain_scores_gemma":[0.99962986,0.0002063607,0.00003626023,0.000015892794,0.000096855554,0.00001475793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008097459,0.0008913849,0.0012337986,0.0010654061,0.0005974914,0.0013426623,0.0013721812,0.0019241257,0.0028122251],"category_scores_gemma":[0.0013848421,0.000644094,0.0013316434,0.00086251734,0.00041267122,0.000920614,0.00070036063,0.00087862456,0.00037460934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006909094,0.000011177739,0.00010946965,0.000021240632,0.00001235393,0.00002267257,0.000009369504,0.9925647,0.000342518,0.0012768802,0.00012212942,0.00550051],"study_design_scores_gemma":[0.0000015940489,0.0000053174704,0.00003219424,0.0000026769328,0.0000045138686,0.0000038062092,0.000003307441,0.9992625,0.00009261049,0.00050162233,0.00008829163,0.0000015511364],"about_ca_topic_score_codex":0.027406082,"about_ca_topic_score_gemma":0.017278204,"teacher_disagreement_score":0.027406082,"about_ca_system_score_codex":0.0011873308,"about_ca_system_score_gemma":0.0016871964,"threshold_uncertainty_score":0.05449313},"labels":[],"label_agreement":null},{"id":"W4382281483","doi":"10.1016/j.eswa.2023.120854","title":"Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Turbine; Wind power; Offshore wind power; Fault (geology); Sensor fusion; Supervised learning; Condition monitoring; Artificial intelligence; Real-time computing; Artificial neural network; Engineering","score_opus":0.012091866676560802,"score_gpt":0.24852448839252442,"score_spread":0.2364326217159636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382281483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04989659,0.0003070286,0.94786507,0.00009256321,0.000056484256,0.000036466787,0.000043841595,0.00044665023,0.0012552368],"genre_scores_gemma":[0.9076585,0.00022805278,0.09061977,0.000038161455,0.00005998889,0.00005306896,0.00013067776,0.00001946078,0.0011922328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998092,0.000021471886,0.000018781879,0.000046678615,0.00007886176,0.000024959376],"domain_scores_gemma":[0.9997663,0.000053059415,0.000038099282,0.000019745035,0.000110300614,0.000012547443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002953953,0.000551427,0.0007305243,0.0006125178,0.0003253896,0.0005130299,0.0005509282,0.00049973937,0.00070254324],"category_scores_gemma":[0.00058421516,0.00022114613,0.0005106333,0.00037133394,0.00021005666,0.0008078777,0.00039608707,0.00040265062,0.00018386942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004717479,0.00023749597,0.0038645864,0.00026155717,0.00016108248,0.0003120903,0.00023909929,0.43685555,0.043196503,0.0054654507,0.0021478916,0.50678694],"study_design_scores_gemma":[0.0000051111706,0.000041673236,0.0006144441,0.0000029334026,0.000015230378,0.000029800132,0.000009538203,0.9959247,0.0024432356,0.00072039687,0.00018858384,0.000004320166],"about_ca_topic_score_codex":0.002143941,"about_ca_topic_score_gemma":0.0027209618,"teacher_disagreement_score":0.002143941,"about_ca_system_score_codex":0.00022008407,"about_ca_system_score_gemma":0.0005168198,"threshold_uncertainty_score":0.0042628646},"labels":[],"label_agreement":null},{"id":"W4383100615","doi":"10.1016/j.eswa.2023.120914","title":"Time series classification, augmentation and artificial-intelligence-enabled software for emergency response in freight transportation fires","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Dynamic time warping; Machine learning","score_opus":0.031422474823847385,"score_gpt":0.2737521831468777,"score_spread":0.24232970832303033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383100615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27322924,0.0003968061,0.69441205,0.0007275789,0.0002899573,0.00013294446,0.00069022697,0.0256902,0.004431064],"genre_scores_gemma":[0.74678296,0.00023779742,0.24768236,0.00012827854,0.000096766475,0.000114455215,0.0007880828,0.00038618967,0.0037830118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997309,0.000074599746,0.00003166899,0.000071843955,0.00007047292,0.000020482752],"domain_scores_gemma":[0.9985789,0.0008077681,0.00012743243,0.00018788317,0.00025573952,0.000042315725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092211907,0.0004018061,0.00034290354,0.0005910534,0.00027505352,0.0008724474,0.0006066886,0.000452196,0.0026054382],"category_scores_gemma":[0.0034989982,0.0002273111,0.00051480543,0.0005068185,0.00024633147,0.000933381,0.000484859,0.0007196379,0.00058618793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087565393,0.00040754382,0.00801827,0.00015846634,0.000109095585,0.00030249066,0.00032966767,0.18281552,0.031645108,0.0045176214,0.008112942,0.76270765],"study_design_scores_gemma":[0.000010513912,0.00003783314,0.001577708,0.000008061781,0.000023325889,0.000042577693,0.00002607313,0.9843747,0.010512113,0.0016837452,0.0016958532,0.0000074018362],"about_ca_topic_score_codex":0.003546406,"about_ca_topic_score_gemma":0.003769344,"teacher_disagreement_score":0.003546406,"about_ca_system_score_codex":0.0004429625,"about_ca_system_score_gemma":0.000554643,"threshold_uncertainty_score":0.008716047},"labels":[],"label_agreement":null},{"id":"W4385554858","doi":"10.1016/j.eswa.2023.121086","title":"Incorporating global–local neighbors with Gaussian mixture embedding for few-shot knowledge graph completion","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Encoder; Embedding; Encoding (memory); Relation (database); Graph; Geospatial analysis; k-nearest neighbors algorithm; Metric (unit); Gaussian; Task (project management); ENCODE; Benchmark (surveying); Data mining; Artificial intelligence; Theoretical computer science","score_opus":0.024626177860893484,"score_gpt":0.3069194756039586,"score_spread":0.2822932977430651,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385554858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013624705,0.00043377414,0.984124,0.0001727036,0.00005506275,0.000056991434,0.00013727805,0.00077705295,0.0006184424],"genre_scores_gemma":[0.5392947,0.0006918756,0.44930714,0.00046382114,0.00023656184,0.00025146865,0.0023758458,0.000517234,0.006861288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988876,0.00026989283,0.00005633427,0.00044636897,0.0002366066,0.000103218896],"domain_scores_gemma":[0.9975299,0.0013516258,0.00016258922,0.00043101975,0.00037232134,0.00015254412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013891449,0.0012365939,0.002411846,0.001856709,0.0007997174,0.0012979493,0.0033634605,0.0029637418,0.0028084833],"category_scores_gemma":[0.006387613,0.00097128184,0.0016117678,0.0018145578,0.0012023395,0.0037940012,0.0024487937,0.0027625088,0.0013223044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004342129,0.00047167696,0.0014663069,0.00037526284,0.00028699284,0.00019489389,0.00034293835,0.59496456,0.006361051,0.019326154,0.006774068,0.3690019],"study_design_scores_gemma":[0.000005361144,0.000018266077,0.00008681748,0.0000056166677,0.000011490402,0.000017381455,0.000012936811,0.9914587,0.0004087207,0.0076824767,0.00028436564,0.000007882746],"about_ca_topic_score_codex":0.017048297,"about_ca_topic_score_gemma":0.024202418,"teacher_disagreement_score":0.017048297,"about_ca_system_score_codex":0.00081728614,"about_ca_system_score_gemma":0.0013080998,"threshold_uncertainty_score":0.033898115},"labels":[],"label_agreement":null},{"id":"W4385621628","doi":"10.1016/j.eswa.2023.121121","title":"A supplier selection &amp; order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Quality and Supply Management","field":"Business, Management and Accounting","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University; Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Principal component analysis; Component (thermodynamics); Supply chain; Process (computing); Selection (genetic algorithm); Artificial neural network; Mathematical optimization; Supply chain management; Artificial intelligence; Operations research; Data mining; Mathematics","score_opus":0.014822722989692474,"score_gpt":0.274710680714615,"score_spread":0.25988795772492257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385621628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049068155,0.00014425865,0.99213845,0.00030393296,0.000028853434,0.00002461919,0.00013177382,0.0006927437,0.0016285534],"genre_scores_gemma":[0.32992297,0.00032079412,0.66064787,0.0003356035,0.00011340771,0.0001415944,0.0005534815,0.00022251553,0.0077417563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996946,0.00007627718,0.000014965164,0.00006939436,0.000101650694,0.00004309669],"domain_scores_gemma":[0.9996501,0.00012883267,0.000036348425,0.00002831261,0.00012458581,0.00003188328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070142664,0.00062795996,0.0009291109,0.0006889993,0.00041203227,0.0009925172,0.0013414047,0.0009805219,0.0029828616],"category_scores_gemma":[0.0010655046,0.00067746214,0.0007196081,0.00093479286,0.00037157367,0.0009372396,0.0009801945,0.001469443,0.0005585252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023202745,0.00006497994,0.00035698127,0.000028604005,0.00005008138,0.000034115066,0.000021983278,0.90597165,0.000719601,0.008013041,0.0035576578,0.081158094],"study_design_scores_gemma":[0.0000013339483,0.0000028874101,0.000025398056,0.0000013812127,0.0000023933817,0.0000017243519,0.0000011927687,0.99801576,0.00008050336,0.0016754686,0.00019068281,0.0000012520294],"about_ca_topic_score_codex":0.026564931,"about_ca_topic_score_gemma":0.043598544,"teacher_disagreement_score":0.026564931,"about_ca_system_score_codex":0.0010391893,"about_ca_system_score_gemma":0.0029045178,"threshold_uncertainty_score":0.052820623},"labels":[],"label_agreement":null},{"id":"W4385776789","doi":"10.1016/j.eswa.2023.121180","title":"Secure hierarchical fog computing-based architecture for industry 5.0 using an attribute-based encryption scheme","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Cloud computing; Architecture; Field (mathematics); Encryption; Distributed computing; Computer security; Layer (electronics); Embedded system; Operating system","score_opus":0.04640962118083454,"score_gpt":0.3115153368081297,"score_spread":0.2651057156272952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385776789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17363502,0.0022149715,0.7779125,0.0011300163,0.00064284354,0.00066572137,0.0004763848,0.009952688,0.033369865],"genre_scores_gemma":[0.94749284,0.00032272172,0.046042,0.00026529207,0.00004319048,0.00008542838,0.00033837045,0.000067730776,0.005342369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995134,0.000065095715,0.00003900779,0.0000722973,0.00015741999,0.00015286873],"domain_scores_gemma":[0.99969304,0.000021516022,0.000027603073,0.00012256789,0.00009505679,0.000040205003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048337792,0.00037523484,0.00049188925,0.0005285597,0.0009520602,0.0014364306,0.0011950288,0.0005944536,0.0015182543],"category_scores_gemma":[0.00047419773,0.00019909863,0.0004805411,0.00053756626,0.00043113125,0.0019635716,0.0015137518,0.0009097154,0.0006997232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0035045643,0.00092582626,0.0055026743,0.0007102179,0.00048210003,0.0018568585,0.0010577372,0.085219525,0.18479964,0.30151778,0.07272921,0.3416938],"study_design_scores_gemma":[0.00016447523,0.00056931097,0.0026635996,0.00007774292,0.00024987088,0.0007688869,0.00021242478,0.79250884,0.08786251,0.056476302,0.05829373,0.00015242708],"about_ca_topic_score_codex":0.0028573952,"about_ca_topic_score_gemma":0.0029027658,"teacher_disagreement_score":0.0028573952,"about_ca_system_score_codex":0.00082999043,"about_ca_system_score_gemma":0.0012870615,"threshold_uncertainty_score":0.006022036},"labels":[],"label_agreement":null},{"id":"W4385989064","doi":"10.1016/j.eswa.2023.121207","title":"A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"HORIZON EUROPE Framework Programme; Ministry of Science and Higher Education of the Russian Federation; Ministry of Education and Science of the Russian Federation","keywords":"Electricity price forecasting; Electricity market; Computer science; Bidding; Electricity; Smart grid; Process (computing); Demand response; Artificial intelligence; Econometrics; Mathematical optimization; Operations research; Microeconomics; Economics","score_opus":0.017941515463548406,"score_gpt":0.2563422122477567,"score_spread":0.2384006967842083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385989064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027185313,0.00079454033,0.96741694,0.00027893038,0.0001073395,0.00003331317,0.00008892657,0.0005357192,0.0035590397],"genre_scores_gemma":[0.86454093,0.0007462473,0.12810907,0.00020130584,0.00018359353,0.00008746583,0.00023867821,0.00006608087,0.005826639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985456,0.00002461122,0.00000915261,0.000043703698,0.0000360675,0.00003179481],"domain_scores_gemma":[0.999882,0.00003589715,0.000020633137,0.000010456603,0.000042181648,0.000008801046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003857687,0.00055101403,0.0004467704,0.00052675186,0.00024222428,0.00063357694,0.0011514489,0.00073471846,0.0013873798],"category_scores_gemma":[0.0006753072,0.0003134281,0.0005965135,0.00055790454,0.00030463422,0.0011003462,0.0006209997,0.0006501693,0.00023011913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000063655716,0.00006472355,0.0017088866,0.00005952613,0.00008523878,0.00019836347,0.000052333206,0.8381524,0.00498251,0.01843111,0.0018533972,0.13434793],"study_design_scores_gemma":[6.549921e-7,0.0000035938303,0.00009776541,0.0000010078401,0.0000024341016,0.0000046749396,0.0000011856688,0.998844,0.00016584307,0.0007449129,0.00013264624,0.000001309017],"about_ca_topic_score_codex":0.019509878,"about_ca_topic_score_gemma":0.013951374,"teacher_disagreement_score":0.019509878,"about_ca_system_score_codex":0.0007406856,"about_ca_system_score_gemma":0.00066270906,"threshold_uncertainty_score":0.03879267},"labels":[],"label_agreement":null},{"id":"W4386170403","doi":"10.1016/j.eswa.2023.121287","title":"Consistent penalizing field loss for zero-shot image retrieval","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Discriminative model; Class (philosophy); Image retrieval; Artificial intelligence; Margin (machine learning); Image (mathematics); Matching (statistics); Inference; Field (mathematics); Similarity (geometry); Pattern recognition (psychology); Machine learning; Mathematics; Statistics","score_opus":0.03777184491170358,"score_gpt":0.33470590528433486,"score_spread":0.29693406037263126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386170403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009815433,0.000657837,0.9877015,0.0002788966,0.00005547206,0.000043012085,0.000109345456,0.0004932289,0.00084522297],"genre_scores_gemma":[0.4345319,0.0013396351,0.5447361,0.000697442,0.00035676616,0.00020818115,0.0015005599,0.0005498979,0.016079525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874425,0.00041531317,0.000052855405,0.00021230133,0.0004374946,0.00013772656],"domain_scores_gemma":[0.9969813,0.0016747507,0.00017013164,0.00049166847,0.00055591186,0.00012633829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031258187,0.00095680746,0.0015172672,0.0011687363,0.0004274142,0.0011821233,0.0022827727,0.0023089028,0.0035421636],"category_scores_gemma":[0.008453569,0.00056568236,0.00058718416,0.0009931286,0.0011640037,0.002340021,0.0017346919,0.0017392468,0.0011945473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011819444,0.0003947864,0.0009109846,0.0004522766,0.00015413374,0.00013806437,0.000072595605,0.35574713,0.033592865,0.03239889,0.019201877,0.55575436],"study_design_scores_gemma":[0.000021661299,0.0000746872,0.00029601005,0.000013531038,0.000015011929,0.00008983498,0.000009472674,0.985531,0.003549906,0.009403179,0.0009838279,0.000011997032],"about_ca_topic_score_codex":0.0043018507,"about_ca_topic_score_gemma":0.004182004,"teacher_disagreement_score":0.0043018507,"about_ca_system_score_codex":0.0009542878,"about_ca_system_score_gemma":0.0014778818,"threshold_uncertainty_score":0.01653111},"labels":[],"label_agreement":null},{"id":"W4386318824","doi":"10.1016/j.eswa.2023.121300","title":"Developing deep transfer and machine learning models of chest X-ray for diagnosing COVID-19 cases using probabilistic single-valued neutrosophic hesitant fuzzy","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Weighting; Benchmark (surveying); Machine learning; Probabilistic logic; Benchmarking; Medical diagnosis; Transfer of learning; Fuzzy logic; Data mining; Medicine; Radiology","score_opus":0.1599653336937774,"score_gpt":0.34761301322419536,"score_spread":0.18764767953041794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386318824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11442798,0.00056302367,0.88201004,0.00042697077,0.00006487377,0.000053757634,0.00014977317,0.00044073036,0.0018627977],"genre_scores_gemma":[0.94945157,0.00021751363,0.047974743,0.00010575403,0.000029909483,0.0000661552,0.00018627527,0.000015714511,0.0019523858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986255,0.000027316448,0.000010825198,0.000041066447,0.000029761673,0.000028367524],"domain_scores_gemma":[0.99940073,0.00034744278,0.000046611513,0.000025361462,0.00015091592,0.000028900622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070502586,0.00048793497,0.00049486617,0.0005163783,0.00026791313,0.0006554624,0.000896095,0.0010176842,0.0012437245],"category_scores_gemma":[0.0017679696,0.00033802984,0.0007384602,0.00029191474,0.0002688694,0.00072245626,0.00069750013,0.0010744818,0.0002679379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012060178,0.00010869174,0.0028787416,0.000050188308,0.00006989227,0.0001252505,0.000068497175,0.8912426,0.0036559359,0.0033871636,0.00092479296,0.09736768],"study_design_scores_gemma":[6.7835674e-7,0.000006036817,0.00009308993,0.0000019193008,0.000002966704,0.0000055545706,0.000002579462,0.9991591,0.00019438002,0.0005064459,0.000026050775,0.0000012594694],"about_ca_topic_score_codex":0.008864222,"about_ca_topic_score_gemma":0.0068168878,"teacher_disagreement_score":0.008864222,"about_ca_system_score_codex":0.0006640045,"about_ca_system_score_gemma":0.00082385144,"threshold_uncertainty_score":0.017625272},"labels":[],"label_agreement":null},{"id":"W4386374821","doi":"10.1016/j.eswa.2023.121276","title":"Facial expression analysis using Decomposed Multiscale Spatiotemporal Networks","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Block (permutation group theory); Artificial intelligence; Code (set theory); Encoding (memory); Facial expression; Adaptation (eye); Pattern recognition (psychology); Variety (cybernetics); Machine learning; Data mining","score_opus":0.04611690508751847,"score_gpt":0.35045687187299585,"score_spread":0.30433996678547737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386374821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11828695,0.00046692474,0.8784598,0.00012977741,0.000066247856,0.000046413665,0.0003529363,0.00033386206,0.0018571283],"genre_scores_gemma":[0.8157801,0.00076605804,0.17925279,0.00005722681,0.000056216955,0.00006416915,0.0005485468,0.00008406712,0.003390819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999068,0.000019496349,0.000004515069,0.00003103125,0.000025668558,0.000012516885],"domain_scores_gemma":[0.9998925,0.00003427029,0.00001627706,0.000013571799,0.00003523451,0.000008064248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021609422,0.0003980478,0.0002535407,0.0005229987,0.0001120553,0.00040988837,0.00021793658,0.00019323047,0.0013529481],"category_scores_gemma":[0.00070292666,0.00016125679,0.00040910993,0.00055205333,0.000114001865,0.00039029957,0.00030849862,0.0002564606,0.00027890684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046792434,0.00012487482,0.0064618713,0.00013548881,0.00021898851,0.00028501434,0.00015471729,0.12914798,0.23846011,0.006326091,0.0030192717,0.61519766],"study_design_scores_gemma":[0.0000049108717,0.000034130462,0.0062401937,0.000007911461,0.00003471121,0.0000896879,0.000029801102,0.9836294,0.006581226,0.002553956,0.000785934,0.0000080469235],"about_ca_topic_score_codex":0.0030938834,"about_ca_topic_score_gemma":0.0042488514,"teacher_disagreement_score":0.0030938834,"about_ca_system_score_codex":0.00019117429,"about_ca_system_score_gemma":0.00017443635,"threshold_uncertainty_score":0.0061517954},"labels":[],"label_agreement":null},{"id":"W4386397637","doi":"10.1016/j.eswa.2023.121420","title":"Architecture selection for 5G-radio access network using type-2 neutrosophic numbers based decision making model","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiple-criteria decision analysis; Computer science; Weighting; Selection (genetic algorithm); Fuzzy logic; Context (archaeology); Artificial intelligence; Architecture; Data mining; Machine learning; Management science; Operations research; Mathematics; Engineering","score_opus":0.2162033217602287,"score_gpt":0.47031863143397956,"score_spread":0.25411530967375084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386397637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085585944,0.00037476994,0.9014626,0.00041750039,0.00009519098,0.00016011349,0.00014900324,0.00012398526,0.011630842],"genre_scores_gemma":[0.9120816,0.00023827636,0.08343741,0.00008574988,0.000033474782,0.00022152156,0.0001238139,0.000012058026,0.0037661502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991749,0.0002701505,0.000037281385,0.0001477651,0.00025504187,0.00011482194],"domain_scores_gemma":[0.99945945,0.00032126985,0.000047029425,0.000012245613,0.00013212844,0.000027884616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009523401,0.00084247807,0.0008745564,0.0011961705,0.00072026055,0.0016030326,0.00096082967,0.0008750274,0.0027798014],"category_scores_gemma":[0.001621499,0.0003029422,0.00088664464,0.00077338016,0.00042958526,0.0010083686,0.0005720466,0.0005744455,0.00014132225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045901,0.000043068874,0.0006676712,0.000058892878,0.00003953281,0.00008770679,0.000045950314,0.971376,0.0008103577,0.009554153,0.00063418935,0.016636565],"study_design_scores_gemma":[0.0000044068875,0.00002374013,0.00010415467,0.0000057172147,0.000010217174,0.000013093538,0.000012341484,0.9962224,0.00014977761,0.0033051898,0.00014421015,0.00000474774],"about_ca_topic_score_codex":0.006711593,"about_ca_topic_score_gemma":0.0052109943,"teacher_disagreement_score":0.006711593,"about_ca_system_score_codex":0.001499522,"about_ca_system_score_gemma":0.0012182193,"threshold_uncertainty_score":0.013345063},"labels":[],"label_agreement":null},{"id":"W4386401253","doi":"10.1016/j.eswa.2023.121395","title":"A lightweight open-world pest image classifier using ResNet8-based matching network and NT-Xent loss function","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"PEST analysis; Computer science; Artificial intelligence; Convolutional neural network; Python (programming language); Classifier (UML); Pattern recognition (psychology); Artificial neural network; Machine learning; Computer vision; Biology","score_opus":0.03010625107620761,"score_gpt":0.25513201720453427,"score_spread":0.22502576612832664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386401253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047802866,0.0007991414,0.9172432,0.00033037772,0.00037532847,0.00038574397,0.0016410113,0.025925988,0.0054962826],"genre_scores_gemma":[0.4689153,0.00067791325,0.4950449,0.00057873037,0.00028991475,0.00055894034,0.0081418175,0.0006489919,0.025143502],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994655,0.00002903408,0.000025936806,0.00014949533,0.000237839,0.00009216871],"domain_scores_gemma":[0.9995648,0.000046613073,0.00003749762,0.00007769013,0.00023849135,0.000034876837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006444209,0.00108966,0.0013570975,0.0014661289,0.000551729,0.00081272935,0.0028956323,0.00135172,0.007796352],"category_scores_gemma":[0.0011648667,0.0004949563,0.0007974367,0.0009474782,0.00025139595,0.0019243831,0.001337159,0.001041015,0.00517073],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052612385,0.0004887001,0.001957565,0.00016762581,0.00018603884,0.00020195634,0.000030371917,0.03815688,0.03108171,0.0019825418,0.021326184,0.9038942],"study_design_scores_gemma":[0.000033918277,0.00011932528,0.001174727,0.000013643728,0.00005197587,0.0001639678,0.00001686436,0.97470766,0.017778942,0.0014355493,0.004481733,0.000021654083],"about_ca_topic_score_codex":0.011791259,"about_ca_topic_score_gemma":0.015775807,"teacher_disagreement_score":0.011791259,"about_ca_system_score_codex":0.0008630398,"about_ca_system_score_gemma":0.0011595782,"threshold_uncertainty_score":0.026081383},"labels":[],"label_agreement":null},{"id":"W4386447329","doi":"10.1016/j.eswa.2023.121375","title":"Sustainable group tourist trip planning: An adaptive large neighborhood search algorithm","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Tourism; Computer science; Operations research; Sustainability; Profit (economics); Environmental economics; Sustainable tourism; Heuristic; Mathematical optimization; Marketing; Business; Economics; Microeconomics; Mathematics; Artificial intelligence","score_opus":0.02209203793765084,"score_gpt":0.3019378705525617,"score_spread":0.27984583261491086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386447329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03205745,0.00021609657,0.9619299,0.00013696325,0.000062698506,0.00010009987,0.000056269837,0.00036499542,0.005075411],"genre_scores_gemma":[0.4716046,0.00016549291,0.52195185,0.00011395746,0.000056989735,0.00036400193,0.00018775888,0.00011927352,0.005436075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997515,0.00008873107,0.000010140913,0.000055825156,0.00006573283,0.00002799056],"domain_scores_gemma":[0.9995819,0.00024350312,0.000033230357,0.000026916052,0.000084920415,0.000029491273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000762408,0.0005564908,0.0010887345,0.0008040177,0.00053595647,0.0005986365,0.0016747486,0.001164209,0.0027480037],"category_scores_gemma":[0.0015475258,0.0004642345,0.0006626548,0.00097135856,0.0004914878,0.000790093,0.0010061043,0.0006201793,0.0003444607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100361685,0.000072457464,0.0004062738,0.000033951903,0.000046002857,0.000037969432,0.00003956334,0.9390547,0.00055433426,0.0043019215,0.0014268504,0.05392554],"study_design_scores_gemma":[0.000009504555,0.000012118383,0.00003141567,0.0000012443721,0.0000038442913,0.000003997926,0.0000032594016,0.99926645,0.000044259785,0.00048722554,0.00013522636,0.0000015482944],"about_ca_topic_score_codex":0.009451428,"about_ca_topic_score_gemma":0.011329831,"teacher_disagreement_score":0.009451428,"about_ca_system_score_codex":0.0006319887,"about_ca_system_score_gemma":0.0011127653,"threshold_uncertainty_score":0.018792808},"labels":[],"label_agreement":null},{"id":"W4386566252","doi":"10.1016/j.eswa.2023.121404","title":"Deep learning in stock portfolio selection and predictions","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Hyperparameter; Hyperparameter optimization; Computer science; Portfolio; Portfolio optimization; Artificial intelligence; Machine learning; Feature selection; Deep learning; Project portfolio management; Genetic algorithm; Grid; Novelty; Finance; Economics; Mathematics","score_opus":0.0684588457896083,"score_gpt":0.3824813804268341,"score_spread":0.3140225346372258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386566252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091748826,0.011679241,0.88715947,0.003861217,0.00035376824,0.000038618207,0.00023341413,0.00040265894,0.0045228275],"genre_scores_gemma":[0.8963817,0.0046945876,0.08510558,0.00046590934,0.0004924824,0.00007513828,0.00025140744,0.00006833372,0.012464856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999602,0.00018483044,0.000027326238,0.000059310867,0.000082984574,0.00004366262],"domain_scores_gemma":[0.99592674,0.0032035052,0.00019619291,0.00013828099,0.0004395982,0.000095659925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024260841,0.0005556258,0.0008422694,0.0009524457,0.00029337115,0.0010493711,0.0008108933,0.0012471356,0.0018205721],"category_scores_gemma":[0.008844366,0.0005456931,0.00030932855,0.0010351874,0.00067022844,0.001824757,0.0010197603,0.0016821169,0.00032908178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014832859,0.00016130332,0.0031513383,0.00016892003,0.0001130844,0.00006363322,0.00007597415,0.6562475,0.00093134906,0.04762361,0.0059797154,0.2853353],"study_design_scores_gemma":[0.0000038186054,0.0000061609276,0.00020339091,0.000010635529,0.0000047615044,0.0000032046623,0.0000035784321,0.98378396,0.00019774972,0.015503712,0.00027640013,0.0000026313337],"about_ca_topic_score_codex":0.007892014,"about_ca_topic_score_gemma":0.005721299,"teacher_disagreement_score":0.007892014,"about_ca_system_score_codex":0.00089023437,"about_ca_system_score_gemma":0.00080229837,"threshold_uncertainty_score":0.015692174},"labels":[],"label_agreement":null},{"id":"W4386575409","doi":"10.1016/j.eswa.2023.121506","title":"An unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation for anomaly detection of machines with multiple measuring points","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Anomaly detection; Artificial intelligence; Sensor fusion; Pattern recognition (psychology)","score_opus":0.010360710153165646,"score_gpt":0.22315314590424473,"score_spread":0.2127924357510791,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386575409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027602427,0.00032025116,0.9707308,0.0001084341,0.00006433391,0.000026679287,0.00011157941,0.0004823659,0.0005531729],"genre_scores_gemma":[0.6388321,0.00049566734,0.35667068,0.000112335874,0.00011535508,0.00010898604,0.000750057,0.00007441316,0.002840411],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995509,0.00009369586,0.000026221815,0.00015273076,0.00012239185,0.000054049135],"domain_scores_gemma":[0.99955076,0.00013126795,0.000055611672,0.000056316003,0.00018205016,0.000023896735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010155162,0.0007217815,0.0008202142,0.00084058574,0.0004271566,0.0005358528,0.001117261,0.0008769922,0.0007674328],"category_scores_gemma":[0.0016696985,0.00038853806,0.0007759087,0.0010564442,0.00036942514,0.0013893176,0.0011106663,0.00074312836,0.00033582796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004562539,0.00017234574,0.002979266,0.00008790009,0.0001795876,0.00017026735,0.00012275107,0.37941077,0.02490835,0.007918289,0.0037269644,0.5798672],"study_design_scores_gemma":[0.0000017503517,0.000017618446,0.00026627138,0.0000018647773,0.000011070816,0.000019907278,0.0000038699936,0.9973482,0.0012883799,0.000838913,0.00019790343,0.000004229145],"about_ca_topic_score_codex":0.0067051523,"about_ca_topic_score_gemma":0.006409391,"teacher_disagreement_score":0.0067051523,"about_ca_system_score_codex":0.00049063703,"about_ca_system_score_gemma":0.00071802875,"threshold_uncertainty_score":0.013332248},"labels":[],"label_agreement":null},{"id":"W4386629819","doi":"10.1016/j.eswa.2023.121503","title":"Learning-based acoustic displacement field modeling and micro-particle control","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Displacement (psychology); Computer science; Artificial neural network; Trajectory; Field (mathematics); Nonlinear system; Acoustics; Particle (ecology); Acoustic wave; Process (computing); Artificial intelligence; Physics; Mathematics","score_opus":0.009144518352695745,"score_gpt":0.2186822404258392,"score_spread":0.20953772207314345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386629819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0117176,0.00011485816,0.9866132,0.00011638077,0.000033059816,0.000018236602,0.00001511967,0.00014283536,0.001228616],"genre_scores_gemma":[0.8932853,0.00022042533,0.09907568,0.00009242234,0.000055543456,0.0001123255,0.00006858414,0.000055618573,0.0070340526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983394,0.0000310081,0.000009921698,0.000048435286,0.000058127218,0.000018582456],"domain_scores_gemma":[0.9994294,0.00031640017,0.00006406492,0.00003246795,0.00013501762,0.000022583621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045142527,0.0003853938,0.0006038941,0.0002784581,0.0002614153,0.0006291593,0.00081146916,0.00096319255,0.0014708204],"category_scores_gemma":[0.0016779797,0.0003139485,0.00043621397,0.00027968173,0.0005337964,0.00076084817,0.0006430427,0.00068443775,0.00027882308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021355543,0.00001873522,0.0001837922,0.000021815948,0.000008583984,0.000013554178,0.000021657117,0.9682732,0.0020141501,0.0034926028,0.00021038746,0.025720041],"study_design_scores_gemma":[6.107071e-7,0.000002347946,0.000016612088,4.371609e-7,4.6531227e-7,0.0000013343829,5.4758107e-7,0.99944216,0.00020019812,0.00028877426,0.000045677913,7.538631e-7],"about_ca_topic_score_codex":0.010980116,"about_ca_topic_score_gemma":0.006631915,"teacher_disagreement_score":0.010980116,"about_ca_system_score_codex":0.0006664565,"about_ca_system_score_gemma":0.0007454047,"threshold_uncertainty_score":0.021832407},"labels":[],"label_agreement":null},{"id":"W4386697003","doi":"10.1016/j.eswa.2023.121512","title":"Monthly sodium adsorption ratio forecasting in rivers using a dual interpretable glass-box complementary intelligent system: Hybridization of ensemble TVF-EMD-VMD, Boruta-SHAP, and eXplainable GPR","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Hilbert–Huang transform; Artificial intelligence; Computer science; Wavelet; Perceptron; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Data mining; Mathematics; Energy (signal processing); Statistics","score_opus":0.03588246122780619,"score_gpt":0.2607271769611985,"score_spread":0.22484471573339232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386697003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8260505,0.00018999583,0.17001623,0.00024449587,0.000079024896,0.000027036667,0.0002730914,0.0012974897,0.0018221588],"genre_scores_gemma":[0.97522914,0.000038735918,0.024112077,0.000028444781,0.000013383291,0.000011698512,0.00014498727,0.000017805773,0.00040378215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986625,0.000031166594,0.000009277878,0.000048968188,0.000024591682,0.00001977396],"domain_scores_gemma":[0.9997861,0.00009467678,0.000015329873,0.000017602995,0.000070019785,0.00001637881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049140205,0.00043470776,0.0006650711,0.00045845186,0.00024366642,0.00044540668,0.0004624314,0.0006987013,0.0005107114],"category_scores_gemma":[0.0008096675,0.00022261706,0.00054976204,0.00040372336,0.00012445205,0.0004949244,0.00030319157,0.00042257272,0.00012311967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069704896,0.00039047148,0.026968652,0.000102407386,0.0002316263,0.00017646332,0.0001319843,0.6558876,0.038835764,0.00075020443,0.0014197913,0.27440795],"study_design_scores_gemma":[0.0000051681836,0.000015523334,0.0014551454,8.30278e-7,0.000012567247,0.0000046345685,0.0000044361313,0.9973406,0.001055352,0.000055929453,0.000045354474,0.0000043864775],"about_ca_topic_score_codex":0.008595321,"about_ca_topic_score_gemma":0.009936752,"teacher_disagreement_score":0.008595321,"about_ca_system_score_codex":0.00033709354,"about_ca_system_score_gemma":0.0003616695,"threshold_uncertainty_score":0.017090559},"labels":[],"label_agreement":null},{"id":"W4386753727","doi":"10.1016/j.eswa.2023.121554","title":"Generalized TODIM method based on symmetric intuitionistic fuzzy Jensen–Shannon divergence","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Sichuan Province; National Natural Science Foundation of China","keywords":"Divergence (linguistics); Vagueness; Axiom; Computer science; Measure (data warehouse); Similarity (geometry); Mathematics; Fuzzy logic; Applied mathematics; Algorithm; Artificial intelligence; Data mining","score_opus":0.16139386579907652,"score_gpt":0.441755891558242,"score_spread":0.28036202575916547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386753727","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027786824,0.00017474801,0.995581,0.000044868262,0.00005165463,0.000020822088,0.000019760468,0.000058329475,0.0012703106],"genre_scores_gemma":[0.2376502,0.0005444684,0.75562525,0.00013611463,0.00018922948,0.00024139266,0.00020821419,0.00018284707,0.0052223676],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986733,0.0005250083,0.00008485196,0.0001806271,0.00046191434,0.000074271586],"domain_scores_gemma":[0.9985196,0.00062763103,0.00008527262,0.00012048808,0.0005786893,0.00006827961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020855782,0.00072912034,0.0018542901,0.0016915584,0.00061490195,0.0013884619,0.0015824129,0.001065128,0.0031673487],"category_scores_gemma":[0.0041779964,0.00029579602,0.001310727,0.0012783112,0.0006775209,0.00154002,0.0016022262,0.0011973572,0.00048175693],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022054145,0.00018703942,0.0010753649,0.0008494927,0.00028595873,0.00023015634,0.00024338535,0.33700162,0.012443144,0.25151116,0.004842494,0.3911097],"study_design_scores_gemma":[0.000008044494,0.000037037327,0.00015164884,0.000018057852,0.000018897019,0.00006376522,0.000016293636,0.9753517,0.0011558271,0.021581996,0.0015757318,0.00002103207],"about_ca_topic_score_codex":0.0015031677,"about_ca_topic_score_gemma":0.001510545,"teacher_disagreement_score":0.0031673487,"about_ca_system_score_codex":0.000649014,"about_ca_system_score_gemma":0.0015626758,"threshold_uncertainty_score":0.01102972},"labels":[],"label_agreement":null},{"id":"W4386783354","doi":"10.1016/j.eswa.2023.121542","title":"Nbias: A natural language processing framework for BIAS identification in text","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Vector Institute","funders":"Vector Institute; Government of Ontario; Canadian Institute for Advanced Research","keywords":"Computer science; Security token; Transformer; Identification (biology); Artificial intelligence; Variety (cybernetics); Natural language processing; Data science; Machine learning; Data mining; Computer security","score_opus":0.03851822480585686,"score_gpt":0.3238614681034604,"score_spread":0.2853432432976035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386783354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012682867,0.00018711065,0.98940825,0.000179105,0.000063972,0.00012918215,0.0010617765,0.007227214,0.0004751197],"genre_scores_gemma":[0.052540176,0.00032451417,0.9376739,0.00028699604,0.00028762154,0.00062146113,0.0043119066,0.0014285797,0.0025248763],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9952187,0.0019578848,0.000460486,0.0010000818,0.0011376716,0.0002252405],"domain_scores_gemma":[0.98767567,0.007404427,0.0008877769,0.0013309201,0.0023044755,0.0003967218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007843419,0.0015818652,0.0015617075,0.0044527785,0.0016470882,0.0034572212,0.0026329746,0.0017220475,0.009321353],"category_scores_gemma":[0.02289441,0.00097640615,0.0020918597,0.002510108,0.0010762699,0.005274282,0.003410903,0.0031405166,0.0064195776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001029109,0.00036513276,0.0057419157,0.0018076795,0.0005018627,0.0004819155,0.0019752823,0.022815723,0.028061023,0.14017734,0.06128423,0.7357588],"study_design_scores_gemma":[0.00013873525,0.00014456344,0.0019379497,0.0002445545,0.00022219084,0.00040698858,0.00038874472,0.7047128,0.018394394,0.20419382,0.06909856,0.00011670453],"about_ca_topic_score_codex":0.0052539892,"about_ca_topic_score_gemma":0.007923733,"teacher_disagreement_score":0.009321353,"about_ca_system_score_codex":0.0012710437,"about_ca_system_score_gemma":0.003058372,"threshold_uncertainty_score":0.04148048},"labels":[],"label_agreement":null},{"id":"W4386826669","doi":"10.1016/j.eswa.2023.121623","title":"Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Acceleration; Energy consumption; Kinematics; Motion planning; Range (aeronautics); Energy (signal processing); Motion (physics); Kinetic energy; Efficient energy use; Artificial neural network; Process (computing); Simulation; Control theory (sociology); Artificial intelligence; Engineering; Mathematics; Robot; Aerospace engineering","score_opus":0.029435436862763686,"score_gpt":0.27404676815523216,"score_spread":0.24461133129246848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386826669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13910568,0.00046106335,0.8469909,0.0005106269,0.00011753706,0.00007636483,0.00019244623,0.00093407545,0.011611323],"genre_scores_gemma":[0.946238,0.00008199618,0.04834549,0.000069165275,0.000015487087,0.00007884969,0.00014084535,0.000050185772,0.0049800356],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999213,0.0000073928145,0.0000035940268,0.000026958904,0.00002124809,0.00001948533],"domain_scores_gemma":[0.9998555,0.00006262935,0.000018600553,0.000010437776,0.000035077075,0.000017766164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017984785,0.000609845,0.0007140702,0.0003021438,0.00048703357,0.0004787322,0.0007221706,0.0011145294,0.0028750517],"category_scores_gemma":[0.0004950181,0.0005786813,0.00045150553,0.00029804,0.0003604017,0.0005293372,0.0006969211,0.00066649076,0.00030868838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045833556,0.000016520375,0.00019096766,0.000019843175,0.000008228592,0.00004327069,0.000014750078,0.9834471,0.00101847,0.00077512744,0.00033292492,0.014086932],"study_design_scores_gemma":[0.000001967345,0.0000075867056,0.00003602365,0.0000012651288,0.0000012864299,0.0000029811517,0.000002226046,0.9995204,0.00012177014,0.00025205145,0.000051334733,0.0000011499196],"about_ca_topic_score_codex":0.025247745,"about_ca_topic_score_gemma":0.022116408,"teacher_disagreement_score":0.025247745,"about_ca_system_score_codex":0.00060861657,"about_ca_system_score_gemma":0.0012775316,"threshold_uncertainty_score":0.050201595},"labels":[],"label_agreement":null},{"id":"W4386968797","doi":"10.1016/j.eswa.2023.121757","title":"Deep transfer learning approach for digital circuits vulnerability analysis","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Integrated circuit; Electronic circuit; Deep learning; Convolutional neural network; Digital electronics; Benchmark (surveying); Vulnerability (computing); Computer engineering; Artificial neural network; Computer architecture; Artificial intelligence; Embedded system; Electrical engineering; Engineering; Computer security","score_opus":0.020241229659081234,"score_gpt":0.25364346042460423,"score_spread":0.233402230765523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386968797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03490958,0.0009522387,0.95929796,0.0004092621,0.00006863525,0.000034469173,0.00018413029,0.0011633475,0.002980431],"genre_scores_gemma":[0.8967566,0.00074478943,0.08455543,0.0003021849,0.0001210288,0.00010551851,0.00065553223,0.000120652316,0.016638417],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998435,0.000030265832,0.000007255007,0.000037759157,0.000045676472,0.00003557199],"domain_scores_gemma":[0.9995789,0.00020598546,0.00003564808,0.000044861175,0.00011293209,0.000021686496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044774183,0.00062073517,0.0006022264,0.0006844475,0.00024500737,0.00051392295,0.0010599474,0.0010076714,0.0034502742],"category_scores_gemma":[0.0011877012,0.00026493258,0.0006536545,0.0005280564,0.00037539136,0.0008636868,0.0008738938,0.0014282961,0.0007023434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107147906,0.0001246935,0.0010625059,0.00010700699,0.0000957625,0.0001326853,0.000049159433,0.62592244,0.0062434096,0.0128795635,0.0058609275,0.3474147],"study_design_scores_gemma":[0.000001334505,0.000010346699,0.00010482695,0.000003283616,0.0000056297713,0.000008570046,0.0000031663901,0.99497235,0.00060514756,0.003972784,0.00031053275,0.000002001328],"about_ca_topic_score_codex":0.004674345,"about_ca_topic_score_gemma":0.003879496,"teacher_disagreement_score":0.004674345,"about_ca_system_score_codex":0.00059150707,"about_ca_system_score_gemma":0.0007830186,"threshold_uncertainty_score":0.01154232},"labels":[],"label_agreement":null},{"id":"W4387004586","doi":"10.1016/j.eswa.2023.121734","title":"Transformer-BLS: An efficient learning algorithm based on multi-head attention mechanism and incremental learning algorithms","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"MNIST database; Computer science; Transformer; Algorithm; Artificial intelligence; Machine learning; Pattern recognition (psychology); Artificial neural network; Voltage","score_opus":0.01990274320579866,"score_gpt":0.28765132552054173,"score_spread":0.2677485823147431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387004586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002720628,0.00031211882,0.9857965,0.00015770686,0.00016498452,0.00009982293,0.00013776285,0.009493782,0.001116806],"genre_scores_gemma":[0.08433109,0.00033024073,0.9047173,0.000536922,0.00017794671,0.0002728564,0.0009536718,0.0012662753,0.0074136863],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989718,0.00019390295,0.00007885908,0.00029034977,0.00033793534,0.0001271928],"domain_scores_gemma":[0.9984084,0.0005308671,0.00007316837,0.00026043385,0.000618185,0.000108923065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001852808,0.0017143853,0.002226625,0.0020624404,0.0008805031,0.0017529107,0.0058810296,0.0022444746,0.01675781],"category_scores_gemma":[0.0045116404,0.0009758622,0.0011789808,0.0021143246,0.00078227534,0.00428452,0.0028489314,0.0033748606,0.0073031476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003817687,0.0001793977,0.00027196936,0.00012202481,0.000064745356,0.000038853308,0.000043187163,0.025837753,0.005753837,0.0062848763,0.015469076,0.9455526],"study_design_scores_gemma":[0.000095951145,0.00010154149,0.00014723648,0.000015113451,0.000042829382,0.00006815613,0.000016039849,0.97483563,0.009129976,0.010631815,0.004892608,0.000023010538],"about_ca_topic_score_codex":0.011542683,"about_ca_topic_score_gemma":0.013069389,"teacher_disagreement_score":0.01675781,"about_ca_system_score_codex":0.0015481699,"about_ca_system_score_gemma":0.0032996032,"threshold_uncertainty_score":0.056060493},"labels":[],"label_agreement":null},{"id":"W4387107464","doi":"10.1016/j.eswa.2023.121720","title":"Parallel inference for cross-collection latent generalized Dirichlet allocation model and applications","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Latent Dirichlet allocation; Computer science; Inference; Topic model; Hierarchical Dirichlet process; Prior probability; Dirichlet distribution; Scalability; Machine learning; Data mining; Artificial intelligence; Mathematics; Bayesian probability; Database","score_opus":0.04492236747625851,"score_gpt":0.31832813756510325,"score_spread":0.27340577008884476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387107464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005634527,0.00045876318,0.99129206,0.0003229021,0.000111187255,0.00006509414,0.00031108028,0.0012035454,0.0006008789],"genre_scores_gemma":[0.19945017,0.00092663703,0.78454804,0.00054732856,0.00063379126,0.0007696313,0.004025059,0.001092028,0.008007443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941817,0.002994392,0.00032555984,0.0014928603,0.00065939105,0.0003460401],"domain_scores_gemma":[0.987872,0.0077298926,0.00032042293,0.002574112,0.0011894181,0.00031412859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009639892,0.0016562681,0.0037387311,0.0023003325,0.0019905772,0.0030593346,0.0052486164,0.002193905,0.008430622],"category_scores_gemma":[0.026390193,0.0022027034,0.0032987548,0.004060421,0.0015805343,0.0055399886,0.0043800673,0.004794232,0.0030999475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010296437,0.0005692597,0.0035297647,0.000488135,0.00080907653,0.00027363698,0.00058920705,0.40024447,0.0035259863,0.10689805,0.018335927,0.46370685],"study_design_scores_gemma":[0.000059035912,0.000018790934,0.00029028332,0.000012486211,0.000056609657,0.000035182635,0.000031132775,0.92689055,0.0006144387,0.07042543,0.0015462484,0.000019911942],"about_ca_topic_score_codex":0.022462837,"about_ca_topic_score_gemma":0.037946925,"teacher_disagreement_score":0.022462837,"about_ca_system_score_codex":0.0023056045,"about_ca_system_score_gemma":0.0043443493,"threshold_uncertainty_score":0.050981224},"labels":[],"label_agreement":null},{"id":"W4387409667","doi":"10.1016/j.eswa.2023.122004","title":"Optimizing prices in trade-in strategies for vehicle retailers","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Business; Subsidy; Value (mathematics); Industrial organization; Production (economics); Microeconomics; Economics; Computer science","score_opus":0.011764243586688303,"score_gpt":0.23647527382075098,"score_spread":0.2247110302340627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387409667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44777018,0.00078653113,0.51049244,0.0022843645,0.00020032669,0.00061238493,0.0002770029,0.00047637674,0.03710047],"genre_scores_gemma":[0.97371364,0.00011528889,0.021407463,0.00008097349,0.00002292615,0.000049117873,0.000050566003,0.000052867126,0.004507245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991184,0.00037582754,0.000043306587,0.0001230975,0.00011513486,0.00022412023],"domain_scores_gemma":[0.99672073,0.0024572648,0.00019000706,0.00007925146,0.00031868482,0.00023408073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018086564,0.0013057354,0.0023235283,0.001096271,0.0010263484,0.0035838613,0.0016046254,0.0029970577,0.0081208935],"category_scores_gemma":[0.0075946767,0.0013573774,0.00092062674,0.0009182688,0.0010556972,0.0033598694,0.0012512762,0.0018564385,0.0004669658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054822356,0.0003699815,0.0011151367,0.00013856286,0.000080638834,0.00019626254,0.0001393528,0.9477822,0.0011643746,0.020843612,0.0015823168,0.026039368],"study_design_scores_gemma":[0.000051052517,0.00013695705,0.00020949024,0.000016563012,0.00003124625,0.000025478479,0.00010834845,0.98075783,0.000674931,0.017606443,0.0003695253,0.000012036674],"about_ca_topic_score_codex":0.007192624,"about_ca_topic_score_gemma":0.0072205644,"teacher_disagreement_score":0.0081208935,"about_ca_system_score_codex":0.0024284003,"about_ca_system_score_gemma":0.0024131485,"threshold_uncertainty_score":0.027167082},"labels":[],"label_agreement":null},{"id":"W4387457430","doi":"10.1016/j.eswa.2023.121911","title":"Optimal decisions for selling on an online group buying platform in a competitive fuzzy game environment","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Vendor; Stackelberg competition; Group buying; Service (business); Computer science; Supply chain; Business; Revenue sharing; Common value auction; Revenue; Marketing; Operations research; Microeconomics; Economics","score_opus":0.061952367225664266,"score_gpt":0.2692015192040115,"score_spread":0.2072491519783472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387457430","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9072505,0.00013152463,0.078098744,0.0005218242,0.00005158094,0.00023907445,0.00013636764,0.00009559819,0.013474873],"genre_scores_gemma":[0.98694485,0.0000358467,0.010275915,0.000027618647,0.000008661741,0.000031882624,0.00003649631,0.000013662095,0.0026250067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946207,0.00018890401,0.000017922372,0.00007858149,0.00006872208,0.00018386493],"domain_scores_gemma":[0.99734455,0.0018772002,0.00014416594,0.000045338005,0.00018730656,0.0004014999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015603341,0.0008232017,0.0011748162,0.0009128964,0.00077292474,0.0023160041,0.0011346699,0.0020332113,0.0077632284],"category_scores_gemma":[0.003631165,0.0007319961,0.0007195309,0.00041049757,0.0009172183,0.002208516,0.00078578346,0.0014303201,0.0002970494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022501752,0.0009389108,0.003009962,0.0001535136,0.00012736511,0.0003863445,0.00026557097,0.93554884,0.0051331054,0.023471354,0.0015872719,0.02712752],"study_design_scores_gemma":[0.00007085596,0.00022262427,0.000647965,0.00001086518,0.000030611205,0.000015811547,0.00011589028,0.99319273,0.000532644,0.0049964394,0.00014501768,0.000018621622],"about_ca_topic_score_codex":0.011522717,"about_ca_topic_score_gemma":0.011562132,"teacher_disagreement_score":0.011522717,"about_ca_system_score_codex":0.00186998,"about_ca_system_score_gemma":0.0019041117,"threshold_uncertainty_score":0.025970638},"labels":[],"label_agreement":null},{"id":"W4387522170","doi":"10.1016/j.eswa.2023.122031","title":"A fast local citation recommendation algorithm scalable to multi-topics","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Citation; Similarity (geometry); Margin (machine learning); Information retrieval; Scalability; Space (punctuation); Artificial intelligence; Machine learning; Data mining; Data science; World Wide Web","score_opus":0.03651649654463209,"score_gpt":0.29333741079450815,"score_spread":0.2568209142498761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387522170","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04019489,0.0027724914,0.93010515,0.0009006345,0.00063662813,0.00042610467,0.0024604606,0.018795868,0.0037076846],"genre_scores_gemma":[0.13194151,0.0007835917,0.84297824,0.00033123273,0.0007197152,0.00051047764,0.006653156,0.0006752477,0.015406839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985996,0.00022600094,0.00012484886,0.00038012041,0.0005326094,0.0001368269],"domain_scores_gemma":[0.9965222,0.0011067472,0.00016876806,0.0007569974,0.0011815164,0.0002637801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012978276,0.0010577257,0.0025578532,0.0043985364,0.0014651108,0.00205652,0.0031112186,0.0021367162,0.0077194255],"category_scores_gemma":[0.0065035946,0.0007592656,0.0015115566,0.0063109454,0.00035897354,0.0025968864,0.001871017,0.0015275307,0.0070578116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054569345,0.00044481535,0.003052322,0.00027032572,0.00036707942,0.00013651916,0.00008189396,0.051632367,0.013941881,0.0034796435,0.047388047,0.8786594],"study_design_scores_gemma":[0.0002030924,0.00008901528,0.00092810864,0.000015294185,0.000112720474,0.00014543526,0.00004480163,0.97916085,0.0050193737,0.007426647,0.0068161893,0.000038431834],"about_ca_topic_score_codex":0.016240288,"about_ca_topic_score_gemma":0.035310417,"teacher_disagreement_score":0.016240288,"about_ca_system_score_codex":0.000924445,"about_ca_system_score_gemma":0.0032212455,"threshold_uncertainty_score":0.03229153},"labels":[],"label_agreement":null},{"id":"W4387537438","doi":"10.1016/j.eswa.2023.121923","title":"Unified embedding and clustering","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"European Commission","keywords":"Cluster analysis; Embedding; Computer science; Correlation clustering; Artificial intelligence; Clustering high-dimensional data; CURE data clustering algorithm; Manifold (fluid mechanics); Pattern recognition (psychology); Data mining","score_opus":0.022415870151486193,"score_gpt":0.279926667443693,"score_spread":0.2575107972922068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387537438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034777948,0.0005635954,0.9927874,0.00015431664,0.000082849205,0.000027549178,0.00022215112,0.0007096196,0.0019746344],"genre_scores_gemma":[0.19749388,0.0013308945,0.77061296,0.00024529197,0.0002941001,0.00023111804,0.0033283236,0.00092730834,0.025536153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985481,0.00039749293,0.000088887085,0.0005450267,0.00032293366,0.00009751663],"domain_scores_gemma":[0.9986204,0.00027430578,0.00007789257,0.00065650477,0.00032564308,0.000045354504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010069751,0.0011919161,0.0016120785,0.0023670685,0.0009004248,0.0018346083,0.001913414,0.0016275506,0.005712301],"category_scores_gemma":[0.00396371,0.0008387947,0.0013163358,0.002805858,0.00094599614,0.003051304,0.0027127992,0.001698504,0.004282454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017385934,0.00007979508,0.0005077473,0.00021490868,0.00016508481,0.00009671766,0.00019440208,0.11646108,0.008283883,0.18695462,0.019231088,0.6676369],"study_design_scores_gemma":[0.000013326492,0.00005578836,0.0005959689,0.00003457361,0.000046270645,0.00015319613,0.000075215634,0.80587095,0.0052371696,0.1704637,0.017416446,0.000037332822],"about_ca_topic_score_codex":0.0034420223,"about_ca_topic_score_gemma":0.004408573,"teacher_disagreement_score":0.005712301,"about_ca_system_score_codex":0.0007806203,"about_ca_system_score_gemma":0.0006950301,"threshold_uncertainty_score":0.019109547},"labels":[],"label_agreement":null},{"id":"W4387778634","doi":"10.1016/j.eswa.2023.122151","title":"GAF-Net: Graph attention fusion network for multi-view semi-supervised classification","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Artificial intelligence; Embedding; Graph; Machine learning; Graph embedding; Pattern recognition (psychology); Sensor fusion; Data mining; Theoretical computer science","score_opus":0.06728185403664276,"score_gpt":0.30637809189961035,"score_spread":0.23909623786296758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387778634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015544388,0.0010791338,0.9668743,0.00033567025,0.00021710232,0.00016721724,0.0011644841,0.0126148965,0.0020027936],"genre_scores_gemma":[0.34967726,0.0007197179,0.6288664,0.0008463438,0.0002119237,0.00041297454,0.006558107,0.00084509735,0.011862201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994192,0.00011607181,0.000020956393,0.00024130337,0.00011739301,0.00008515026],"domain_scores_gemma":[0.99924743,0.00023275107,0.000049121925,0.00016407084,0.00024281372,0.00006394206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012714838,0.0017019924,0.0016188495,0.0017158901,0.00083927275,0.00088959927,0.0030715156,0.0028104123,0.00511208],"category_scores_gemma":[0.002586402,0.000708071,0.0012666417,0.0015724286,0.0005800552,0.0018615996,0.0020472119,0.0023665675,0.0025675308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038387658,0.0003418208,0.0011344238,0.00015942023,0.00026943,0.00013997268,0.00009827423,0.11484374,0.00923381,0.005513224,0.032750502,0.8351315],"study_design_scores_gemma":[0.000011552775,0.00004533156,0.00025928594,0.000011168048,0.00002264667,0.000036204154,0.00001276194,0.9891104,0.0025105989,0.0062980354,0.0016697652,0.000012200476],"about_ca_topic_score_codex":0.020511637,"about_ca_topic_score_gemma":0.03155181,"teacher_disagreement_score":0.020511637,"about_ca_system_score_codex":0.001406351,"about_ca_system_score_gemma":0.0012974185,"threshold_uncertainty_score":0.04078448},"labels":[],"label_agreement":null},{"id":"W4387827378","doi":"10.1016/j.eswa.2023.122209","title":"Measurement of adverse cosmesis in breast cancer: A deep learning approach","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University; Juravinski Hospital; Juravinski Cancer Centre","funders":"Hamilton Health Sciences Foundation","keywords":"Cosmesis; Artificial intelligence; Computer science; Support vector machine; Preprocessor; Receiver operating characteristic; Breast cancer; Medicine; Machine learning; Medical physics; Cancer; Internal medicine","score_opus":0.021603929172689128,"score_gpt":0.2544504878006874,"score_spread":0.23284655862799827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387827378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.601543,0.0067087123,0.38389656,0.0009838549,0.0001267609,0.00031739374,0.0018308182,0.0011345211,0.0034583586],"genre_scores_gemma":[0.93896234,0.00091433566,0.05741161,0.00022295598,0.000059877962,0.00022505636,0.0011950488,0.00002819055,0.0009805064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995968,0.0001169503,0.00004114533,0.00010319844,0.00009409757,0.000047842757],"domain_scores_gemma":[0.999223,0.000365546,0.00014586769,0.000050586885,0.00017075484,0.000044157183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001238346,0.0007937322,0.0005942428,0.0010079257,0.00013713687,0.0005179383,0.0007264059,0.00062934787,0.0004793717],"category_scores_gemma":[0.0022304347,0.00022490132,0.0005695267,0.0006429961,0.0002359587,0.00038993492,0.000606549,0.00075522385,0.000121170655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009138107,0.0007313651,0.061065055,0.00046457772,0.000489116,0.00020983498,0.000103912906,0.32309523,0.013803658,0.0008210038,0.0039949426,0.5943075],"study_design_scores_gemma":[0.000042817937,0.00047041892,0.018365018,0.000056261655,0.00012032088,0.00016826832,0.000042893633,0.97268564,0.0050340747,0.00198806,0.001002653,0.000023491939],"about_ca_topic_score_codex":0.0026578861,"about_ca_topic_score_gemma":0.0031896464,"teacher_disagreement_score":0.0026578861,"about_ca_system_score_codex":0.0007040315,"about_ca_system_score_gemma":0.00047803562,"threshold_uncertainty_score":0.0065490603},"labels":[],"label_agreement":null},{"id":"W4388036504","doi":"10.1016/j.eswa.2023.122380","title":"Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart Cities: A comprehensive review","year":2023,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":159,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"National University of Computer and Emerging Sciences","keywords":"Smart city; Automation; Computer science; Home automation; Sustainability; Process (computing); Knowledge management; Data science; Architectural engineering; Engineering management; Process management; Business; Internet of Things; Engineering; Computer security; Telecommunications","score_opus":0.0464812757838032,"score_gpt":0.30301551111144154,"score_spread":0.25653423532763836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388036504","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00004489126,0.99924624,0.000070986134,0.0001813144,0.000106542175,0.0000048915826,0.000012809367,0.0000023173593,0.00032996936],"genre_scores_gemma":[0.00046418465,0.99886775,0.00013684446,0.0002456967,0.00010318358,0.000006026349,0.000015513438,8.235452e-7,0.00015998709],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993974,0.00013422784,0.000109685185,0.000106267544,0.00020096825,0.000051440435],"domain_scores_gemma":[0.9975459,0.0015927712,0.0002933768,0.000035218694,0.00045311407,0.00007966564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018143533,0.0011623928,0.0021579321,0.0038238696,0.0003297655,0.0019817594,0.0011425442,0.0024118046,0.0057890466],"category_scores_gemma":[0.0031695776,0.00052921387,0.0011475824,0.0040572193,0.0006201415,0.0029117202,0.0012222072,0.0015667478,0.0015272719],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008698005,0.00008393325,0.00027926275,0.090942256,0.00034689708,0.00011188555,0.00011604536,0.00039197187,0.0005516467,0.0037354378,0.031041866,0.8723119],"study_design_scores_gemma":[0.000082075916,0.00015701473,0.0017862988,0.046096966,0.001295539,0.0005938259,0.00020701747,0.00022121012,0.00039102868,0.0026254125,0.94648635,0.000057231053],"about_ca_topic_score_codex":0.0024957743,"about_ca_topic_score_gemma":0.0059877983,"teacher_disagreement_score":0.0057890466,"about_ca_system_score_codex":0.0008034403,"about_ca_system_score_gemma":0.0027968944,"threshold_uncertainty_score":0.019366264},"labels":[],"label_agreement":null},{"id":"W4388099759","doi":"10.1016/j.eswa.2023.122156","title":"Financial fraud detection using graph neural networks: A systematic review","year":2023,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Artificial neural network; Artificial intelligence; Machine learning; Theoretical computer science","score_opus":0.06262470066207466,"score_gpt":0.33965272587246653,"score_spread":0.2770280252103919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388099759","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006347402,0.9980107,0.00040981692,0.00036046695,0.00010528412,0.00007928715,0.00017396153,0.000008074073,0.00021775033],"genre_scores_gemma":[0.0075950976,0.99048656,0.001146335,0.00036448453,0.0000832348,0.00007258823,0.00015598089,0.00000446777,0.00009113003],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.996783,0.0011005884,0.00088404404,0.00034860751,0.00081408443,0.00006962369],"domain_scores_gemma":[0.9823139,0.013677509,0.0022332082,0.00028437283,0.0013404713,0.00015050633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060892943,0.0013439283,0.0048832432,0.008125808,0.0004024787,0.002039836,0.0021882816,0.0015206315,0.0034408823],"category_scores_gemma":[0.031335488,0.0005719428,0.004421169,0.0068471846,0.00083324185,0.002563997,0.0012376463,0.0011811373,0.00039823906],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003202207,0.00009157657,0.0026966766,0.36980283,0.009754799,0.00010903181,0.00010321703,0.0007231575,0.0001595294,0.0008858114,0.009572819,0.60578036],"study_design_scores_gemma":[0.00065568957,0.00072477513,0.011188632,0.7553541,0.09850694,0.0014395274,0.00049698254,0.0026843625,0.0008203189,0.006581341,0.12137344,0.0001738553],"about_ca_topic_score_codex":0.0040515317,"about_ca_topic_score_gemma":0.014937457,"teacher_disagreement_score":0.008125808,"about_ca_system_score_codex":0.0014177597,"about_ca_system_score_gemma":0.004939363,"threshold_uncertainty_score":0.032203674},"labels":[],"label_agreement":null},{"id":"W4388153871","doi":"10.1016/j.eswa.2023.122402","title":"SRTNet: Scanning, Reading, and Thinking Network for myocardial infarction detection and localization","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Overfitting; Computer science; Artificial intelligence; Pattern recognition (psychology); Deep learning; Sensitivity (control systems); Perspective (graphical); Feature (linguistics); Machine learning; Artificial neural network","score_opus":0.01389833318439415,"score_gpt":0.28176480141589183,"score_spread":0.2678664682314977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388153871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11670306,0.0027753112,0.39836898,0.0029906589,0.0013772199,0.002398426,0.118412286,0.3106239,0.046350077],"genre_scores_gemma":[0.42561176,0.0017349172,0.36796284,0.0024772815,0.0006544881,0.0030129962,0.13641347,0.0055709076,0.05656131],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967563,0.00006926749,0.000023820397,0.000085228174,0.00009807832,0.000047987553],"domain_scores_gemma":[0.9991092,0.00029473746,0.00007995441,0.0001417163,0.00021610453,0.00015834597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007484084,0.0008786065,0.00061756815,0.0017171499,0.00034017727,0.0006740283,0.0010733099,0.0006635405,0.01832179],"category_scores_gemma":[0.0025076866,0.000250255,0.00037583697,0.00072628364,0.00016550104,0.0007442027,0.0011288895,0.0006507235,0.010380067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022649632,0.00047358274,0.019840552,0.0004911839,0.00017147785,0.00048076425,0.00017427627,0.007547376,0.01367571,0.0025218297,0.39789864,0.55445963],"study_design_scores_gemma":[0.0011709201,0.00170993,0.04650181,0.00036827513,0.00070572796,0.0027919952,0.00038521286,0.58216274,0.060273748,0.017740572,0.2858851,0.00030402857],"about_ca_topic_score_codex":0.004023928,"about_ca_topic_score_gemma":0.0076452186,"teacher_disagreement_score":0.01832179,"about_ca_system_score_codex":0.00043808884,"about_ca_system_score_gemma":0.0009944973,"threshold_uncertainty_score":0.06129247},"labels":[],"label_agreement":null},{"id":"W4388294700","doi":"10.1016/j.eswa.2023.122335","title":"Lévy Arithmetic Algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Algorithm; Metaheuristic; Mathematics; Benchmark (surveying); Mathematical optimization; Computer science; Arithmetic; Optimization problem","score_opus":0.015276394716650077,"score_gpt":0.28143076615970714,"score_spread":0.26615437144305704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388294700","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008489339,0.00072799466,0.984874,0.00026761333,0.00018021518,0.0000382262,0.000029894636,0.00025933643,0.0051332777],"genre_scores_gemma":[0.28170586,0.0014872185,0.7061618,0.00031366604,0.00030544944,0.00024020887,0.00011020578,0.00021164202,0.009463976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999653,0.00011954651,0.0000150097785,0.000026288346,0.00016496207,0.000021209573],"domain_scores_gemma":[0.9994491,0.00023845646,0.000042695032,0.000041256135,0.00020038745,0.000028208688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083131273,0.0007009512,0.0009215662,0.001033622,0.0003677436,0.0008417472,0.0011267456,0.0014242621,0.0018070207],"category_scores_gemma":[0.0019603,0.00023451833,0.00065594446,0.001543431,0.0005632414,0.0010162615,0.0008632874,0.0011035227,0.00058984937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011186528,0.00008422457,0.00050510553,0.00012741335,0.00008956694,0.00011945624,0.000062891224,0.73182076,0.0079496885,0.06537465,0.0044078887,0.1893465],"study_design_scores_gemma":[0.000014823894,0.000033034732,0.00007816938,0.000005106996,0.000010838465,0.000028452065,0.0000043163323,0.98778915,0.0009435068,0.008616462,0.0024655168,0.000010583182],"about_ca_topic_score_codex":0.0015551524,"about_ca_topic_score_gemma":0.0014805511,"teacher_disagreement_score":0.0018070207,"about_ca_system_score_codex":0.00047030565,"about_ca_system_score_gemma":0.0008183078,"threshold_uncertainty_score":0.006045103},"labels":[],"label_agreement":null},{"id":"W4388312160","doi":"10.1016/j.eswa.2023.122303","title":"Distributionally-robust chance-constrained optimization of selective maintenance under uncertain repair duration","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mathematical optimization; Computer science; Probabilistic logic; Benchmark (surveying); Piecewise linear function; Preventive maintenance; Linear programming; Ambiguity; Mathematics; Reliability engineering; Artificial intelligence","score_opus":0.012011024546715664,"score_gpt":0.2229362957933242,"score_spread":0.21092527124660854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388312160","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07820184,0.0012027179,0.91283774,0.000988347,0.00011065916,0.000081011945,0.00045209276,0.00025212162,0.0058735115],"genre_scores_gemma":[0.9598232,0.00051344326,0.033299223,0.0001380954,0.00008774212,0.00014395328,0.00032935868,0.00016151302,0.0055034827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872845,0.00051141245,0.000058918507,0.00024957958,0.00020772687,0.00024389155],"domain_scores_gemma":[0.9922891,0.005943069,0.0007749157,0.00021740013,0.0005352188,0.00024024492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004450418,0.0016018508,0.0032678468,0.0011403554,0.00041082466,0.0019880515,0.002279029,0.0023845886,0.0023980446],"category_scores_gemma":[0.013645613,0.0015488488,0.00124635,0.001262743,0.0019334722,0.0020872392,0.0018008153,0.0016021233,0.00033255198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053530715,0.000010809186,0.0001094534,0.000040327683,0.000028896855,0.000023860843,0.000011358078,0.9932961,0.00025233303,0.0044482956,0.00019796367,0.0015270183],"study_design_scores_gemma":[0.0000071701097,0.000015013472,0.000080360296,0.0000043936884,0.000006229404,0.0000053920458,0.0000040859004,0.99729246,0.00009396566,0.0024380733,0.00004857305,0.0000042372526],"about_ca_topic_score_codex":0.00996354,"about_ca_topic_score_gemma":0.0050406507,"teacher_disagreement_score":0.00996354,"about_ca_system_score_codex":0.002305419,"about_ca_system_score_gemma":0.0020226366,"threshold_uncertainty_score":0.023536384},"labels":[],"label_agreement":null},{"id":"W4388766745","doi":"10.1016/j.eswa.2023.122500","title":"GGI-DDI: Identification for key molecular substructures by granule learning to interpret predicted drug–drug interactions","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Interpretability; Computer science; Artificial intelligence; Machine learning; Drug-drug interaction; Drug target; Drug; Training set; Key (lock); Pharmacology; Medicine","score_opus":0.009174085614773241,"score_gpt":0.3018731458323867,"score_spread":0.2926990602176135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388766745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16621044,0.0012223106,0.78852797,0.00083357486,0.0002541154,0.0006893425,0.0064120675,0.032724883,0.0031252739],"genre_scores_gemma":[0.40815273,0.00054770306,0.57885545,0.00032245956,0.00008854835,0.00044011947,0.00843428,0.0007667954,0.0023919484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997739,0.000033737262,0.000021988622,0.00007402176,0.000061628816,0.000034757282],"domain_scores_gemma":[0.99940383,0.00022442063,0.000093398936,0.00012931578,0.00009057043,0.00005847014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092985213,0.0011504758,0.0013476375,0.0018822522,0.00035530733,0.0012196765,0.0015113645,0.0009899777,0.0030496016],"category_scores_gemma":[0.0024171579,0.00034309505,0.0010258758,0.0012197805,0.00057398446,0.0010806152,0.0015219193,0.0013754594,0.0012188641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001574824,0.00069294235,0.019742116,0.0008623285,0.00046814847,0.0006259037,0.00020077443,0.09486913,0.07122028,0.01035992,0.028187167,0.7711964],"study_design_scores_gemma":[0.00014300276,0.00024892506,0.0030003237,0.000036206173,0.00009098906,0.00018211539,0.00005556012,0.9623157,0.019910866,0.009755161,0.0042236038,0.000037581813],"about_ca_topic_score_codex":0.0015883878,"about_ca_topic_score_gemma":0.0023562394,"teacher_disagreement_score":0.0030496016,"about_ca_system_score_codex":0.0005950349,"about_ca_system_score_gemma":0.001218669,"threshold_uncertainty_score":0.010201991},"labels":[],"label_agreement":null},{"id":"W4388773811","doi":"10.1016/j.eswa.2023.122582","title":"A machine learning tool for collecting and analyzing subjective road safety data from Twitter","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Support vector machine; Naive Bayes classifier; Random forest; Artificial intelligence; Machine learning; Crowdsourcing; Classifier (UML); Social media; World Wide Web","score_opus":0.05363648456002865,"score_gpt":0.3167575060949625,"score_spread":0.2631210215349339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388773811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20101772,0.00046212008,0.61565214,0.0012606948,0.00040935187,0.0034360606,0.09401327,0.07014266,0.013605963],"genre_scores_gemma":[0.2939183,0.00027852049,0.6374465,0.00037407948,0.00026673643,0.0031358325,0.05457849,0.00053606543,0.009465471],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987803,0.00021230352,0.00019697865,0.0002161927,0.00049954007,0.000094650924],"domain_scores_gemma":[0.9960198,0.0019249811,0.00042219216,0.00035020325,0.0010972073,0.00018555889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001670942,0.00096252025,0.00077402964,0.0057084993,0.00085823063,0.0010728417,0.0007969167,0.0008010768,0.0042898776],"category_scores_gemma":[0.0063242903,0.00034169678,0.0005893821,0.003741586,0.00021169917,0.0018567425,0.0010382873,0.00080912415,0.004484753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006458568,0.0013258979,0.053461745,0.0010281702,0.00036544979,0.00063598715,0.0011509213,0.008451971,0.057215035,0.0030224144,0.11289562,0.7598009],"study_design_scores_gemma":[0.00019689526,0.00077587843,0.08696429,0.00019219135,0.00031205642,0.00070602953,0.0015223835,0.75663584,0.060021125,0.009328162,0.0831401,0.00020510529],"about_ca_topic_score_codex":0.0038698434,"about_ca_topic_score_gemma":0.00900937,"teacher_disagreement_score":0.0057084993,"about_ca_system_score_codex":0.0005990501,"about_ca_system_score_gemma":0.0010516865,"threshold_uncertainty_score":0.01435101},"labels":[],"label_agreement":null},{"id":"W4388923662","doi":"10.1016/j.eswa.2023.122666","title":"A comprehensive survey on applications of transformers for deep learning tasks","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":421,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformer; Artificial intelligence; Machine learning; Data science; Electrical engineering; Engineering","score_opus":0.06332082183036745,"score_gpt":0.3130403542702057,"score_spread":0.24971953243983824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388923662","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009557407,0.2035396,0.7668938,0.0011170103,0.00057340076,0.00012869498,0.00080599,0.0019357703,0.015448385],"genre_scores_gemma":[0.1982407,0.38642886,0.39370963,0.0010186264,0.0014704298,0.00028568628,0.0032401776,0.0011634914,0.014442407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99845064,0.00031232822,0.000224888,0.00025697006,0.00066216616,0.0000930959],"domain_scores_gemma":[0.9968656,0.0018911039,0.00013648605,0.00042538377,0.0006094954,0.00007186016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001957915,0.001378178,0.0011209121,0.0031919382,0.00033186248,0.0021885848,0.0014980764,0.0010703487,0.0064321514],"category_scores_gemma":[0.009170354,0.00080444803,0.0010386835,0.004989816,0.0007101179,0.003953989,0.0019552882,0.0018505246,0.0032719777],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011429489,0.000052443218,0.0008351198,0.0017797353,0.000055515986,0.00004851889,0.00004242472,0.00869375,0.0026652238,0.033312712,0.007443093,0.9449572],"study_design_scores_gemma":[0.000077929966,0.00072907616,0.0033658377,0.0021906127,0.00030327938,0.002798769,0.00022830308,0.273726,0.037419904,0.27201137,0.4070143,0.00013464347],"about_ca_topic_score_codex":0.0014126189,"about_ca_topic_score_gemma":0.0017422448,"teacher_disagreement_score":0.0064321514,"about_ca_system_score_codex":0.0007832786,"about_ca_system_score_gemma":0.0015988522,"threshold_uncertainty_score":0.021517694},"labels":[],"label_agreement":null},{"id":"W4388947366","doi":"10.1016/j.eswa.2023.122695","title":"Scale-pyramid dynamic atrous convolution for pixel-level labeling","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Pixel; Convolutional neural network; Convolution (computer science); Kernel (algebra); Artificial intelligence; Scale (ratio); Granularity; Upsampling; Exploit; Pattern recognition (psychology); Algorithm; Image (mathematics); Artificial neural network; Mathematics","score_opus":0.030097741912789345,"score_gpt":0.2960657762361329,"score_spread":0.26596803432334354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388947366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006623941,0.0001248561,0.9899301,0.00007532828,0.000023842638,0.000027117638,0.000103232924,0.0014454192,0.0016460948],"genre_scores_gemma":[0.223159,0.00034300206,0.7698502,0.00017931551,0.000037276503,0.000075516575,0.0006440545,0.00031650328,0.00539512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967,0.000036744455,0.000015950252,0.000087030916,0.00013506712,0.00005517227],"domain_scores_gemma":[0.99968195,0.000056220622,0.000023055061,0.00012071223,0.00009028719,0.000027795604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043320426,0.0004825773,0.0005638836,0.000644593,0.0003643597,0.00087860145,0.0010972102,0.0008425552,0.005028032],"category_scores_gemma":[0.0010861586,0.000310324,0.00057583407,0.0011151986,0.0003987141,0.00086100155,0.0010846164,0.00094660616,0.002010056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020913561,0.00013180173,0.0006522597,0.00013033391,0.00006436467,0.00010308157,0.0000972061,0.06300973,0.08610965,0.027701247,0.00781869,0.81397253],"study_design_scores_gemma":[0.0000070346778,0.000032089207,0.0003981105,0.000009938308,0.000014461201,0.00012895375,0.000017974236,0.95800513,0.02656279,0.009535701,0.00527611,0.000011810184],"about_ca_topic_score_codex":0.0072159846,"about_ca_topic_score_gemma":0.012763212,"teacher_disagreement_score":0.0072159846,"about_ca_system_score_codex":0.00086111407,"about_ca_system_score_gemma":0.0011257597,"threshold_uncertainty_score":0.01682049},"labels":[],"label_agreement":null},{"id":"W4389071095","doi":"10.1016/j.eswa.2023.122754","title":"A new tree-based approach to mine sequential patterns","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Pruning; Data mining; Tree (set theory); Sequence database; Depth-first search; Set (abstract data type); Heuristic; Search tree; Sequence (biology); Database; Artificial intelligence; Search algorithm; Algorithm; Mathematics","score_opus":0.03183737925473632,"score_gpt":0.27552079480129293,"score_spread":0.24368341554655662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389071095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005603025,0.0006924522,0.9887656,0.00020898435,0.00013069976,0.00022291354,0.0014979966,0.001834117,0.00104423],"genre_scores_gemma":[0.037067108,0.0005280755,0.9558922,0.00019587648,0.000115758834,0.00024376619,0.0031786265,0.00017439967,0.0026040834],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806315,0.00022083396,0.00022135372,0.0005074439,0.00088853197,0.00009869997],"domain_scores_gemma":[0.9964855,0.0015101156,0.0002653528,0.00037270808,0.0011773261,0.0001890106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011462719,0.00095078745,0.0014454954,0.005181421,0.00091498275,0.002179546,0.0017390788,0.0014531147,0.0034626487],"category_scores_gemma":[0.006276879,0.00053296465,0.0014876674,0.006890525,0.00047414683,0.0032197132,0.0012171987,0.0014413998,0.0025420033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035993714,0.0003879938,0.0059992247,0.0007802835,0.00042321024,0.0005842296,0.0003792069,0.020975605,0.026327137,0.017863039,0.024713349,0.90120685],"study_design_scores_gemma":[0.00013103218,0.00037678378,0.0034991251,0.00020269709,0.00040791126,0.0020647277,0.00023104248,0.86252236,0.010699335,0.06888806,0.050857387,0.00011958478],"about_ca_topic_score_codex":0.004251118,"about_ca_topic_score_gemma":0.007826901,"teacher_disagreement_score":0.005181421,"about_ca_system_score_codex":0.00044373464,"about_ca_system_score_gemma":0.0018289578,"threshold_uncertainty_score":0.011583686},"labels":[],"label_agreement":null},{"id":"W4389195994","doi":"10.1016/j.eswa.2023.122710","title":"A multi-level wavelet-based underwater image enhancement network with color compensation prior","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Wavelet; Wavelet transform; Pattern recognition (psychology); Normalization (sociology); Frequency domain; Color image; Image processing; Image (mathematics)","score_opus":0.03348413158142738,"score_gpt":0.2829039192260364,"score_spread":0.24941978764460904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389195994","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027008634,0.00025043136,0.96935844,0.000090547954,0.000045953726,0.000034011613,0.000045274774,0.00042182562,0.002744872],"genre_scores_gemma":[0.34923154,0.0006856205,0.63754684,0.0001290645,0.000054576223,0.00007779737,0.00020006397,0.00006693767,0.012007539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998406,0.000019279698,0.0000065952786,0.000039300325,0.000075818476,0.000018367364],"domain_scores_gemma":[0.9998555,0.000029957151,0.000014939165,0.000020668058,0.00006732419,0.000011641049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023407713,0.00037624995,0.00039203544,0.00038417467,0.00022630012,0.00032384225,0.00060959737,0.00040234847,0.0017577215],"category_scores_gemma":[0.0003746865,0.00023775313,0.00028462242,0.00038655155,0.00020722648,0.00070098095,0.0006368066,0.00046710603,0.0007158056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003680075,0.00016893176,0.0009539954,0.0001236733,0.000050665993,0.00015293244,0.0000698413,0.07675997,0.3070326,0.0051481714,0.0027067102,0.60646445],"study_design_scores_gemma":[0.000013871684,0.00012305398,0.00076329964,0.000015259433,0.000040096424,0.00016715036,0.00001746159,0.9147267,0.079710625,0.000773243,0.0036282067,0.000021073345],"about_ca_topic_score_codex":0.002294213,"about_ca_topic_score_gemma":0.0043569426,"teacher_disagreement_score":0.002294213,"about_ca_system_score_codex":0.00029340334,"about_ca_system_score_gemma":0.00048045907,"threshold_uncertainty_score":0.0058801174},"labels":[],"label_agreement":null},{"id":"W4389264937","doi":"10.1016/j.eswa.2023.122749","title":"Computer vision defect detection on unseen backgrounds for manufacturing inspection","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Deep learning; Visual inspection; Task (project management); Machine learning; Variety (cybernetics); Object detection; Parameterized complexity; Pattern recognition (psychology); Computer vision; Engineering","score_opus":0.01970219987755033,"score_gpt":0.2603581894886739,"score_spread":0.24065598961112356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389264937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3288759,0.0020097748,0.6621215,0.0002886018,0.0001427514,0.00008919406,0.0003493026,0.0025210846,0.0036019657],"genre_scores_gemma":[0.78056115,0.00082822685,0.21355486,0.00015299894,0.000062252046,0.000033916487,0.0006654107,0.00024017213,0.0039009764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953043,0.000059080026,0.000014283439,0.00010913786,0.00022403696,0.000063038504],"domain_scores_gemma":[0.9990915,0.0003099996,0.00009176032,0.00012738287,0.00029999606,0.000079371275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046824818,0.0007020526,0.0006634984,0.0017726785,0.0002835685,0.00070411473,0.0007309166,0.0009591504,0.0014130332],"category_scores_gemma":[0.0016892265,0.00033540593,0.00039367747,0.00065889145,0.00032797083,0.0006526709,0.00075122726,0.00073666614,0.00074438006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008047197,0.00035968603,0.003551555,0.0003509306,0.00006516256,0.00036722817,0.00010389895,0.022747258,0.49565747,0.0014192293,0.0032542374,0.47131863],"study_design_scores_gemma":[0.000027181775,0.00041336866,0.0124056265,0.00004181761,0.00008352287,0.0008157799,0.00007251149,0.831603,0.14967585,0.0014888591,0.003343913,0.000028638713],"about_ca_topic_score_codex":0.0014234404,"about_ca_topic_score_gemma":0.0026515322,"teacher_disagreement_score":0.0017726785,"about_ca_system_score_codex":0.0002936048,"about_ca_system_score_gemma":0.00048665437,"threshold_uncertainty_score":0.004727125},"labels":[],"label_agreement":null},{"id":"W4389396137","doi":"10.1016/j.eswa.2023.122856","title":"Model checking combined trust and commitments in Multi-Agent Systems","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Model checking; Computation tree logic; Theoretical computer science; Temporal logic; Binary decision diagram; Formalism (music); Computation; Probabilistic CTL; Subjective logic; Algorithm; Artificial intelligence; Probabilistic logic","score_opus":0.05874410564406512,"score_gpt":0.2932519827685964,"score_spread":0.23450787712453125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389396137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15139703,0.00020885476,0.84270775,0.0012423799,0.00013901852,0.00023314414,0.00021641128,0.0012361757,0.0026192472],"genre_scores_gemma":[0.9421321,0.000055145796,0.05627429,0.00008430305,0.00003673348,0.00011335272,0.00013809891,0.00010341938,0.0010625066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9846044,0.008055381,0.00093544845,0.002126514,0.003000732,0.0012773991],"domain_scores_gemma":[0.936089,0.04748234,0.003885781,0.006463021,0.0045006094,0.0015793276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012142336,0.0012454226,0.002125404,0.0016595379,0.0015984161,0.0053477837,0.004261356,0.0025187954,0.00283901],"category_scores_gemma":[0.0651877,0.001560297,0.0021371096,0.0012844134,0.0041941386,0.0104313,0.00664284,0.0049021505,0.00031536623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085781375,0.00018476041,0.0030318382,0.00022135314,0.00030734713,0.00042406862,0.0005583098,0.8689429,0.0020866701,0.10236222,0.0009554857,0.02006722],"study_design_scores_gemma":[0.00004423467,0.00002704393,0.00009186377,0.000012196016,0.000030398654,0.000020116577,0.00004998893,0.9535885,0.0009113059,0.044994336,0.00021782686,0.000012195526],"about_ca_topic_score_codex":0.013832327,"about_ca_topic_score_gemma":0.014034117,"teacher_disagreement_score":0.013832327,"about_ca_system_score_codex":0.0037129952,"about_ca_system_score_gemma":0.004432433,"threshold_uncertainty_score":0.06421554},"labels":[],"label_agreement":null},{"id":"W4389472577","doi":"10.1016/j.eswa.2023.122676","title":"Top2Label: Explainable zero shot topic labelling using knowledge graphs","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Natural language processing; Graph; Sentence; Language model; Knowledge graph; Semantic similarity; Information retrieval; Theoretical computer science","score_opus":0.06576893595542228,"score_gpt":0.3079112960256666,"score_spread":0.2421423600702443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389472577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058355336,0.000777616,0.8437394,0.0002790982,0.00026810062,0.00028277835,0.02097715,0.12299141,0.0048488663],"genre_scores_gemma":[0.08054455,0.000606363,0.81147665,0.00036208573,0.00017180627,0.0005573466,0.08514921,0.010715685,0.010416256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985415,0.0002976097,0.0000628984,0.00059857935,0.00033545826,0.00016401576],"domain_scores_gemma":[0.99765825,0.0010630785,0.00009016296,0.000749165,0.00032607015,0.00011324489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001430656,0.0025390417,0.0014007405,0.0041318056,0.0015019809,0.0032467702,0.003613044,0.003356816,0.026176536],"category_scores_gemma":[0.007411274,0.0013632169,0.0023792202,0.0029559052,0.0006429465,0.0045011695,0.004063828,0.002849583,0.014485311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010412239,0.00032789822,0.0015280048,0.0015016799,0.000360536,0.00040260077,0.0005965977,0.020479841,0.014152605,0.018675929,0.23270497,0.7082282],"study_design_scores_gemma":[0.00023789342,0.00013796001,0.001498957,0.00031844713,0.00021785364,0.00041777207,0.00034182705,0.7209419,0.026937407,0.11899109,0.12981753,0.00014133997],"about_ca_topic_score_codex":0.01282687,"about_ca_topic_score_gemma":0.027108569,"teacher_disagreement_score":0.026176536,"about_ca_system_score_codex":0.0015774852,"about_ca_system_score_gemma":0.0017863757,"threshold_uncertainty_score":0.08756924},"labels":[],"label_agreement":null},{"id":"W4389476392","doi":"10.1016/j.eswa.2023.122824","title":"Channel strategy and the management of fake reviews in a catering platform service supply chain","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Supply chain; Supply chain management; Channel (broadcasting); Computer science; Service (business); Service management; Process management; Business; Chain (unit); Telecommunications; Computer security; Marketing","score_opus":0.024132704511995222,"score_gpt":0.25664329594254204,"score_spread":0.2325105914305468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389476392","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9341176,0.00065932394,0.044853315,0.0027480202,0.00009707477,0.00018646223,0.00022319035,0.00032463938,0.016790342],"genre_scores_gemma":[0.9973489,0.000061218925,0.0009311649,0.000029310673,0.000014925408,0.000012886278,0.000013823428,0.000007782482,0.0015799842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969041,0.0010865446,0.00013885484,0.00046628018,0.00064027344,0.0007639537],"domain_scores_gemma":[0.9513617,0.030435102,0.009263679,0.0018570438,0.0050029303,0.0020795942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0070510344,0.00048682632,0.0008981242,0.0022766364,0.0013643554,0.005248786,0.0010366302,0.003247962,0.0068803723],"category_scores_gemma":[0.03513547,0.0005132963,0.0003985129,0.0013092534,0.0016846498,0.004543402,0.0013351331,0.0013779701,0.00078182673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006219682,0.0014020468,0.10823786,0.0006593272,0.00051151897,0.0047175586,0.0037647814,0.45224693,0.028540716,0.18681516,0.014370285,0.19251408],"study_design_scores_gemma":[0.00018220393,0.0009657879,0.018601904,0.00012647225,0.00018812867,0.0005129434,0.002033222,0.89125425,0.0065342123,0.07570539,0.003700113,0.00019532275],"about_ca_topic_score_codex":0.0050068623,"about_ca_topic_score_gemma":0.0043428456,"teacher_disagreement_score":0.0070510344,"about_ca_system_score_codex":0.0034030797,"about_ca_system_score_gemma":0.0033457184,"threshold_uncertainty_score":0.037289858},"labels":[],"label_agreement":null},{"id":"W4389611245","doi":"10.1016/j.eswa.2023.122902","title":"Integrating social media data: Venues, groups and activities","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Interoperability; Ontology; Social media; Variety (cybernetics); Task (project management); Semantics (computer science); Data science; World Wide Web; Data integration; Ontology-based data integration; Knowledge management; Database; Semantic Web; Artificial intelligence","score_opus":0.0336398441966502,"score_gpt":0.3020800828935923,"score_spread":0.2684402386969421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389611245","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23662543,0.004064201,0.25278294,0.002202857,0.001273091,0.0009364833,0.44480377,0.008423356,0.048887845],"genre_scores_gemma":[0.58541375,0.0021866425,0.20898187,0.00024318883,0.00092385843,0.0008553376,0.1857461,0.00063197187,0.015017278],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822694,0.00041396936,0.00021993527,0.00040597253,0.00061803736,0.000115179006],"domain_scores_gemma":[0.99587435,0.0017813531,0.00042058865,0.0008204773,0.00075894996,0.00034428455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010790213,0.00088399247,0.0007495318,0.015283402,0.0006588924,0.0026299777,0.00089837966,0.0008710996,0.0061313733],"category_scores_gemma":[0.0062036556,0.00033454227,0.00078502006,0.01274781,0.00025720106,0.0041986876,0.0018870636,0.0008912792,0.0040721153],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059626787,0.0006304093,0.2309464,0.0025519896,0.0008603807,0.00056463305,0.0030642247,0.012381225,0.01511459,0.013644956,0.077204056,0.6424408],"study_design_scores_gemma":[0.000053441836,0.00021191011,0.2978126,0.0008113311,0.0008896014,0.0013100815,0.011872261,0.16734827,0.019899078,0.052348826,0.44719028,0.0002524162],"about_ca_topic_score_codex":0.0120938085,"about_ca_topic_score_gemma":0.032649714,"teacher_disagreement_score":0.015283402,"about_ca_system_score_codex":0.00066409976,"about_ca_system_score_gemma":0.0007107456,"threshold_uncertainty_score":0.024046838},"labels":[],"label_agreement":null},{"id":"W4389752793","doi":"10.1016/j.eswa.2023.122946","title":"MSER: Multimodal speech emotion recognition using cross-attention with deep fusion","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":137,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Ministry of Science and ICT, South Korea; State Fund for Fundamental Research of Ukraine","keywords":"Computer science; Discriminative model; Robustness (evolution); Speech recognition; Artificial intelligence; Encoder; Feature (linguistics); Fusion mechanism; Pattern recognition (psychology); Fusion","score_opus":0.05091831193250718,"score_gpt":0.3489223053887625,"score_spread":0.29800399345625533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389752793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08601354,0.003077655,0.8454103,0.0006198844,0.0013041819,0.00052265153,0.008139846,0.045238364,0.009673579],"genre_scores_gemma":[0.41958198,0.0012329206,0.5281466,0.0012673824,0.00046126667,0.00081019115,0.018408626,0.0015680868,0.028523015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948597,0.000074736614,0.000026326501,0.00017307718,0.00014215897,0.000097709],"domain_scores_gemma":[0.99971944,0.0000863225,0.000017952889,0.00004842877,0.00009779307,0.000029986459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009895015,0.001522415,0.001091687,0.00084490725,0.00032313503,0.0007719896,0.0010008619,0.0010898152,0.010262561],"category_scores_gemma":[0.001120752,0.00036762338,0.0009585264,0.00060772774,0.00020800365,0.0010999354,0.0020022895,0.001287875,0.0054148273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000923071,0.00044405734,0.0015172146,0.00020955228,0.0002854437,0.00023064892,0.000085583066,0.009229257,0.09884325,0.0015713762,0.033186954,0.85347366],"study_design_scores_gemma":[0.00011945078,0.00059616077,0.010615533,0.00006639655,0.00022655996,0.00047960316,0.00010840226,0.8657713,0.098550476,0.0054787737,0.017860431,0.00012687367],"about_ca_topic_score_codex":0.0034619,"about_ca_topic_score_gemma":0.0061952793,"teacher_disagreement_score":0.010262561,"about_ca_system_score_codex":0.00037491604,"about_ca_system_score_gemma":0.00045101694,"threshold_uncertainty_score":0.03433162},"labels":[],"label_agreement":null},{"id":"W4389841773","doi":"10.1016/j.eswa.2023.122975","title":"An innovative unsupervised gait recognition based tracking system for safeguarding large-scale nature reserves in complex terrain","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Terrain; Computer science; Gait; Scale (ratio); Safeguarding; Tracking (education); Artificial intelligence; Pattern recognition (psychology); Machine learning; Computer vision; Physical medicine and rehabilitation; Geography; Cartography; Medicine; Psychology","score_opus":0.030750003186386717,"score_gpt":0.28780132343354103,"score_spread":0.2570513202471543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389841773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13241906,0.0003154605,0.85111576,0.00014505384,0.00033180963,0.00025309107,0.00082404655,0.010658002,0.0039376845],"genre_scores_gemma":[0.5520761,0.0002489756,0.43434522,0.00035096117,0.000109424735,0.00032352755,0.0013146356,0.00014481015,0.01108632],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998512,0.00000950291,0.000010044495,0.000056512385,0.000054943346,0.000017806864],"domain_scores_gemma":[0.99978274,0.000027499082,0.000032472766,0.000024707067,0.00010981007,0.00002289528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002003194,0.00039396805,0.00051581534,0.0008038905,0.0002800513,0.0003679414,0.00076474296,0.00062107365,0.0019661728],"category_scores_gemma":[0.0003666595,0.00020139803,0.00021825393,0.00055189943,0.00013668073,0.00038329637,0.00037405334,0.00028803304,0.0011957341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035807275,0.00034214265,0.004928311,0.00015092191,0.0000728502,0.00022106282,0.00007257359,0.00790228,0.2393517,0.0005063226,0.007804197,0.73828965],"study_design_scores_gemma":[0.00014879165,0.0008523953,0.04955318,0.00005374377,0.00019045701,0.0012332937,0.00008521478,0.8221783,0.111408845,0.0009881557,0.013201414,0.00010617121],"about_ca_topic_score_codex":0.002451319,"about_ca_topic_score_gemma":0.0065072775,"teacher_disagreement_score":0.002451319,"about_ca_system_score_codex":0.00019802629,"about_ca_system_score_gemma":0.0004606678,"threshold_uncertainty_score":0.0065775514},"labels":[],"label_agreement":null},{"id":"W4390022205","doi":"10.1016/j.eswa.2023.122960","title":"Cross-database and cross-channel electrocardiogram arrhythmia heartbeat classification based on unsupervised domain adaptation","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Centroid; Artificial intelligence; Pattern recognition (psychology); Domain (mathematical analysis); Heartbeat; Feature (linguistics); Deep learning; Data mining; Machine learning; Mathematics","score_opus":0.032344854929741124,"score_gpt":0.32389968114730394,"score_spread":0.2915548262175628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390022205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22582428,0.001015601,0.7673076,0.00014259081,0.00019983327,0.000102944556,0.00048240722,0.0020895456,0.002835247],"genre_scores_gemma":[0.8417273,0.0003693207,0.15136246,0.00013776725,0.00008236883,0.00009116738,0.00208684,0.00014218182,0.004000603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926716,0.00021445607,0.000052250973,0.00022532039,0.00014449413,0.00009625072],"domain_scores_gemma":[0.99890137,0.00038144056,0.00005661954,0.00021613478,0.0003996664,0.000044795124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012948556,0.00049918424,0.00086741755,0.0008425332,0.00028728734,0.00073997054,0.0005852267,0.0007423359,0.0014405698],"category_scores_gemma":[0.0020145387,0.00015204275,0.00081503193,0.0008189853,0.00020924529,0.0007288807,0.00080696284,0.00067242683,0.0010651164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010942256,0.00069715077,0.009859387,0.000100649595,0.00033056756,0.00019235884,0.000118894066,0.05499494,0.042850368,0.0012312327,0.004104984,0.88442516],"study_design_scores_gemma":[0.000019885816,0.00012724247,0.014345369,0.000007885228,0.00007209866,0.0002073563,0.00006463923,0.9714806,0.011933789,0.0007880508,0.0009263016,0.00002678758],"about_ca_topic_score_codex":0.002137055,"about_ca_topic_score_gemma":0.0029955392,"teacher_disagreement_score":0.002137055,"about_ca_system_score_codex":0.00019550517,"about_ca_system_score_gemma":0.00041311287,"threshold_uncertainty_score":0.006847918},"labels":[],"label_agreement":null},{"id":"W4390431290","doi":"10.1016/j.eswa.2023.123060","title":"A novel two-stage dynamic pricing model for logistics planning using an exploration–exploitation framework: A multi-armed bandit problem","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Dynamic pricing; Mathematical optimization; Demand curve; Revenue; Function (biology); Operations research; Revenue management; Economics; Microeconomics; Mathematics","score_opus":0.18176962022134283,"score_gpt":0.3574000146320499,"score_spread":0.17563039441070707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390431290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031616442,0.0008339432,0.9574918,0.0009764636,0.00012614782,0.000123989,0.0002612341,0.00021952804,0.008350273],"genre_scores_gemma":[0.863721,0.00085465726,0.11507243,0.00037344656,0.00018842993,0.0004150138,0.00036373577,0.000118926415,0.018892352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984341,0.0007533194,0.00006155327,0.00027441425,0.00017995804,0.00029660927],"domain_scores_gemma":[0.9976326,0.0016535522,0.00022034392,0.00006953712,0.00022902466,0.00019486663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029640081,0.0017055245,0.004359105,0.0011841784,0.0010241664,0.004033721,0.0044007124,0.006319917,0.0063094897],"category_scores_gemma":[0.0043903226,0.0023209343,0.0017411048,0.0020975599,0.001973946,0.0032893082,0.002581626,0.0034013644,0.0006593959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004767355,0.00005437522,0.00015967841,0.000043291016,0.00003294745,0.00006482101,0.000026877387,0.9888947,0.0001593395,0.007109554,0.0004062876,0.0030003646],"study_design_scores_gemma":[0.0000083494615,0.000010727887,0.000023480803,0.0000025333281,0.0000067536735,0.0000044658814,0.0000036060696,0.9985612,0.000016801581,0.0012758891,0.00008196942,0.0000043038244],"about_ca_topic_score_codex":0.014418401,"about_ca_topic_score_gemma":0.010598698,"teacher_disagreement_score":0.014418401,"about_ca_system_score_codex":0.001907565,"about_ca_system_score_gemma":0.0025379332,"threshold_uncertainty_score":0.02866894},"labels":[],"label_agreement":null},{"id":"W4390570875","doi":"10.1016/j.eswa.2023.123113","title":"MV-Checker: A software tool for multi-valued model checking intelligent applications with trust and commitment","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Khalifa University of Science, Technology and Research; Ministry of Higher Education, Malaysia; Concordia University; Ministry of Higher Education and Scientific Research","keywords":"Computer science; Model checking; Scalability; Computation tree logic; Domain (mathematical analysis); Software; CTL*; Theoretical computer science; Distributed computing; Formal verification; Temporal logic; Software engineering; Programming language; Database","score_opus":0.03088365301964105,"score_gpt":0.28975672822735693,"score_spread":0.25887307520771585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390570875","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045319246,0.00007662552,0.9486494,0.00007360673,0.000055050667,0.00007487491,0.00061198394,0.04441719,0.0015092994],"genre_scores_gemma":[0.32344592,0.00026101046,0.6608543,0.0003213254,0.000054364005,0.00037009074,0.002453041,0.007661223,0.0045787385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978963,0.00079987687,0.00024080566,0.00033773974,0.000562159,0.00016305585],"domain_scores_gemma":[0.9934163,0.004423017,0.0003915425,0.0011229789,0.0005421983,0.00010406831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029075076,0.0014428893,0.0011076392,0.0016926489,0.00058734475,0.001969296,0.0025076545,0.0014102169,0.01325958],"category_scores_gemma":[0.012403524,0.0012576663,0.0020355976,0.00082040566,0.0010211685,0.0034630746,0.002912551,0.0020341044,0.0021227682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017402838,0.00043910212,0.008024234,0.0027976774,0.00095955055,0.001191392,0.00068838155,0.30604672,0.03105127,0.21743716,0.04432853,0.38529572],"study_design_scores_gemma":[0.00017266639,0.00010722091,0.00040578246,0.00017807182,0.00012426368,0.0002393731,0.00004927063,0.88760686,0.022348229,0.07293089,0.015772182,0.00006513543],"about_ca_topic_score_codex":0.0031698125,"about_ca_topic_score_gemma":0.003977442,"teacher_disagreement_score":0.01325958,"about_ca_system_score_codex":0.0008832764,"about_ca_system_score_gemma":0.002033606,"threshold_uncertainty_score":0.044357717},"labels":[],"label_agreement":null},{"id":"W4390590169","doi":"10.1016/j.eswa.2023.123131","title":"APDF: An active preference-based deep forest expert system for overall survival prediction in gastric cancer","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Machine learning; Artificial intelligence; Estimator; Credibility; Feature selection; Relevance (law); Curse of dimensionality; Data mining; Statistics; Mathematics","score_opus":0.028539155342008283,"score_gpt":0.31223180197785383,"score_spread":0.28369264663584554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390590169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12359445,0.0022956429,0.8552123,0.00076429924,0.00031502388,0.00021448011,0.0033332927,0.011449937,0.0028205735],"genre_scores_gemma":[0.7528512,0.00049040717,0.23560467,0.0008430426,0.00018260264,0.00018449644,0.003618482,0.00025554092,0.0059695505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997054,0.000053332053,0.000021285154,0.00008411845,0.000083100946,0.000052722677],"domain_scores_gemma":[0.99940753,0.0002653018,0.000032695367,0.00004502083,0.00020056285,0.00004905302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009420719,0.0008342118,0.0009668372,0.0007872652,0.00029600423,0.0005037445,0.0015711496,0.0013218336,0.0026830647],"category_scores_gemma":[0.0021786417,0.0003134281,0.0006468873,0.0005290615,0.00014607134,0.0009070539,0.00087111164,0.0012100701,0.0009972573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006646437,0.00039833487,0.005348876,0.00013300915,0.00014748782,0.00016989121,0.000043442054,0.12299412,0.007077468,0.0006134459,0.019650731,0.84275854],"study_design_scores_gemma":[0.000030798354,0.00008073013,0.0006568245,0.000010415654,0.00002246786,0.000059781534,0.0000070412966,0.99545926,0.0017871307,0.0010236173,0.00085135567,0.000010580596],"about_ca_topic_score_codex":0.011214773,"about_ca_topic_score_gemma":0.019002225,"teacher_disagreement_score":0.011214773,"about_ca_system_score_codex":0.00045673782,"about_ca_system_score_gemma":0.0008767641,"threshold_uncertainty_score":0.022298992},"labels":[],"label_agreement":null},{"id":"W4390843766","doi":"10.1016/j.eswa.2024.123199","title":"A real-time anchor-free defect detector with global and local feature enhancement for surface defect detection","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Detector; Computer science; Artificial intelligence; Noise (video); Pattern recognition (psychology); Field (mathematics); Feature extraction; Mathematics","score_opus":0.007278602425634316,"score_gpt":0.23387733784913706,"score_spread":0.22659873542350273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390843766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03918581,0.0005023542,0.95619184,0.00010331189,0.00014328602,0.0000812406,0.00011947232,0.0026393016,0.0010333534],"genre_scores_gemma":[0.28930518,0.0003846655,0.7035087,0.00022630404,0.00009375315,0.000112056834,0.00041513715,0.00016363335,0.0057905833],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954647,0.000039640927,0.000019454412,0.000088620116,0.00027140547,0.00003443538],"domain_scores_gemma":[0.9994655,0.00012682448,0.000041524956,0.00008493839,0.00023990657,0.000041134394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052998593,0.0006641659,0.0010462247,0.0008945249,0.00020998057,0.00050707074,0.0011161631,0.00112592,0.0022888866],"category_scores_gemma":[0.0005892282,0.00038689587,0.00047063906,0.0005988483,0.00023175115,0.00091732916,0.00073214463,0.00058211276,0.0011910241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005319927,0.00017603592,0.0010164648,0.00015204636,0.00007073639,0.00016796487,0.000041661857,0.0038329603,0.583239,0.00069535366,0.0032861815,0.40678963],"study_design_scores_gemma":[0.00011867912,0.00087018166,0.0047782096,0.000016740021,0.00016535712,0.0015168953,0.000033392178,0.6251972,0.3561341,0.0006088346,0.010470485,0.00008990191],"about_ca_topic_score_codex":0.000569506,"about_ca_topic_score_gemma":0.001277848,"teacher_disagreement_score":0.0022888866,"about_ca_system_score_codex":0.00018577157,"about_ca_system_score_gemma":0.00043805919,"threshold_uncertainty_score":0.007657051},"labels":[],"label_agreement":null},{"id":"W4391058219","doi":"10.1016/j.eswa.2024.123289","title":"Covariance matrix adaptation evolution strategy based on correlated evolution paths with application to reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; CHEO Research Institute","keywords":"CMA-ES; Reinforcement learning; Benchmark (surveying); Evolution strategy; Computer science; Suite; Covariance matrix; Population; Mathematical optimization; Algorithm; Adaptation (eye); Path (computing); Covariance; Artificial intelligence; Evolutionary algorithm; Mathematics","score_opus":0.009727315906889298,"score_gpt":0.24972470431190918,"score_spread":0.23999738840501988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391058219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013624978,0.0001887344,0.98384017,0.000118176715,0.000055453533,0.00004400189,0.000010825868,0.00013533168,0.0019823231],"genre_scores_gemma":[0.69176924,0.00033891306,0.3023739,0.00015574248,0.00007602627,0.00034821045,0.00006416638,0.00013047106,0.0047434205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995204,0.00016473063,0.00002467106,0.0000950007,0.0001526239,0.000042590957],"domain_scores_gemma":[0.9977102,0.0014267644,0.00014997297,0.000100883364,0.0005137326,0.00009850262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014311778,0.00070626417,0.0011643409,0.0007525654,0.0005844014,0.0007610944,0.0012887985,0.0013138835,0.0020222978],"category_scores_gemma":[0.004767166,0.00047264644,0.0006926964,0.00087048346,0.00100521,0.00091258157,0.0012662158,0.0013463155,0.00022829673],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006191573,0.00007923411,0.00055133656,0.000058057987,0.00006384387,0.00008680721,0.00007380481,0.9173306,0.0018945907,0.027910389,0.0008066929,0.05108275],"study_design_scores_gemma":[0.000006379072,0.000014079774,0.00004086149,0.000002185272,0.0000047621697,0.000008990779,0.0000013538481,0.9982938,0.0001013415,0.0014251613,0.00009776851,0.000003351749],"about_ca_topic_score_codex":0.0056328527,"about_ca_topic_score_gemma":0.0037462676,"teacher_disagreement_score":0.0056328527,"about_ca_system_score_codex":0.0007432767,"about_ca_system_score_gemma":0.001547866,"threshold_uncertainty_score":0.01120013},"labels":[],"label_agreement":null},{"id":"W4391188490","doi":"10.1016/j.eswa.2024.123228","title":"MetaUSACC: Unlabeled scene adaptation for crowd counting via meta-auxiliary learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Scholarship Council","keywords":"Computer science; Task (project management); Artificial intelligence; Adaptation (eye); Machine learning; Supervised learning; Computer vision; Pattern recognition (psychology); Artificial neural network","score_opus":0.0623486884662975,"score_gpt":0.3248349720059056,"score_spread":0.2624862835396081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391188490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076869046,0.00036193302,0.9772329,0.00011093062,0.00020918708,0.0001536245,0.0005034274,0.012211595,0.0015294934],"genre_scores_gemma":[0.16742176,0.0002418343,0.8179493,0.00055587076,0.0002639982,0.00047892076,0.0051979143,0.0021728193,0.0057176384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978961,0.00050338235,0.00007090868,0.00082497596,0.00043319096,0.00027133693],"domain_scores_gemma":[0.9974342,0.000752228,0.000099189434,0.0008762561,0.0006371842,0.00020091157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025388882,0.003594511,0.0035661554,0.0028122747,0.0014228238,0.0022988205,0.00703197,0.004150737,0.006466801],"category_scores_gemma":[0.006594012,0.0015875855,0.0026674124,0.0022656992,0.0013853684,0.0035460938,0.0062124864,0.003775141,0.005221971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009998328,0.00085640536,0.0016939952,0.00035869982,0.00044036887,0.0002906566,0.00025645195,0.17876546,0.017605191,0.006152048,0.033699296,0.75888157],"study_design_scores_gemma":[0.000026076597,0.00005745516,0.00021617877,0.000021578073,0.00003257576,0.000055783712,0.000023855337,0.988751,0.0040251357,0.004522721,0.0022454557,0.000022098737],"about_ca_topic_score_codex":0.009926952,"about_ca_topic_score_gemma":0.01723789,"teacher_disagreement_score":0.009926952,"about_ca_system_score_codex":0.0010107682,"about_ca_system_score_gemma":0.0020922737,"threshold_uncertainty_score":0.021633625},"labels":[],"label_agreement":null},{"id":"W4391264353","doi":"10.1016/j.eswa.2024.123314","title":"Enhancing pavement health assessment: An attention-based approach for accurate crack detection, measurement, and mapping","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Indian Institute of Technology Delhi","keywords":"Computer science; Artificial intelligence; Data mining; Machine learning","score_opus":0.02381689299513571,"score_gpt":0.27954029867179786,"score_spread":0.25572340567666213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391264353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08420467,0.00082668447,0.9083793,0.00039486025,0.00010675492,0.00014180828,0.00024713506,0.0017079597,0.0039907685],"genre_scores_gemma":[0.80842507,0.0005451326,0.1867163,0.00030967846,0.00014529307,0.00007934019,0.0002925632,0.000117000396,0.0033696468],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939275,0.00008350778,0.000023456449,0.00018977947,0.00021724755,0.0000932089],"domain_scores_gemma":[0.9987835,0.00038818642,0.00010394463,0.000101508675,0.0005496893,0.00007306948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006740993,0.0011402316,0.0010543751,0.0023460458,0.00041048328,0.0010201072,0.0012193362,0.0012377676,0.002376572],"category_scores_gemma":[0.002455273,0.0003127416,0.00069092686,0.0008790411,0.0004003341,0.0012128593,0.0016977595,0.0008484304,0.00059019716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057756476,0.00054533524,0.0072257244,0.00034357948,0.00016410655,0.0002772,0.00027732164,0.042025324,0.1150083,0.0019852985,0.004056189,0.827514],"study_design_scores_gemma":[0.000033146982,0.00036536035,0.01531162,0.00004998181,0.00026573168,0.00035654314,0.00014376,0.9400827,0.036160976,0.0047402685,0.0024393608,0.00005070156],"about_ca_topic_score_codex":0.0074581895,"about_ca_topic_score_gemma":0.009463048,"teacher_disagreement_score":0.0074581895,"about_ca_system_score_codex":0.0005098534,"about_ca_system_score_gemma":0.00090880174,"threshold_uncertainty_score":0.014829516},"labels":[],"label_agreement":null},{"id":"W4391512963","doi":"10.1016/j.eswa.2024.123387","title":"A novel governing equation for shale gas production prediction via physics-informed neural networks","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Artificial neural network; Extrapolation; Computer science; Hyperbolic function; Production (economics); Shale gas; Unconventional oil; Oil shale; Applied mathematics; Mathematical optimization; Econometrics; Machine learning; Mathematics; Geology; Economics; Statistics; Microeconomics; Mathematical analysis","score_opus":0.027910998597894653,"score_gpt":0.2699491845586697,"score_spread":0.24203818596077503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391512963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04697127,0.0011962827,0.939585,0.0007288622,0.00017951238,0.00008358035,0.0004891011,0.00029590918,0.010470471],"genre_scores_gemma":[0.9164051,0.0012062326,0.06701227,0.0002936419,0.00014956482,0.0003204098,0.00072660233,0.000067815876,0.013818397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981576,0.000028093611,0.000013026039,0.000061697145,0.000060856237,0.000020534406],"domain_scores_gemma":[0.99976593,0.000106006766,0.000032368247,0.00001029351,0.00007641537,0.000008955823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046215602,0.00062713336,0.00063423277,0.00039885932,0.0003305316,0.000681076,0.00092550257,0.0011253807,0.0017405993],"category_scores_gemma":[0.0011530085,0.00040551848,0.00061120105,0.0004584964,0.0005141042,0.00096813176,0.0007241222,0.0012482402,0.00024826772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011893571,0.000009756346,0.0007779547,0.000031626543,0.000010262075,0.00006619962,0.000019662702,0.98615533,0.0010511864,0.0041236854,0.00038292818,0.007359496],"study_design_scores_gemma":[9.2107325e-7,0.0000012374978,0.00006053279,0.0000014210135,0.000001086154,0.0000028129577,9.4386144e-7,0.9992963,0.00006151267,0.00045807977,0.000113977556,0.0000012296956],"about_ca_topic_score_codex":0.017338287,"about_ca_topic_score_gemma":0.012471956,"teacher_disagreement_score":0.017338287,"about_ca_system_score_codex":0.0007065231,"about_ca_system_score_gemma":0.0010038571,"threshold_uncertainty_score":0.03447473},"labels":[],"label_agreement":null},{"id":"W4391589833","doi":"10.1016/j.eswa.2024.123388","title":"An efficient indoor large map global path planning for robot navigation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Motion planning; Representation (politics); Computer science; Tile; Path (computing); Robot; Any-angle path planning; Artificial intelligence; Computer vision","score_opus":0.01691856003676576,"score_gpt":0.31515997401507034,"score_spread":0.29824141397830456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391589833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004677699,0.000100161706,0.99190044,0.000040018604,0.000037135684,0.00003733725,0.00008958432,0.00152709,0.0015905298],"genre_scores_gemma":[0.1483496,0.0001319438,0.84708446,0.000050716673,0.000026397618,0.00014686963,0.00036662753,0.00020583335,0.00363752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972755,0.000036171827,0.0000087711815,0.00007187881,0.00012632809,0.00002926637],"domain_scores_gemma":[0.99985504,0.000045124707,0.00000912994,0.000030111523,0.000048698974,0.0000117594755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018114965,0.0006888394,0.0007361879,0.00056879706,0.00045553708,0.00041815703,0.0009516784,0.0005254819,0.004308035],"category_scores_gemma":[0.0005971936,0.00040179977,0.0004942074,0.00081543205,0.00028006246,0.00061506435,0.0011528537,0.00068825705,0.001050573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020192006,0.00010281173,0.00039194367,0.00014956793,0.000048071266,0.00010963441,0.000079469704,0.37178925,0.018212887,0.00852086,0.011554605,0.58883893],"study_design_scores_gemma":[0.000017409788,0.000051066516,0.00021695829,0.000004714904,0.000011278981,0.000047886526,0.00001230527,0.99120975,0.0028639084,0.002698684,0.0028563677,0.000009702388],"about_ca_topic_score_codex":0.00868507,"about_ca_topic_score_gemma":0.012829973,"teacher_disagreement_score":0.00868507,"about_ca_system_score_codex":0.00042248872,"about_ca_system_score_gemma":0.0011094428,"threshold_uncertainty_score":0.017269075},"labels":[],"label_agreement":null},{"id":"W4392135127","doi":"10.1016/j.eswa.2024.123561","title":"An efficient hybrid adaptive large neighborhood search method for the capacitated team orienteering problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Air Liquide (Canada)","funders":"","keywords":"Orienteering; Computer science; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.017482284406608904,"score_gpt":0.3035203629416793,"score_spread":0.28603807853507035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392135127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013091513,0.00023952837,0.98303014,0.00010633467,0.00007301567,0.000040497158,0.000022912469,0.00012097473,0.0032750438],"genre_scores_gemma":[0.4767528,0.0003159608,0.5113813,0.0001859795,0.0001073692,0.00040932564,0.00016373488,0.00016469863,0.010518827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976236,0.000087228254,0.000008467551,0.00003719379,0.00007696633,0.000027722546],"domain_scores_gemma":[0.999546,0.00026551803,0.000030143065,0.000020649368,0.00010591817,0.000031779844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007157121,0.00060512015,0.0011173625,0.00049059634,0.00038262134,0.0005869522,0.0014651421,0.0013021004,0.0030888019],"category_scores_gemma":[0.0013699392,0.00039958567,0.00051625085,0.00053588214,0.00042770317,0.0007319308,0.00092734344,0.0007004334,0.00040371547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095092466,0.000059617596,0.00021540205,0.00006150285,0.000032057964,0.000043776356,0.000041594754,0.930845,0.0015709947,0.007779963,0.0017165064,0.057538524],"study_design_scores_gemma":[0.0000061591168,0.000011340985,0.000014781435,0.0000015790805,0.0000016311602,0.000003375526,0.0000024455505,0.9993161,0.000052980115,0.00040736815,0.0001807206,0.000001467047],"about_ca_topic_score_codex":0.006193301,"about_ca_topic_score_gemma":0.005477173,"teacher_disagreement_score":0.006193301,"about_ca_system_score_codex":0.00049388176,"about_ca_system_score_gemma":0.0009120383,"threshold_uncertainty_score":0.012314498},"labels":[],"label_agreement":null},{"id":"W4392154185","doi":"10.1016/j.eswa.2024.123565","title":"Covariance matrix adaptation evolution strategy based on ensemble of mutations for parking navigation and maneuver of autonomous vehicles","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education, Kenya; CHEO Research Institute","keywords":"CMA-ES; Computer science; Mathematical optimization; Scalability; Motion planning; Task (project management); Evolution strategy; Gaussian; Time horizon; Covariance matrix; Genetic algorithm; Artificial intelligence; Algorithm; Machine learning; Evolutionary algorithm; Mathematics; Robot; Engineering","score_opus":0.021549305920962467,"score_gpt":0.2874718961859683,"score_spread":0.2659225902650058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392154185","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06089937,0.00034422925,0.93578637,0.00015302573,0.0001060229,0.00003753105,0.000028912931,0.0002938479,0.002350762],"genre_scores_gemma":[0.8793218,0.00018016357,0.11646016,0.00015813892,0.00004923909,0.0001229673,0.00011336386,0.00006736703,0.0035267584],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996835,0.00006570015,0.000018848055,0.00008092193,0.0001041904,0.00004693346],"domain_scores_gemma":[0.9992692,0.00031240578,0.000054000935,0.000053802978,0.00026842445,0.000042228265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006149732,0.0005555334,0.000892995,0.000457144,0.0004712206,0.00048266057,0.0010628854,0.00088278716,0.0011103029],"category_scores_gemma":[0.002208516,0.00030275455,0.00062198564,0.00040357694,0.00043187762,0.0007074043,0.0008105839,0.00088505144,0.0001980903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007855476,0.00007874528,0.0013887343,0.00003878476,0.00008945529,0.000090396315,0.000083083905,0.87559,0.006421991,0.0067175883,0.001386816,0.10803586],"study_design_scores_gemma":[0.0000033680328,0.000017548751,0.000117360716,0.0000015002033,0.0000067237397,0.000011568393,0.0000028292147,0.9989919,0.00028004433,0.00044457868,0.00011962512,0.0000028484499],"about_ca_topic_score_codex":0.008175768,"about_ca_topic_score_gemma":0.0068595605,"teacher_disagreement_score":0.008175768,"about_ca_system_score_codex":0.0005085764,"about_ca_system_score_gemma":0.0009507187,"threshold_uncertainty_score":0.016256332},"labels":[],"label_agreement":null},{"id":"W4392337651","doi":"10.1016/j.eswa.2024.123484","title":"An NLP-based system for modulating virtual experiences using speech instructions","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence; Speech recognition","score_opus":0.02394420026868275,"score_gpt":0.289669436656138,"score_spread":0.2657252363874552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392337651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10422771,0.00045298375,0.7756534,0.00033814568,0.0006149305,0.0016127959,0.0029259368,0.094557896,0.019616231],"genre_scores_gemma":[0.47652632,0.0003305977,0.485688,0.00083062256,0.00023041612,0.003206979,0.0032596553,0.0032718827,0.026655592],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961156,0.0000984785,0.000040407635,0.00012003048,0.00010341148,0.000026111435],"domain_scores_gemma":[0.99884653,0.00068034604,0.000054113498,0.00011503286,0.00019689629,0.00010701299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064172054,0.00082963903,0.0005832821,0.00060961396,0.00032092,0.0009001083,0.0010850726,0.00095011014,0.027096057],"category_scores_gemma":[0.003027881,0.00028075997,0.00023154421,0.00030434274,0.00028690058,0.00088430254,0.0011694394,0.00056494656,0.0067282994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033215275,0.0004742733,0.001551456,0.00090652064,0.00007355738,0.0007046617,0.0011532246,0.0024631857,0.35657078,0.0026709468,0.018893838,0.61121595],"study_design_scores_gemma":[0.0020133248,0.0027181206,0.02634716,0.00043341494,0.000571215,0.0026089866,0.00084485265,0.4036514,0.42900765,0.009325634,0.121988624,0.0004895992],"about_ca_topic_score_codex":0.0008650256,"about_ca_topic_score_gemma":0.0008141083,"teacher_disagreement_score":0.027096057,"about_ca_system_score_codex":0.0002731018,"about_ca_system_score_gemma":0.00037217527,"threshold_uncertainty_score":0.09064537},"labels":[],"label_agreement":null},{"id":"W4392467796","doi":"10.1016/j.eswa.2024.123577","title":"A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Associative property; Counterfeit; Artificial intelligence; Pattern recognition (psychology); Fuzzy logic; Classifier (UML); Data mining; Machine learning; Mathematics","score_opus":0.0442918992567024,"score_gpt":0.3288920458183135,"score_spread":0.2846001465616111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392467796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055936226,0.00026821464,0.9924286,0.0000533005,0.000038141734,0.00004352613,0.000036866204,0.0005730579,0.0009646284],"genre_scores_gemma":[0.24616496,0.0005837003,0.74725056,0.00014151247,0.000113119,0.00015839534,0.00023780814,0.00010027717,0.0052496125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937266,0.00006602646,0.000035156092,0.00015269699,0.00029600854,0.00007743245],"domain_scores_gemma":[0.9994998,0.000084078936,0.0000312934,0.00007239635,0.00028239284,0.00003001105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006799417,0.0004341818,0.0011924295,0.001556708,0.0006998132,0.0012114402,0.0021985874,0.0013095315,0.0021087544],"category_scores_gemma":[0.0012752429,0.0003507792,0.00082626357,0.0010533364,0.00055464427,0.001288069,0.0008416212,0.0008282425,0.0010936304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018906432,0.00023760543,0.0014978222,0.0002122869,0.00013376247,0.00048272358,0.00020630803,0.13974494,0.068936035,0.041546855,0.0043974244,0.7424152],"study_design_scores_gemma":[0.0000050646368,0.0000397808,0.00031267264,0.000015748465,0.000030413741,0.00016784511,0.000021465945,0.9824965,0.008813729,0.005508607,0.0025684857,0.00001963887],"about_ca_topic_score_codex":0.010092488,"about_ca_topic_score_gemma":0.009662388,"teacher_disagreement_score":0.010092488,"about_ca_system_score_codex":0.00063283613,"about_ca_system_score_gemma":0.0013282456,"threshold_uncertainty_score":0.020067513},"labels":[],"label_agreement":null},{"id":"W4392638957","doi":"10.1016/j.eswa.2024.123537","title":"Retailing encroaching decision in an E-commerce platform supply chain with multiple online retailers","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; National Office for Philosophy and Social Sciences; National Natural Science Foundation of China","keywords":"Stackelberg competition; Commission; Key (lock); Supply chain; Benchmark (surveying); Product (mathematics); Computer science; Set (abstract data type); Business; Industrial organization; Microeconomics; Marketing; Economics; Computer security; Mathematics","score_opus":0.028598934371838757,"score_gpt":0.2595693919316856,"score_spread":0.23097045755984685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392638957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.966417,0.00009478638,0.020311233,0.00055998977,0.000053523672,0.00007092202,0.00007779647,0.00012712338,0.012287538],"genre_scores_gemma":[0.9927395,0.000028811119,0.003598115,0.000031192205,0.000008771806,0.000009411278,0.000029438377,0.000006233588,0.0035485462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986726,0.00032732968,0.00007231189,0.00035065008,0.00023832165,0.00033882773],"domain_scores_gemma":[0.9964426,0.0018271249,0.00028739622,0.00026091546,0.00060142216,0.00058057706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014102256,0.0004002637,0.0006167073,0.0012501308,0.0027597265,0.0037807599,0.001397287,0.0020398127,0.009669504],"category_scores_gemma":[0.0032951948,0.0006015414,0.0007288568,0.0015540074,0.001331145,0.0036836644,0.002438458,0.0011839996,0.00063511316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00443869,0.0034660306,0.12855645,0.00028121044,0.00030995178,0.019727707,0.0028289417,0.646514,0.01635279,0.0540457,0.0049418896,0.11853674],"study_design_scores_gemma":[0.000053730724,0.00031869378,0.005964208,0.000024942965,0.0001256714,0.00041451305,0.0021241999,0.9773581,0.0032420454,0.008762475,0.0015501066,0.00006131626],"about_ca_topic_score_codex":0.014171619,"about_ca_topic_score_gemma":0.01603065,"teacher_disagreement_score":0.014171619,"about_ca_system_score_codex":0.0016331434,"about_ca_system_score_gemma":0.002120869,"threshold_uncertainty_score":0.03234774},"labels":[],"label_agreement":null},{"id":"W4392973662","doi":"10.1016/j.eswa.2024.123764","title":"Attention-based ConvNeXt with a parallel multiscale dilated convolution residual module for fault diagnosis of rotating machinery","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Residual; Computer science; Convolution (computer science); Fault (geology); Artificial intelligence; Algorithm","score_opus":0.01112376936594493,"score_gpt":0.273895778167408,"score_spread":0.26277200880146306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392973662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122176915,0.0017535982,0.8669621,0.00032977774,0.0002997905,0.00009316546,0.00027150512,0.0040425914,0.004070649],"genre_scores_gemma":[0.83201396,0.00033826783,0.16010636,0.00032113673,0.0000993091,0.000049649934,0.0005432272,0.00017501162,0.006353101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980265,0.000022669947,0.000010639466,0.0000666837,0.000051863328,0.00004557788],"domain_scores_gemma":[0.9998118,0.00005713409,0.000015777749,0.00002897738,0.000067446745,0.000018917885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042558223,0.0007614622,0.0008333613,0.0003934953,0.00027042063,0.0003409513,0.0010891835,0.00069390435,0.0027722244],"category_scores_gemma":[0.00068646483,0.0002598783,0.00062275084,0.0003482968,0.00022507552,0.00060621236,0.0007411071,0.0006383224,0.0005842831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006557487,0.00022482904,0.0010610287,0.00016222268,0.00014102488,0.00023231856,0.00007837794,0.1275175,0.060591202,0.0026484944,0.00656048,0.80012673],"study_design_scores_gemma":[0.000014045948,0.0000969604,0.00055797125,0.000006341888,0.000037594604,0.00006001914,0.000010802204,0.98726463,0.01039668,0.0007560193,0.0007909128,0.000008029508],"about_ca_topic_score_codex":0.01151542,"about_ca_topic_score_gemma":0.017363936,"teacher_disagreement_score":0.01151542,"about_ca_system_score_codex":0.00036835094,"about_ca_system_score_gemma":0.0009950228,"threshold_uncertainty_score":0.022896767},"labels":[],"label_agreement":null},{"id":"W4393132109","doi":"10.1016/j.eswa.2024.123709","title":"OENet: An overexposure correction network fused with residual block and transformer","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Residual; Computer science; Transformer; Block (permutation group theory); Algorithm; Electrical engineering; Mathematics; Voltage","score_opus":0.01013189593889547,"score_gpt":0.24704922649607236,"score_spread":0.2369173305571769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393132109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025817627,0.0003857167,0.9643784,0.00011461376,0.00022531938,0.0000557046,0.00036312704,0.0056635076,0.0029960242],"genre_scores_gemma":[0.4710096,0.0004068889,0.5055689,0.00034282342,0.000102694954,0.000072430856,0.0015848788,0.000581806,0.020330021],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986434,0.00001197075,0.0000059478393,0.00004027468,0.000057439876,0.000019951032],"domain_scores_gemma":[0.9998423,0.000029126659,0.000013046786,0.00003019689,0.00007540779,0.000009935188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004046394,0.0007636077,0.00048373145,0.00048220003,0.00023389596,0.00041915095,0.00090097013,0.0006575167,0.00430842],"category_scores_gemma":[0.00066249404,0.00030251793,0.0004059927,0.00039364255,0.00021842858,0.00082750845,0.00076463004,0.0007496343,0.0012195476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000530885,0.00013727501,0.0015363601,0.00013081447,0.00015979966,0.00018244736,0.00004145364,0.11837894,0.06670712,0.0027328518,0.0098716635,0.7995904],"study_design_scores_gemma":[0.00002216393,0.000099864985,0.0009975509,0.000015082507,0.00006866186,0.00016257727,0.00001341723,0.93919677,0.051646113,0.0015101931,0.006245516,0.000021920674],"about_ca_topic_score_codex":0.0066520246,"about_ca_topic_score_gemma":0.012505547,"teacher_disagreement_score":0.0066520246,"about_ca_system_score_codex":0.00036815574,"about_ca_system_score_gemma":0.0006023969,"threshold_uncertainty_score":0.014413118},"labels":[],"label_agreement":null},{"id":"W4393970287","doi":"10.1016/j.eswa.2024.123718","title":"Memory-enhanced spatial-temporal encoding framework for industrial anomaly detection system","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"China Scholarship Council","keywords":"Anomaly detection; Computer science; Encoding (memory); Reliability (semiconductor); Artificial intelligence; Consistency (knowledge bases); Workspace; Data mining; Process (computing); Spatial analysis; Temporal database","score_opus":0.024396139954589443,"score_gpt":0.27519592361198475,"score_spread":0.2507997836573953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393970287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027692122,0.000809321,0.96257263,0.00012862327,0.00008202166,0.000045699104,0.000496629,0.006422283,0.0017506917],"genre_scores_gemma":[0.60496294,0.0007816417,0.38764387,0.00016338733,0.00008669618,0.000099285775,0.0011553584,0.00021335874,0.0048934375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967647,0.000036327205,0.000029281457,0.00007620092,0.0001318687,0.000049767037],"domain_scores_gemma":[0.99962103,0.00005445158,0.000039270228,0.00008879516,0.00017767139,0.000018733752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033825825,0.000490831,0.00048435273,0.000915094,0.0002469285,0.0008079943,0.0011204338,0.00036893485,0.002644422],"category_scores_gemma":[0.0009601855,0.00016429038,0.00043614124,0.0008322333,0.00017617678,0.0013263886,0.00062081387,0.00051849964,0.000705618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006403343,0.00024934596,0.0030343288,0.00023172348,0.000104404025,0.0002767966,0.00013685788,0.080098726,0.06291804,0.015272809,0.008051097,0.8289855],"study_design_scores_gemma":[0.000019181252,0.00011852583,0.0008135389,0.000015244112,0.00006515071,0.00020993153,0.00004600896,0.9512111,0.03329464,0.007843683,0.0063383225,0.000024681463],"about_ca_topic_score_codex":0.009642154,"about_ca_topic_score_gemma":0.008114137,"teacher_disagreement_score":0.009642154,"about_ca_system_score_codex":0.0004453479,"about_ca_system_score_gemma":0.0010580341,"threshold_uncertainty_score":0.019172072},"labels":[],"label_agreement":null},{"id":"W4394015524","doi":"10.1016/j.eswa.2024.123908","title":"An efficient multi-objective adaptive large neighborhood search algorithm for solving a disassembly line balancing model considering idle rate, smoothness, labor cost, and energy consumption","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Heuristics; Mathematical optimization; Idle; Process (computing); Remanufacturing; Product (mathematics); Energy consumption; Workstation; Smoothness; Algorithm; Industrial engineering; Manufacturing engineering; Mathematics; Engineering","score_opus":0.018407668765964454,"score_gpt":0.26803568250854554,"score_spread":0.2496280137425811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394015524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01815936,0.0005154279,0.977985,0.00011084853,0.00006874436,0.000071981536,0.00003521565,0.00025633283,0.0027971065],"genre_scores_gemma":[0.55818045,0.00034607426,0.4352997,0.00016100578,0.000072422,0.00060265075,0.00021593747,0.00011046397,0.0050113453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996445,0.00011270538,0.000020647818,0.00008077706,0.00009896011,0.000042461426],"domain_scores_gemma":[0.9994129,0.00036225424,0.00005366261,0.00001982631,0.00012139565,0.00002999522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011214007,0.0010957296,0.0019409604,0.00069843465,0.0006321935,0.00074546685,0.001632011,0.0017567513,0.0020612068],"category_scores_gemma":[0.001646621,0.00066423364,0.00075457856,0.0007669777,0.0005017488,0.0008753639,0.0009679631,0.00093089056,0.0002806706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072740026,0.000054191652,0.00023636376,0.000057161476,0.00003408353,0.00003633914,0.000031163927,0.96494937,0.0006757172,0.0021773325,0.00074291753,0.030932637],"study_design_scores_gemma":[0.000008591882,0.00001480149,0.000024850175,0.0000021682574,0.0000029988314,0.0000031759964,0.0000025310605,0.9996007,0.000048338537,0.00019405595,0.000096171345,0.0000016144498],"about_ca_topic_score_codex":0.014680779,"about_ca_topic_score_gemma":0.014125986,"teacher_disagreement_score":0.014680779,"about_ca_system_score_codex":0.0006665918,"about_ca_system_score_gemma":0.0015872319,"threshold_uncertainty_score":0.02919066},"labels":[],"label_agreement":null},{"id":"W4394688228","doi":"10.1016/j.eswa.2024.123923","title":"One-step abductive multi-target learning with diverse noisy samples and its application to tumour segmentation for breast cancer","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Machine learning; Breast cancer; Pattern recognition (psychology); Cancer; Computer vision; Medicine; Internal medicine","score_opus":0.02848520687439513,"score_gpt":0.30339922850753454,"score_spread":0.2749140216331394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394688228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07261426,0.00093820516,0.92465574,0.00022799743,0.000039181443,0.000052699856,0.00005094924,0.0006143182,0.0008067147],"genre_scores_gemma":[0.7562114,0.00022628678,0.24086386,0.00014719511,0.000028051021,0.00009445675,0.0001948752,0.00008925263,0.002144606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992053,0.00023669629,0.00006352824,0.00019813472,0.0002097563,0.00008663479],"domain_scores_gemma":[0.99774504,0.0015940855,0.00011870997,0.00014675104,0.00034699138,0.00004848339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025934908,0.0009937888,0.0016131419,0.0006859573,0.00061875914,0.00080416136,0.001550136,0.002370077,0.001059646],"category_scores_gemma":[0.005304181,0.00071576535,0.0011414569,0.00085252814,0.0011176831,0.00093070796,0.0021417863,0.0015271432,0.00023900454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072205625,0.00015171958,0.0016695412,0.0002611618,0.00017589612,0.0003477667,0.00035214247,0.69338274,0.01757205,0.0038434893,0.00080188015,0.2807195],"study_design_scores_gemma":[0.000005847246,0.000051431995,0.00019945261,0.000004646802,0.00001312187,0.00003407906,0.000012281118,0.99676836,0.0019530621,0.0008388912,0.00011227397,0.0000065433983],"about_ca_topic_score_codex":0.005425348,"about_ca_topic_score_gemma":0.0049169245,"teacher_disagreement_score":0.005425348,"about_ca_system_score_codex":0.00047279813,"about_ca_system_score_gemma":0.0008130811,"threshold_uncertainty_score":0.013715863},"labels":[],"label_agreement":null},{"id":"W4394787069","doi":"10.1016/j.eswa.2024.123974","title":"Accelerated semantic segmentation of additively manufactured metal matrix composites: Generating datasets, evaluating convolutional and transformer models, and developing the MicroSegQ+ Tool","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; National Research Council Canada","funders":"National Research Council Canada","keywords":"Computer science; Segmentation; Modular design; Exploit; Transformer; Process (computing); Data mining; Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.035143718547167496,"score_gpt":0.2931258139229141,"score_spread":0.25798209537574657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394787069","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5170386,0.0014169348,0.39645433,0.0007695124,0.00039080722,0.0005717773,0.026216527,0.045461655,0.0116797285],"genre_scores_gemma":[0.57893246,0.0006664662,0.35783666,0.00030337734,0.00006524281,0.00037805497,0.05333774,0.0030133196,0.005466766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999571,0.000048740283,0.000023560588,0.00016666546,0.00013245408,0.000057481873],"domain_scores_gemma":[0.9994037,0.00023020076,0.000053542444,0.00012339951,0.00016120427,0.00002799732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066834973,0.0017060473,0.00072052074,0.002257043,0.0005604909,0.0014448529,0.0019009706,0.0020790347,0.0031095492],"category_scores_gemma":[0.001615256,0.00057544507,0.0017357366,0.0014090596,0.0006651053,0.0011485722,0.0010231147,0.0009908779,0.0016701897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015757551,0.0009323498,0.0071130283,0.0016714917,0.0004361016,0.00084703596,0.00035953207,0.4262922,0.106236264,0.009131907,0.041767433,0.4036369],"study_design_scores_gemma":[0.00006909545,0.00017494915,0.0028060288,0.000050820938,0.00006900213,0.00031719528,0.00017076645,0.9114149,0.071052134,0.0055583776,0.008275867,0.00004083491],"about_ca_topic_score_codex":0.01023344,"about_ca_topic_score_gemma":0.021755254,"teacher_disagreement_score":0.01023344,"about_ca_system_score_codex":0.0010764571,"about_ca_system_score_gemma":0.0012602216,"threshold_uncertainty_score":0.020347714},"labels":[],"label_agreement":null},{"id":"W4394876029","doi":"10.1016/j.eswa.2024.124022","title":"A novel jujube tree trunk and branch salient object detection method for catch-and-shake robotic visual perception","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Canadian Stroke Consortium; China Scholarship Council; National Natural Science Foundation of China; McGill University","keywords":"Shake; Computer science; Artificial intelligence; Computer vision; Perception; Trunk; Tree (set theory); Object (grammar); Salient; Mathematics; Psychology; Botany; Biology","score_opus":0.016289850730972794,"score_gpt":0.2713557818005047,"score_spread":0.2550659310695319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394876029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043276876,0.0005660769,0.9524541,0.00008232557,0.0001413304,0.000085557054,0.000073604286,0.001383624,0.0019365783],"genre_scores_gemma":[0.3893327,0.0005943122,0.60062635,0.00021898674,0.00009915636,0.00013075142,0.0003664462,0.00019997328,0.008431318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997918,0.000009057944,0.000005609352,0.00006164233,0.00010040196,0.000031581916],"domain_scores_gemma":[0.99984145,0.000024050623,0.000012610744,0.000020897138,0.000075152006,0.000025712696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018146049,0.0006310844,0.00087641174,0.0009250675,0.0004779506,0.0004803439,0.0010734976,0.00073790277,0.0020972725],"category_scores_gemma":[0.00034988846,0.000431009,0.00051253755,0.0005622309,0.0002600711,0.00089497113,0.00077023264,0.0006254698,0.0006702563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002881845,0.00012931254,0.001254149,0.00015881962,0.00006714688,0.0001898284,0.00010909446,0.008415856,0.31030542,0.0018096787,0.0037486793,0.67352384],"study_design_scores_gemma":[0.00005451088,0.0003337076,0.0077551138,0.00002518766,0.0001367071,0.000765966,0.00011128802,0.85507697,0.12204874,0.0022958445,0.01130865,0.00008733298],"about_ca_topic_score_codex":0.0027734372,"about_ca_topic_score_gemma":0.006424933,"teacher_disagreement_score":0.0027734372,"about_ca_system_score_codex":0.00020459467,"about_ca_system_score_gemma":0.0007129449,"threshold_uncertainty_score":0.0070161223},"labels":[],"label_agreement":null},{"id":"W4394960615","doi":"10.1016/j.eswa.2024.123918","title":"Designing a sustainable plastic bottle reverse logistics network: A data-driven optimization approach","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reverse logistics; Container (type theory); Operations research; Mathematical optimization; Prioritization; Supply chain; Business; Process management","score_opus":0.027764512182337397,"score_gpt":0.24530488124709987,"score_spread":0.21754036906476248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394960615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03273457,0.0002832691,0.9619456,0.00037983706,0.000035705292,0.00016130907,0.0002524922,0.00014581846,0.0040613767],"genre_scores_gemma":[0.6808935,0.0004893762,0.31397155,0.00016355947,0.00003731397,0.0005402863,0.0005825106,0.00007972887,0.003242222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941635,0.00021233705,0.000031304935,0.00012211423,0.00013308752,0.00008479694],"domain_scores_gemma":[0.9990564,0.00050372473,0.00013778733,0.00003779202,0.00020211615,0.00006221184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014164445,0.0011863433,0.0012106657,0.0014242451,0.00060398824,0.0018657235,0.0014897472,0.0015074149,0.0020944453],"category_scores_gemma":[0.0022181429,0.0009388962,0.0011963237,0.0015031399,0.0005795857,0.001555205,0.0013167697,0.0011167172,0.0002071516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007245016,0.000009574182,0.00018585887,0.000019769868,0.0000111935215,0.000021149945,0.000008577937,0.99521625,0.00017784638,0.0011256725,0.00009116874,0.0031256757],"study_design_scores_gemma":[0.0000022465974,0.00000757548,0.000042122356,0.0000038077412,0.0000037216607,0.0000034360828,0.0000104969295,0.99872893,0.00008534705,0.00094955985,0.00015998774,0.000002737139],"about_ca_topic_score_codex":0.014816029,"about_ca_topic_score_gemma":0.013709961,"teacher_disagreement_score":0.014816029,"about_ca_system_score_codex":0.001970644,"about_ca_system_score_gemma":0.0025075483,"threshold_uncertainty_score":0.029459536},"labels":[],"label_agreement":null},{"id":"W4396508640","doi":"10.1016/j.eswa.2024.124014","title":"Optimal control of Boolean control networks with state-triggered impulses","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Control (management); Computer science; Boolean network; State (computer science); Boolean function; Impulse control; Artificial intelligence; Algorithm; Neuroscience; Psychology","score_opus":0.0035429652022934185,"score_gpt":0.22069211798651164,"score_spread":0.21714915278421823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396508640","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1152945,0.0005765461,0.8693563,0.00067049597,0.00017926878,0.00005542515,0.00013925829,0.00044196137,0.013286334],"genre_scores_gemma":[0.9861233,0.00015258319,0.010767427,0.00006990666,0.000025383768,0.000054640765,0.0000352646,0.00002567887,0.0027456884],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99946684,0.00015571991,0.000017759969,0.000111789974,0.00011947729,0.00012841511],"domain_scores_gemma":[0.99813396,0.0013387768,0.00021019788,0.00005519014,0.00017590029,0.000085982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011170454,0.0007818428,0.0008214321,0.00051850954,0.00029002837,0.0014247153,0.0008408785,0.00093779207,0.0022387197],"category_scores_gemma":[0.0041278736,0.00035929793,0.00049354334,0.00038808366,0.0013071339,0.0007500777,0.0009352668,0.00088418764,0.00014025124],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028180558,0.000075626216,0.00025879598,0.0001015409,0.000041147072,0.00008968887,0.00007521939,0.91370314,0.008596786,0.055601276,0.0007532061,0.020421717],"study_design_scores_gemma":[0.000019174691,0.000031024767,0.000053059128,0.0000042750953,0.0000068515224,0.000004945831,0.0000051250777,0.99150777,0.0006832033,0.0075448914,0.00013407638,0.0000055217897],"about_ca_topic_score_codex":0.0053721806,"about_ca_topic_score_gemma":0.0045527695,"teacher_disagreement_score":0.0053721806,"about_ca_system_score_codex":0.0016212735,"about_ca_system_score_gemma":0.0010290684,"threshold_uncertainty_score":0.011763215},"labels":[],"label_agreement":null},{"id":"W4396620509","doi":"10.1016/j.eswa.2024.124129","title":"Integrating deep transformer and temporal convolutional networks for SMEs revenue and employment growth prediction","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"","keywords":"Computer science; Revenue; Transformer; Artificial intelligence; Convolutional neural network; Machine learning; Finance; Business; Electrical engineering","score_opus":0.053615560260757276,"score_gpt":0.3618383054063002,"score_spread":0.30822274514554293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396620509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3965834,0.003267302,0.5837278,0.0012706032,0.0004043963,0.00006230387,0.0021285824,0.0037091093,0.008846519],"genre_scores_gemma":[0.9632766,0.0006269107,0.029748319,0.0001228602,0.000080759244,0.00002487402,0.0013559452,0.000044059,0.004719549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989235,0.000015769843,0.000006403164,0.000030922998,0.000023239134,0.000031240375],"domain_scores_gemma":[0.9997383,0.00009040446,0.000028744882,0.000026135349,0.00009128051,0.000025151381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055713323,0.00057286595,0.0003842917,0.00073609315,0.00016408498,0.0005338585,0.00073193014,0.00050508016,0.0017872986],"category_scores_gemma":[0.0010829371,0.00021630799,0.00046763435,0.00074132887,0.00013218005,0.0008753851,0.0005088797,0.0008069436,0.00075173535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046761814,0.00044015926,0.01696282,0.00011982554,0.00020818897,0.00020080882,0.00006346668,0.37765622,0.01099352,0.006827057,0.010562579,0.5754977],"study_design_scores_gemma":[0.0000027864116,0.0000113289525,0.00070147216,0.0000043761474,0.000013073736,0.000010122052,0.000004752139,0.99661297,0.0009500282,0.0014148388,0.00027141298,0.0000028702925],"about_ca_topic_score_codex":0.015714997,"about_ca_topic_score_gemma":0.026831733,"teacher_disagreement_score":0.015714997,"about_ca_system_score_codex":0.0005932401,"about_ca_system_score_gemma":0.00080205296,"threshold_uncertainty_score":0.03124708},"labels":[],"label_agreement":null},{"id":"W4396766974","doi":"10.1016/j.eswa.2024.124167","title":"Artificial intelligence in education: A systematic literature review","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":681,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Saskatchewan","funders":"","keywords":"Computer science; Systematic review; Artificial intelligence; Data science; MEDLINE","score_opus":0.014395288466626811,"score_gpt":0.31872940913674647,"score_spread":0.30433412067011967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396766974","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009911585,0.9966702,0.00028042897,0.0007680431,0.00012821218,0.00014926857,0.00032495632,0.000007740626,0.00068007386],"genre_scores_gemma":[0.0052450057,0.9929543,0.000801927,0.00045814726,0.00006453787,0.00017652634,0.0002049234,0.0000038271255,0.00009084614],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99519956,0.0015219414,0.0016933425,0.00036366688,0.0010350992,0.00018632994],"domain_scores_gemma":[0.9621771,0.029488018,0.0034623938,0.00044426904,0.0039181076,0.0005102742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0081412755,0.0010288032,0.0032219894,0.025105998,0.0009899475,0.003257818,0.0014224213,0.0017571804,0.005746521],"category_scores_gemma":[0.03328953,0.00074434665,0.0025570383,0.025864627,0.0010411231,0.003965787,0.0020741464,0.001469086,0.00070679147],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093343035,0.00006529994,0.0019932522,0.737448,0.0013682174,0.0003607581,0.0010216236,0.00023193311,0.00024541703,0.002009766,0.008304881,0.2468575],"study_design_scores_gemma":[0.00004249452,0.00009695826,0.0045400215,0.8899293,0.0058164527,0.0006762406,0.0014349106,0.000106943146,0.00014890125,0.001131042,0.09603821,0.000038457332],"about_ca_topic_score_codex":0.006308183,"about_ca_topic_score_gemma":0.021815779,"teacher_disagreement_score":0.025105998,"about_ca_system_score_codex":0.0030148288,"about_ca_system_score_gemma":0.020974236,"threshold_uncertainty_score":0.043055713},"labels":[],"label_agreement":null},{"id":"W4396793108","doi":"10.1016/j.eswa.2024.124198","title":"A web GIS based integrated water resources assessment tool for Javeh Reservoir","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Web application; Water resources; Water resource management; Data mining; World Wide Web; Environmental science","score_opus":0.009837199186572236,"score_gpt":0.23625374715350117,"score_spread":0.22641654796692892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396793108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10855782,0.00057827367,0.553847,0.00069942104,0.00022699169,0.00082888495,0.046259362,0.24619268,0.042809576],"genre_scores_gemma":[0.45055556,0.00080408726,0.46145248,0.00030882118,0.00006478042,0.0009559792,0.041249808,0.006816245,0.03779224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998036,0.000025008556,0.000025369118,0.000030344392,0.0000997101,0.000015860698],"domain_scores_gemma":[0.999553,0.00015693891,0.00003107282,0.000052489002,0.00016939842,0.00003698215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043411192,0.00063829165,0.00064647157,0.0024671436,0.00030650597,0.0012137266,0.0007923114,0.0005638298,0.021157011],"category_scores_gemma":[0.0010989645,0.0004323489,0.0005308059,0.0013716285,0.000117069285,0.0014149898,0.0009748325,0.0004471645,0.003968753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059974164,0.0008057433,0.018024152,0.0013836297,0.00038538603,0.0017023823,0.000816699,0.12413031,0.036504872,0.0063352655,0.14039062,0.66892123],"study_design_scores_gemma":[0.00025518375,0.00016519752,0.020438014,0.00020996148,0.00019176939,0.0008188816,0.0006620945,0.8001881,0.026065806,0.008213793,0.14257114,0.00022001592],"about_ca_topic_score_codex":0.005042215,"about_ca_topic_score_gemma":0.007414291,"teacher_disagreement_score":0.021157011,"about_ca_system_score_codex":0.00027196968,"about_ca_system_score_gemma":0.0006713853,"threshold_uncertainty_score":0.07077718},"labels":[],"label_agreement":null},{"id":"W4396887657","doi":"10.1016/j.eswa.2024.124201","title":"A novel robust black-box fingerprinting scheme for deep classification neural networks","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"","keywords":"Computer science; Black box; Artificial intelligence; Deep neural networks; Artificial neural network; Scheme (mathematics); Pattern recognition (psychology); Classification scheme; Deep learning; Machine learning; Data mining; Mathematics","score_opus":0.02777965028994986,"score_gpt":0.25420990466357873,"score_spread":0.22643025437362888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396887657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011417591,0.00034330014,0.98575586,0.0001186038,0.000074301664,0.00004248599,0.000099388315,0.0010755887,0.0010728437],"genre_scores_gemma":[0.36820272,0.00047823816,0.62082404,0.00031907758,0.00010681794,0.00012761996,0.00041608876,0.00015930756,0.009366137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914384,0.00013324474,0.00004742984,0.0001819915,0.0003537935,0.00013975229],"domain_scores_gemma":[0.99903715,0.00018157664,0.00010548391,0.00031058548,0.0003067944,0.000058434918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009540579,0.0010803316,0.0011616893,0.0008710598,0.00060618605,0.0010548488,0.0021520117,0.0014090717,0.004326532],"category_scores_gemma":[0.002209833,0.0004429733,0.000612432,0.0009694826,0.0005403112,0.0020176913,0.0022858786,0.0015588654,0.0016016184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005799901,0.00015883276,0.00079679355,0.0001269751,0.000093428265,0.00009010385,0.00004553552,0.054456394,0.060526457,0.013063438,0.0045722765,0.8654898],"study_design_scores_gemma":[0.000018643595,0.00009717923,0.00032884246,0.000018546803,0.000029916628,0.00011718778,0.000009268801,0.9638365,0.028347988,0.0050928574,0.0020797024,0.000023349901],"about_ca_topic_score_codex":0.0027421007,"about_ca_topic_score_gemma":0.0041192006,"teacher_disagreement_score":0.004326532,"about_ca_system_score_codex":0.00074220006,"about_ca_system_score_gemma":0.0012231563,"threshold_uncertainty_score":0.014473677},"labels":[],"label_agreement":null},{"id":"W4399019070","doi":"10.1016/j.eswa.2024.124310","title":"Bridging the simulation-to-real gap of depth images for deep reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Encoder; Bridging (networking); Bridge (graph theory); Perception; Virtual reality; Machine learning; Deep learning; Computer vision","score_opus":0.025106323295264334,"score_gpt":0.3069211344319391,"score_spread":0.2818148111366747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399019070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051116355,0.00046263935,0.9436867,0.0008103324,0.000092290975,0.000032974494,0.000047002322,0.00045258566,0.003299057],"genre_scores_gemma":[0.94141537,0.00016762178,0.056956466,0.00016302831,0.000024730201,0.000046074983,0.00004724343,0.000079101555,0.0011003913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954814,0.00016372031,0.000022228101,0.000091823094,0.00012139621,0.000052594318],"domain_scores_gemma":[0.99642867,0.0025645052,0.00022568431,0.00034115114,0.00027510276,0.00016486744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014539266,0.0006751512,0.00075736875,0.0002484349,0.00033856928,0.0009371083,0.001226212,0.0012455021,0.0033216607],"category_scores_gemma":[0.010221165,0.00050606014,0.0002614452,0.00018077712,0.0012195314,0.002088953,0.0023542014,0.0024654532,0.00026464855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035999026,0.00013369323,0.00090141484,0.00016091272,0.00003647476,0.000094675306,0.00019204973,0.87780255,0.0063709626,0.03383234,0.0014137293,0.07870112],"study_design_scores_gemma":[0.000007868801,0.000028378014,0.000058804762,0.000007906125,0.0000019078761,0.0000069788075,0.00000718037,0.9897726,0.0007233365,0.009148688,0.0002336772,0.000002754359],"about_ca_topic_score_codex":0.0029550185,"about_ca_topic_score_gemma":0.0026714632,"teacher_disagreement_score":0.0033216607,"about_ca_system_score_codex":0.0010048758,"about_ca_system_score_gemma":0.0011977861,"threshold_uncertainty_score":0.011112034},"labels":[],"label_agreement":null},{"id":"W4399207478","doi":"10.1016/j.eswa.2024.124315","title":"On efficient computation in active inference","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Embodied and Extended Cognition","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"","keywords":"Computer science; Inference; Computation; Artificial intelligence; Machine learning; Algorithm","score_opus":0.029883013985373987,"score_gpt":0.31904224700543554,"score_spread":0.2891592330200616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399207478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040919613,0.00038124813,0.9898078,0.0006128469,0.00006286022,0.000036807796,0.00004579773,0.0002533059,0.0047073895],"genre_scores_gemma":[0.30441675,0.0008151345,0.68723685,0.00053927297,0.00025063675,0.0004568879,0.0002596262,0.00031767384,0.005707148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99867517,0.00058288843,0.00008684337,0.0002245093,0.00029180478,0.00013876791],"domain_scores_gemma":[0.99064696,0.007863635,0.0002138006,0.00070845644,0.00043181825,0.00013533152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030666732,0.001085993,0.0012896139,0.0009468528,0.0010437146,0.0019913104,0.0025450736,0.0016939959,0.005828284],"category_scores_gemma":[0.015285792,0.0007099217,0.001293856,0.0013595684,0.0025555259,0.003930194,0.0030707053,0.0034013337,0.0011515087],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018884146,0.0000739231,0.00064860197,0.00015447798,0.00006434892,0.000096709315,0.00014921204,0.5142875,0.001129755,0.40047553,0.0025662398,0.08016489],"study_design_scores_gemma":[0.000020586906,0.000013328564,0.000049087255,0.000012891412,0.0000064733813,0.000010567873,0.000009945944,0.8212632,0.00028857746,0.1772599,0.0010591529,0.000006285055],"about_ca_topic_score_codex":0.006875558,"about_ca_topic_score_gemma":0.008807204,"teacher_disagreement_score":0.006875558,"about_ca_system_score_codex":0.0017050918,"about_ca_system_score_gemma":0.001864808,"threshold_uncertainty_score":0.019497573},"labels":[],"label_agreement":null},{"id":"W4399245390","doi":"10.1016/j.eswa.2024.124247","title":"Precision refined: Integrating micromachining constraints for enhanced product accuracy through topology optimization","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Topology optimization; Topology (electrical circuits); Product (mathematics); Surface micromachining; Mathematical optimization; Mathematics; Physics; Geometry","score_opus":0.01137071555146123,"score_gpt":0.2777937294845028,"score_spread":0.2664230139330416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399245390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030538142,0.00021041445,0.9598602,0.000102470294,0.000050967377,0.000032263026,0.000063562846,0.00047884925,0.008663111],"genre_scores_gemma":[0.5766341,0.00025507138,0.41667607,0.00009647781,0.00003558989,0.00007115429,0.00017289727,0.0005097941,0.005548707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927694,0.00010494385,0.000034239984,0.0001233848,0.00040862744,0.000051977837],"domain_scores_gemma":[0.99923646,0.00020567526,0.0001027228,0.0002631758,0.00016645911,0.000025598321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075557444,0.0009132292,0.0008220573,0.00055789045,0.00033373028,0.0011498755,0.0015150632,0.000930164,0.0044455384],"category_scores_gemma":[0.002449298,0.0006321664,0.0005704274,0.0005876631,0.0005954553,0.0016556288,0.001560826,0.0011771377,0.00078082905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093776354,0.00007820541,0.0005198157,0.00018092006,0.00006064903,0.00008977075,0.00010186629,0.7907207,0.06347373,0.036628153,0.0011037269,0.10694873],"study_design_scores_gemma":[0.00002015855,0.00009999941,0.00022835954,0.000021494609,0.000026445754,0.00006130063,0.000017526881,0.96365345,0.019789794,0.012047217,0.0040154094,0.000018858806],"about_ca_topic_score_codex":0.0012655086,"about_ca_topic_score_gemma":0.0031386646,"teacher_disagreement_score":0.0044455384,"about_ca_system_score_codex":0.00049810385,"about_ca_system_score_gemma":0.0009412922,"threshold_uncertainty_score":0.014871836},"labels":[],"label_agreement":null},{"id":"W4399795905","doi":"10.1016/j.eswa.2024.124523","title":"Label-semantics enhanced multi-layer heterogeneous graph convolutional network for Aspect Sentiment Quadruplet Extraction","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Science and Technology Service Network Plan; Key Science and Technology Program of Shaanxi Province; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China","keywords":"Computer science; Graph; Artificial intelligence; Semantics (computer science); Layer (electronics); Natural language processing; Theoretical computer science; Programming language; Chemistry","score_opus":0.038061305910356225,"score_gpt":0.32247039089730317,"score_spread":0.28440908498694695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399795905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18830827,0.0014693422,0.7762946,0.0006478761,0.00040229844,0.0002122442,0.0032749502,0.014025743,0.015364687],"genre_scores_gemma":[0.73768073,0.0007543892,0.23002681,0.00045374146,0.00014148069,0.00014785535,0.010294241,0.00052578485,0.019974928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985397,0.000013391973,0.00000763163,0.00005107338,0.000038525304,0.000035369063],"domain_scores_gemma":[0.9998505,0.00002943305,0.00001697632,0.00002753858,0.000063716936,0.000011766221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021659546,0.00091095007,0.00041927546,0.0010348039,0.00040016335,0.00055307825,0.0006630474,0.0006303179,0.0029687423],"category_scores_gemma":[0.0004854732,0.00024364854,0.0006516858,0.0009243283,0.0001966576,0.00093276624,0.0006034784,0.00072698,0.0014693086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005191426,0.0004248995,0.0045902105,0.00024977268,0.00021465178,0.00035981432,0.0001464802,0.04631526,0.12301239,0.007687878,0.030310567,0.7861689],"study_design_scores_gemma":[0.00002085906,0.00007357776,0.0023550799,0.000020901993,0.00009591609,0.00008553063,0.000046899553,0.9584616,0.026276937,0.006268132,0.0062760487,0.000018651672],"about_ca_topic_score_codex":0.010010943,"about_ca_topic_score_gemma":0.026011776,"teacher_disagreement_score":0.010010943,"about_ca_system_score_codex":0.0005592046,"about_ca_system_score_gemma":0.0007241795,"threshold_uncertainty_score":0.019905388},"labels":[],"label_agreement":null},{"id":"W4399913729","doi":"10.1016/j.eswa.2024.124557","title":"Basis path coverage testing of MPI programs based on multi-task evolutionary optimization","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Path (computing); Basis (linear algebra); Machine learning; Artificial intelligence; Mathematics; Programming language","score_opus":0.028408197792053412,"score_gpt":0.26922609703886186,"score_spread":0.24081789924680844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399913729","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6189334,0.00018427834,0.37212923,0.00037582844,0.000046484653,0.00008561724,0.00018141493,0.0028970542,0.0051668235],"genre_scores_gemma":[0.9345937,0.000033922868,0.06435997,0.00003851097,0.0000070401743,0.00005880819,0.00014430827,0.00018073544,0.0005831594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858,0.0006009619,0.000041000396,0.00012377246,0.00044853435,0.00020580002],"domain_scores_gemma":[0.9930656,0.0050537246,0.00032113885,0.0006007914,0.0008005733,0.00015808146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012475656,0.00069641776,0.0007012953,0.0012061617,0.0006927846,0.0005470552,0.0013205581,0.0006913136,0.0018544401],"category_scores_gemma":[0.01056342,0.00026492248,0.0005528026,0.00071751646,0.00085561693,0.000926681,0.0009882075,0.0007327369,0.00013354373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011776005,0.0003420323,0.01001905,0.00024916834,0.00010548169,0.0004022789,0.00022262067,0.82577085,0.022694036,0.021557568,0.00229084,0.11516842],"study_design_scores_gemma":[0.000022657945,0.000056840403,0.0005846111,0.000005269112,0.000009473454,0.000021787167,0.000015325255,0.99150825,0.0040968303,0.003549073,0.0001253814,0.0000044923545],"about_ca_topic_score_codex":0.0045542023,"about_ca_topic_score_gemma":0.004695305,"teacher_disagreement_score":0.0045542023,"about_ca_system_score_codex":0.000688076,"about_ca_system_score_gemma":0.001512854,"threshold_uncertainty_score":0.009055436},"labels":[],"label_agreement":null},{"id":"W4400038571","doi":"10.1016/j.eswa.2024.124606","title":"Designing a resilient cloud network fulfilled by reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reinforcement learning; Computer science; Cloud computing; Artificial intelligence; Machine learning; Operating system","score_opus":0.010558495178767361,"score_gpt":0.24150529542143304,"score_spread":0.23094680024266567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400038571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06310631,0.00014770566,0.9314012,0.00027170003,0.00009207632,0.00007852583,0.000034065513,0.00061629666,0.004252163],"genre_scores_gemma":[0.9414252,0.00006919161,0.056287266,0.00008890192,0.000019031848,0.00005819798,0.000022090473,0.00002936784,0.002000633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975866,0.000043981592,0.00000890072,0.000078376506,0.00003943503,0.000070648624],"domain_scores_gemma":[0.99973685,0.00008896829,0.000029901337,0.000028626113,0.0000743482,0.00004124299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035226395,0.00042036077,0.00049055216,0.00022347858,0.00062079285,0.00063260307,0.0011962018,0.00068652857,0.001910094],"category_scores_gemma":[0.000847693,0.00022725972,0.00032630478,0.0001973338,0.00038821838,0.000687551,0.0008810964,0.00060945423,0.00026922417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017178869,0.00010194668,0.0011727832,0.000072438226,0.000051109513,0.0002876908,0.00007983113,0.9224665,0.015927395,0.013505298,0.0015382784,0.04462488],"study_design_scores_gemma":[0.0000046926516,0.000020837095,0.00004968612,0.000002292643,0.000004997789,0.000019188472,0.0000087099015,0.9974232,0.0008726284,0.0013151646,0.0002758881,0.0000027140784],"about_ca_topic_score_codex":0.0043122303,"about_ca_topic_score_gemma":0.003689463,"teacher_disagreement_score":0.0043122303,"about_ca_system_score_codex":0.00057611126,"about_ca_system_score_gemma":0.0009063809,"threshold_uncertainty_score":0.008574247},"labels":[],"label_agreement":null},{"id":"W4400142953","doi":"10.1016/j.eswa.2024.124647","title":"Drug–target binding affinity prediction model based on multi-scale diffusion and interactive learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scale (ratio); Diffusion; Machine learning; Artificial intelligence; Drug; Pharmacology; Thermodynamics; Medicine","score_opus":0.018594485507896722,"score_gpt":0.29981513641846486,"score_spread":0.28122065091056814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400142953","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09034544,0.0010030357,0.9029993,0.0005357371,0.000101452,0.000074466356,0.00017204194,0.0010168729,0.0037516814],"genre_scores_gemma":[0.91387373,0.00057259994,0.08000912,0.00022520848,0.000085210246,0.00014694479,0.00028980756,0.000083071805,0.0047142846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953437,0.000073405434,0.000030300454,0.00017245187,0.0001355678,0.000053929667],"domain_scores_gemma":[0.9991954,0.0004591231,0.000084047155,0.00004567195,0.00016371686,0.000052051273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081172324,0.00072884286,0.0015400765,0.0009472305,0.00063555,0.0008616569,0.0022161945,0.0012384309,0.0017565064],"category_scores_gemma":[0.0019140752,0.0004626027,0.0010877065,0.0008331152,0.00054379896,0.001929515,0.00077348517,0.0011766035,0.0003680924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018056706,0.00019091864,0.0023910154,0.00013676936,0.000117999516,0.00018360134,0.000058776885,0.9211012,0.003759558,0.0073488387,0.001881562,0.0626492],"study_design_scores_gemma":[0.000004318336,0.0000058922747,0.000075857446,7.205873e-7,0.000006843115,0.000011466117,9.130925e-7,0.99912566,0.00015256958,0.00055216247,0.000060730308,0.0000028512231],"about_ca_topic_score_codex":0.0121120475,"about_ca_topic_score_gemma":0.008242886,"teacher_disagreement_score":0.0121120475,"about_ca_system_score_codex":0.001088947,"about_ca_system_score_gemma":0.0012649857,"threshold_uncertainty_score":0.024083078},"labels":[],"label_agreement":null},{"id":"W4400340432","doi":"10.1016/j.eswa.2024.124678","title":"Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring","year":2024,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning; Physics","score_opus":0.03957793814183662,"score_gpt":0.34584854767913187,"score_spread":0.30627060953729524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400340432","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00064116606,0.9820279,0.012292023,0.0006546074,0.0004170858,0.000029542252,0.000091944494,0.00009945392,0.0037463177],"genre_scores_gemma":[0.006316512,0.98540145,0.005974035,0.00030881414,0.0007458269,0.000038860082,0.00019918072,0.000033830813,0.0009815132],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993368,0.00013625367,0.000083241204,0.00014305802,0.00025799972,0.00004253305],"domain_scores_gemma":[0.9975079,0.0017872249,0.00015258206,0.00008682546,0.00041080246,0.000054590357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015990265,0.0016140514,0.0017698347,0.003731596,0.0004310417,0.0017520514,0.001772395,0.0015317518,0.004401331],"category_scores_gemma":[0.00374894,0.0006409984,0.0014007738,0.005029886,0.0007067258,0.002434902,0.0011957678,0.0019338424,0.0021887908],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000051032683,0.00010469309,0.00083821075,0.017329358,0.00017252349,0.00016857489,0.00013827886,0.0070712036,0.0010033274,0.01630209,0.021577617,0.935243],"study_design_scores_gemma":[0.000020198671,0.00028022612,0.0027696155,0.011086161,0.00043041314,0.0011843537,0.0001858688,0.016136037,0.00205754,0.030113434,0.9355837,0.00015232578],"about_ca_topic_score_codex":0.0017329504,"about_ca_topic_score_gemma":0.0015747974,"teacher_disagreement_score":0.004401331,"about_ca_system_score_codex":0.00067565806,"about_ca_system_score_gemma":0.0015731249,"threshold_uncertainty_score":0.014723897},"labels":[],"label_agreement":null},{"id":"W4400381330","doi":"10.1016/j.eswa.2024.124656","title":"Accurate synthesis of sensor-to-machined-surface image generation in carbon fiber-reinforced plastic drilling","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Delamination (geology); Drilling; Durability; Fibre-reinforced plastic; Computer science; Materials science; Machining; Mechanical engineering; Composite material; Geology; Engineering","score_opus":0.00873153816691692,"score_gpt":0.24358766910832275,"score_spread":0.23485613094140584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400381330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053517528,0.00016293736,0.94428355,0.000059336486,0.00003571607,0.00003342972,0.000044843724,0.00070208893,0.0011605694],"genre_scores_gemma":[0.8105568,0.00016614169,0.1877908,0.000032717642,0.000011109992,0.00005193978,0.000109813154,0.00007141767,0.0012092323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99990773,0.000011219764,0.0000035155915,0.000021511225,0.000045848526,0.0000101416945],"domain_scores_gemma":[0.99983823,0.000060697224,0.0000243067,0.000020547248,0.000048119513,0.00000800498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017787976,0.00034957888,0.0002678876,0.00017809562,0.00009488856,0.0002450779,0.00040196124,0.00038747833,0.000750874],"category_scores_gemma":[0.0005298877,0.0002160252,0.00029877343,0.00013825858,0.00019456643,0.00034641605,0.0002265877,0.0003076759,0.00024621995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001811854,0.000062205196,0.00093717093,0.00016596945,0.000018489178,0.00021000102,0.00013108946,0.63990843,0.12959717,0.0025426936,0.00091408996,0.22533159],"study_design_scores_gemma":[0.0000034941377,0.000036262307,0.00020711917,0.0000023566927,0.0000021054761,0.000027149681,0.0000044893563,0.9857137,0.013411598,0.00019702966,0.00039017343,0.000004564013],"about_ca_topic_score_codex":0.0018665297,"about_ca_topic_score_gemma":0.0017569512,"teacher_disagreement_score":0.0018665297,"about_ca_system_score_codex":0.00023843416,"about_ca_system_score_gemma":0.00035496996,"threshold_uncertainty_score":0.0037112832},"labels":[],"label_agreement":null},{"id":"W4400383844","doi":"10.1016/j.eswa.2024.124643","title":"CARD: Comprehensive approach based on relative difference for decision-making problems with dual evaluation forms − Application to sustainable renewable energy selection","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Iran University of Science and Technology; Iran National Science Foundation; Iran's National Elites Foundation","keywords":"Dual (grammatical number); Computer science; Selection (genetic algorithm); Renewable energy; Sustainable energy; Artificial intelligence; Environmental economics; Machine learning; Risk analysis (engineering); Operations research; Mathematics; Business","score_opus":0.0673593248607758,"score_gpt":0.38357851599157544,"score_spread":0.31621919113079966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400383844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001708324,0.00014283494,0.99679035,0.0000809291,0.00006079117,0.000087532106,0.000037758768,0.00008835241,0.0010030874],"genre_scores_gemma":[0.12659937,0.00032022904,0.8683044,0.00016692623,0.00009999669,0.0006246945,0.00020877444,0.00011296301,0.0035626572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951757,0.0024894888,0.00026161288,0.00043970573,0.0014205243,0.00021294893],"domain_scores_gemma":[0.9925465,0.0050048353,0.00024633086,0.00045577777,0.0015286027,0.00021789847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010287932,0.0014797435,0.0029092596,0.0024671908,0.0009632353,0.0023193748,0.0024792699,0.0018376267,0.007866607],"category_scores_gemma":[0.01690102,0.0007380971,0.0017524692,0.0024218329,0.0011686928,0.0032910502,0.0028212578,0.0029182103,0.0006265231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039650625,0.00038631522,0.00093140604,0.00075277255,0.000256605,0.00014480302,0.00021795755,0.40429685,0.0019017713,0.2314422,0.0069674617,0.35230538],"study_design_scores_gemma":[0.000033616583,0.0001153526,0.00017154508,0.000039973063,0.00003121629,0.000040652958,0.000017984155,0.96000123,0.0005313689,0.036256358,0.0027352541,0.000025368765],"about_ca_topic_score_codex":0.0023965598,"about_ca_topic_score_gemma":0.0023949358,"teacher_disagreement_score":0.010287932,"about_ca_system_score_codex":0.0012677116,"about_ca_system_score_gemma":0.0025253429,"threshold_uncertainty_score":0.05440837},"labels":[],"label_agreement":null},{"id":"W4400400027","doi":"10.1016/j.eswa.2024.124710","title":"Survey on Explainable AI: Techniques, challenges and open issues","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Computer science; Data science; Artificial intelligence","score_opus":0.07160439259705363,"score_gpt":0.35152822231162967,"score_spread":0.279923829714576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400400027","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035581063,0.8175179,0.12698483,0.016882392,0.0009915266,0.00010069689,0.00044356688,0.0005731846,0.032947853],"genre_scores_gemma":[0.031147541,0.86846465,0.08834262,0.0022289758,0.0025148385,0.00014454068,0.001245015,0.00021281165,0.005698947],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.997682,0.000684673,0.00023211578,0.0003546743,0.00092321134,0.00012339222],"domain_scores_gemma":[0.9738643,0.02093609,0.00059975346,0.0018374907,0.0024035734,0.00035879438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005242317,0.0008660722,0.0014623551,0.0049791504,0.0007312622,0.004298537,0.0030676168,0.0014471118,0.01160062],"category_scores_gemma":[0.014730827,0.00073260075,0.0010861901,0.008317958,0.0019322397,0.0098172305,0.00229715,0.0034641495,0.0024917414],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006947647,0.00013210054,0.0015102573,0.007195679,0.0001340459,0.00008006598,0.00041027647,0.0035253665,0.0007007285,0.25772318,0.032793835,0.6957249],"study_design_scores_gemma":[0.000022486518,0.00012374263,0.0019715682,0.0047913357,0.00012020847,0.00043181324,0.0005110828,0.012128601,0.0012278411,0.27048734,0.70812374,0.000060226652],"about_ca_topic_score_codex":0.0024615969,"about_ca_topic_score_gemma":0.0026442006,"teacher_disagreement_score":0.01160062,"about_ca_system_score_codex":0.0021415409,"about_ca_system_score_gemma":0.0028584334,"threshold_uncertainty_score":0.03880793},"labels":[],"label_agreement":null},{"id":"W4400617959","doi":"10.1016/j.eswa.2024.124762","title":"Open-vocabulary object detection via debiased curriculum self-training","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Vocabulary; Curriculum; Natural language processing; Machine learning; Object (grammar); Computer vision; Pattern recognition (psychology); Psychology; Linguistics; Pedagogy","score_opus":0.014389172244817729,"score_gpt":0.28630577759657566,"score_spread":0.27191660535175793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400617959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18481068,0.00085366593,0.78641194,0.0004135979,0.0004122044,0.00023846299,0.0006442942,0.0132221775,0.012992915],"genre_scores_gemma":[0.7321156,0.00024755576,0.24743183,0.00048774478,0.000099360426,0.00020688259,0.0023896703,0.00046704218,0.016554328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917597,0.000103358536,0.000039892562,0.00033001538,0.00017326456,0.0001774565],"domain_scores_gemma":[0.99870574,0.00036532176,0.00007700092,0.00028714578,0.00046575724,0.00009897785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009284986,0.00085732865,0.00086829497,0.0010569476,0.0005872169,0.0007632126,0.00170871,0.0013353709,0.006709638],"category_scores_gemma":[0.0030350406,0.00031176014,0.0006242867,0.0008005789,0.000453295,0.001651496,0.0028077716,0.0014193146,0.004164573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026500126,0.00024256227,0.0024810391,0.000081457736,0.000038236663,0.00008896677,0.00010679047,0.008190936,0.038053557,0.0014798356,0.005459833,0.94351184],"study_design_scores_gemma":[0.00006475227,0.00037428967,0.005421062,0.000047861624,0.00007638279,0.00035718715,0.00022814109,0.90031594,0.07820019,0.0057467753,0.009125718,0.000041726384],"about_ca_topic_score_codex":0.0040166215,"about_ca_topic_score_gemma":0.006255871,"teacher_disagreement_score":0.006709638,"about_ca_system_score_codex":0.00046147278,"about_ca_system_score_gemma":0.0011738647,"threshold_uncertainty_score":0.022445977},"labels":[],"label_agreement":null},{"id":"W4400620876","doi":"10.1016/j.eswa.2024.124780","title":"Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Machine learning; Disease; Deep learning; Data science; Medicine; Pathology","score_opus":0.09059684299010333,"score_gpt":0.3029390564757793,"score_spread":0.21234221348567595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400620876","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11626876,0.31254342,0.54587996,0.014581195,0.0009028324,0.00013233375,0.0012400327,0.0013591951,0.0070923325],"genre_scores_gemma":[0.6710929,0.099142805,0.21923143,0.0018257762,0.0022543864,0.0001452253,0.0022053097,0.00012666898,0.003975485],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9995384,0.000132167,0.000041083134,0.000113265436,0.000121024896,0.00005394763],"domain_scores_gemma":[0.99797493,0.0011194272,0.00012745656,0.00012716505,0.0005334374,0.000117555624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002441113,0.0008812307,0.0013755653,0.0011370525,0.0001487664,0.0016716239,0.0010559944,0.0011339308,0.0009703241],"category_scores_gemma":[0.0041385326,0.00028619712,0.000886913,0.001098874,0.00043269747,0.002139856,0.0008403383,0.0013559968,0.00038904586],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026747235,0.0003336868,0.020457216,0.00079301,0.00047450434,0.0001117407,0.000109506946,0.02975384,0.0051112054,0.005447373,0.009672387,0.9274681],"study_design_scores_gemma":[0.000042276406,0.00032531025,0.014128206,0.00055839005,0.00038093133,0.0003571778,0.000369779,0.9121548,0.005533012,0.046335995,0.019703664,0.0001104737],"about_ca_topic_score_codex":0.004918866,"about_ca_topic_score_gemma":0.006695965,"teacher_disagreement_score":0.004918866,"about_ca_system_score_codex":0.0006164499,"about_ca_system_score_gemma":0.0010910238,"threshold_uncertainty_score":0.012909949},"labels":[],"label_agreement":null},{"id":"W4400927401","doi":"10.1016/j.eswa.2024.124852","title":"A systematic review of trimodal affective computing approaches: Text, audio, and visual integration in emotion recognition and sentiment analysis","year":2024,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Firat University Scientific Research Projects Management Unit; University of Alberta","keywords":"Computer science; Sentiment analysis; Emotion recognition; Affective computing; Audio visual; Speech recognition; Natural language processing; Artificial intelligence; Human–computer interaction; Pattern recognition (psychology); Multimedia","score_opus":0.07219832333873055,"score_gpt":0.37573116914719984,"score_spread":0.3035328458084693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400927401","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0001401519,0.99912494,0.00023313196,0.00013370914,0.000059743645,0.000057335634,0.00010361953,0.0000064003248,0.00014104087],"genre_scores_gemma":[0.0014842147,0.9966757,0.001016324,0.00038647058,0.000073848474,0.00013140142,0.000101451165,0.0000054038724,0.00012516619],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9980276,0.0006036759,0.00062949513,0.00027336186,0.00040709827,0.000058675814],"domain_scores_gemma":[0.991585,0.006546414,0.0008675385,0.0001314703,0.0007464769,0.00012311626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045624026,0.0014694331,0.005492618,0.006393141,0.00043869184,0.0024396982,0.0019034676,0.0014978361,0.0069701658],"category_scores_gemma":[0.014948701,0.0005545317,0.0043942514,0.0060473904,0.00073076657,0.0021192844,0.0015703188,0.0013491438,0.0009648077],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002594371,0.00005076411,0.00023337331,0.58256215,0.0034556845,0.00006228023,0.0001124951,0.00015593081,0.000493255,0.00048806056,0.006462836,0.4056637],"study_design_scores_gemma":[0.00059882354,0.00062850147,0.006912995,0.7435234,0.05208723,0.0007895415,0.0004250677,0.0003832416,0.00085188483,0.00245173,0.1911888,0.00015874344],"about_ca_topic_score_codex":0.0050136778,"about_ca_topic_score_gemma":0.017887883,"teacher_disagreement_score":0.0069701658,"about_ca_system_score_codex":0.0012018878,"about_ca_system_score_gemma":0.005627532,"threshold_uncertainty_score":0.024128616},"labels":[],"label_agreement":null},{"id":"W4401100658","doi":"10.1016/j.eswa.2024.124913","title":"Reservoir computing based encryption-then-compression scheme of image achieving lossless compression","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Southwest University; Chongqing Science and Technology Commission","keywords":"Lossless compression; Computer science; Image compression; Compression (physics); Encryption; Scheme (mathematics); Lossy compression; Lossless JPEG; Image (mathematics); Data compression; Computer vision; Theoretical computer science; Artificial intelligence; Image processing; Computer security; Mathematics","score_opus":0.014538906799180368,"score_gpt":0.2785556375792903,"score_spread":0.26401673078010995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401100658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1010629,0.00086329895,0.88432854,0.00069641153,0.00022828943,0.00010878191,0.00017869259,0.0005989132,0.011934216],"genre_scores_gemma":[0.851885,0.00043022865,0.13853133,0.00013811048,0.00006310297,0.000059829417,0.00011414144,0.000030877596,0.008747372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998307,0.000026110558,0.000012650226,0.000029106885,0.00006803987,0.000033349064],"domain_scores_gemma":[0.99983525,0.000041431158,0.00001861938,0.000049377322,0.000043813063,0.0000114679915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023469616,0.00026098717,0.00040155835,0.00022024591,0.00030530235,0.00044730943,0.00059073407,0.0004718627,0.0023879702],"category_scores_gemma":[0.00048227498,0.00010936708,0.0002457811,0.0003272609,0.00034827457,0.0009250014,0.00058979535,0.00061844755,0.00032828024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008577271,0.0001893847,0.00095844094,0.00042461866,0.00010478264,0.0005966228,0.00022779236,0.12954625,0.31659994,0.21043804,0.0070875855,0.3329689],"study_design_scores_gemma":[0.000037701615,0.00020799169,0.00036430382,0.000025287925,0.00003040994,0.0005119121,0.000022735976,0.8409534,0.13703707,0.0168699,0.0039016898,0.000037545393],"about_ca_topic_score_codex":0.00048462843,"about_ca_topic_score_gemma":0.0007156651,"teacher_disagreement_score":0.0023879702,"about_ca_system_score_codex":0.00027837418,"about_ca_system_score_gemma":0.00049809436,"threshold_uncertainty_score":0.007988572},"labels":[],"label_agreement":null},{"id":"W4401155163","doi":"10.1016/j.eswa.2024.124900","title":"Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Computer science; Index (typography); Ensemble forecasting; Probabilistic forecasting; Artificial intelligence; Weather forecasting; Meteorology; Geography","score_opus":0.033017089358276006,"score_gpt":0.21492470522490223,"score_spread":0.18190761586662624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401155163","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9158894,0.0018816541,0.048720207,0.0020556836,0.00043254817,0.000064360334,0.016308358,0.003595129,0.011052639],"genre_scores_gemma":[0.9782154,0.00024027641,0.011561571,0.000084250416,0.0000627284,0.000011183629,0.006288618,0.00011985274,0.0034160393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998356,0.000012456325,0.000008077859,0.000045184028,0.00004610074,0.00005245399],"domain_scores_gemma":[0.99958485,0.00006150802,0.000022921658,0.000033302982,0.00025366002,0.000043797703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050262053,0.0006772481,0.0004869862,0.0007896503,0.00071690156,0.0011888009,0.0010167389,0.0005875239,0.001659432],"category_scores_gemma":[0.0015996919,0.00025254692,0.0004694029,0.0012043294,0.00022306474,0.00087393535,0.00044872603,0.0009897023,0.0005160391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027707903,0.00017271466,0.055046305,0.00009764373,0.00028504006,0.0002130684,0.000110716515,0.7560862,0.005388333,0.0013532967,0.019554134,0.16141543],"study_design_scores_gemma":[0.0000136956405,0.0000051461316,0.012481,0.000007686303,0.000025318386,0.0000061078676,0.000049826558,0.984781,0.0008425852,0.0003279452,0.0014396413,0.000020024061],"about_ca_topic_score_codex":0.8836691,"about_ca_topic_score_gemma":0.9096462,"teacher_disagreement_score":0.11633092,"about_ca_system_score_codex":0.0029240642,"about_ca_system_score_gemma":0.006162149,"threshold_uncertainty_score":0.23403198},"labels":[],"label_agreement":null},{"id":"W4401155360","doi":"10.1016/j.eswa.2024.124836","title":"A paradigm shift in appointment Scheduling: Introducing a decentralized integrated Online booking system","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Paradigm shift; Computer science; Scheduling (production processes); Distributed computing; Operations research; Operations management","score_opus":0.04041137937182063,"score_gpt":0.38465508213540833,"score_spread":0.34424370276358773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401155360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02676326,0.00030632343,0.9598929,0.0019362464,0.0004426572,0.00015250912,0.00006816299,0.0012672887,0.009170593],"genre_scores_gemma":[0.49653435,0.00038258865,0.4931748,0.0008765408,0.0005911544,0.00015422756,0.00011883496,0.00022085126,0.0079467185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876297,0.0004457239,0.000059340542,0.00027577198,0.0003254524,0.00013082144],"domain_scores_gemma":[0.99848855,0.0004956293,0.0001019935,0.00031381144,0.00033969118,0.00026026106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017153583,0.00027724376,0.0005964894,0.00026978148,0.0007186568,0.0021829854,0.0017198905,0.0010536686,0.004116713],"category_scores_gemma":[0.0028914995,0.00039375405,0.0004246376,0.00057083776,0.0005359532,0.0019467162,0.001781869,0.0018305505,0.0010523134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086821994,0.0014945995,0.0027623302,0.0003310563,0.00017690078,0.0003607554,0.000819605,0.22001517,0.044069417,0.13983719,0.023033364,0.5662313],"study_design_scores_gemma":[0.00011890351,0.00024575944,0.000504269,0.00002419884,0.000038461396,0.00015120051,0.000099253375,0.9356417,0.0045480225,0.0285392,0.030051626,0.000037318223],"about_ca_topic_score_codex":0.0026090108,"about_ca_topic_score_gemma":0.0031023398,"teacher_disagreement_score":0.004116713,"about_ca_system_score_codex":0.0007432431,"about_ca_system_score_gemma":0.0022797696,"threshold_uncertainty_score":0.013771772},"labels":[],"label_agreement":null},{"id":"W4401197937","doi":"10.1016/j.eswa.2024.124879","title":"A scale-equivariant CNN-based method for estimating human weight and height from multi-view clinic silhouette images","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bioinformatics Solutions (Canada)","funders":"Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Silhouette; Equivariant map; Computer science; Scale (ratio); Artificial intelligence; Computer vision; Pattern recognition (psychology); Mathematics; Cartography; Geography","score_opus":0.028003472488757928,"score_gpt":0.37173460983673096,"score_spread":0.343731137347973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401197937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044469405,0.0012238519,0.94800466,0.00017919349,0.00025957034,0.00010223876,0.0006191515,0.0025293708,0.0026125778],"genre_scores_gemma":[0.51265985,0.0019077063,0.46772388,0.00048356282,0.00032187183,0.00015192633,0.0026200698,0.0004053951,0.013725697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996983,0.000022013503,0.000013414148,0.000109759756,0.000102476275,0.000054075877],"domain_scores_gemma":[0.99979967,0.000026947606,0.000023639966,0.000036219244,0.00009402508,0.00001955418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003934589,0.0010164721,0.00089333754,0.00089896616,0.00021496683,0.00048108233,0.0011173174,0.0007843344,0.001998036],"category_scores_gemma":[0.0007394232,0.0005070988,0.00082049484,0.0008947972,0.00020601683,0.00045336093,0.00082188693,0.00068941544,0.0014308201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028751334,0.00012281306,0.0029794122,0.000109710345,0.00019981767,0.00023624346,0.00004531326,0.033842575,0.0681635,0.00096117274,0.006976418,0.88607544],"study_design_scores_gemma":[0.00001421338,0.00007794394,0.0055491356,0.000021426737,0.00009288889,0.0004783766,0.000018143823,0.974032,0.016325425,0.00079837505,0.0025649513,0.000027130554],"about_ca_topic_score_codex":0.013398673,"about_ca_topic_score_gemma":0.02237147,"teacher_disagreement_score":0.013398673,"about_ca_system_score_codex":0.00047049488,"about_ca_system_score_gemma":0.0006774343,"threshold_uncertainty_score":0.026641369},"labels":[],"label_agreement":null},{"id":"W4401198692","doi":"10.1016/j.eswa.2024.124919","title":"Subspace learning for feature selection via rank revealing QR factorization: Fast feature selection","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Seneca Polytechnic","funders":"","keywords":"Feature selection; Computer science; Subspace topology; Rank (graph theory); Feature (linguistics); Selection (genetic algorithm); Factorization; Artificial intelligence; Pattern recognition (psychology); QR decomposition; Minimum redundancy feature selection; Machine learning; Mathematics; Algorithm; Combinatorics","score_opus":0.010690355960508728,"score_gpt":0.28618774206053027,"score_spread":0.27549738610002156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401198692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002417078,0.00023989851,0.99638474,0.00007463287,0.000028473836,0.000040149604,0.00006682421,0.0005126515,0.00023550629],"genre_scores_gemma":[0.114620626,0.0005686156,0.88052344,0.00014315153,0.00009484368,0.00025017312,0.00077887153,0.00019979465,0.0028204878],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854314,0.00046484367,0.0000794759,0.00026652403,0.00050573517,0.00014020955],"domain_scores_gemma":[0.99822253,0.0008424578,0.000104644336,0.00029071086,0.00046981737,0.00006994235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014896883,0.0014233731,0.0017787192,0.0008986639,0.00057918124,0.0010008634,0.0012173663,0.0010340525,0.0051388214],"category_scores_gemma":[0.0048208763,0.0006450917,0.0009419992,0.0015915933,0.0005937028,0.0015423716,0.0015166136,0.0017943905,0.0032312311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056215934,0.00024220288,0.0006988864,0.00038256173,0.0001309889,0.00016053062,0.00013299752,0.08631939,0.036338095,0.015658848,0.015331699,0.8440417],"study_design_scores_gemma":[0.00004973113,0.00014526215,0.00041043464,0.000014243058,0.000025283685,0.00011696288,0.00003881828,0.9789659,0.0090608075,0.007688483,0.0034555176,0.000028563469],"about_ca_topic_score_codex":0.0042923368,"about_ca_topic_score_gemma":0.0049944753,"teacher_disagreement_score":0.0051388214,"about_ca_system_score_codex":0.00034528447,"about_ca_system_score_gemma":0.0015879333,"threshold_uncertainty_score":0.017191052},"labels":[],"label_agreement":null},{"id":"W4401361894","doi":"10.1016/j.eswa.2024.124966","title":"Extraction of sequential patterns from the web for human activity recognition","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; USable; Exploit; Autonomy; Field (mathematics); Activity recognition; Activities of daily living; Assisted living; Artificial intelligence; Machine learning; Data science; World Wide Web; Computer security","score_opus":0.059416170887688455,"score_gpt":0.3251723815078517,"score_spread":0.26575621062016325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401361894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38883317,0.0065095234,0.5040401,0.00075027114,0.00046820653,0.0010354128,0.0628372,0.018653015,0.016873159],"genre_scores_gemma":[0.6851042,0.0025224288,0.26276532,0.00013775736,0.00021816605,0.00059482385,0.040398814,0.00038844635,0.007870162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996996,0.000028973574,0.000039011935,0.000094216455,0.00009068342,0.00004759827],"domain_scores_gemma":[0.9993926,0.00014976654,0.00011024172,0.00009727165,0.00017829286,0.000071784045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001899797,0.0007917484,0.00061746483,0.006093356,0.00024318835,0.00073657,0.000471599,0.0005998042,0.0036719753],"category_scores_gemma":[0.0011981889,0.00022601527,0.0007173937,0.004995603,0.00014126055,0.0008075247,0.0004906284,0.00044024477,0.004426415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007646687,0.0005492684,0.03566014,0.00085209665,0.00019519134,0.0016481283,0.00018423612,0.0037960275,0.076041795,0.0014759193,0.018582575,0.86025],"study_design_scores_gemma":[0.0001564589,0.0009001511,0.272921,0.00061057374,0.00067175855,0.008748556,0.0013378295,0.49905136,0.10455104,0.024498712,0.086379945,0.00017270033],"about_ca_topic_score_codex":0.0038062294,"about_ca_topic_score_gemma":0.007862161,"teacher_disagreement_score":0.006093356,"about_ca_system_score_codex":0.0002008716,"about_ca_system_score_gemma":0.0006652733,"threshold_uncertainty_score":0.012283981},"labels":[],"label_agreement":null},{"id":"W4401649637","doi":"10.1016/j.eswa.2024.125120","title":"Logic-oriented fuzzy neural networks: A survey","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fuzzy logic; Artificial neural network; Artificial intelligence; Neuro-fuzzy; Machine learning; Fuzzy control system","score_opus":0.02237162754913257,"score_gpt":0.2724644143570859,"score_spread":0.2500927868079533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401649637","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005248364,0.8571014,0.10085531,0.001859635,0.0011990515,0.00008832154,0.0003000612,0.00024310891,0.033104807],"genre_scores_gemma":[0.05417846,0.88818324,0.046170995,0.0008046591,0.0015896267,0.000105768675,0.000643723,0.00005779903,0.008265666],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970156,0.000049318063,0.0000398002,0.00006402556,0.0001234477,0.000021825814],"domain_scores_gemma":[0.9995059,0.00025872522,0.000039431907,0.000021594069,0.0001576607,0.000016724996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005701347,0.00096706615,0.0007322758,0.0020160098,0.00036543986,0.0012197695,0.0012240292,0.0012246538,0.004233543],"category_scores_gemma":[0.0015756185,0.00039275177,0.0006208488,0.0027672458,0.00039242298,0.0019000914,0.0005478943,0.0009205359,0.0013900403],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007880338,0.000104930135,0.001249951,0.006543063,0.000107770844,0.0002774636,0.0001333885,0.015269598,0.0016840304,0.0371112,0.019154016,0.91828585],"study_design_scores_gemma":[0.000022970278,0.00030882677,0.0028961985,0.006887858,0.0002467381,0.0022899106,0.00029387616,0.09608543,0.0041124746,0.10175654,0.78495044,0.00014868517],"about_ca_topic_score_codex":0.0025136769,"about_ca_topic_score_gemma":0.002115636,"teacher_disagreement_score":0.004233543,"about_ca_system_score_codex":0.0006701762,"about_ca_system_score_gemma":0.0009814252,"threshold_uncertainty_score":0.0141626},"labels":[],"label_agreement":null},{"id":"W4401852659","doi":"10.1016/j.eswa.2024.125175","title":"Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"Mudanjiang Normal University; Natural Science Foundation of Heilongjiang Province","keywords":"Backtracking; Computer science; Robot; Workload; Workstation; Constraint (computer-aided design); Process (computing); Position (finance); Sequence (biology); Efficient energy use; Work (physics); Mathematical optimization; Algorithm; Artificial intelligence; Operating system; Engineering","score_opus":0.01051210704468864,"score_gpt":0.23651997128265886,"score_spread":0.22600786423797023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401852659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053866595,0.0002216396,0.9409171,0.00017912789,0.00010669268,0.00009306726,0.00007323998,0.00054307847,0.0039994707],"genre_scores_gemma":[0.8140815,0.00015066554,0.17854103,0.00008234952,0.00004935534,0.00013528414,0.00021523057,0.00008528848,0.0066592842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996542,0.00005743673,0.000013783579,0.00009163792,0.000081879814,0.000101108846],"domain_scores_gemma":[0.9995983,0.00013612852,0.000050193205,0.000044590084,0.000105171006,0.00006563557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005502499,0.0007473885,0.0012711218,0.0006661018,0.0010012911,0.001094201,0.0020637154,0.00072425784,0.004878815],"category_scores_gemma":[0.00092236913,0.0004451155,0.00063126965,0.0012933174,0.00051426765,0.0009920247,0.0013917264,0.00077279145,0.0004418881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036315227,0.00012761193,0.0007749558,0.00009960992,0.000053448137,0.000113076996,0.0001103063,0.880969,0.002740317,0.00767285,0.0034546414,0.10352103],"study_design_scores_gemma":[0.000022485485,0.000032213884,0.00012926589,0.0000022711656,0.0000067891697,0.000018318213,0.00002503416,0.9972696,0.0003475523,0.0017707866,0.00037034345,0.000005321253],"about_ca_topic_score_codex":0.008298514,"about_ca_topic_score_gemma":0.0077690776,"teacher_disagreement_score":0.008298514,"about_ca_system_score_codex":0.00091803574,"about_ca_system_score_gemma":0.0015545432,"threshold_uncertainty_score":0.016500413},"labels":[],"label_agreement":null},{"id":"W4402036074","doi":"10.1016/j.eswa.2024.125238","title":"A deep residual reinforcement learning algorithm based on Soft Actor-Critic for autonomous navigation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Residual; Artificial intelligence; Algorithm; Machine learning; Computer vision","score_opus":0.01574082428586497,"score_gpt":0.27792289904326045,"score_spread":0.26218207475739547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402036074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014384241,0.00024104283,0.9832101,0.00013148798,0.000053671964,0.000032635322,0.0000176051,0.0004673263,0.0014619415],"genre_scores_gemma":[0.84221804,0.00021641262,0.15298302,0.00018673111,0.00004376888,0.00017860794,0.00010313421,0.000084195206,0.0039861146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997063,0.00007039045,0.000017129727,0.000077902354,0.00007623132,0.000051938416],"domain_scores_gemma":[0.9994778,0.0002475208,0.00006720845,0.000033942848,0.00012770401,0.000045806486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079656666,0.0008226407,0.00091859215,0.00036493485,0.0002696516,0.0005208307,0.0011207691,0.000806419,0.0013649995],"category_scores_gemma":[0.0016719319,0.0003988829,0.00051266555,0.00029363728,0.00065252354,0.00057250675,0.00077688094,0.001311328,0.00027100084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006199903,0.000034359487,0.0004816823,0.00004217222,0.00004061709,0.00006331543,0.000044100958,0.9433911,0.0025837168,0.0042860224,0.0008464291,0.04812456],"study_design_scores_gemma":[0.000005493536,0.000016720713,0.000028577999,0.0000018344866,0.0000028201869,0.0000056383224,0.000001302431,0.9991289,0.00019931822,0.00050232734,0.00010502421,0.0000021029664],"about_ca_topic_score_codex":0.0062568644,"about_ca_topic_score_gemma":0.0046665533,"teacher_disagreement_score":0.0062568644,"about_ca_system_score_codex":0.00062432775,"about_ca_system_score_gemma":0.0012264602,"threshold_uncertainty_score":0.01244092},"labels":[],"label_agreement":null},{"id":"W4402130536","doi":"10.1016/j.eswa.2024.125183","title":"Prize-collecting Electric Vehicle routing model for parcel delivery problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Vehicle routing problem; Operations research; Service (business); Last mile (transportation); Electric vehicle; Outsourcing; Metaheuristic; Total cost; Loyalty; Routing (electronic design automation); Total cost of ownership; Integer programming; Quality of service; Work (physics); Business; Computer network; Engineering; Marketing","score_opus":0.022918078238572306,"score_gpt":0.2738865281985426,"score_spread":0.2509684499599703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402130536","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08732235,0.000986186,0.87158996,0.0024010907,0.00035959543,0.00026109483,0.0015856747,0.0003531097,0.035141017],"genre_scores_gemma":[0.86521983,0.0009419727,0.047062285,0.0002392612,0.000174799,0.00029213866,0.00089073164,0.00011601859,0.085062906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990858,0.00032466772,0.000030533567,0.0002097668,0.00016128476,0.00018797441],"domain_scores_gemma":[0.9991654,0.00036169303,0.00011964442,0.000050880455,0.00017711513,0.00012531466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557582,0.0010259412,0.0022543587,0.0010073383,0.00070792256,0.0019882785,0.004128226,0.0028063662,0.009843189],"category_scores_gemma":[0.0024651743,0.000666974,0.0010166803,0.0018911753,0.001027517,0.0020693708,0.0012819604,0.001791414,0.0007528806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006051377,0.000052431726,0.0003355232,0.00007473729,0.000020153058,0.00011752893,0.000025638128,0.9622597,0.00032225344,0.028818144,0.002394476,0.0055188793],"study_design_scores_gemma":[0.000010609901,0.000019131929,0.00011103118,0.0000047608337,0.0000077443565,0.000022164239,0.000012814349,0.99156064,0.00006439083,0.007434034,0.00074520597,0.0000075167623],"about_ca_topic_score_codex":0.010455915,"about_ca_topic_score_gemma":0.008906407,"teacher_disagreement_score":0.010455915,"about_ca_system_score_codex":0.0019898147,"about_ca_system_score_gemma":0.0016280247,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4402308721","doi":"10.1016/j.eswa.2024.125304","title":"Normal wiggly hesitant fuzzy modelling approach for 6G frameworks based blockchain technology","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Blockchain; Computer science; Fuzzy logic; Distributed computing; Artificial intelligence; Computer security","score_opus":0.012057560235207968,"score_gpt":0.23441391064296097,"score_spread":0.222356350407753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402308721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014670531,0.00013970604,0.972152,0.00022320874,0.000031819123,0.000046197536,0.00006581083,0.00007005913,0.01260062],"genre_scores_gemma":[0.8842452,0.0002573235,0.098048046,0.00007900104,0.00003220283,0.00013410697,0.00012649676,0.000029635565,0.01704792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992155,0.0002630135,0.00004329221,0.00014231351,0.00024896773,0.00008698653],"domain_scores_gemma":[0.99950314,0.00020355002,0.000048687172,0.000045752287,0.00016923982,0.000029669996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010407661,0.0005549325,0.00077734975,0.0007878078,0.0007980425,0.0018333566,0.0013451806,0.0011640759,0.0049441885],"category_scores_gemma":[0.0016504949,0.00030173964,0.00088814547,0.00062831736,0.0010133339,0.00218033,0.0012394576,0.0011467345,0.0004542681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080528414,0.000041523774,0.00040787423,0.00009248226,0.000049392358,0.00032051106,0.00032817057,0.69780535,0.002238152,0.27692592,0.0006967569,0.021013245],"study_design_scores_gemma":[0.000004037825,0.000020814945,0.000054038228,0.000011016068,0.0000088936795,0.000026271082,0.000040749874,0.93589634,0.0003897541,0.062517054,0.0010218304,0.0000092327355],"about_ca_topic_score_codex":0.010008665,"about_ca_topic_score_gemma":0.008392255,"teacher_disagreement_score":0.010008665,"about_ca_system_score_codex":0.0013438654,"about_ca_system_score_gemma":0.0012245366,"threshold_uncertainty_score":0.019900799},"labels":[],"label_agreement":null},{"id":"W4402604564","doi":"10.1016/j.eswa.2024.125369","title":"Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"European Regional Development Fund; European Society of Regional Anaesthesia and Pain Therapy; Agence Nationale de la Recherche","keywords":"Interpretation (philosophy); Cluster analysis; Computer science; Series (stratigraphy); Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning","score_opus":0.010374370100567104,"score_gpt":0.24441729035909435,"score_spread":0.23404292025852724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402604564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13618404,0.00039219484,0.8607109,0.00027854496,0.000061402156,0.000115162635,0.00064976275,0.0007492458,0.00085874635],"genre_scores_gemma":[0.725577,0.00019754529,0.27189857,0.00003206432,0.000033678094,0.00010147806,0.0012358102,0.00008321478,0.0008407649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958116,0.00010683953,0.000050782735,0.000120240504,0.00009158964,0.000049316215],"domain_scores_gemma":[0.9980812,0.0009014473,0.0002222493,0.00018891506,0.00055417675,0.00005197964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012423635,0.0005057409,0.000507459,0.0021418813,0.00054322806,0.00094236,0.0008243883,0.00071021984,0.0012464415],"category_scores_gemma":[0.006111443,0.00021906347,0.00091396255,0.001387197,0.00031494742,0.0006227775,0.0005804637,0.0008633022,0.00032313567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045502422,0.00021600306,0.016637996,0.00031215145,0.0002434503,0.00023783109,0.00083915016,0.5639976,0.009635293,0.011821089,0.0040149773,0.39158937],"study_design_scores_gemma":[0.000005561764,0.000026194646,0.005017508,0.000018633335,0.000016574759,0.00003750607,0.00007180877,0.988672,0.0009577173,0.004584104,0.00057659234,0.00001591977],"about_ca_topic_score_codex":0.013675797,"about_ca_topic_score_gemma":0.01106382,"teacher_disagreement_score":0.013675797,"about_ca_system_score_codex":0.00067241985,"about_ca_system_score_gemma":0.0010779446,"threshold_uncertainty_score":0.027192414},"labels":[],"label_agreement":null},{"id":"W4402637210","doi":"10.1016/j.eswa.2024.125412","title":"A multi-objective optimization approach for sustainable and personalized trip planning: A self-adaptive evolutionary algorithm with case study","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Transportation and Mobility Innovations","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Evolutionary algorithm; Mathematical optimization; Genetic algorithm; Optimization algorithm; Artificial intelligence; Algorithm; Machine learning; Mathematics","score_opus":0.01669337235149348,"score_gpt":0.2626366321404224,"score_spread":0.2459432597889289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402637210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42638412,0.000682869,0.54639244,0.00061495963,0.00008353972,0.0003039515,0.00014923874,0.00033595692,0.025052857],"genre_scores_gemma":[0.84841883,0.0002174675,0.14593206,0.00004236306,0.000014183191,0.00016509618,0.00006748822,0.000035509634,0.00510703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997639,0.00012295862,0.000009552097,0.000030791234,0.000042876454,0.000029911444],"domain_scores_gemma":[0.99940515,0.00041893695,0.000026666037,0.000036785554,0.00008523243,0.000027285783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000799736,0.00058527297,0.00056461606,0.0006889182,0.00052323606,0.0007777717,0.0009829496,0.0018002368,0.0022193347],"category_scores_gemma":[0.0015890835,0.00029344528,0.0006683691,0.0008395883,0.00034643552,0.00058387086,0.0005528743,0.000557262,0.00016938054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036540197,0.00010494837,0.00056758145,0.000045636883,0.000027078597,0.0002116139,0.00004396816,0.97503006,0.00059556274,0.0035871193,0.00036979895,0.019380076],"study_design_scores_gemma":[0.000007901695,0.000029742894,0.000117976284,0.0000027967828,0.0000071069867,0.000025061845,0.00001546565,0.99890196,0.00017280162,0.00046822472,0.00024818114,0.0000028284392],"about_ca_topic_score_codex":0.006834997,"about_ca_topic_score_gemma":0.006373848,"teacher_disagreement_score":0.006834997,"about_ca_system_score_codex":0.0005510038,"about_ca_system_score_gemma":0.0005529781,"threshold_uncertainty_score":0.013590395},"labels":[],"label_agreement":null},{"id":"W4402733211","doi":"10.1016/j.eswa.2024.125405","title":"Evaluating driver-pedestrian interaction behavior in different environments via Markov-game-based inverse reinforcement learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Traffic control and management","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Pedestrian; Reinforcement learning; Artificial intelligence; Machine learning; Markov chain; Hidden Markov model; Human–computer interaction; Transport engineering","score_opus":0.016981012490646392,"score_gpt":0.26454349463170035,"score_spread":0.24756248214105395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402733211","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8744267,0.000087781715,0.12327696,0.00016400707,0.00002523089,0.00008682184,0.000070859496,0.00023025683,0.0016314151],"genre_scores_gemma":[0.9953962,0.000009690611,0.0043011955,0.000013757722,0.0000014446534,0.000020476416,0.00003319656,0.0000036548706,0.00022029829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955064,0.00020734465,0.000017320535,0.00008002018,0.00007009428,0.000074689146],"domain_scores_gemma":[0.997678,0.0016225097,0.00021481613,0.00007139811,0.00023524457,0.00017807966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013586405,0.00071472954,0.00053094764,0.0004033268,0.00026493182,0.00045601802,0.00082892907,0.00056854094,0.00068620313],"category_scores_gemma":[0.003791281,0.0003491299,0.00043733336,0.00013970026,0.00061899534,0.0005531239,0.0007059022,0.0009333671,0.000076125994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041694166,0.00004832237,0.002240199,0.000007361718,0.00001440126,0.000022799157,0.000017428483,0.9953992,0.00021812748,0.0003877448,0.00004160543,0.0015612241],"study_design_scores_gemma":[0.0000019101178,0.000012855769,0.00014028656,3.3663275e-7,0.0000010848663,9.777583e-7,0.00000221616,0.9996748,0.0000619078,0.00009493865,0.000007618796,0.0000011100627],"about_ca_topic_score_codex":0.028987292,"about_ca_topic_score_gemma":0.01913183,"teacher_disagreement_score":0.028987292,"about_ca_system_score_codex":0.0013634993,"about_ca_system_score_gemma":0.0011234286,"threshold_uncertainty_score":0.057637155},"labels":[],"label_agreement":null},{"id":"W4402910088","doi":"10.1016/j.eswa.2024.125454","title":"Multi-view neutrosophic <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si2.svg\" display=\"inline\" id=\"d1e2958\"><mml:mi>c</mml:mi></mml:math>-means clustering algorithms","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Algorithm; Computer science; Cluster analysis; Database; Data mining; Artificial intelligence","score_opus":0.06396399516179124,"score_gpt":0.3418368717969467,"score_spread":0.2778728766351555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402910088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044424287,0.00008202856,0.99312145,0.00010366533,0.000018885989,0.00004251991,0.0000750331,0.00011583445,0.0019981274],"genre_scores_gemma":[0.18389553,0.00025571056,0.81314707,0.00012705663,0.00003703205,0.0001297023,0.00033664185,0.00006814505,0.0020031142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990447,0.00023533578,0.000058993057,0.00021971426,0.00038905133,0.000052227384],"domain_scores_gemma":[0.99921083,0.0002578634,0.000102864564,0.00009979174,0.00028474364,0.000043883854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012596804,0.00085768243,0.0007480433,0.0013665089,0.0008376841,0.0017688291,0.0013079267,0.0010805612,0.002418453],"category_scores_gemma":[0.0028983846,0.00034406985,0.0008485262,0.001175747,0.0007340131,0.0013926034,0.0013969077,0.0012673036,0.0006688646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014730565,0.0000959362,0.0013518668,0.0003333571,0.00011078938,0.00020021487,0.00026844663,0.5768953,0.0167405,0.12149902,0.0086898655,0.27366737],"study_design_scores_gemma":[0.0000052424634,0.000019785548,0.00021501962,0.000016816375,0.000008666967,0.00005480462,0.000035328918,0.9766516,0.0029807203,0.01744001,0.002552916,0.000019064177],"about_ca_topic_score_codex":0.0051105353,"about_ca_topic_score_gemma":0.0060070483,"teacher_disagreement_score":0.0051105353,"about_ca_system_score_codex":0.0013365587,"about_ca_system_score_gemma":0.001333974,"threshold_uncertainty_score":0.010161579},"labels":[],"label_agreement":null},{"id":"W4403147773","doi":"10.1016/j.eswa.2024.125472","title":"SFINet: A semantic feature interactive learning network for full-time infrared and visible image fusion","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"National Natural Science Foundation of China","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Semantic feature; Fusion; Infrared; Image (mathematics); Computer vision; Pattern recognition (psychology)","score_opus":0.0037517997993182636,"score_gpt":0.24175441309757142,"score_spread":0.23800261329825317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403147773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016321898,0.00027342897,0.9667565,0.00013129666,0.00011392281,0.000094069226,0.0009770888,0.013101593,0.0022302507],"genre_scores_gemma":[0.30681187,0.0003290593,0.672527,0.00037453472,0.00012708198,0.00030917124,0.0058162375,0.00087494025,0.012830032],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999655,0.000042483825,0.000012406705,0.00011549469,0.00011818329,0.000056431127],"domain_scores_gemma":[0.9996973,0.000073785486,0.000021852466,0.0000673725,0.00011171875,0.000028031918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008381248,0.0011580714,0.0007940241,0.0010049136,0.0007091384,0.00070116634,0.0021411579,0.0014214774,0.0059096063],"category_scores_gemma":[0.0013717115,0.0004524379,0.0008752955,0.00095342775,0.00047004345,0.0016759576,0.0018686159,0.0012993804,0.0020295708],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007849093,0.00039431057,0.001726729,0.0001357284,0.00025565282,0.00018055324,0.000100315294,0.10931757,0.024326399,0.0055618268,0.028038839,0.8291771],"study_design_scores_gemma":[0.000026843005,0.00008040704,0.00050577504,0.00000958273,0.000029472165,0.000059209156,0.000019825302,0.9785766,0.010531801,0.0053592576,0.0047803386,0.000020806026],"about_ca_topic_score_codex":0.010451619,"about_ca_topic_score_gemma":0.017181499,"teacher_disagreement_score":0.010451619,"about_ca_system_score_codex":0.0007819318,"about_ca_system_score_gemma":0.00085446285,"threshold_uncertainty_score":0.020781577},"labels":[],"label_agreement":null},{"id":"W4403262390","doi":"10.1016/j.eswa.2024.125534","title":"A geographic-semantic context-aware urban commuting flow prediction model using graph neural network","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Computer science; Artificial neural network; Graph; Context (archaeology); Artificial intelligence; Machine learning; Data mining; Theoretical computer science; Geography","score_opus":0.0261890593102568,"score_gpt":0.29257085631996865,"score_spread":0.2663817970097119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403262390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2902055,0.0019916664,0.6880138,0.0028090505,0.00035826376,0.00014478719,0.0054118093,0.0035161283,0.007549056],"genre_scores_gemma":[0.9546307,0.00043397755,0.03773593,0.00026463374,0.00007781138,0.00010038599,0.0024831132,0.00006183929,0.0042116395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986327,0.00001722097,0.000006151157,0.00006885945,0.000018154395,0.000026336262],"domain_scores_gemma":[0.9998474,0.000054659038,0.000023657716,0.000011493051,0.000046083303,0.000016721302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002612628,0.00078474707,0.00053133565,0.0009518062,0.00028937875,0.0006010632,0.0015024865,0.00087768526,0.0014526393],"category_scores_gemma":[0.00082899106,0.000424483,0.00064859574,0.001078207,0.00041541836,0.0010223823,0.00062245794,0.001070696,0.0002829282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006029018,0.00006907196,0.004733366,0.000031939602,0.00004791868,0.00005917111,0.000028394074,0.95868874,0.00053656887,0.0026033737,0.002761842,0.03037928],"study_design_scores_gemma":[0.0000019712309,0.0000030790704,0.00026021712,0.0000020519383,0.000005114822,0.0000033113558,0.0000027516978,0.9987361,0.000048736227,0.00079630915,0.00013859227,0.0000018017166],"about_ca_topic_score_codex":0.08407448,"about_ca_topic_score_gemma":0.10441002,"teacher_disagreement_score":0.08407448,"about_ca_system_score_codex":0.001490852,"about_ca_system_score_gemma":0.0011172348,"threshold_uncertainty_score":0.16717029},"labels":[],"label_agreement":null},{"id":"W4403281680","doi":"10.1016/j.eswa.2024.125500","title":"A large-scale supplier evaluation approach for circular economy in the presence of circular criteria interactions and weight consistency","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Consistency (knowledge bases); Circular economy; Scale (ratio); Computer science; Data mining; Artificial intelligence; Physics","score_opus":0.019270478790694115,"score_gpt":0.27349018535984665,"score_spread":0.25421970656915255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403281680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017715521,0.000065482105,0.978519,0.00009525354,0.000013008018,0.00017401547,0.00005230326,0.00020008879,0.0031653242],"genre_scores_gemma":[0.46746147,0.000089439534,0.52758974,0.000094709176,0.00003221654,0.0003260628,0.00020653516,0.000110121364,0.0040896926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963283,0.0016711657,0.00018330381,0.0005380185,0.0010914191,0.00018785273],"domain_scores_gemma":[0.9930306,0.004039349,0.00039522685,0.00058255426,0.0017758389,0.00017647055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005821654,0.0008781763,0.0016178464,0.0021969134,0.0013504288,0.002425326,0.0022347665,0.0013746755,0.006025618],"category_scores_gemma":[0.01471261,0.0007663443,0.0011694501,0.0025735092,0.0010746573,0.0028800427,0.002773676,0.0012527341,0.00057131593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004003198,0.0004806785,0.0048524314,0.00041476177,0.00037641372,0.0008427526,0.0008821297,0.5991702,0.007473979,0.065470785,0.0035803511,0.31605524],"study_design_scores_gemma":[0.000015195468,0.000057559795,0.00045179742,0.0000149655625,0.000041948348,0.0000517749,0.000107654785,0.9833429,0.0007191164,0.014491199,0.00068838574,0.000017496366],"about_ca_topic_score_codex":0.008717505,"about_ca_topic_score_gemma":0.012951824,"teacher_disagreement_score":0.008717505,"about_ca_system_score_codex":0.0013817205,"about_ca_system_score_gemma":0.0030364373,"threshold_uncertainty_score":0.030788243},"labels":[],"label_agreement":null},{"id":"W4403524826","doi":"10.1016/j.eswa.2024.125487","title":"ADA-UDA: A transferable transformer framework for rumor detection using Adversarial Domain Alignment within Unsupervised Domain Adaptation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Science and Technology Service Network Plan; Key Science and Technology Program of Shaanxi Province; Sichuan Province Science and Technology Support Program; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Adversarial system; Domain adaptation; Transformer; Rumor; Adaptation (eye); Domain (mathematical analysis); Artificial intelligence; Data mining; Machine learning; Mathematics; Electrical engineering","score_opus":0.040878525868206894,"score_gpt":0.32504788864605316,"score_spread":0.28416936277784627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403524826","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00290108,0.0002379717,0.99241126,0.00009176998,0.0000521261,0.000045584064,0.00010123379,0.0035514298,0.0006075855],"genre_scores_gemma":[0.27545127,0.00048147596,0.71400803,0.0005111678,0.00014606712,0.0001967859,0.0012224873,0.00092473056,0.007058042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989587,0.00041051343,0.000045259756,0.00025103491,0.00023134143,0.00010307868],"domain_scores_gemma":[0.99839646,0.00079839374,0.00009898126,0.00033172013,0.0002931756,0.00008127098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002178733,0.0009973339,0.0014071274,0.0011969678,0.00056269165,0.0012391147,0.0028152086,0.0015385916,0.0041151866],"category_scores_gemma":[0.0046909438,0.00066305534,0.0012577598,0.0008771201,0.0008649613,0.0021309392,0.002816395,0.0030818018,0.002880674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052292447,0.0003541385,0.0011931031,0.0002384124,0.00028454143,0.00023587988,0.0001902347,0.2113505,0.011945576,0.022603527,0.016616562,0.7344646],"study_design_scores_gemma":[0.000010825971,0.00003070506,0.000091400296,0.000007879946,0.000014026705,0.0000555103,0.000015099535,0.9865498,0.0028146817,0.008762413,0.0016373931,0.000010204139],"about_ca_topic_score_codex":0.00365734,"about_ca_topic_score_gemma":0.0051385066,"teacher_disagreement_score":0.0041151866,"about_ca_system_score_codex":0.0005677736,"about_ca_system_score_gemma":0.0011627926,"threshold_uncertainty_score":0.013766706},"labels":[],"label_agreement":null},{"id":"W4403539812","doi":"10.1016/j.eswa.2024.125557","title":"VGTS: Visually Guided Text Spotting for novel categories in historical manuscripts","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Spotting; Computer science; Natural language processing; Artificial intelligence; Information retrieval","score_opus":0.03840895304255738,"score_gpt":0.301956647548362,"score_spread":0.2635476945058046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403539812","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3506529,0.009517549,0.32926983,0.0011895042,0.0020181602,0.0018801322,0.06884051,0.21669646,0.019934876],"genre_scores_gemma":[0.3939219,0.0016781528,0.46112987,0.00064192136,0.000352931,0.0007082453,0.121062405,0.0030122208,0.017492315],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944764,0.00006465391,0.000038830192,0.00021581724,0.00014439404,0.00008865835],"domain_scores_gemma":[0.9994192,0.00013285356,0.00005293558,0.00018976224,0.00013383108,0.000071486174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059609127,0.0020357866,0.0011531898,0.0042534317,0.000513482,0.0015479097,0.0023505634,0.0014926788,0.00583063],"category_scores_gemma":[0.0019550677,0.0003167665,0.0013276866,0.0023429773,0.0005437459,0.0018408123,0.0019170793,0.00094933284,0.005639665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011181384,0.00030824248,0.0039966977,0.0011626396,0.00024248075,0.0006702314,0.0005223246,0.020336902,0.04291359,0.0015458995,0.12787405,0.7993088],"study_design_scores_gemma":[0.00043854726,0.00081620243,0.015450188,0.00026467678,0.00017996192,0.0022045444,0.0017827601,0.7316284,0.10467534,0.0117003545,0.13068986,0.00016916856],"about_ca_topic_score_codex":0.0068608695,"about_ca_topic_score_gemma":0.012135422,"teacher_disagreement_score":0.0068608695,"about_ca_system_score_codex":0.0007254221,"about_ca_system_score_gemma":0.0007966024,"threshold_uncertainty_score":0.019505382},"labels":[],"label_agreement":null},{"id":"W4403541664","doi":"10.1016/j.eswa.2024.125556","title":"IFusionQuad: A novel framework for improved aspect-based sentiment quadruple analysis in dialogue contexts with advanced feature integration and contextual CloBlock","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Department of Science and Technology of Sichuan Province; Ministry of Science and Technology of the People's Republic of China; Science and Technology Service Network Plan; National Natural Science Foundation of China","keywords":"Computer science; Sentiment analysis; Feature (linguistics); Artificial intelligence; Natural language processing; Human–computer interaction; Linguistics","score_opus":0.011994315867215279,"score_gpt":0.2825469148215361,"score_spread":0.2705525989543208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403541664","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034702057,0.00021195147,0.98158365,0.00005340228,0.000048243244,0.00011163371,0.0004934533,0.012538269,0.0014891261],"genre_scores_gemma":[0.10586624,0.000273101,0.8838072,0.00016820597,0.00009215785,0.0002666472,0.002238669,0.002985413,0.0043024397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991099,0.00016722846,0.000065489854,0.0002400862,0.00031147103,0.000105766405],"domain_scores_gemma":[0.99936277,0.00018655848,0.000057754776,0.00012932095,0.00021035412,0.000053169064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009949565,0.0013187533,0.0010506565,0.0020410644,0.00078820845,0.0023341752,0.0017034303,0.00074155536,0.0072361412],"category_scores_gemma":[0.003156442,0.00065373204,0.0013510173,0.0013351891,0.0005562381,0.00272866,0.0027768973,0.0015836864,0.0032791328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006553744,0.00021760479,0.0040474334,0.0007717777,0.00022807765,0.0006379356,0.001657604,0.012713649,0.063555315,0.035937604,0.029346239,0.85023135],"study_design_scores_gemma":[0.00007756401,0.00016847123,0.0029531508,0.00017868949,0.00017988241,0.000545198,0.00066300656,0.79438394,0.03664649,0.057713617,0.1062924,0.00019751849],"about_ca_topic_score_codex":0.00515497,"about_ca_topic_score_gemma":0.009550104,"teacher_disagreement_score":0.0072361412,"about_ca_system_score_codex":0.0004767358,"about_ca_system_score_gemma":0.0010664373,"threshold_uncertainty_score":0.024207354},"labels":[],"label_agreement":null},{"id":"W4403574402","doi":"10.1016/j.eswa.2024.125582","title":"Modeling competing guidance on evacuation choices under time pressure using virtual reality and machine learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"National Institutes of Health; National Science Foundation","keywords":"Computer science; Virtual reality; Human–computer interaction; Machine learning; Artificial intelligence; Simulation","score_opus":0.024700094076433646,"score_gpt":0.27602948599232324,"score_spread":0.2513293919158896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403574402","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62516856,0.0008029952,0.36456993,0.0015208917,0.00026889378,0.0000894443,0.00047826752,0.0003063256,0.006794642],"genre_scores_gemma":[0.9894879,0.00010935472,0.006885976,0.00005743266,0.000040688075,0.000039241073,0.000099415265,0.00001835924,0.0032617196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915576,0.0003129058,0.000033560525,0.00017634504,0.00010855262,0.00021283508],"domain_scores_gemma":[0.99265563,0.0056756837,0.00060425233,0.00012895529,0.00048235973,0.00045315985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017336467,0.0008710415,0.0014868184,0.0014247056,0.0006778593,0.0018314115,0.0018106822,0.002960269,0.0031177504],"category_scores_gemma":[0.010130976,0.0013581397,0.0011029483,0.0009513846,0.0015501162,0.0018424743,0.0014193484,0.0018483625,0.00024509054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006719546,0.000030036485,0.00049043883,0.000008557394,0.000012262144,0.000033588876,0.000023734838,0.995593,0.000060621347,0.0022810916,0.0001276595,0.0012718193],"study_design_scores_gemma":[0.0000029998923,0.0000071188315,0.00008931336,0.0000011977794,0.0000017674603,0.0000027175242,0.0000045413117,0.9993413,0.000012082812,0.00051178684,0.000022118562,0.000002956232],"about_ca_topic_score_codex":0.05444713,"about_ca_topic_score_gemma":0.033548467,"teacher_disagreement_score":0.05444713,"about_ca_system_score_codex":0.0018870843,"about_ca_system_score_gemma":0.0018341095,"threshold_uncertainty_score":0.10826039},"labels":[],"label_agreement":null},{"id":"W4403601156","doi":"10.1016/j.eswa.2024.125581","title":"Rethinking prediction-based video anomaly detection from local–global normality perspective","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Scholarship Council; Science and Technology Commission of Shanghai Municipality","keywords":"Normality; Perspective (graphical); Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Data mining; Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.011100133485326973,"score_gpt":0.25510862476355034,"score_spread":0.24400849127822336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403601156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034701075,0.00039701498,0.9619037,0.0004933586,0.00016716261,0.000031884167,0.000084085346,0.0011239514,0.0010978293],"genre_scores_gemma":[0.8209567,0.0003880603,0.17605773,0.00023407537,0.00022258949,0.00003165145,0.000315137,0.00023573762,0.0015584383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987208,0.00023955801,0.000075786375,0.00040338168,0.00043264238,0.00012777047],"domain_scores_gemma":[0.9964803,0.0012522738,0.00025547456,0.00065353216,0.0011826686,0.00017577507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016691778,0.00088300556,0.0012410614,0.00093105034,0.0004637305,0.0015030897,0.0016408727,0.0008798229,0.0009836119],"category_scores_gemma":[0.0064874236,0.0003116199,0.0005928212,0.0007381975,0.0008103645,0.0025786664,0.0019351642,0.001963581,0.00045630088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048566525,0.00033064737,0.024071835,0.00018681452,0.0002472187,0.0005175405,0.00038864484,0.1972715,0.055029415,0.018597823,0.006207167,0.69666576],"study_design_scores_gemma":[0.0000056237586,0.000055928365,0.0018566424,0.000011029965,0.000024806797,0.000091418646,0.00005903259,0.98306686,0.004950527,0.008850364,0.0010141975,0.0000135635055],"about_ca_topic_score_codex":0.0067622466,"about_ca_topic_score_gemma":0.005668747,"teacher_disagreement_score":0.0067622466,"about_ca_system_score_codex":0.00044382518,"about_ca_system_score_gemma":0.0008744848,"threshold_uncertainty_score":0.013445735},"labels":[],"label_agreement":null},{"id":"W4403667757","doi":"10.1016/j.eswa.2024.125614","title":"Multi-scale neural network for accurate determination of the ash content of coal flotation concentrate using froth images","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakes Environmental (Canada); University of Waterloo; Ministry of Education and Child Care","funders":"Fundamental Research Funds for the Central Universities","keywords":"Computer science; Artificial neural network; Coal; Artificial intelligence; Scale (ratio); Content (measure theory); Process engineering; Froth flotation; Pulp and paper industry; Pattern recognition (psychology); Computer vision; Chemistry; Mathematics; Materials science; Metallurgy","score_opus":0.048832019291871136,"score_gpt":0.31707527916146866,"score_spread":0.2682432598695975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403667757","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54485554,0.0010054888,0.44916987,0.0001790664,0.00010914784,0.00007437574,0.00030782574,0.0010709632,0.003227694],"genre_scores_gemma":[0.92255896,0.00026273384,0.07387857,0.000045199024,0.00002091925,0.000040578285,0.00020439363,0.000019213028,0.0029693712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992895,0.000007757659,0.0000042877214,0.000022129438,0.000024337205,0.000012580699],"domain_scores_gemma":[0.99990165,0.000031576197,0.000012441784,0.00000698347,0.00004218256,0.000005161348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023228982,0.00033508198,0.00021647263,0.00051390234,0.00015773709,0.00023808953,0.00031683478,0.00048568033,0.0007630312],"category_scores_gemma":[0.0003472314,0.00016686886,0.0002757756,0.00035843716,0.00010630685,0.0003278086,0.00018763992,0.00029424316,0.00018229776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005289655,0.0002672427,0.0059621157,0.00013202123,0.00010335167,0.00014587339,0.00008385381,0.15992765,0.16775413,0.0007095842,0.0019237036,0.66246146],"study_design_scores_gemma":[0.0000038345092,0.000026610043,0.003510726,0.000003177268,0.000012334124,0.000017729659,0.0000090410485,0.9858737,0.010260892,0.000102533595,0.00017351861,0.000005865918],"about_ca_topic_score_codex":0.0077918903,"about_ca_topic_score_gemma":0.010547742,"teacher_disagreement_score":0.0077918903,"about_ca_system_score_codex":0.00029214306,"about_ca_system_score_gemma":0.00024096959,"threshold_uncertainty_score":0.015493035},"labels":[],"label_agreement":null},{"id":"W4403740969","doi":"10.1016/j.eswa.2024.125589","title":"FRGEM: Feature integration pre-training based Gaussian embedding model for Chinese word representation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Sichuan Province Science and Technology Support Program; National University's Basic Research Foundation of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Word embedding; Feature (linguistics); Word (group theory); Artificial intelligence; Representation (politics); Embedding; Training (meteorology); Natural language processing; Pattern recognition (psychology); Gaussian; Training set; Machine learning; Mathematics; Linguistics","score_opus":0.023426597846608593,"score_gpt":0.34731843608318186,"score_spread":0.32389183823657325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403740969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02792366,0.00068734755,0.9593274,0.00016828786,0.00016567278,0.0001019097,0.0009356288,0.009295538,0.0013945848],"genre_scores_gemma":[0.3781573,0.0009791713,0.5913173,0.00036997983,0.00014099131,0.000486101,0.009229979,0.0011174683,0.018201714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996643,0.000059791808,0.00002007674,0.00012017851,0.00007484569,0.00006088504],"domain_scores_gemma":[0.999718,0.000074562864,0.000013531141,0.000056594592,0.000116132316,0.000021136844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056750875,0.0011012475,0.00089508883,0.0006723179,0.00042902748,0.0005144797,0.0013467696,0.0007539209,0.004394558],"category_scores_gemma":[0.001135721,0.00037231972,0.0008547193,0.0010396859,0.00029535842,0.0014178178,0.0011242105,0.0016251612,0.0024532473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003469872,0.00017578236,0.001375814,0.0001926466,0.000118328884,0.00018060148,0.00014460608,0.06817334,0.022549687,0.00692983,0.021355828,0.87845653],"study_design_scores_gemma":[0.000031430107,0.00010005266,0.00076491025,0.000011974341,0.00003624282,0.000080036174,0.000027873428,0.9821035,0.009434029,0.003076725,0.0043109707,0.000022198488],"about_ca_topic_score_codex":0.021827802,"about_ca_topic_score_gemma":0.022864446,"teacher_disagreement_score":0.021827802,"about_ca_system_score_codex":0.0004804424,"about_ca_system_score_gemma":0.0013952425,"threshold_uncertainty_score":0.04340154},"labels":[],"label_agreement":null},{"id":"W4403897142","doi":"10.1016/j.eswa.2024.125613","title":"Artificial bee colony algorithm based on multiple indicators for many-objective optimization with irregular Pareto fronts","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Computer science; Pareto principle; Artificial bee colony algorithm; Multi-objective optimization; Mathematical optimization; Optimization algorithm; Pareto optimal; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.010151162525947797,"score_gpt":0.25836093192546555,"score_spread":0.24820976939951775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403897142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017410932,0.00030079487,0.9796397,0.00008876103,0.00007341515,0.000039489016,0.000019547735,0.00017048692,0.002256803],"genre_scores_gemma":[0.48713407,0.0003665182,0.5094237,0.00007459752,0.00006139151,0.00029097265,0.00012807372,0.00009537711,0.0024253842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991709,0.00029832797,0.000050270646,0.00007449492,0.00034664854,0.000059456455],"domain_scores_gemma":[0.9990507,0.00044580663,0.000075015196,0.000053538082,0.00033351232,0.000041490348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015031004,0.0009782657,0.0012810994,0.001113279,0.00059156487,0.0010391863,0.0013040085,0.0011003935,0.0012018078],"category_scores_gemma":[0.0034910997,0.0004421553,0.0008781554,0.0014979786,0.00052285887,0.0010207974,0.0011657058,0.0012226793,0.00020355775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111764246,0.00007418087,0.0006538188,0.0001026072,0.00008607913,0.000064490036,0.00006419366,0.9005245,0.0033490492,0.009871437,0.0011114536,0.0839864],"study_design_scores_gemma":[0.000006237205,0.0000130012995,0.000056922243,0.000003458709,0.0000050910617,0.000005916891,0.000002552696,0.9988757,0.00022310179,0.0006456283,0.00015937023,0.0000029539617],"about_ca_topic_score_codex":0.0034691524,"about_ca_topic_score_gemma":0.002272752,"teacher_disagreement_score":0.0034691524,"about_ca_system_score_codex":0.00051758735,"about_ca_system_score_gemma":0.00094025326,"threshold_uncertainty_score":0.007949233},"labels":[],"label_agreement":null},{"id":"W4404003110","doi":"10.1016/j.eswa.2024.125663","title":"Development of A deep Learning-based algorithm for High-Pitch helical computed tomography imaging","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Light Source (Canada); University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation; National Research Council; University of Saskatchewan","keywords":"Computer science; Computed tomography; Artificial intelligence; Algorithm; Tomography; Deep learning; Computer vision; Radiology; Optics; Physics; Medicine","score_opus":0.012740887698427858,"score_gpt":0.2965306980549348,"score_spread":0.28378981035650697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404003110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008048429,0.00010149247,0.99040496,0.000088876724,0.000021270997,0.000031688523,0.000023596389,0.00070220436,0.00057755836],"genre_scores_gemma":[0.23271109,0.0002044481,0.7633842,0.00023370225,0.000033575463,0.00013877149,0.0002541948,0.00012083081,0.0029192243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997571,0.00003146712,0.000017211292,0.000065966306,0.0000924356,0.00003585285],"domain_scores_gemma":[0.99959296,0.00014267009,0.000047781614,0.00003857811,0.0001467469,0.00003122706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064766797,0.0005879962,0.00048263572,0.00039281242,0.00025747073,0.00056501606,0.0012497677,0.0010764648,0.0015923019],"category_scores_gemma":[0.00176327,0.00041169327,0.00043717248,0.00038574744,0.00036869015,0.00087348546,0.00097439514,0.0013312041,0.0005555604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012699918,0.00008809922,0.0013746646,0.000098028846,0.000051739316,0.00009722174,0.000058462116,0.56051856,0.024109617,0.006509567,0.0020309521,0.40493608],"study_design_scores_gemma":[0.0000043915666,0.000017913464,0.00006136582,0.0000027995357,0.0000026943958,0.000014592587,0.000003018058,0.9960867,0.002887385,0.0005721858,0.00034450795,0.0000025201118],"about_ca_topic_score_codex":0.0044652503,"about_ca_topic_score_gemma":0.004891263,"teacher_disagreement_score":0.0044652503,"about_ca_system_score_codex":0.0006798993,"about_ca_system_score_gemma":0.0016037968,"threshold_uncertainty_score":0.008878529},"labels":[],"label_agreement":null},{"id":"W4404033128","doi":"10.1016/j.eswa.2024.125643","title":"AuthorNet: Leveraging attention-based early fusion of transformers for low-resource authorship attribution","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University; York University","funders":"","keywords":"Computer science; Attribution; Transformer; Fusion; Resource (disambiguation); Artificial intelligence; Psychology; Linguistics; Electrical engineering; Engineering; Social psychology","score_opus":0.028416718557299034,"score_gpt":0.2835948748019529,"score_spread":0.25517815624465384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404033128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04453895,0.0028317494,0.9009433,0.00088636857,0.0011304729,0.00024390577,0.005784365,0.03668801,0.006952922],"genre_scores_gemma":[0.64920074,0.0014387639,0.319355,0.0004158846,0.0009743755,0.00022072998,0.014470157,0.0018945753,0.01202974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637324,0.0007689222,0.0003756337,0.0010013374,0.0011112711,0.00036950334],"domain_scores_gemma":[0.986668,0.0063386816,0.00095779664,0.002767425,0.0025675225,0.00070056895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003507828,0.0018614702,0.001512113,0.009539934,0.0010014543,0.0030975991,0.0021535105,0.0017622396,0.0068068416],"category_scores_gemma":[0.021651115,0.0004975729,0.0010095331,0.0069446573,0.0005764983,0.006647848,0.0038501443,0.0017049033,0.006960374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00087877404,0.00042764642,0.010011385,0.00043666817,0.00016584394,0.00036242206,0.00038813558,0.010219563,0.016177801,0.01014469,0.039817397,0.9109697],"study_design_scores_gemma":[0.0001004988,0.0002598178,0.0051415656,0.0001234719,0.00022477638,0.00068529753,0.00028047775,0.7962707,0.047457103,0.11892128,0.030403204,0.00013178721],"about_ca_topic_score_codex":0.002641688,"about_ca_topic_score_gemma":0.0075680204,"teacher_disagreement_score":0.009539934,"about_ca_system_score_codex":0.00087979564,"about_ca_system_score_gemma":0.0019806498,"threshold_uncertainty_score":0.02277118},"labels":[],"label_agreement":null},{"id":"W4404202083","doi":"10.1016/j.eswa.2024.125681","title":"SD-ABM-ISM: An integrated system dynamics and agent-based modeling framework for information security management in complex information systems with multi-actor threat dynamics","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Information and Cyber Security","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Dynamics (music); System dynamics; Computer security; Information system; Knowledge management; Risk analysis (engineering); Human–computer interaction; Process management; Artificial intelligence; Business","score_opus":0.018480219035708612,"score_gpt":0.26697756456860466,"score_spread":0.24849734553289604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404202083","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015317091,0.000080277634,0.99524575,0.00010913,0.00003671444,0.000033368102,0.000120524324,0.00083792774,0.002004628],"genre_scores_gemma":[0.2741911,0.0004963968,0.7183644,0.00022587644,0.00011693733,0.0004613754,0.00067629496,0.00045751984,0.0050100316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993926,0.00024408865,0.000039193696,0.00007599497,0.00019162321,0.000056506124],"domain_scores_gemma":[0.9993444,0.0002594931,0.000080137535,0.0000878841,0.0001536285,0.00007438304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014441196,0.0009813855,0.0010590587,0.00079175783,0.0005793121,0.0015708116,0.0022836134,0.0011340285,0.003578925],"category_scores_gemma":[0.0019287155,0.00057936955,0.0016545935,0.00058863737,0.0006653101,0.001577498,0.0022376252,0.0020347491,0.00094572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032889136,0.00006282058,0.0006394172,0.0001177292,0.00010144362,0.000092631526,0.00012029909,0.8916252,0.0011263391,0.08132063,0.0019630066,0.02279756],"study_design_scores_gemma":[0.000004620068,0.0000070052947,0.000033670338,0.000008991006,0.000008265046,0.000008863642,0.00000768022,0.98654896,0.00014018301,0.010836524,0.002390891,0.0000043349814],"about_ca_topic_score_codex":0.008095135,"about_ca_topic_score_gemma":0.011711484,"teacher_disagreement_score":0.008095135,"about_ca_system_score_codex":0.0007857071,"about_ca_system_score_gemma":0.002131295,"threshold_uncertainty_score":0.016096056},"labels":[],"label_agreement":null},{"id":"W4404278292","doi":"10.1016/j.eswa.2024.125728","title":"A novel data-driven rolling horizon production planning approach for the plastic industry under the uncertainty of demand and recycling rate","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Production (economics); Computer science; Horizon; Production planning; Industrial engineering; Manufacturing engineering; Economics; Microeconomics; Mathematics; Engineering","score_opus":0.04647947774418688,"score_gpt":0.28368402836838125,"score_spread":0.23720455062419438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404278292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025458613,0.00036646568,0.96792734,0.0002046036,0.000039422677,0.00008499388,0.00027684183,0.00045556226,0.0051861713],"genre_scores_gemma":[0.8341328,0.00036063063,0.16125317,0.00009691964,0.000025140782,0.000246029,0.00048195114,0.00009246788,0.0033108355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967694,0.000069748225,0.000015855605,0.00008778324,0.00008911232,0.00006053668],"domain_scores_gemma":[0.99965775,0.00015379631,0.00006317328,0.0000241764,0.00007049492,0.00003057658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055647304,0.0010112827,0.000876212,0.00047106255,0.0004084243,0.0011392138,0.0010619092,0.000813124,0.0030563269],"category_scores_gemma":[0.00089295924,0.00076036766,0.0008770805,0.0006717649,0.00033626432,0.0007272173,0.0007384081,0.0009214551,0.00030883763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027491487,0.000012382158,0.00014798863,0.00004329293,0.000011467875,0.00005925331,0.000020891117,0.98664373,0.0009875722,0.0011238895,0.00021180467,0.01071015],"study_design_scores_gemma":[0.0000032773926,0.00001859314,0.00005643319,0.000003434973,0.000005323296,0.000007688234,0.000007870608,0.99859864,0.000305336,0.00072181114,0.00026807885,0.000003592574],"about_ca_topic_score_codex":0.01204724,"about_ca_topic_score_gemma":0.011805174,"teacher_disagreement_score":0.01204724,"about_ca_system_score_codex":0.00075701403,"about_ca_system_score_gemma":0.0018595841,"threshold_uncertainty_score":0.023954213},"labels":[],"label_agreement":null},{"id":"W4404341915","doi":"10.1016/j.eswa.2024.125669","title":"Consistent positive correlation sample distribution: Alleviating the negative sample noise issue in contrastive adaptation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sample (material); Adaptation (eye); Computer science; Correlation; Noise (video); Distribution (mathematics); Statistics; Pattern recognition (psychology); Artificial intelligence; Mathematics; Psychology; Physics; Mathematical analysis; Image (mathematics)","score_opus":0.019696307811565714,"score_gpt":0.2846219291296285,"score_spread":0.2649256213180628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404341915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016222026,0.00009700915,0.98179835,0.0002012948,0.0000430634,0.000061375875,0.000032353702,0.00056467607,0.0009798497],"genre_scores_gemma":[0.51509607,0.00018546975,0.47922018,0.00066900405,0.00021495803,0.00027963048,0.00021265153,0.0008344122,0.0032876397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9939084,0.0029161954,0.00030526132,0.0010236917,0.0015776278,0.00026889885],"domain_scores_gemma":[0.9579761,0.028686287,0.001921652,0.0071660974,0.0035661876,0.0006836506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009268849,0.0010899019,0.001096541,0.00075058785,0.00083302474,0.0012040621,0.002686614,0.0024361531,0.0031107496],"category_scores_gemma":[0.06723494,0.00093084056,0.0006592504,0.0008068798,0.0021158573,0.00245649,0.0045604734,0.0032335445,0.0008584392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022167077,0.0008516733,0.011498945,0.000569385,0.00039313035,0.000994111,0.0012428936,0.13050596,0.15945849,0.09628351,0.0063343127,0.5896509],"study_design_scores_gemma":[0.00015803399,0.00026985825,0.004664854,0.0000517176,0.00014028889,0.0005226252,0.00006977093,0.9281893,0.032303374,0.030310584,0.00326465,0.000054969998],"about_ca_topic_score_codex":0.0012353543,"about_ca_topic_score_gemma":0.0033140306,"teacher_disagreement_score":0.009268849,"about_ca_system_score_codex":0.0006908285,"about_ca_system_score_gemma":0.001732976,"threshold_uncertainty_score":0.04901898},"labels":[],"label_agreement":null},{"id":"W4404406221","doi":"10.1016/j.eswa.2024.125804","title":"Optimization framework for surveillance camera layouts considering infiltration routes in general outposts (GOPs)","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.012396628899297251,"score_gpt":0.24942534542084938,"score_spread":0.23702871652155214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404406221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009723673,0.00036824684,0.986413,0.00014813064,0.000052136693,0.00008145597,0.00020988636,0.0003077977,0.0026956666],"genre_scores_gemma":[0.56666344,0.0010713148,0.41493878,0.0002561587,0.00021034217,0.0005749306,0.0013933996,0.00053187145,0.014359722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928266,0.0001678078,0.000024870382,0.00023022117,0.00014614599,0.00014830977],"domain_scores_gemma":[0.9993235,0.00030609372,0.00008950174,0.000041479674,0.00017824993,0.00006132286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001174217,0.001962711,0.0030516335,0.0011931617,0.0005806222,0.0021854858,0.002491399,0.002843198,0.0061046835],"category_scores_gemma":[0.0023612867,0.0016654854,0.001994537,0.0016787747,0.00082607224,0.0014543426,0.0016357102,0.0015014311,0.0010973901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032234668,0.00002336445,0.00021350798,0.000075472955,0.00002616517,0.000056755558,0.000027109001,0.9841382,0.0006403707,0.002279238,0.0008463351,0.011641255],"study_design_scores_gemma":[0.0000049182936,0.000018419565,0.00008163579,0.0000063711095,0.00000825852,0.000009426544,0.000013114235,0.9986149,0.000108105196,0.00084833323,0.00028180456,0.0000047336853],"about_ca_topic_score_codex":0.028641582,"about_ca_topic_score_gemma":0.018817708,"teacher_disagreement_score":0.028641582,"about_ca_system_score_codex":0.0015387037,"about_ca_system_score_gemma":0.0023810894,"threshold_uncertainty_score":0.056949735},"labels":[],"label_agreement":null},{"id":"W4404412796","doi":"10.1016/j.eswa.2024.125648","title":"A new approach for competency frameworks mapping using large language models","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université TÉLUQ","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Natural language processing; Artificial intelligence; Data science","score_opus":0.03832938816186795,"score_gpt":0.2950653646710826,"score_spread":0.25673597650921465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404412796","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00095338793,0.0000728928,0.995808,0.00017141829,0.000043977154,0.00006221149,0.00025652567,0.0014583921,0.0011731558],"genre_scores_gemma":[0.055318624,0.00022378587,0.9384555,0.00019928467,0.000071940834,0.0003049842,0.0015842145,0.00065702107,0.0031846361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99498796,0.0014774202,0.00044348484,0.001164479,0.001715283,0.00021148149],"domain_scores_gemma":[0.99399513,0.0025727395,0.00023782156,0.001668079,0.001253102,0.0002731895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031378292,0.001155357,0.0011701448,0.0038794263,0.0017308927,0.0044923113,0.0030366755,0.0015279134,0.007336215],"category_scores_gemma":[0.013140687,0.0011817586,0.003711237,0.0030402204,0.0010437354,0.008628591,0.006479226,0.004498946,0.0036540802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019168165,0.00042497367,0.0024762903,0.0006040387,0.0004301336,0.000533591,0.0021766257,0.04163129,0.011176356,0.33505777,0.02097641,0.5843208],"study_design_scores_gemma":[0.00003539387,0.00006770779,0.0007414659,0.00014910246,0.00015437456,0.0005038845,0.0006736601,0.6012244,0.006823597,0.33466065,0.054855987,0.000109795415],"about_ca_topic_score_codex":0.008542319,"about_ca_topic_score_gemma":0.014066769,"teacher_disagreement_score":0.008542319,"about_ca_system_score_codex":0.0014233482,"about_ca_system_score_gemma":0.0032046272,"threshold_uncertainty_score":0.024542153},"labels":[],"label_agreement":null},{"id":"W4404565450","doi":"10.1016/j.eswa.2024.125790","title":"Adaptive and soft constrained vision-map vehicle localization using Gaussian processes and instance segmentation","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Artificial intelligence; Computer vision; Segmentation; Gaussian process; Gaussian; Pattern recognition (psychology)","score_opus":0.010368505002270342,"score_gpt":0.24271032437210116,"score_spread":0.2323418193698308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404565450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011580256,0.00008053214,0.9874113,0.000079185294,0.000017277614,0.000014705557,0.00003149599,0.00038801445,0.00039715972],"genre_scores_gemma":[0.609224,0.0001918732,0.3865365,0.00014753058,0.00008605145,0.00008412487,0.0003355749,0.00031738146,0.0030768062],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944204,0.00009436859,0.000026984684,0.00018665355,0.00015976839,0.00009027224],"domain_scores_gemma":[0.9991443,0.00036241775,0.000115347866,0.00013228999,0.00017997892,0.00006573603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060266105,0.0006493553,0.0016203461,0.00096142164,0.00045881447,0.0014891459,0.0020266937,0.0015713972,0.0013832821],"category_scores_gemma":[0.0029335287,0.00082628167,0.001084357,0.0012671787,0.0010297946,0.0014641944,0.001950175,0.0012539267,0.00042488988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000376653,0.000101699756,0.0010787807,0.000106884676,0.000111461864,0.00010475697,0.00011533701,0.74402165,0.015964525,0.017067708,0.0017087332,0.21924183],"study_design_scores_gemma":[0.000003612968,0.000007203007,0.0001231144,0.0000017380152,0.0000037108573,0.000011270188,0.00000387917,0.9969958,0.000968041,0.0017642011,0.00011333601,0.0000040456493],"about_ca_topic_score_codex":0.014147738,"about_ca_topic_score_gemma":0.014323796,"teacher_disagreement_score":0.014147738,"about_ca_system_score_codex":0.0010678733,"about_ca_system_score_gemma":0.0016433821,"threshold_uncertainty_score":0.02813077},"labels":[],"label_agreement":null},{"id":"W4404763830","doi":"10.1016/j.eswa.2024.125962","title":"Graph-Transformer with spatial-spectral features fusion for hyperspectral image classification","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Stroke Consortium; China Scholarship Council; National Natural Science Foundation of China; McGill University","keywords":"Hyperspectral imaging; Computer science; Pattern recognition (psychology); Artificial intelligence; Graph; Fusion; Transformer; Theoretical computer science; Physics","score_opus":0.011569077002822914,"score_gpt":0.24327598641237574,"score_spread":0.23170690940955282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404763830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05634556,0.00044629065,0.9356364,0.00023518527,0.0000916392,0.00010188714,0.00036511468,0.0035693652,0.0032086808],"genre_scores_gemma":[0.77680755,0.00037477625,0.21623282,0.00023244548,0.000059546826,0.00008897736,0.0015565497,0.00016896341,0.004478338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996747,0.000052832896,0.000015466665,0.00009345633,0.00011700594,0.000046581088],"domain_scores_gemma":[0.99972147,0.00006628001,0.00003403931,0.000053758427,0.00009856742,0.000025886606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005022762,0.0009815258,0.0006185734,0.0015356635,0.0003512544,0.0006013789,0.0012391413,0.0006445515,0.0021059748],"category_scores_gemma":[0.0011628418,0.00023047991,0.0009429869,0.001638427,0.0004488888,0.0018573331,0.0010280331,0.00080066704,0.00077208335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030424117,0.00026577315,0.0030411894,0.00013364665,0.00014649797,0.0001610092,0.000099565186,0.20870984,0.027630359,0.0092901215,0.007824787,0.7423931],"study_design_scores_gemma":[0.000008369072,0.00005268699,0.00065627764,0.0000047496633,0.000030764568,0.000065994915,0.000032800726,0.9807668,0.009271071,0.007599143,0.0014996572,0.0000116931715],"about_ca_topic_score_codex":0.006886373,"about_ca_topic_score_gemma":0.009463879,"teacher_disagreement_score":0.006886373,"about_ca_system_score_codex":0.0008459206,"about_ca_system_score_gemma":0.0007572215,"threshold_uncertainty_score":0.013692558},"labels":[],"label_agreement":null},{"id":"W4404838573","doi":"10.1016/j.eswa.2024.125820","title":"Hierarchical candidate recursive network for highlight restoration in endoscopic videos","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence","score_opus":0.011494996805427045,"score_gpt":0.2811939768989583,"score_spread":0.26969898009353127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404838573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016676564,0.00020635113,0.9821034,0.00005743491,0.000014032289,0.000022675958,0.000036826903,0.00035492147,0.00052774575],"genre_scores_gemma":[0.4142023,0.00047264568,0.57843506,0.00009314671,0.00005712338,0.000109639506,0.0002906363,0.00017116328,0.0061681694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997658,0.000059024493,0.000009029782,0.00006535747,0.000056782792,0.000044045373],"domain_scores_gemma":[0.99954706,0.0002262378,0.000047585654,0.000048928803,0.000102785016,0.000027424356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006461359,0.0005705299,0.00071464485,0.0006756516,0.00035180195,0.0005405219,0.0009911243,0.00097038224,0.0021028726],"category_scores_gemma":[0.0015160519,0.00039981064,0.00058183813,0.00045475957,0.00033961504,0.0008091088,0.0007483367,0.0008133643,0.00056481577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054575305,0.00014940646,0.0010790176,0.00015436953,0.00008722336,0.0001977418,0.00014977454,0.35682848,0.04531542,0.0105716,0.0036954505,0.5812258],"study_design_scores_gemma":[0.0000036667373,0.000026342239,0.00014061919,0.000004135019,0.000009119822,0.000023983986,0.000005937869,0.9956801,0.0029605203,0.0007955733,0.00034615686,0.0000038030198],"about_ca_topic_score_codex":0.00518444,"about_ca_topic_score_gemma":0.008661443,"teacher_disagreement_score":0.00518444,"about_ca_system_score_codex":0.00048342723,"about_ca_system_score_gemma":0.0007336847,"threshold_uncertainty_score":0.010308504},"labels":[],"label_agreement":null},{"id":"W4404851119","doi":"10.1016/j.eswa.2024.125924","title":"Pairwise dual-level alignment for cross-prompt automated essay scoring","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"Taishan Scholar Project of Shandong Province; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Pairwise comparison; Dual (grammatical number); Computer science; Artificial intelligence; Cross-validation; Data mining; Natural language processing; Pattern recognition (psychology)","score_opus":0.04273792357509929,"score_gpt":0.3207816349410548,"score_spread":0.2780437113659555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404851119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024395388,0.0005568009,0.94456565,0.00020943314,0.00040970228,0.00028985547,0.002119701,0.02171845,0.0057349806],"genre_scores_gemma":[0.26531228,0.00020718151,0.7112133,0.00017488876,0.00022470237,0.0006217745,0.010946666,0.0030066767,0.0082924785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99220383,0.0031421562,0.00063602603,0.0020722959,0.001310861,0.00063482346],"domain_scores_gemma":[0.9867264,0.004715504,0.0007212324,0.0023441787,0.0047997725,0.00069286523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045725508,0.0015663204,0.0016190321,0.003968651,0.0020371461,0.0032883768,0.0024490603,0.0021743483,0.0171283],"category_scores_gemma":[0.022230895,0.00093558105,0.0010025029,0.0038833346,0.0005705011,0.003068874,0.0050858147,0.0032689294,0.016809154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000962982,0.00038446797,0.003875869,0.00055669324,0.0001575169,0.00023939474,0.0009241128,0.0062953876,0.05327974,0.0077091707,0.04031002,0.8853047],"study_design_scores_gemma":[0.00028411055,0.0007982772,0.010983554,0.00024788314,0.00029602443,0.0010251828,0.0019762942,0.73788714,0.108667836,0.053125534,0.08444628,0.00026184827],"about_ca_topic_score_codex":0.0017349215,"about_ca_topic_score_gemma":0.0045644254,"teacher_disagreement_score":0.0171283,"about_ca_system_score_codex":0.00073869,"about_ca_system_score_gemma":0.002787334,"threshold_uncertainty_score":0.057299852},"labels":[],"label_agreement":null},{"id":"W4404893488","doi":"10.1016/j.eswa.2024.126001","title":"Interpretable model committee for monitoring and early prediction of intracranial pressure crises","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universitätsklinikum Heidelberg; Vilnius University; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico; Turun Yliopisto; Seventh Framework Programme; Charité – Universitätsmedizin Berlin; Narodowe Centrum Nauki; European Commission; University of Manitoba; Berlin Institute of Health; Kauno Technologijos Universitetas; Medizinische Universität Innsbruck; Helsingin ja Uudenmaan Sairaanhoitopiiri","keywords":"Computer science; Intracranial pressure; Artificial intelligence; Machine learning; Data mining; Medicine; Radiology","score_opus":0.02727952253824304,"score_gpt":0.2883741996372028,"score_spread":0.2610946770989597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404893488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21566524,0.0012699775,0.7704799,0.0013356481,0.0002441577,0.0003017396,0.0024779828,0.0054614935,0.002763774],"genre_scores_gemma":[0.9288059,0.00028645893,0.06435527,0.00027837118,0.00011266171,0.00026538564,0.0036334142,0.00014021886,0.0021222278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921227,0.00032917646,0.000058416463,0.00021740104,0.00009492722,0.00008782464],"domain_scores_gemma":[0.9971934,0.001771539,0.00029997915,0.00015885,0.00047279074,0.000103606086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026888906,0.0016431952,0.0010173809,0.0015761738,0.0004978592,0.0013980577,0.0013866178,0.0012140388,0.0020246578],"category_scores_gemma":[0.007604337,0.00047808897,0.0012923128,0.0005923525,0.00036276312,0.00088050676,0.0009443437,0.0016865669,0.0007388397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044431258,0.00020024195,0.016984317,0.0000789317,0.00027517282,0.00021545035,0.00010099042,0.90992224,0.001970968,0.0017290538,0.0029904004,0.065088004],"study_design_scores_gemma":[0.000005433142,0.000025748916,0.0005072284,0.0000063630946,0.0000146753755,0.000011091942,0.0000057067464,0.99825126,0.0002453704,0.00073803513,0.0001840581,0.0000049266405],"about_ca_topic_score_codex":0.013391942,"about_ca_topic_score_gemma":0.010048171,"teacher_disagreement_score":0.013391942,"about_ca_system_score_codex":0.001078133,"about_ca_system_score_gemma":0.0014685567,"threshold_uncertainty_score":0.026627958},"labels":[],"label_agreement":null},{"id":"W4405045292","doi":"10.1016/j.eswa.2024.126033","title":"Stacked fuzzy envelope consistency imbalanced ensemble classification method","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Chongqing Municipal Education Commission; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Computer science; Consistency (knowledge bases); Artificial intelligence; Pattern recognition (psychology); Ensemble learning; Envelope (radar); Fuzzy logic; Machine learning; Data mining","score_opus":0.031137723218206563,"score_gpt":0.31543773548748494,"score_spread":0.28430001226927837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405045292","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034513816,0.0006479152,0.9612898,0.00015618725,0.00018356799,0.000046871413,0.00012594312,0.00049607287,0.0025398568],"genre_scores_gemma":[0.6965719,0.0006353748,0.29429662,0.00026229155,0.00041057367,0.000114175185,0.00083599734,0.0001396218,0.00673344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883336,0.00019651907,0.00007532329,0.00026276926,0.0005219929,0.00010995928],"domain_scores_gemma":[0.9987244,0.00023277172,0.000077674944,0.00025189755,0.0006618326,0.000051386876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021223603,0.0007139035,0.0011595478,0.001275966,0.0006827595,0.0011070062,0.0012831845,0.00096374704,0.0022817566],"category_scores_gemma":[0.0026435317,0.0002539614,0.0009604192,0.0011500968,0.0003568177,0.0016790384,0.0013625589,0.00094784744,0.0007499233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005451409,0.00019030162,0.005166134,0.000098669865,0.00035307748,0.00010506498,0.00012340085,0.098532304,0.0164016,0.010589023,0.0071333554,0.86076176],"study_design_scores_gemma":[0.000014088365,0.00007755846,0.0022090073,0.000014729686,0.00010030591,0.00010893982,0.000037810478,0.9841069,0.005628718,0.0054298188,0.00225532,0.000016848626],"about_ca_topic_score_codex":0.0017332383,"about_ca_topic_score_gemma":0.002136756,"teacher_disagreement_score":0.0022817566,"about_ca_system_score_codex":0.00039029884,"about_ca_system_score_gemma":0.0007080589,"threshold_uncertainty_score":0.01122421},"labels":[],"label_agreement":null},{"id":"W4405045402","doi":"10.1016/j.eswa.2024.125996","title":"A new branch-and-Benders-cut algorithm for the time-dependent vehicle routing problem","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Vehicle routing problem; Branch and cut; Computer science; Algorithm; Mathematical optimization; Routing (electronic design automation); Integer programming; Mathematics; Computer network","score_opus":0.012920647537111862,"score_gpt":0.2621486258310455,"score_spread":0.24922797829393362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405045402","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002970395,0.00019340862,0.9938066,0.0001314517,0.000112653186,0.00006915533,0.000081209255,0.00038697422,0.0022483063],"genre_scores_gemma":[0.03389503,0.0002698551,0.9594429,0.00015522256,0.0000983727,0.00023517234,0.0004317169,0.00023749925,0.005234215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993067,0.00013588661,0.00003468853,0.00013319532,0.0003244683,0.00006504025],"domain_scores_gemma":[0.99911124,0.00043811786,0.000057882484,0.0000652248,0.00025652503,0.00007098189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088685297,0.0012151662,0.0015637134,0.0012933773,0.00062497164,0.001408374,0.0021018023,0.0022503296,0.0097501],"category_scores_gemma":[0.0025316593,0.00087750214,0.00095073535,0.0015779389,0.00052700326,0.0018737075,0.0014247485,0.0026735757,0.0017510576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002616235,0.0002629971,0.00037353789,0.0002314752,0.000090453825,0.00009316649,0.00007076777,0.47951514,0.0055113286,0.021671467,0.013392604,0.47852552],"study_design_scores_gemma":[0.000059863632,0.000052270687,0.000079530386,0.000014193456,0.000015900314,0.000032259744,0.000009993718,0.98842865,0.0007777873,0.006904339,0.0036155954,0.000009588434],"about_ca_topic_score_codex":0.0041342843,"about_ca_topic_score_gemma":0.005794489,"teacher_disagreement_score":0.0097501,"about_ca_system_score_codex":0.0009652596,"about_ca_system_score_gemma":0.0020892792,"threshold_uncertainty_score":0.03261733},"labels":[],"label_agreement":null},{"id":"W4405133505","doi":"10.1016/j.eswa.2024.125915","title":"Personnel scheduling problem for ready-mixed concrete delivery","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Center for Interuniversity Research and Analysis on Organizations; Global Affairs Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Scheduling (production processes); Job shop scheduling; Operations research; Mathematical optimization; Operating system; Schedule; Mathematics","score_opus":0.09929024395607605,"score_gpt":0.368135883397784,"score_spread":0.268845639441708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405133505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20684715,0.001062275,0.76136893,0.0016894613,0.00040316777,0.0007945435,0.0019728008,0.00051016436,0.025351569],"genre_scores_gemma":[0.72927237,0.00091863616,0.25214654,0.00021679679,0.00023964011,0.0006249416,0.0019797604,0.0002397368,0.014361527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989685,0.00041972386,0.000052209456,0.0001876033,0.00018265832,0.00018935006],"domain_scores_gemma":[0.9990664,0.00056312693,0.00013938302,0.00003931632,0.000084330706,0.00010749853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015327849,0.0012805948,0.001229372,0.00071279745,0.0007455571,0.0014230208,0.0015130413,0.0016852723,0.0082233865],"category_scores_gemma":[0.0022522449,0.00067109487,0.0014868092,0.00092884357,0.0005685694,0.0011386079,0.0007976176,0.0013586617,0.0006845731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019646369,0.00017483126,0.00090073346,0.0003050886,0.0000801948,0.0003312343,0.00010730554,0.9519865,0.0021631615,0.016143618,0.004009985,0.023600833],"study_design_scores_gemma":[0.00012359773,0.00023466858,0.0008290995,0.00004000404,0.000043367236,0.00017689994,0.00020946152,0.97570634,0.0014037787,0.013259838,0.007939553,0.000033445547],"about_ca_topic_score_codex":0.008201141,"about_ca_topic_score_gemma":0.006204755,"teacher_disagreement_score":0.0082233865,"about_ca_system_score_codex":0.0015256505,"about_ca_system_score_gemma":0.0026876058,"threshold_uncertainty_score":0.027509987},"labels":[],"label_agreement":null},{"id":"W4405137555","doi":"10.1016/j.eswa.2024.126034","title":"RQFormer: Rotated Query Transformer for end-to-end oriented object detection","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; End-to-end principle; Transformer; Artificial intelligence; Computer vision; Electrical engineering","score_opus":0.012589328320885667,"score_gpt":0.27383363220767726,"score_spread":0.2612443038867916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405137555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048567145,0.00029182862,0.9463075,0.00013301744,0.00016626118,0.00016371631,0.0013344449,0.044867057,0.0018795667],"genre_scores_gemma":[0.18082714,0.0004022618,0.7900719,0.00091705576,0.00021798216,0.00039638503,0.0076227263,0.0048894924,0.014655078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880743,0.000102841965,0.000078548896,0.00030427714,0.00053223275,0.0001746695],"domain_scores_gemma":[0.99892056,0.0002603307,0.000059381724,0.00032012677,0.0003588482,0.000080781094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013338393,0.001777651,0.0015627012,0.001523623,0.00047461555,0.0019004623,0.0029380736,0.0013663624,0.021767845],"category_scores_gemma":[0.0037211482,0.0006987749,0.0008324903,0.0012350118,0.0006056602,0.002502944,0.0026131433,0.0014233108,0.012648967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025682615,0.0003252163,0.0018459177,0.0005560031,0.00016143655,0.0005364365,0.00021859951,0.0083532175,0.14280808,0.015560416,0.11717182,0.7098946],"study_design_scores_gemma":[0.00034451258,0.0005737145,0.002186431,0.00006914344,0.00014567173,0.0010561296,0.000205102,0.62092733,0.28493738,0.02721531,0.062182594,0.00015665272],"about_ca_topic_score_codex":0.0046936586,"about_ca_topic_score_gemma":0.0063953646,"teacher_disagreement_score":0.021767845,"about_ca_system_score_codex":0.0008138698,"about_ca_system_score_gemma":0.0012353273,"threshold_uncertainty_score":0.07282072},"labels":[],"label_agreement":null},{"id":"W4405235374","doi":"10.1016/j.eswa.2024.126130","title":"Joint entity and relation extraction with table filling based on graph convolutional Networks","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"","keywords":"Computer science; Relationship extraction; Joint (building); Graph; Table (database); Relation (database); Artificial intelligence; Data mining; Pattern recognition (psychology); Theoretical computer science","score_opus":0.017283427904659514,"score_gpt":0.23851524714268935,"score_spread":0.22123181923802984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405235374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043385725,0.0022885303,0.9003984,0.00068714307,0.00035273572,0.00038207718,0.015923135,0.030731525,0.0058508674],"genre_scores_gemma":[0.29882008,0.001518433,0.6410229,0.0002634335,0.00019225963,0.00027262454,0.04501647,0.0009913284,0.0119025335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991202,0.00007690163,0.00007928413,0.0004226155,0.00018075635,0.00012014575],"domain_scores_gemma":[0.99883324,0.00048481615,0.000094110575,0.00029763533,0.00023035871,0.000059833783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006883092,0.0015699839,0.0013737071,0.0046546375,0.0008510193,0.0017827216,0.0017105354,0.0011746545,0.006957787],"category_scores_gemma":[0.002388368,0.00070529233,0.002051131,0.0055716815,0.0003924963,0.0040022316,0.0014940866,0.001518949,0.005003856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005617625,0.000314474,0.0048249844,0.0005773803,0.00027181543,0.00047854567,0.00024942541,0.02130968,0.028850157,0.010833085,0.041784305,0.88994443],"study_design_scores_gemma":[0.00005593504,0.00015208841,0.005911811,0.0001262224,0.00045621363,0.0005186098,0.00025587535,0.85775965,0.050599985,0.044634014,0.039426308,0.00010330572],"about_ca_topic_score_codex":0.018953864,"about_ca_topic_score_gemma":0.037290417,"teacher_disagreement_score":0.018953864,"about_ca_system_score_codex":0.0010294149,"about_ca_system_score_gemma":0.0023854792,"threshold_uncertainty_score":0.037687063},"labels":[],"label_agreement":null},{"id":"W4405300201","doi":"10.1016/j.eswa.2024.125943","title":"Online health-aware energy management strategy of a fuel cell hybrid autonomous mobile robot under startup–shutdown condition","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shutdown; Computer science; Mobile robot; Energy (signal processing); Fuel cells; Energy management; Robot; Automotive engineering; Embedded system; Artificial intelligence; Nuclear engineering; Engineering","score_opus":0.009673336013518387,"score_gpt":0.2433396829233283,"score_spread":0.23366634690980992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405300201","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7385938,0.0004884437,0.24139957,0.000620205,0.00019080371,0.00010939383,0.000100031066,0.001337157,0.017160565],"genre_scores_gemma":[0.997331,0.00002207068,0.0015657869,0.000019500185,0.0000045083284,0.000011662197,0.000013241896,0.0000041438584,0.0010280367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99993527,0.0000071253858,0.0000029190478,0.000017394354,0.000014609289,0.000022639204],"domain_scores_gemma":[0.9998766,0.000032869757,0.000019832805,0.000008762022,0.000038517326,0.000023417724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010779393,0.0004905905,0.00042159518,0.00025356654,0.00046420394,0.00039107777,0.00052877516,0.00053165836,0.0025834388],"category_scores_gemma":[0.00026114128,0.00015418009,0.00017011084,0.0000878065,0.00022707108,0.00040631526,0.00047869934,0.00021483285,0.00024129651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026788516,0.0005763145,0.01010855,0.0005174136,0.00013074675,0.0043170485,0.00078487414,0.5720135,0.17981657,0.004475596,0.004946409,0.2196342],"study_design_scores_gemma":[0.000041496478,0.00037146916,0.003867593,0.000011381913,0.000033944816,0.00014452537,0.00016310316,0.9844861,0.009235354,0.0007916034,0.00083594833,0.0000174917],"about_ca_topic_score_codex":0.0033110094,"about_ca_topic_score_gemma":0.0040565818,"teacher_disagreement_score":0.0033110094,"about_ca_system_score_codex":0.00015214599,"about_ca_system_score_gemma":0.00030656176,"threshold_uncertainty_score":0.008642495},"labels":[],"label_agreement":null},{"id":"W4405337780","doi":"10.1016/j.eswa.2024.126122","title":"Semi-mobile in-pit crushing and conveying vs. truck-shovel systems: Long-term scheduling with road and conveyor networks integration","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Shovel; Truck; Computer science; Term (time); Scheduling (production processes); Automotive engineering; Mining engineering; Geology; Operations management; Engineering; Mechanical engineering","score_opus":0.009579919816307078,"score_gpt":0.23217650898860687,"score_spread":0.2225965891722998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405337780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87767565,0.00017017899,0.11525707,0.00012536968,0.000025306093,0.00019703057,0.00015626587,0.00023732612,0.0061558327],"genre_scores_gemma":[0.98477674,0.000046914418,0.014220693,0.000008981045,0.0000038122876,0.000031266227,0.00006757377,0.000018129947,0.00082589645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996263,0.000110380286,0.000014358631,0.000058716414,0.0000680052,0.00012228696],"domain_scores_gemma":[0.9992192,0.00028435336,0.0001555764,0.00007726787,0.00014140004,0.00012226771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006910448,0.000587323,0.00052777637,0.00032793614,0.00040703826,0.0008930384,0.0012099681,0.0005538508,0.0014848596],"category_scores_gemma":[0.0012036207,0.00034557775,0.0004699749,0.000664815,0.000318949,0.00077068486,0.00057599513,0.00050463766,0.00012837294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016308656,0.00015300808,0.0012550576,0.000047807735,0.000023457815,0.000060668448,0.000031863583,0.9793173,0.0032999562,0.0007734685,0.00017238522,0.014701825],"study_design_scores_gemma":[0.000015926134,0.00029365296,0.0011694441,0.0000033331455,0.0000159399,0.000018494136,0.000073318646,0.9963952,0.0012844792,0.00043108183,0.0002919566,0.00000720621],"about_ca_topic_score_codex":0.01851822,"about_ca_topic_score_gemma":0.025474694,"teacher_disagreement_score":0.01851822,"about_ca_system_score_codex":0.0013834732,"about_ca_system_score_gemma":0.0019241221,"threshold_uncertainty_score":0.03682083},"labels":[],"label_agreement":null},{"id":"W4405394884","doi":"10.1016/j.eswa.2024.126017","title":"Learning adversarially robust kernel ensembles with kernel average pooling","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Kernel (algebra); Pooling; Computer science; Artificial intelligence; Multiple kernel learning; Machine learning; Kernel method; Radial basis function kernel; Mathematics; Support vector machine; Discrete mathematics","score_opus":0.011579720252070871,"score_gpt":0.2465815101318245,"score_spread":0.23500178987975362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405394884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036136664,0.0003127427,0.96100456,0.00022013977,0.000046802837,0.000028755196,0.00008061056,0.0010907946,0.0010789088],"genre_scores_gemma":[0.90338206,0.00026806555,0.09318336,0.00021177274,0.00006451353,0.00008010178,0.00033416782,0.00017503682,0.002300968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899036,0.00030325074,0.000055400087,0.00025014026,0.00026313233,0.00013770729],"domain_scores_gemma":[0.99808025,0.00066025107,0.00028643687,0.0005714837,0.00028090124,0.000120682496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020838124,0.0015136366,0.0014337301,0.0004966228,0.00045966284,0.0009477048,0.001585731,0.0014077284,0.0010136584],"category_scores_gemma":[0.0062740957,0.0006793069,0.0010781756,0.0004643925,0.0010990645,0.0029863578,0.0027477215,0.0026500355,0.0005354709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084699386,0.00004279942,0.0009068353,0.00003802244,0.00012090456,0.00007317995,0.000052452648,0.94000435,0.005236167,0.012301308,0.0015212406,0.039618015],"study_design_scores_gemma":[0.0000016737765,0.000017286719,0.00006696731,0.0000029129749,0.000005105472,0.000013856411,0.000003119657,0.993436,0.0010669532,0.005235533,0.00014618097,0.000004412843],"about_ca_topic_score_codex":0.0017483492,"about_ca_topic_score_gemma":0.0019259178,"teacher_disagreement_score":0.0020838124,"about_ca_system_score_codex":0.00084946334,"about_ca_system_score_gemma":0.0007522796,"threshold_uncertainty_score":0.011020362},"labels":[],"label_agreement":null},{"id":"W4405616994","doi":"10.1016/j.eswa.2024.126213","title":"A bi-objective data-driven chance-constrained optimization for sustainable urban medical waste management","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Healthcare and Environmental Waste Management","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Multi-objective optimization; Mathematical optimization; Risk analysis (engineering); Machine learning; Business; Mathematics","score_opus":0.020290609322495816,"score_gpt":0.3027723864484073,"score_spread":0.2824817771259115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405616994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038476225,0.0007555438,0.9537541,0.00044949685,0.000091393835,0.00011852374,0.00026607382,0.0002087203,0.0058798706],"genre_scores_gemma":[0.84561574,0.000636513,0.14679053,0.0002677938,0.00007057436,0.0005050969,0.00047985712,0.00009534915,0.0055385865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939,0.00018422799,0.000030541938,0.0001256267,0.0001668019,0.00010286564],"domain_scores_gemma":[0.99925786,0.00044036872,0.000090062145,0.000023412387,0.000131376,0.000056883448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012814325,0.0014473805,0.0014081569,0.0008990973,0.0005458411,0.0015033209,0.0013670152,0.0016776866,0.0025612582],"category_scores_gemma":[0.0018442712,0.00095276075,0.001311338,0.0012410276,0.0006922787,0.001035762,0.0013631651,0.0012667581,0.00020927322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011427172,0.000008736377,0.00013884458,0.000022514474,0.000013216418,0.000024508154,0.000006592991,0.9966659,0.00014678888,0.00082844374,0.00012989702,0.0020030835],"study_design_scores_gemma":[0.0000036762972,0.000009474494,0.000040963885,0.0000031275997,0.0000034677169,0.0000027797425,0.0000033896458,0.99936575,0.000046015004,0.00041750938,0.0001015326,0.0000023297362],"about_ca_topic_score_codex":0.015665613,"about_ca_topic_score_gemma":0.00911716,"teacher_disagreement_score":0.015665613,"about_ca_system_score_codex":0.0011208192,"about_ca_system_score_gemma":0.0020275735,"threshold_uncertainty_score":0.03114885},"labels":[],"label_agreement":null},{"id":"W4405999073","doi":"10.1016/j.eswa.2024.126367","title":"A group decision-making method for public opinion response plans: Herd behavior and two-stage consensus-reaching","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Xi’an Jiaotong University","keywords":"Group decision-making; Computer science; Stage (stratigraphy); Public opinion; Group (periodic table); Decision-making models; Herd; Operations research; Artificial intelligence; Mathematics; Psychology; Political science; Social psychology; Medicine; Biology; Veterinary medicine","score_opus":0.023088967286774512,"score_gpt":0.37167782345847517,"score_spread":0.34858885617170066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405999073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005153561,0.00003858181,0.9939697,0.00008212869,0.000024792025,0.000117671734,0.000021480932,0.00011032846,0.00048186159],"genre_scores_gemma":[0.20723161,0.00007882676,0.78907853,0.00014575582,0.00007719654,0.0009773375,0.00015029185,0.00010251184,0.0021578767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99364096,0.0035223023,0.00038152855,0.0010288253,0.0010831147,0.00034330745],"domain_scores_gemma":[0.97173315,0.023468483,0.0008533413,0.00094552437,0.0024177725,0.0005816131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015038624,0.0013640871,0.0030267097,0.0019924755,0.0015050884,0.0016242554,0.0036804178,0.003777561,0.0058229733],"category_scores_gemma":[0.030514387,0.0011098133,0.0018626392,0.0014651205,0.0017014975,0.003162282,0.0027840396,0.0027423068,0.00070873636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008041548,0.00044231318,0.0020485853,0.00044351045,0.0004155229,0.00018440695,0.00084930076,0.6911873,0.004056423,0.04563657,0.002712723,0.25121918],"study_design_scores_gemma":[0.0000460045,0.000058055677,0.00007177278,0.000010658695,0.00002349902,0.000010873565,0.00001758737,0.99086237,0.00042800856,0.008201057,0.00025594607,0.000014221485],"about_ca_topic_score_codex":0.0053808787,"about_ca_topic_score_gemma":0.004838565,"teacher_disagreement_score":0.015038624,"about_ca_system_score_codex":0.0016775205,"about_ca_system_score_gemma":0.0030163142,"threshold_uncertainty_score":0.0795328},"labels":[],"label_agreement":null},{"id":"W4406063203","doi":"10.1016/j.eswa.2024.126303","title":"A distance similarity-based genetic optimization algorithm for satellite ground network planning considering feeding mode","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Satellite Communication Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Similarity (geometry); Genetic algorithm; Mode (computer interface); Satellite; Algorithm; Optimization algorithm; Artificial intelligence; Mathematical optimization; Machine learning; Mathematics","score_opus":0.021108027405517038,"score_gpt":0.2762190699338666,"score_spread":0.25511104252834954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406063203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027086034,0.0001966884,0.96805364,0.00011096015,0.000061410814,0.00009326154,0.000037356862,0.00021357228,0.004147029],"genre_scores_gemma":[0.48496935,0.0001806159,0.5100379,0.00014599004,0.000056033095,0.0003418207,0.00016984683,0.00009973568,0.0039986684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995503,0.00012777321,0.000021916385,0.00007838992,0.00017570276,0.0000458816],"domain_scores_gemma":[0.9991454,0.00046262148,0.00006199552,0.000036233465,0.0002518613,0.00004182148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011105071,0.0006580913,0.001274696,0.0012466818,0.00063496426,0.0008366354,0.0016309533,0.0017477068,0.0025327634],"category_scores_gemma":[0.0026306466,0.0005071244,0.000796547,0.0015715588,0.000596706,0.0008526126,0.0010353014,0.0008227681,0.00030762222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041975956,0.00006738714,0.0003121744,0.000027948898,0.000025435826,0.00002629044,0.000035797133,0.9437012,0.0007193657,0.0032890565,0.00048410406,0.051269375],"study_design_scores_gemma":[0.000008162777,0.000019290717,0.000049131275,0.0000023343675,0.0000040135947,0.00000495878,0.0000035869612,0.9992422,0.00010158686,0.00044024843,0.00012227077,0.0000022628249],"about_ca_topic_score_codex":0.015055804,"about_ca_topic_score_gemma":0.009902266,"teacher_disagreement_score":0.015055804,"about_ca_system_score_codex":0.0011944148,"about_ca_system_score_gemma":0.0019096406,"threshold_uncertainty_score":0.029936314},"labels":[],"label_agreement":null},{"id":"W4406160139","doi":"10.1016/j.eswa.2025.126416","title":"Inter-frame residual frequency-based reconstruction learning for deep video frame interpolation detection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Guangdong Provincial Key Laboratory of Robotics and Intelligent Systems; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Frame (networking); Residual; Artificial intelligence; Residual frame; Computer vision; Interpolation (computer graphics); Deep learning; Motion interpolation; Pattern recognition (psychology); Reference frame; Algorithm; Video tracking; Video processing; Telecommunications; Block-matching algorithm","score_opus":0.008576228013502676,"score_gpt":0.2788852912507866,"score_spread":0.27030906323728393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406160139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017970515,0.00041619764,0.9796292,0.00010419527,0.000053515163,0.00003405607,0.00011292711,0.0008768647,0.0008025572],"genre_scores_gemma":[0.3770093,0.0008378047,0.6139194,0.00022895388,0.00009171898,0.00009282431,0.0009589461,0.00018168917,0.006679384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969447,0.00004478095,0.000016006537,0.000076357384,0.00010817914,0.00006026792],"domain_scores_gemma":[0.999524,0.00013700908,0.00004535318,0.00007771896,0.00018656102,0.000029290368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072026916,0.00073875196,0.00077371334,0.0008844104,0.0002216276,0.00046248012,0.0010424893,0.00087548856,0.0030577744],"category_scores_gemma":[0.0017962295,0.00027060608,0.000522894,0.00071370625,0.00026213282,0.0007049788,0.00076409464,0.0011865654,0.0013258877],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003813122,0.00015687282,0.0011280122,0.00009347885,0.000050135637,0.00006501983,0.00004828897,0.047866613,0.037526414,0.0033845608,0.003578645,0.90572065],"study_design_scores_gemma":[0.0000085751835,0.000071901915,0.0004829825,0.00001540314,0.000017793509,0.000057100573,0.000011999102,0.9827679,0.014040613,0.0013696278,0.0011469831,0.000009036716],"about_ca_topic_score_codex":0.005701748,"about_ca_topic_score_gemma":0.008534516,"teacher_disagreement_score":0.005701748,"about_ca_system_score_codex":0.0003843062,"about_ca_system_score_gemma":0.0010049625,"threshold_uncertainty_score":0.011337101},"labels":[],"label_agreement":null},{"id":"W4406161035","doi":"10.1016/j.eswa.2025.126398","title":"t-SNE-PSO: Optimizing t-SNE using particle swarm optimization","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Bishop's University","funders":"","keywords":"Particle swarm optimization; Computer science; Metaheuristic; Multi-swarm optimization; Mathematical optimization; Particle (ecology); Algorithm; Mathematics","score_opus":0.02750459388104861,"score_gpt":0.31741090542930855,"score_spread":0.28990631154825997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406161035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010336813,0.0003010427,0.9804534,0.000133175,0.00027981462,0.00010701566,0.00008607072,0.0011300951,0.0071726255],"genre_scores_gemma":[0.18682629,0.00025485636,0.80374515,0.00022672192,0.00009797321,0.00029932603,0.0003657704,0.00046563795,0.0077182557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967885,0.000101703205,0.000020908928,0.000055827855,0.00011642739,0.000026302117],"domain_scores_gemma":[0.9995461,0.00019530929,0.000034807104,0.000048596903,0.00015450384,0.000020632224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064840505,0.00091741135,0.000965084,0.0007091086,0.00041856422,0.0006991702,0.0009966342,0.0015782455,0.0034154186],"category_scores_gemma":[0.0021821829,0.00040911933,0.0009480878,0.00091861095,0.0003386895,0.0007427559,0.00059528305,0.0010637996,0.0008274071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000091225455,0.00012715456,0.0005845747,0.00012992724,0.00012318826,0.00008622774,0.000040031115,0.8617046,0.0022900137,0.006280289,0.0054371543,0.12310563],"study_design_scores_gemma":[0.000010908958,0.000016062939,0.000051903353,0.0000038758776,0.000005591323,0.000011183525,0.0000028459779,0.9981458,0.00042131887,0.0005184782,0.00080877676,0.0000032879504],"about_ca_topic_score_codex":0.006459709,"about_ca_topic_score_gemma":0.0066794455,"teacher_disagreement_score":0.006459709,"about_ca_system_score_codex":0.00032848184,"about_ca_system_score_gemma":0.00078929344,"threshold_uncertainty_score":0.012844205},"labels":[],"label_agreement":null},{"id":"W4406185642","doi":"10.1016/j.eswa.2025.126430","title":"Optimizing portfolio selection through stock ranking and matching: A reinforcement learning approach","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Computer science; Portfolio; Matching (statistics); Selection (genetic algorithm); Machine learning; Ranking (information retrieval); Artificial intelligence; Stock (firearms); Reinforcement; Finance; Mathematics; Statistics; Business; Psychology; Engineering","score_opus":0.06819578480297202,"score_gpt":0.37976847082293974,"score_spread":0.31157268601996774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406185642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09972875,0.00043468917,0.8950751,0.00057643634,0.00006787845,0.00011644733,0.000048354977,0.00029772334,0.0036546038],"genre_scores_gemma":[0.8963373,0.00016753608,0.10062781,0.00016556829,0.00006566247,0.00010762817,0.000062685984,0.000030822695,0.0024350148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931216,0.00032489773,0.00003540124,0.00011321874,0.00012484934,0.000089543064],"domain_scores_gemma":[0.99503255,0.0038739746,0.00035507386,0.00014298708,0.00041374602,0.00018171496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002696307,0.0007021858,0.0016511086,0.00083871075,0.0003936788,0.0009106464,0.0014989211,0.0017296615,0.0024006001],"category_scores_gemma":[0.008097141,0.00061177893,0.0005479058,0.0005895565,0.00080222887,0.0012879194,0.0009517477,0.0011918361,0.00025185803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010947145,0.00015048608,0.0009594746,0.00003411138,0.00006286237,0.000036847512,0.000024192814,0.96060413,0.00054082926,0.0048080473,0.0004910678,0.032178503],"study_design_scores_gemma":[0.000013862965,0.00001900286,0.000049786948,0.0000017290153,0.000005182109,0.0000031911557,0.0000013759733,0.9988612,0.00007043512,0.0009382576,0.000034179648,0.0000018788197],"about_ca_topic_score_codex":0.006998137,"about_ca_topic_score_gemma":0.0051512886,"teacher_disagreement_score":0.006998137,"about_ca_system_score_codex":0.0008580642,"about_ca_system_score_gemma":0.00139895,"threshold_uncertainty_score":0.014259577},"labels":[],"label_agreement":null},{"id":"W4406194178","doi":"10.1016/j.eswa.2024.126287","title":"Public opinion prediction on social media by using machine learning methods","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Humanities and Social Science Fund of Ministry of Education of China; Fundamental Research Funds for the Central Universities; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Public opinion; Social media; Artificial intelligence; Machine learning; Sentiment analysis; Support vector machine; Data science; World Wide Web; Political science","score_opus":0.07570998929517869,"score_gpt":0.35653529430967523,"score_spread":0.28082530501449654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406194178","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6609391,0.0014932271,0.31202394,0.0015914425,0.0006801953,0.0002619486,0.005018922,0.0026960138,0.015295214],"genre_scores_gemma":[0.9598538,0.00035754257,0.03308657,0.000093952156,0.0006181388,0.00008288071,0.0030271376,0.000049107068,0.002830938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993505,0.0001801117,0.00004077092,0.00012783664,0.00020548931,0.000095366486],"domain_scores_gemma":[0.99684685,0.0016631208,0.00034020748,0.0001289548,0.00093145133,0.00008941779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009946352,0.0007832648,0.00060382823,0.0033723162,0.0004221033,0.0013854034,0.0003986546,0.00065573293,0.0022855296],"category_scores_gemma":[0.0046611964,0.00019023675,0.00069962844,0.0015711774,0.00018451783,0.0017007653,0.0003898227,0.0008398479,0.0020459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010305155,0.0014427279,0.12702398,0.0003045928,0.00058339915,0.00053522136,0.00027934354,0.07446167,0.01679552,0.0040274044,0.03163201,0.74188364],"study_design_scores_gemma":[0.000012018126,0.00005376666,0.0075192563,0.000013396568,0.00004960951,0.00003009187,0.0000659204,0.9867443,0.0023043838,0.002234635,0.00096239493,0.000010273904],"about_ca_topic_score_codex":0.0043688817,"about_ca_topic_score_gemma":0.005576352,"teacher_disagreement_score":0.0043688817,"about_ca_system_score_codex":0.0005344768,"about_ca_system_score_gemma":0.00033549804,"threshold_uncertainty_score":0.0086869},"labels":[],"label_agreement":null},{"id":"W4406199199","doi":"10.1016/j.eswa.2025.126461","title":"Rethinking detection based table structure recognition for visually rich document images","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Mitacs","keywords":"Computer science; Table (database); Artificial intelligence; Pattern recognition (psychology); Computer vision; Information retrieval; Data mining","score_opus":0.01266045042178457,"score_gpt":0.27734803628360416,"score_spread":0.2646875858618196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406199199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12518907,0.0035541716,0.77011925,0.00092603755,0.00095877383,0.00069381285,0.0068597402,0.07919716,0.01250208],"genre_scores_gemma":[0.39971277,0.0014972016,0.55626416,0.0008788208,0.00018096117,0.0002758997,0.01964133,0.0014954007,0.020053381],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935466,0.00005083974,0.000038580096,0.00028680588,0.00017330663,0.00009570656],"domain_scores_gemma":[0.99919385,0.00018207265,0.00007385868,0.00030475968,0.00018526665,0.000060308612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072479364,0.0016772114,0.0011054234,0.0014958107,0.00030130986,0.0018326777,0.0028381704,0.0011165874,0.007216231],"category_scores_gemma":[0.0024810326,0.0005429182,0.0013914322,0.00096767995,0.00041274168,0.0031538922,0.001360941,0.0013973748,0.00747501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004369494,0.00023932509,0.0038025184,0.00044216705,0.00014263265,0.00030964825,0.0001201203,0.034251813,0.059871733,0.003060765,0.02886358,0.86845875],"study_design_scores_gemma":[0.000041772095,0.0002779149,0.002646636,0.0000781627,0.00009344382,0.0004627903,0.00012153922,0.9128697,0.06335775,0.0036891792,0.016317539,0.000043641016],"about_ca_topic_score_codex":0.008472155,"about_ca_topic_score_gemma":0.0140004335,"teacher_disagreement_score":0.008472155,"about_ca_system_score_codex":0.0009294412,"about_ca_system_score_gemma":0.0011876266,"threshold_uncertainty_score":0.024140716},"labels":[],"label_agreement":null},{"id":"W4406291830","doi":"10.1016/j.eswa.2025.126432","title":"MTCloud: Multi-type convolutional linkage network for point cloud instance segmentation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Xiamen Southern Oceanographic Center; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Linkage (software); Point cloud; Segmentation; Type (biology); Artificial intelligence; Convolutional neural network; Cloud computing; Data mining; Pattern recognition (psychology); Operating system","score_opus":0.01406384592608546,"score_gpt":0.261084336711436,"score_spread":0.24702049078535054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406291830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018881757,0.000597381,0.9066951,0.00035394426,0.0002026929,0.00024783466,0.0065944465,0.06318551,0.0032412992],"genre_scores_gemma":[0.19338997,0.0005759035,0.7641782,0.00055057433,0.00011853816,0.000468777,0.022087745,0.0036815386,0.014948702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995654,0.000035734596,0.000017863405,0.00017864881,0.00012858317,0.00007377054],"domain_scores_gemma":[0.999526,0.000096623626,0.000040817922,0.0001432473,0.0001446554,0.000048601818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006492495,0.0019891341,0.0011633636,0.002036896,0.00090026774,0.0017474022,0.0038447417,0.0025108268,0.011592291],"category_scores_gemma":[0.0020329047,0.0012902846,0.0014551579,0.002220728,0.0004555169,0.0020610325,0.0028065036,0.0023655938,0.006044593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064942514,0.00033815246,0.0029956223,0.00040059615,0.00048499694,0.00032950335,0.00013945125,0.17872527,0.023157014,0.008602756,0.08810776,0.6960695],"study_design_scores_gemma":[0.000020960328,0.000035490048,0.00039064194,0.00001688932,0.000023997514,0.00006214161,0.000015587033,0.9830625,0.008151692,0.0035416004,0.00466324,0.000015227557],"about_ca_topic_score_codex":0.022777198,"about_ca_topic_score_gemma":0.041863006,"teacher_disagreement_score":0.022777198,"about_ca_system_score_codex":0.0015494846,"about_ca_system_score_gemma":0.0017145127,"threshold_uncertainty_score":0.04528922},"labels":[],"label_agreement":null},{"id":"W4406308611","doi":"10.1016/j.eswa.2025.126482","title":"Towards a performance characteristic curve for model evaluation: An application in information diffusion prediction","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; HORIZON EUROPE Framework Programme; National Natural Science Foundation of China; National University's Basic Research Foundation of China; Ministerstwo Edukacji i Nauki; European Research Executive Agency; European Commission","keywords":"Computer science; Diffusion; Data mining; Artificial intelligence; Thermodynamics","score_opus":0.013891746191541973,"score_gpt":0.29638134677665845,"score_spread":0.28248960058511646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406308611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098558806,0.0045194677,0.8846086,0.0023001358,0.00029440137,0.00044090013,0.0013677259,0.0027883716,0.005121611],"genre_scores_gemma":[0.72561806,0.001615106,0.26715344,0.00062027964,0.00018782832,0.0006758554,0.0023124418,0.0006428168,0.0011741258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9877851,0.0060509974,0.0008008543,0.001487102,0.0032608937,0.0006149746],"domain_scores_gemma":[0.9119825,0.06191435,0.005937572,0.007554005,0.011374503,0.0012371287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02505793,0.0032098682,0.002179127,0.0074417903,0.0011656693,0.0036605194,0.001833928,0.0035632267,0.0015742554],"category_scores_gemma":[0.10468771,0.0005810465,0.002371436,0.0052470537,0.0027392898,0.005757522,0.0038511448,0.0050643287,0.00066994975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006558914,0.0003615755,0.031426165,0.0009456701,0.0005676278,0.00035625542,0.0010411144,0.74604774,0.0039519793,0.045183346,0.00838887,0.16107365],"study_design_scores_gemma":[0.000020770744,0.0002483654,0.0033172646,0.00014608866,0.00004596432,0.00013319802,0.00017093297,0.9716276,0.0021241328,0.020137869,0.0019408563,0.00008699194],"about_ca_topic_score_codex":0.01053482,"about_ca_topic_score_gemma":0.0033885255,"teacher_disagreement_score":0.02505793,"about_ca_system_score_codex":0.0028445078,"about_ca_system_score_gemma":0.0022287406,"threshold_uncertainty_score":0.13252056},"labels":[],"label_agreement":null},{"id":"W4406518114","doi":"10.1016/j.eswa.2025.126547","title":"Group matching method for search-space reduction, development, proof, and comparison","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reduction (mathematics); Computer science; Matching (statistics); Space (punctuation); Proof of concept; Development (topology); Group (periodic table); Artificial intelligence; Mathematics; Statistics","score_opus":0.014457827180369226,"score_gpt":0.2934325992702911,"score_spread":0.27897477208992183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406518114","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019183601,0.00024302611,0.99412954,0.00014713957,0.00008450151,0.00008097374,0.000045770503,0.00053786836,0.0028128503],"genre_scores_gemma":[0.119559534,0.0005598559,0.8709629,0.00027483128,0.00019390883,0.00038226272,0.000366824,0.00046994217,0.007229954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99582005,0.0011660932,0.00024056785,0.00062038447,0.0018413843,0.00031146937],"domain_scores_gemma":[0.99391174,0.002948215,0.00023686343,0.0013256667,0.0014202403,0.00015732662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029584274,0.0011677931,0.0014205972,0.002419614,0.000961998,0.001503908,0.0024676768,0.0016086778,0.011691048],"category_scores_gemma":[0.011100919,0.000596751,0.0022170108,0.0018682763,0.0016807329,0.0035183125,0.0033233012,0.002595964,0.0034665158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049912464,0.00040904764,0.00079866796,0.0009599177,0.00022345537,0.00028180567,0.00032088393,0.05150452,0.014782166,0.32305995,0.018540302,0.5886202],"study_design_scores_gemma":[0.00022165722,0.0006402949,0.00077273947,0.00017209144,0.00026467894,0.0009134854,0.000159901,0.52748024,0.03220855,0.3894251,0.04764949,0.000091795846],"about_ca_topic_score_codex":0.0015138636,"about_ca_topic_score_gemma":0.0013771377,"teacher_disagreement_score":0.011691048,"about_ca_system_score_codex":0.0010072395,"about_ca_system_score_gemma":0.0027167434,"threshold_uncertainty_score":0.039110422},"labels":[],"label_agreement":null},{"id":"W4406518354","doi":"10.1016/j.eswa.2025.126437","title":"Safe deep reinforcement learning for flow control within the Internet of Vehicles","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Traffic control and management","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Université Mohammed VI Polytechnique","keywords":"Reinforcement learning; Computer science; Artificial intelligence; The Internet; Control (management); Flow (mathematics); Machine learning; World Wide Web; Mathematics","score_opus":0.005252705768151372,"score_gpt":0.20860168951016808,"score_spread":0.2033489837420167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406518354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0531361,0.00038885986,0.94215375,0.0004785351,0.00008628126,0.000032023778,0.00008161634,0.0007387291,0.0029041455],"genre_scores_gemma":[0.952965,0.0001161389,0.042324893,0.00012084437,0.00003777021,0.000052025498,0.00012173387,0.00008616778,0.0041754916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970347,0.00008714986,0.000010301056,0.00006747984,0.00006327011,0.000068370864],"domain_scores_gemma":[0.998579,0.0009006936,0.000097743956,0.00008284242,0.00023152337,0.00010819702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009418036,0.0006287292,0.000924897,0.00041685137,0.00045340866,0.0007187058,0.0010965066,0.0011061007,0.0025528374],"category_scores_gemma":[0.0034726388,0.00046278883,0.0003575256,0.00029699333,0.0010420136,0.0010052186,0.0011686232,0.0019624478,0.0002693312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004594904,0.000027720476,0.00024507754,0.00001534672,0.0000099853205,0.000018117778,0.000021092408,0.9764573,0.0003110344,0.0075851576,0.00070583983,0.014557406],"study_design_scores_gemma":[0.0000021367205,0.000004491096,0.000012630341,0.0000010631143,7.673554e-7,7.2329937e-7,0.0000010374015,0.99763,0.000046858313,0.0022572088,0.00004228676,7.119058e-7],"about_ca_topic_score_codex":0.019627096,"about_ca_topic_score_gemma":0.016491422,"teacher_disagreement_score":0.019627096,"about_ca_system_score_codex":0.001565349,"about_ca_system_score_gemma":0.0019615514,"threshold_uncertainty_score":0.039025724},"labels":[],"label_agreement":null},{"id":"W4406614545","doi":"10.1016/j.eswa.2025.126534","title":"Deep reinforcement learning-based column generation for the two-dimensional vector variable-sized packing problem","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Column (typography); Variable (mathematics); Artificial intelligence; Reinforcement; Mathematical optimization; Mathematics; Materials science; Composite material; Mathematical analysis","score_opus":0.012331397136158447,"score_gpt":0.24001417987003454,"score_spread":0.2276827827338761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406614545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049416434,0.00096960546,0.942886,0.00068862387,0.00018525534,0.00011086941,0.00025763165,0.0012717389,0.004213827],"genre_scores_gemma":[0.7072442,0.00035661465,0.2845176,0.0005478473,0.00014872228,0.00024236488,0.00068756385,0.0002802982,0.0059747756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955183,0.00011241327,0.00002181158,0.00010675197,0.00010744394,0.000099644414],"domain_scores_gemma":[0.9978181,0.0014516241,0.00015011411,0.0001245588,0.00032780966,0.00012782116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087273336,0.0009973376,0.0018217181,0.0005941259,0.0004237708,0.0009365442,0.0017702837,0.0017548266,0.006103787],"category_scores_gemma":[0.0036733882,0.0007192588,0.0006491456,0.00092875026,0.00084605813,0.0014534843,0.0012218335,0.002139741,0.0007019336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018247562,0.00018782039,0.00046871416,0.00014390188,0.000033679575,0.00006927909,0.000039953018,0.8850578,0.0016230871,0.007635967,0.0063277767,0.098229535],"study_design_scores_gemma":[0.000007281111,0.00001166725,0.000016722926,0.0000030138162,0.0000019645477,0.0000036955573,0.0000019053056,0.9981743,0.00014362238,0.0015534488,0.00008061342,0.0000016869869],"about_ca_topic_score_codex":0.008514644,"about_ca_topic_score_gemma":0.009943932,"teacher_disagreement_score":0.008514644,"about_ca_system_score_codex":0.0011123116,"about_ca_system_score_gemma":0.001773297,"threshold_uncertainty_score":0.02041918},"labels":[],"label_agreement":null},{"id":"W4406788159","doi":"10.1016/j.eswa.2025.126607","title":"Toward a generic information system for projects portfolio management using Physics of Decision (POD)","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Portfolio; Computer science; Point of delivery; Project portfolio management; Decision support system; Knowledge management; Management science; Systems engineering; Artificial intelligence; Business; Project management; Finance; Engineering","score_opus":0.06957275567177534,"score_gpt":0.29873187325419653,"score_spread":0.2291591175824212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406788159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054841363,0.000101158184,0.9712549,0.00026066345,0.00007056143,0.0002344968,0.0006481865,0.018305037,0.0036409064],"genre_scores_gemma":[0.10337962,0.00025729695,0.8896679,0.00027692606,0.00005962134,0.00020977466,0.0022199135,0.0005596227,0.0033692273],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988148,0.00023727512,0.00025160942,0.00024420663,0.0003686615,0.000083525025],"domain_scores_gemma":[0.99760735,0.00047103272,0.00019489738,0.00093258475,0.0006215607,0.00017250821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026889842,0.0005836074,0.0009782881,0.0035711993,0.0008033384,0.006143621,0.0016014507,0.0012925639,0.0041377703],"category_scores_gemma":[0.006812589,0.00063538196,0.0012315296,0.0024345997,0.0005686561,0.004624915,0.003130321,0.0012718042,0.0030683093],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003673507,0.0006140636,0.01642319,0.00075585506,0.00036466782,0.00072188646,0.00067769276,0.041714597,0.020664925,0.16763549,0.040155243,0.7099049],"study_design_scores_gemma":[0.00009093298,0.00018047346,0.0047197817,0.0002407464,0.00028479294,0.0005910971,0.00023595623,0.7407677,0.026441181,0.087662235,0.13866232,0.000122724],"about_ca_topic_score_codex":0.003268928,"about_ca_topic_score_gemma":0.0033633423,"teacher_disagreement_score":0.006143621,"about_ca_system_score_codex":0.0009141284,"about_ca_system_score_gemma":0.0021924304,"threshold_uncertainty_score":0.014220893},"labels":[],"label_agreement":null},{"id":"W4406900260","doi":"10.1016/j.eswa.2025.126656","title":"DeepUKF-VIN: Adaptively-tuned Deep Unscented Kalman Filter for 3D Visual-Inertial Navigation based on IMU-Vision-Net","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Kalman filter; Computer vision; Computer science; Artificial intelligence; Inertial frame of reference; Extended Kalman filter; Physics","score_opus":0.007168347885564663,"score_gpt":0.25429141696662755,"score_spread":0.24712306908106288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406900260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007615106,0.0003036235,0.98937935,0.00005770667,0.00007120612,0.000022327888,0.00008109474,0.0015999117,0.0008696761],"genre_scores_gemma":[0.64432544,0.0004370745,0.3486275,0.00035724678,0.00008278446,0.00018937864,0.000892292,0.00021312728,0.004875091],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965966,0.000038484843,0.000019041407,0.00010325219,0.00012369588,0.000055842494],"domain_scores_gemma":[0.99962366,0.0001053177,0.000059858005,0.000062114515,0.00012427162,0.00002477805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065627875,0.0009648936,0.0008336178,0.00042542792,0.00035718802,0.0005383093,0.0016072524,0.0010118078,0.0015123967],"category_scores_gemma":[0.0017908657,0.00049859454,0.0006344519,0.00043615184,0.00041582406,0.00087004155,0.0010728152,0.0014259585,0.0006583818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016460955,0.00008373346,0.0033119419,0.00012652494,0.00013461718,0.00011580337,0.0001182579,0.63993096,0.011334297,0.0066283233,0.0047482573,0.3333026],"study_design_scores_gemma":[0.0000072849916,0.000025142343,0.00028226097,0.000007617674,0.000008267259,0.00002141229,0.0000050598856,0.99533844,0.001976663,0.0010528925,0.0012652025,0.000009745505],"about_ca_topic_score_codex":0.018329598,"about_ca_topic_score_gemma":0.025278885,"teacher_disagreement_score":0.018329598,"about_ca_system_score_codex":0.00075745065,"about_ca_system_score_gemma":0.0013243872,"threshold_uncertainty_score":0.036445796},"labels":[],"label_agreement":null},{"id":"W4407120710","doi":"10.1016/j.eswa.2025.126667","title":"Graph-enhanced anomaly detection framework in multivariate time series using Graph Attention and Enhanced Generative Adversarial Networks","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Key Science and Technology Program of Shaanxi Province; National Key Research and Development Program of China; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China","keywords":"Computer science; Multivariate statistics; Graph; Anomaly detection; Adversarial system; Generative grammar; Series (stratigraphy); Generative adversarial network; Anomaly (physics); Time series; Theoretical computer science; Artificial intelligence; Machine learning; Image (mathematics)","score_opus":0.006834604503249495,"score_gpt":0.25249767912061066,"score_spread":0.24566307461736117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407120710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015735343,0.00017976007,0.98261917,0.00018101554,0.000041763295,0.000013735098,0.00005151038,0.000591987,0.0005857015],"genre_scores_gemma":[0.83662736,0.0003709276,0.15686141,0.00026970287,0.0001799105,0.00006132336,0.00043451707,0.0002779345,0.0049169413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995135,0.00013464924,0.000016427963,0.00015330379,0.00011606583,0.00006604635],"domain_scores_gemma":[0.9986607,0.0007903943,0.00013678531,0.00011519411,0.00022593372,0.00007099902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093946885,0.00078880764,0.0009993379,0.0011350634,0.00032031263,0.00078423356,0.0019762847,0.0012631814,0.0017441806],"category_scores_gemma":[0.003156187,0.00043176668,0.00092050084,0.00094944,0.00074841833,0.0013911885,0.0013031971,0.0017243483,0.00040406277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011559619,0.00008318772,0.0015178273,0.000066933026,0.00009377295,0.00013817081,0.00007460441,0.8619835,0.005761141,0.0251607,0.0021595894,0.10284497],"study_design_scores_gemma":[8.9025275e-7,0.000003790955,0.00006817309,7.694876e-7,0.0000031485358,0.0000074138893,0.0000012222441,0.9967347,0.00021612842,0.0028802285,0.000081863975,0.0000015881085],"about_ca_topic_score_codex":0.0062600225,"about_ca_topic_score_gemma":0.006561209,"teacher_disagreement_score":0.0062600225,"about_ca_system_score_codex":0.0007193276,"about_ca_system_score_gemma":0.00062935444,"threshold_uncertainty_score":0.012447178},"labels":[],"label_agreement":null},{"id":"W4407230512","doi":"10.1016/j.eswa.2025.126648","title":"Dynamic link prediction: Using language models and graph structures for temporal knowledge graph completion with emerging entities and relations","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Computer science; Knowledge graph; Graph; Link (geometry); Theoretical computer science; Artificial intelligence","score_opus":0.01790363908459883,"score_gpt":0.27997578535207374,"score_spread":0.2620721462674749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407230512","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025645519,0.0003960007,0.9669662,0.00050860207,0.00007566496,0.00016782363,0.0015719556,0.0036938,0.00097435265],"genre_scores_gemma":[0.3905848,0.00052691344,0.5948303,0.00026224295,0.0001476579,0.00041060153,0.008790635,0.0005727187,0.0038742402],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858594,0.0003824748,0.000097341086,0.00054216414,0.0002682583,0.0001237661],"domain_scores_gemma":[0.9934801,0.0044138706,0.00044523363,0.0007784331,0.00065808353,0.00022433362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022506574,0.0011554819,0.0011400828,0.003707346,0.0008911009,0.0017421932,0.002969951,0.001479859,0.003087375],"category_scores_gemma":[0.010813633,0.00074257725,0.0017061213,0.003151778,0.0007248493,0.004860673,0.0021067294,0.0027192314,0.0014454982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078728254,0.0009757179,0.0060277823,0.00048304023,0.0002973238,0.0004859558,0.0007100082,0.32786298,0.0056342166,0.03187243,0.020768195,0.60409504],"study_design_scores_gemma":[0.000014267699,0.000017462124,0.00019049326,0.000013097773,0.000022151671,0.000021497837,0.000036659596,0.978973,0.0006570507,0.019218795,0.0008249923,0.000010481986],"about_ca_topic_score_codex":0.025207877,"about_ca_topic_score_gemma":0.03369194,"teacher_disagreement_score":0.025207877,"about_ca_system_score_codex":0.0010971889,"about_ca_system_score_gemma":0.0022472932,"threshold_uncertainty_score":0.05012232},"labels":[],"label_agreement":null},{"id":"W4407243450","doi":"10.1016/j.eswa.2025.126810","title":"Data augmentation of flavor information for electronic nose and electronic tongue: An olfactory-taste synesthesia model combined with multiblock reconstruction method","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Taishan Scholar Project of Shandong Province; Taishan Scholar Foundation of Shandong Province; Yunnan Provincial Science and Technology Department; Natural Science Foundation of Shandong Province","keywords":"Electronic nose; Electronic tongue; Taste; Computer science; Synesthesia; Flavor; Olfactory system; Artificial intelligence; Tongue; Pattern recognition (psychology); Neuroscience; Medicine; Psychology; Perception; Pathology","score_opus":0.014146910275851963,"score_gpt":0.2760546679164766,"score_spread":0.2619077576406246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407243450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06372908,0.00051722076,0.93338853,0.00024932597,0.00008499732,0.000043550055,0.0002029079,0.00031329106,0.001471146],"genre_scores_gemma":[0.80309474,0.0010741123,0.18841173,0.00015855573,0.0000628415,0.00013158628,0.0006384174,0.0000736182,0.006354538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999925,0.000016804725,0.000005163699,0.000021051932,0.000018642479,0.000013450871],"domain_scores_gemma":[0.99980634,0.00007963156,0.000017792756,0.000023013068,0.00005921808,0.000014007738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031591364,0.0004211678,0.00038523824,0.0002370146,0.00014985113,0.00032832217,0.00054270506,0.0007724974,0.0016965694],"category_scores_gemma":[0.00060807157,0.00024714426,0.00069508806,0.00027761748,0.00024274517,0.00053384854,0.00040014103,0.00075847266,0.00036711153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007635092,0.00028242165,0.0024543714,0.0003312583,0.00014047457,0.00030187482,0.00010838172,0.6823944,0.096775465,0.010234548,0.0019647127,0.20424852],"study_design_scores_gemma":[0.0000047038566,0.000029594923,0.00023632501,0.0000040254904,0.000011822776,0.000027573613,0.0000031205404,0.9958689,0.0031060562,0.00044721703,0.0002551973,0.0000054688694],"about_ca_topic_score_codex":0.0036705206,"about_ca_topic_score_gemma":0.0031061305,"teacher_disagreement_score":0.0036705206,"about_ca_system_score_codex":0.00018330646,"about_ca_system_score_gemma":0.0006135143,"threshold_uncertainty_score":0.0072982907},"labels":[],"label_agreement":null},{"id":"W4407316205","doi":"10.1016/j.eswa.2025.126826","title":"Assessment of renewable energy alternatives for sustainable resource policies with knowledge-based expert prioritized quantum picture fuzzy rough modelling","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Renewable energy; Fuzzy logic; Resource (disambiguation); Artificial intelligence; Knowledge management; Environmental economics; Data mining","score_opus":0.01711147733682172,"score_gpt":0.29352886666659567,"score_spread":0.27641738932977394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407316205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20180376,0.0003110201,0.78060555,0.00044666094,0.00004523678,0.00013036451,0.00019813015,0.00012107942,0.016338311],"genre_scores_gemma":[0.9590499,0.00015221743,0.039289705,0.000029800183,0.000008770449,0.00007270858,0.00007029698,0.000009272916,0.0013172895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993783,0.0002445927,0.00003198487,0.000056831956,0.00023487482,0.000053506225],"domain_scores_gemma":[0.99942243,0.00032095966,0.00007533954,0.000033313452,0.00011848087,0.000029587189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010791632,0.0004262105,0.00086207903,0.0010235099,0.00036191527,0.0015704705,0.0007854649,0.0008391333,0.0019584983],"category_scores_gemma":[0.0024781479,0.00034035387,0.0008980057,0.00082011457,0.0005376424,0.001660257,0.0008203841,0.00056248065,0.00015167249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033446246,0.000022318285,0.00027200286,0.000042173386,0.000028145123,0.00005324767,0.000040447892,0.9736242,0.0009912619,0.01643116,0.00014822553,0.008313311],"study_design_scores_gemma":[0.0000040533364,0.000012612368,0.0001322266,0.0000060485263,0.0000069787484,0.0000049940736,0.000018259934,0.9904233,0.00018195018,0.009064276,0.00013983811,0.0000055798478],"about_ca_topic_score_codex":0.0068904962,"about_ca_topic_score_gemma":0.006117865,"teacher_disagreement_score":0.0068904962,"about_ca_system_score_codex":0.0012557344,"about_ca_system_score_gemma":0.0012921798,"threshold_uncertainty_score":0.013700783},"labels":[],"label_agreement":null},{"id":"W4407406870","doi":"10.1016/j.eswa.2025.126785","title":"Comprehensive analysis of Transformer networks in identifying informative sentences containing customer needs","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Transformer; Natural language processing; Artificial intelligence","score_opus":0.02054406995083354,"score_gpt":0.29838290153105684,"score_spread":0.2778388315802233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407406870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5808621,0.0026493848,0.4002051,0.0007355031,0.00011856151,0.00024108801,0.0032142862,0.0015284058,0.01044552],"genre_scores_gemma":[0.9402591,0.0006990587,0.052658033,0.000060887418,0.000074427575,0.00007841793,0.0035717979,0.00007582781,0.002522449],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994273,0.00021894753,0.00003604556,0.00010442011,0.00015669277,0.00005655014],"domain_scores_gemma":[0.9970937,0.002003371,0.0001506484,0.00011374098,0.0005785003,0.000060096398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014779373,0.0006169657,0.00033088063,0.0021765148,0.00049589574,0.00075655465,0.00037067485,0.00054778834,0.001624916],"category_scores_gemma":[0.004646757,0.00018933522,0.00040347865,0.0012083442,0.00019973484,0.0017075205,0.00044814724,0.0005358311,0.00059048325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001397384,0.00056439824,0.034246262,0.0007447315,0.00039757363,0.001468325,0.0010068694,0.0935083,0.08340752,0.01694161,0.017689211,0.7486278],"study_design_scores_gemma":[0.000022760867,0.00015666166,0.012950931,0.00003589684,0.000240845,0.0003781183,0.0003477995,0.95945364,0.013810947,0.008383139,0.0041971775,0.000022135087],"about_ca_topic_score_codex":0.0027022671,"about_ca_topic_score_gemma":0.0067869327,"teacher_disagreement_score":0.0027022671,"about_ca_system_score_codex":0.00043421227,"about_ca_system_score_gemma":0.000665741,"threshold_uncertainty_score":0.0078161955},"labels":[],"label_agreement":null},{"id":"W4407596847","doi":"10.1016/j.eswa.2025.126825","title":"A multi-task minutiae transformer network for fingerprint recognition of young children","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Minutiae; Computer science; Fingerprint (computing); Fingerprint recognition; Pattern recognition (psychology); Transformer; Task (project management); Artificial intelligence; Machine learning; Engineering; Electrical engineering","score_opus":0.02056990255885342,"score_gpt":0.2677597977606301,"score_spread":0.24718989520177667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407596847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21392095,0.0009493513,0.7796885,0.0001575801,0.00013227815,0.00012111099,0.0005919279,0.0015550654,0.002883308],"genre_scores_gemma":[0.8708839,0.00061364117,0.12284601,0.00010446997,0.000052569438,0.000105698404,0.00056897465,0.000041482148,0.0047832397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997192,0.00005901983,0.000009909553,0.000067630695,0.00009945383,0.000044736727],"domain_scores_gemma":[0.9998,0.00004388955,0.00001665221,0.000023385848,0.00009098707,0.000025072228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041006674,0.00033191027,0.00046201417,0.0006496422,0.00021642847,0.00033560855,0.00059238804,0.00040405142,0.0019330776],"category_scores_gemma":[0.00049699977,0.00014993345,0.0002589088,0.00051083777,0.00012135173,0.00049863855,0.00047686277,0.0002858235,0.00080728816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010771585,0.00027163656,0.0075730747,0.00012455965,0.000063369844,0.00017393743,0.00006683119,0.014037814,0.12372951,0.0011222591,0.0023593982,0.8494005],"study_design_scores_gemma":[0.00006175972,0.0008730336,0.021898177,0.000027783099,0.00011704945,0.001313794,0.00011984782,0.86392516,0.106311806,0.000921701,0.004382658,0.000047323025],"about_ca_topic_score_codex":0.0037913818,"about_ca_topic_score_gemma":0.0052809357,"teacher_disagreement_score":0.0037913818,"about_ca_system_score_codex":0.00028066014,"about_ca_system_score_gemma":0.00055598206,"threshold_uncertainty_score":0.0075386763},"labels":[],"label_agreement":null},{"id":"W4407640895","doi":"10.1016/j.eswa.2025.126928","title":"Federal underwater acoustic spectrum sensing algorithm based on DCYOLO","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Xidian University; National Natural Science Foundation of China","keywords":"Underwater; Computer science; Spectrum (functional analysis); Algorithm; Acoustic sensor; Acoustics; Artificial intelligence; Geology; Physics; Oceanography","score_opus":0.009106456601398513,"score_gpt":0.2269556467366524,"score_spread":0.2178491901352539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407640895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06905898,0.0005709386,0.91455144,0.00031584516,0.00039339572,0.00013387737,0.00032851376,0.001689437,0.012957644],"genre_scores_gemma":[0.52676433,0.00040279844,0.45520684,0.00023830723,0.00017284029,0.00026922312,0.00115712,0.00009602165,0.015692508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976915,0.000017975266,0.000012761551,0.00007547631,0.000085400105,0.000039239505],"domain_scores_gemma":[0.999843,0.000022028828,0.000011828687,0.000020806134,0.000087909,0.000014405612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019707279,0.00042929273,0.00051408366,0.0007546232,0.0007511617,0.00059393735,0.00052031636,0.00039080816,0.0030004464],"category_scores_gemma":[0.00057277415,0.00017547784,0.00024660828,0.0004347293,0.00022629257,0.000505275,0.000711883,0.00037326486,0.00090024935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069543254,0.00018826201,0.0047729895,0.00014489345,0.00004755844,0.00014508219,0.000107659245,0.034334075,0.115602456,0.011093676,0.010656594,0.8222113],"study_design_scores_gemma":[0.0000756945,0.00013238585,0.0032965362,0.000021562442,0.000036316338,0.00019278441,0.00008650984,0.93348396,0.048745293,0.0021953133,0.011699111,0.000034566052],"about_ca_topic_score_codex":0.0058153705,"about_ca_topic_score_gemma":0.010299732,"teacher_disagreement_score":0.0058153705,"about_ca_system_score_codex":0.00037189596,"about_ca_system_score_gemma":0.0015987471,"threshold_uncertainty_score":0.011563003},"labels":[],"label_agreement":null},{"id":"W4407742191","doi":"10.1016/j.eswa.2025.126944","title":"Gaseous fuel supply chain configuration selection: A life cycle thinking-based decision support framework","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"FortisBC; Mitacs","keywords":"Supply chain; Computer science; Decision support system; Selection (genetic algorithm); Chain (unit); Supply chain risk management; Life-cycle assessment; Process management; Operations research; Risk analysis (engineering); Artificial intelligence; Supply chain management; Business; Service management; Microeconomics; Engineering","score_opus":0.0072013400828362184,"score_gpt":0.2515760688887591,"score_spread":0.2443747288059229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407742191","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017149802,0.00034835617,0.9739533,0.0006091211,0.000024016985,0.00023224454,0.00014529459,0.00023035219,0.007307572],"genre_scores_gemma":[0.4459445,0.00055208226,0.5510048,0.00012408584,0.000046310437,0.0006408592,0.00034089078,0.00004209855,0.0013042586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978993,0.0010905879,0.0001215483,0.00022318108,0.0004937894,0.00017159736],"domain_scores_gemma":[0.99815255,0.0010333764,0.00024830876,0.00006187564,0.00035266188,0.00015112858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040133456,0.0016201008,0.001015569,0.003523879,0.0009877327,0.003520286,0.0024922288,0.0014295671,0.0032685646],"category_scores_gemma":[0.0042557344,0.00056288874,0.001512527,0.0023916594,0.0010802024,0.002435012,0.0020505942,0.0012811573,0.00034109905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080797276,0.00018451325,0.001503566,0.00032956566,0.00014977292,0.00026228672,0.00043622914,0.8294266,0.0015603557,0.087574445,0.0010475039,0.07744436],"study_design_scores_gemma":[0.000024105299,0.00007288234,0.0001907609,0.00008158676,0.000040708408,0.000027735345,0.00017854567,0.9605391,0.00052587705,0.03603128,0.002266982,0.000020417943],"about_ca_topic_score_codex":0.005954362,"about_ca_topic_score_gemma":0.0065566506,"teacher_disagreement_score":0.005954362,"about_ca_system_score_codex":0.003342119,"about_ca_system_score_gemma":0.004018833,"threshold_uncertainty_score":0.024248898},"labels":[],"label_agreement":null},{"id":"W4407776479","doi":"10.1016/j.eswa.2025.126821","title":"Bio-inspired algorithms for the characterization of excellent performance in handball players: A data-driven methodology","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Regional Development Fund; European Commission; Emissions Reduction Alberta","keywords":"Computer science; Characterization (materials science); Machine learning; Algorithm; Artificial intelligence; Nanotechnology","score_opus":0.11928865167312998,"score_gpt":0.30575512257367155,"score_spread":0.18646647090054158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407776479","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01643228,0.0005940561,0.9803447,0.0004203924,0.00005085194,0.00016124043,0.00021731814,0.00022188286,0.00155713],"genre_scores_gemma":[0.49156934,0.0009510791,0.50271803,0.0003612179,0.00012305596,0.0010151444,0.00107553,0.00009762138,0.0020889675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986161,0.000455005,0.00010747293,0.00034632508,0.00038485738,0.000090225854],"domain_scores_gemma":[0.99603707,0.0024821544,0.0005320184,0.00021967977,0.00062997773,0.00009900441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037092452,0.0013956372,0.0010827014,0.002993135,0.0005131238,0.0021294602,0.0016931156,0.0015609761,0.0011120267],"category_scores_gemma":[0.008753849,0.00049450283,0.0015672416,0.0017611033,0.0009753765,0.0016006799,0.0013913686,0.0018211833,0.00036054966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007196735,0.0001796265,0.011175633,0.00026344377,0.00024511697,0.00011029054,0.0002125628,0.8486583,0.0030391226,0.03069757,0.0013203279,0.10402602],"study_design_scores_gemma":[0.000006166695,0.000042148873,0.0011278419,0.00003848932,0.000019142697,0.000030793894,0.000055761604,0.9850891,0.00068266486,0.01185133,0.0010404846,0.000016096017],"about_ca_topic_score_codex":0.0046719247,"about_ca_topic_score_gemma":0.0023421794,"teacher_disagreement_score":0.0046719247,"about_ca_system_score_codex":0.001440272,"about_ca_system_score_gemma":0.001480592,"threshold_uncertainty_score":0.019616604},"labels":[],"label_agreement":null},{"id":"W4407837134","doi":"10.1016/j.eswa.2025.127002","title":"How effective are discrete-continuous multi-task learning compared to single-output models? Insights from travel mode and departure time analysis","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Mode (computer interface); Travel time; Multi-task learning; Artificial intelligence; Human–computer interaction; Economics","score_opus":0.006894765585209466,"score_gpt":0.21321496290949388,"score_spread":0.20632019732428442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407837134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3125469,0.009711886,0.6469379,0.01177881,0.0007315892,0.00021779093,0.00065716007,0.0013007644,0.016117161],"genre_scores_gemma":[0.95371467,0.00080472307,0.04305336,0.0005079528,0.00014722224,0.000068647765,0.00021530774,0.00005485116,0.0014333638],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99621516,0.0023318427,0.00014571543,0.00070408266,0.00040574328,0.00019737551],"domain_scores_gemma":[0.9853677,0.011607494,0.0006266914,0.0010583177,0.0009351933,0.00040462377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009812314,0.001272338,0.0013927367,0.00067175686,0.00053970877,0.002872998,0.0020614846,0.002404081,0.0029111083],"category_scores_gemma":[0.03900414,0.0005350105,0.00073428446,0.00088752457,0.0012062053,0.006745332,0.0018921015,0.003277198,0.00061217113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054728583,0.00026423906,0.0057364525,0.00024335277,0.00023307453,0.00006956631,0.00014654615,0.84941024,0.00038125867,0.013832909,0.00219338,0.12694164],"study_design_scores_gemma":[0.00002185989,0.00006156925,0.0004832958,0.000023317538,0.000016060838,0.000011621498,0.00003613961,0.9875882,0.0001977997,0.01122332,0.00032614675,0.00001061991],"about_ca_topic_score_codex":0.010576565,"about_ca_topic_score_gemma":0.0060152677,"teacher_disagreement_score":0.010576565,"about_ca_system_score_codex":0.0015506351,"about_ca_system_score_gemma":0.0011696061,"threshold_uncertainty_score":0.051893055},"labels":[],"label_agreement":null},{"id":"W4407942355","doi":"10.1016/j.eswa.2025.126998","title":"Exploring the soluble (pro)renin receptor (sPRR) as a biomarker in pathophysiological disorders: Integrating machine learning and meta-analysis for insights into gestational diabetes","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Pregnancy and preeclampsia studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"University of Utah","keywords":"Gestational diabetes; Biomarker; Pathophysiology; Computer science; Diabetes mellitus; Renin–angiotensin system; Bioinformatics; Medicine; Computational biology; Pregnancy; Endocrinology; Gestation; Chemistry; Biology; Biochemistry","score_opus":0.07297859250524846,"score_gpt":0.31644278069827686,"score_spread":0.2434641881930284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407942355","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3376681,0.5894085,0.060798984,0.0051957136,0.0012362609,0.00021340088,0.003174088,0.00040152535,0.0019035131],"genre_scores_gemma":[0.97395974,0.014317486,0.009584598,0.0006612335,0.0002965276,0.00008091313,0.00076775666,0.00006252566,0.0002692761],"study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","domain_scores_codex":[0.980667,0.01313929,0.0022043246,0.002308269,0.0013346566,0.0003463378],"domain_scores_gemma":[0.95620286,0.037215482,0.0020883344,0.003124406,0.0009886897,0.0003801323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028063316,0.0022173033,0.005324687,0.0045839027,0.0006347449,0.004300578,0.0017858614,0.0019120004,0.0015576235],"category_scores_gemma":[0.03746807,0.0007934758,0.018506074,0.0044221343,0.00053990603,0.0022983318,0.0017775105,0.0021556385,0.00021401953],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004138323,0.00013017452,0.24863665,0.00631799,0.69080746,0.00030791658,0.0002345592,0.00703883,0.0017359518,0.0010119297,0.0009450568,0.038695138],"study_design_scores_gemma":[0.0005520969,0.0010289773,0.09606327,0.0019200358,0.8518118,0.0004850971,0.0003918111,0.031727713,0.002172784,0.009756438,0.0039711744,0.00011868919],"about_ca_topic_score_codex":0.0025912418,"about_ca_topic_score_gemma":0.0034941083,"teacher_disagreement_score":0.028063316,"about_ca_system_score_codex":0.00063702394,"about_ca_system_score_gemma":0.0013962919,"threshold_uncertainty_score":0.14841473},"labels":[],"label_agreement":null},{"id":"W4408014605","doi":"10.1016/j.eswa.2025.126965","title":"Referring Expression Comprehension in semi-structured human–robot interaction","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Young Scientists Fund; National Natural Science Foundation of China","keywords":"Computer science; Expression (computer science); Human–robot interaction; Human–computer interaction; Artificial intelligence; Robot; Comprehension; Natural language processing; Programming language","score_opus":0.01705013750644576,"score_gpt":0.3216777303456205,"score_spread":0.3046275928391748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408014605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4103078,0.0008432426,0.56044537,0.00049641833,0.00012378601,0.00031161826,0.00047260296,0.003754941,0.02324427],"genre_scores_gemma":[0.9551744,0.0001687341,0.040941574,0.0000959941,0.000033429318,0.00013983373,0.00049029343,0.00025451957,0.002701229],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99670863,0.0022043881,0.00010344024,0.00048488975,0.0003618378,0.00013682267],"domain_scores_gemma":[0.9912845,0.0062753498,0.00069412816,0.0007167738,0.0008889663,0.00014030369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018467824,0.0007223867,0.00056765374,0.00036971032,0.0005216052,0.001963224,0.0010674553,0.0014531479,0.007032926],"category_scores_gemma":[0.021170074,0.0005130896,0.00049441494,0.00044628663,0.0010681043,0.0038738123,0.0017418375,0.00092941505,0.0016973474],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004265593,0.00036521198,0.012106461,0.0023290413,0.00030689285,0.003971555,0.093242034,0.04998659,0.35811865,0.08760499,0.01346058,0.37424237],"study_design_scores_gemma":[0.00022705081,0.0015690243,0.04179871,0.00043655516,0.00027335653,0.0025500453,0.017740868,0.6520669,0.09380702,0.16250014,0.0266501,0.00038028933],"about_ca_topic_score_codex":0.001587912,"about_ca_topic_score_gemma":0.0007910041,"teacher_disagreement_score":0.007032926,"about_ca_system_score_codex":0.0004483034,"about_ca_system_score_gemma":0.0004978613,"threshold_uncertainty_score":0.023527503},"labels":[],"label_agreement":null},{"id":"W4408211992","doi":"10.1016/j.eswa.2025.127170","title":"Balanced Uncertainty Sets for Closed-Loop Supply Chain Design: A Data-Driven Robust Optimization Framework with Fairness Considerations","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Process Optimization and Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Supply chain; Robust optimization; Mathematical optimization; Closed loop; Loop (graph theory); Chain (unit); Mathematics; Control engineering; Business","score_opus":0.029822072330294696,"score_gpt":0.27779579526450215,"score_spread":0.24797372293420747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408211992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002832809,0.00012856188,0.99593484,0.0000772241,0.000015567974,0.000025961634,0.000022717119,0.000041889944,0.0009204427],"genre_scores_gemma":[0.8006,0.0005808005,0.19498622,0.00016416592,0.00010249558,0.00042259516,0.00019333976,0.00010980662,0.0028406258],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829656,0.0006160461,0.000078592995,0.00033280705,0.0004936299,0.0001824721],"domain_scores_gemma":[0.99650794,0.0022099575,0.00041568425,0.0001417577,0.0006192112,0.000105376734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004146539,0.0019775229,0.0015977822,0.0010447452,0.0006730828,0.0023017183,0.001670961,0.0016606657,0.0019986255],"category_scores_gemma":[0.008077779,0.0008552065,0.0014353804,0.0010166197,0.0015029465,0.0020449657,0.0023082297,0.0021090184,0.00026300852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001810924,0.000009180967,0.00010103442,0.000029986784,0.000016133443,0.000022833443,0.000019232308,0.98728347,0.00029454994,0.0078064087,0.00010329928,0.004295868],"study_design_scores_gemma":[0.0000033036154,0.000013502224,0.00002003602,0.0000051621305,0.000004108644,0.0000033372762,0.0000041499734,0.9950303,0.00016650275,0.0045924354,0.0001532905,0.0000038595485],"about_ca_topic_score_codex":0.0051348736,"about_ca_topic_score_gemma":0.00261401,"teacher_disagreement_score":0.0051348736,"about_ca_system_score_codex":0.0016073289,"about_ca_system_score_gemma":0.001834947,"threshold_uncertainty_score":0.021929324},"labels":[],"label_agreement":null},{"id":"W4408252119","doi":"10.1016/j.eswa.2025.127107","title":"Support group formation for users with depression in social networks","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Toronto","funders":"","keywords":"Depression (economics); Group (periodic table); Computer science; Chemistry","score_opus":0.022848485012274673,"score_gpt":0.37141127250686756,"score_spread":0.3485627874945929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408252119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9863956,0.00015250673,0.0013537293,0.002235144,0.0001389729,0.000380122,0.00006440032,0.0000632529,0.009216273],"genre_scores_gemma":[0.99511576,0.00007556785,0.0016363569,0.00018978743,0.00003723766,0.00016119896,0.00006242579,0.0000040680065,0.0027176382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993319,0.00033183434,0.000030285564,0.000056920493,0.000103361956,0.00014562129],"domain_scores_gemma":[0.99518675,0.0017952232,0.00048162532,0.0003089114,0.00042377406,0.0018036929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015538401,0.00021085009,0.00019826661,0.00054658117,0.0020514259,0.0009255678,0.000806099,0.000746414,0.019323342],"category_scores_gemma":[0.012582406,0.000105752784,0.00027493946,0.0002389348,0.00028071876,0.0009390014,0.0014600299,0.00062798476,0.0012183157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004433291,0.012358334,0.2113213,0.0004259652,0.00015740571,0.00087709154,0.02140197,0.0006975659,0.004231607,0.0039068735,0.019215083,0.72097355],"study_design_scores_gemma":[0.0058750096,0.026020722,0.6523087,0.0011247426,0.0011164027,0.00291209,0.14873777,0.02488073,0.011071049,0.03291531,0.0928848,0.00015265202],"about_ca_topic_score_codex":0.0018715088,"about_ca_topic_score_gemma":0.0043786746,"teacher_disagreement_score":0.019323342,"about_ca_system_score_codex":0.0005026782,"about_ca_system_score_gemma":0.0013962055,"threshold_uncertainty_score":0.064643025},"labels":[],"label_agreement":null},{"id":"W4408389725","doi":"10.1016/j.eswa.2025.127155","title":"RSSD: A regional-level Resource-Saving Snow Detection Model for winter road surface maintenance","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Transportation; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Snow; Resource (disambiguation); Computer science; Environmental science; Road surface; Meteorology; Geography; Civil engineering; Engineering","score_opus":0.019324021298542226,"score_gpt":0.24493691075235832,"score_spread":0.2256128894538161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408389725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25155044,0.00046052694,0.72685885,0.0004046591,0.00014514614,0.00013114161,0.0033728075,0.008828926,0.008247593],"genre_scores_gemma":[0.9363961,0.00010333,0.058431383,0.00005855012,0.000019793002,0.00008970482,0.0011719957,0.00024386928,0.0034853613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998803,0.000022606655,0.000006760712,0.0000385322,0.000027778884,0.00002402318],"domain_scores_gemma":[0.99974436,0.00010797035,0.000017921604,0.0000260591,0.000080373,0.000023397663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037952856,0.0007402894,0.0008430009,0.0003954144,0.00027646692,0.00068407226,0.0019008315,0.00085654476,0.0039948025],"category_scores_gemma":[0.0009361454,0.0003973786,0.00084034586,0.00030970477,0.00024732505,0.0006318134,0.0005529499,0.0005797145,0.0005124764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030463189,0.000017831882,0.00035243662,0.000012935006,0.0000134576685,0.000012397774,0.000005419462,0.992875,0.00030011256,0.00029311996,0.00047129087,0.0056155375],"study_design_scores_gemma":[0.0000031636744,0.0000040035866,0.0000462977,7.1968776e-7,0.0000028253953,0.0000016740744,0.00000111891,0.9996196,0.00009172078,0.00012042313,0.000107312866,0.0000011734478],"about_ca_topic_score_codex":0.058182877,"about_ca_topic_score_gemma":0.043798745,"teacher_disagreement_score":0.058182877,"about_ca_system_score_codex":0.0010323137,"about_ca_system_score_gemma":0.0012722664,"threshold_uncertainty_score":0.11568838},"labels":[],"label_agreement":null},{"id":"W4408397987","doi":"10.1016/j.eswa.2025.127233","title":"Unbiased criteria identification for two-sided matching: An environment-based design approach","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Identification (biology); Matching (statistics); Data mining; Artificial intelligence; Machine learning; Statistics; Mathematics","score_opus":0.05475716662173801,"score_gpt":0.2818182935625275,"score_spread":0.2270611269407895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408397987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025966691,0.000029406847,0.9961958,0.0000583847,0.0000059895156,0.00012024036,0.00001227178,0.000043324108,0.00093788607],"genre_scores_gemma":[0.15531611,0.0000987402,0.8421956,0.000104206134,0.000014100481,0.0009241613,0.00007440192,0.000054992488,0.0012177778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9763722,0.016270617,0.0009659213,0.0022396392,0.0035444095,0.0006072546],"domain_scores_gemma":[0.98291576,0.010931397,0.0015178927,0.0018478021,0.0023556498,0.00043150256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018064054,0.0013782714,0.0015231654,0.0025574141,0.00089693465,0.0029512546,0.0023131005,0.0017877794,0.004760908],"category_scores_gemma":[0.029073477,0.0010673185,0.0022912845,0.0015954616,0.002423723,0.0030332387,0.0038188107,0.001957084,0.00068564556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021961213,0.00039147088,0.0032735833,0.00048605178,0.00021447716,0.0002322919,0.0008247253,0.5398671,0.0052879173,0.31054503,0.0007785708,0.13787922],"study_design_scores_gemma":[0.00006565913,0.00039174466,0.00042866723,0.000079706704,0.00005490683,0.00008808831,0.00015900399,0.86048913,0.0024185139,0.13194098,0.0038427266,0.00004088798],"about_ca_topic_score_codex":0.0011410422,"about_ca_topic_score_gemma":0.0012515635,"teacher_disagreement_score":0.018064054,"about_ca_system_score_codex":0.0021264742,"about_ca_system_score_gemma":0.0038867947,"threshold_uncertainty_score":0.095532954},"labels":[],"label_agreement":null},{"id":"W4408434866","doi":"10.1016/j.eswa.2025.127180","title":"Application of Soft Actor-Critic algorithms in optimizing wastewater treatment with time delays integration","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kruger (Canada)","funders":"H2020 Marie Skłodowska-Curie Actions; Horizon 2020; Research Executive Agency; Horizon 2020 Framework Programme; European Commission","keywords":"Computer science; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.009996203683530259,"score_gpt":0.25450466053906284,"score_spread":0.24450845685553257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408434866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074684076,0.00048975745,0.91834843,0.0004302291,0.00010404939,0.000047268324,0.000047434005,0.00075330446,0.0050955047],"genre_scores_gemma":[0.9531183,0.00013233416,0.044291776,0.000121314886,0.000023440218,0.00006488404,0.00005167618,0.000047909656,0.0021483582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997775,0.000071297436,0.000012309878,0.000053342716,0.000051213105,0.00003435768],"domain_scores_gemma":[0.99925786,0.00046257762,0.00007592982,0.00003907883,0.0001224545,0.000042106945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006930029,0.0009528593,0.00066968834,0.00026636102,0.00023273875,0.00063301984,0.00071648153,0.0008564501,0.0009938296],"category_scores_gemma":[0.0018938495,0.0004057745,0.00045070055,0.00019820518,0.00061978726,0.00046689544,0.00068441196,0.0010495626,0.00015363314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016561788,0.000007916788,0.0001619767,0.000014184188,0.000010671913,0.000018388731,0.0000075812654,0.99411976,0.00053192384,0.0007315003,0.000091898604,0.004287513],"study_design_scores_gemma":[0.0000020181458,0.0000051511374,0.000014763881,0.0000010159765,0.0000013689764,0.0000013790095,7.492728e-7,0.9995419,0.00014604232,0.00023935126,0.000045331246,9.104133e-7],"about_ca_topic_score_codex":0.0075641936,"about_ca_topic_score_gemma":0.0058844592,"teacher_disagreement_score":0.0075641936,"about_ca_system_score_codex":0.00073475734,"about_ca_system_score_gemma":0.0011468716,"threshold_uncertainty_score":0.015040338},"labels":[],"label_agreement":null},{"id":"W4408500529","doi":"10.1016/j.eswa.2025.127262","title":"Dynamics of information propagation and intervention in the multiplatform coupled networks","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Fundamental Research Funds for the Central Universities; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Communication University of China; Networks of Centres of Excellence of Canada; Canada Research Chairs; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; International Development Research Centre","keywords":"Computer science; Dynamics (music); Intervention (counseling); Artificial intelligence; Physics","score_opus":0.004711384839767409,"score_gpt":0.2551195561518124,"score_spread":0.250408171312045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408500529","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7057886,0.0007981063,0.26318473,0.0064174123,0.00014741057,0.00013439034,0.0005259026,0.0003190806,0.022684405],"genre_scores_gemma":[0.98160076,0.00041817068,0.0064792405,0.00019755414,0.00010340199,0.00008941573,0.0000812279,0.000042173764,0.010987992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988944,0.00043183885,0.000035233494,0.00027572727,0.00017495797,0.00018794449],"domain_scores_gemma":[0.9823827,0.013441132,0.001902005,0.00047049293,0.000776011,0.0010277011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022905052,0.0007004294,0.0015304484,0.0016838842,0.00135272,0.003033432,0.0022599364,0.0030420115,0.008681364],"category_scores_gemma":[0.021495605,0.00080017885,0.0009299815,0.0009547322,0.0031839192,0.005473473,0.0023202498,0.0019377439,0.000693778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028442018,0.00017637767,0.0046999943,0.00017829513,0.000158986,0.0006894061,0.0012994247,0.49069065,0.004531337,0.47775364,0.0040716627,0.015465896],"study_design_scores_gemma":[0.00004604476,0.000047654485,0.0016531161,0.000016228443,0.000025917203,0.00009251032,0.00019190223,0.88659716,0.0002718086,0.1102483,0.0007685113,0.000040834653],"about_ca_topic_score_codex":0.009854336,"about_ca_topic_score_gemma":0.0066756504,"teacher_disagreement_score":0.009854336,"about_ca_system_score_codex":0.0019605313,"about_ca_system_score_gemma":0.0009854884,"threshold_uncertainty_score":0.029042006},"labels":[],"label_agreement":null},{"id":"W4408673728","doi":"10.1016/j.eswa.2025.127280","title":"Expert evaluation system for pothole defect detection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western University","keywords":"Pothole (geology); Computer science; Artificial intelligence","score_opus":0.00802648541540311,"score_gpt":0.25664185942610407,"score_spread":0.24861537401070097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408673728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029017797,0.0012611309,0.8564805,0.00082501775,0.000643861,0.0029883967,0.014944627,0.063261405,0.030577334],"genre_scores_gemma":[0.4600354,0.0007982006,0.49034926,0.0008620924,0.00017929882,0.004032289,0.020738028,0.0018758262,0.021129593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99683714,0.00090473355,0.00046056978,0.0006016795,0.0010483309,0.00014744762],"domain_scores_gemma":[0.99306905,0.002320193,0.00043356957,0.00068488234,0.0032797225,0.00021270454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044569043,0.0011561727,0.00090704265,0.0020070127,0.0003556625,0.0012851947,0.0015580679,0.0010464471,0.02170982],"category_scores_gemma":[0.012741079,0.00027984308,0.0006597863,0.00066431594,0.00019927489,0.0009918687,0.00093445013,0.00074737245,0.008368261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017914101,0.00057537033,0.0049538454,0.0015269653,0.00027659303,0.00048240816,0.00021017218,0.03657203,0.025564717,0.0046910853,0.11795372,0.8054017],"study_design_scores_gemma":[0.000539041,0.00078318047,0.008392093,0.00033973088,0.00017195319,0.0004897477,0.00024205617,0.81580186,0.034730975,0.008524168,0.12980236,0.00018289627],"about_ca_topic_score_codex":0.003370339,"about_ca_topic_score_gemma":0.0036139057,"teacher_disagreement_score":0.02170982,"about_ca_system_score_codex":0.0007244211,"about_ca_system_score_gemma":0.0011407982,"threshold_uncertainty_score":0.07262659},"labels":[],"label_agreement":null},{"id":"W4408765368","doi":"10.1016/j.eswa.2025.127315","title":"A novel method for identifying sudden degradation changes in remaining useful life prediction for bearing","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Degradation (telecommunications); Bearing (navigation); Artificial intelligence; Data mining; Reliability engineering; Engineering; Telecommunications","score_opus":0.03712892324979427,"score_gpt":0.34962896036434093,"score_spread":0.31250003711454666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408765368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02957988,0.00041975762,0.96733934,0.00010272632,0.000085502965,0.000059399936,0.00026963922,0.0009299958,0.0012137677],"genre_scores_gemma":[0.8268714,0.00046180704,0.16934387,0.000107409345,0.00014230228,0.000115389244,0.00059700233,0.00008304923,0.002277691],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970907,0.00003103588,0.00001708758,0.00011187051,0.000102680664,0.000028193243],"domain_scores_gemma":[0.9994091,0.00021946324,0.000106644744,0.000052050636,0.00018634611,0.000026402417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046667963,0.0008965518,0.0005816186,0.0012271303,0.00025959918,0.00042443772,0.00073326775,0.0005998022,0.00089628284],"category_scores_gemma":[0.0020387287,0.00019097717,0.00041496853,0.00062174886,0.00024877788,0.0007360725,0.00040874621,0.00059029955,0.00035916368],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028580363,0.00015902756,0.010210156,0.00019714986,0.00010958088,0.0003137481,0.00012751698,0.43670282,0.029180132,0.0027793006,0.0040081167,0.5159267],"study_design_scores_gemma":[0.0000046588357,0.000038805436,0.0014824824,0.0000066421967,0.0000129258915,0.000093421404,0.000010221253,0.9938373,0.0026553844,0.0010238488,0.000825363,0.000008980895],"about_ca_topic_score_codex":0.0034300082,"about_ca_topic_score_gemma":0.0036051613,"teacher_disagreement_score":0.0034300082,"about_ca_system_score_codex":0.0003953307,"about_ca_system_score_gemma":0.00048964494,"threshold_uncertainty_score":0.0068200827},"labels":[],"label_agreement":null},{"id":"W4409164491","doi":"10.1016/j.eswa.2025.127421","title":"How do LLMs perform on Turkish? A multi-faceted multi-prompt evaluation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Zoo","funders":"Boğaziçi Üniversitesi","keywords":"Turkish; Computer science; Business; Risk analysis (engineering)","score_opus":0.02679430898198465,"score_gpt":0.3229170655409644,"score_spread":0.29612275655897974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409164491","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97520965,0.00063195167,0.00877607,0.00047207615,0.0001775415,0.00029421854,0.0035553167,0.0025744338,0.008308785],"genre_scores_gemma":[0.9771184,0.00019080791,0.010559531,0.00016491849,0.00003190436,0.0002574943,0.0077748573,0.000432841,0.0034692762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99307764,0.00420296,0.0005945786,0.0006981787,0.000909845,0.0005168265],"domain_scores_gemma":[0.9774374,0.012655114,0.0011656124,0.0021253792,0.0054795123,0.0011369362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00517498,0.0013287447,0.0011088785,0.0017289525,0.0011627725,0.001954598,0.0010622006,0.0018298059,0.006590028],"category_scores_gemma":[0.03417983,0.00028736817,0.00078574306,0.0013243493,0.0005789503,0.004099123,0.0025278209,0.0011147243,0.005375652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.034060992,0.0065785805,0.14628392,0.0065158447,0.00095216034,0.004037867,0.020139443,0.04393933,0.060342,0.003712528,0.088892065,0.5845453],"study_design_scores_gemma":[0.0026639625,0.015601075,0.39490324,0.0013147487,0.0016025711,0.003977785,0.048880484,0.2784905,0.10266734,0.008799523,0.13998084,0.0011179909],"about_ca_topic_score_codex":0.0064967894,"about_ca_topic_score_gemma":0.008302894,"teacher_disagreement_score":0.006590028,"about_ca_system_score_codex":0.0012100172,"about_ca_system_score_gemma":0.0013262464,"threshold_uncertainty_score":0.027368248},"labels":[],"label_agreement":null},{"id":"W4409189376","doi":"10.1016/j.eswa.2025.127356","title":"Unified link prediction modeling for enhanced knowledge graph completion task","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; National Research Foundation of Korea","keywords":"Computer science; Link (geometry); Knowledge graph; Task (project management); Graph; Machine learning; Artificial intelligence; Theoretical computer science; Computer network","score_opus":0.019156331097123728,"score_gpt":0.2816174827888249,"score_spread":0.2624611516917012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409189376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050387755,0.0006438614,0.94177294,0.0005794808,0.00012965007,0.000103232465,0.0012446798,0.0027244785,0.002413898],"genre_scores_gemma":[0.78428537,0.0005846475,0.1974259,0.00029494034,0.00017448516,0.00025337108,0.004963962,0.00032425157,0.011693163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994197,0.00013916315,0.000027372891,0.00023571661,0.000105819476,0.00007224917],"domain_scores_gemma":[0.9984584,0.0007550204,0.000110287605,0.0002649025,0.00032844915,0.00008301296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000990264,0.00083985756,0.00091332186,0.0011679133,0.00043478256,0.0010517732,0.002057441,0.0014336373,0.005342967],"category_scores_gemma":[0.0040255855,0.00036985855,0.0008347621,0.0014571873,0.00029327467,0.0023941044,0.000988594,0.0020994032,0.0015570853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042605758,0.0005808829,0.0024240785,0.000230771,0.00017948756,0.00023658108,0.000113602946,0.6330083,0.0069630477,0.013058087,0.01545289,0.32732624],"study_design_scores_gemma":[0.000004145858,0.000014535874,0.00015700271,0.00000394916,0.000012135695,0.000009890462,0.000003841971,0.9955728,0.00046105438,0.0034457361,0.0003108666,0.0000040241634],"about_ca_topic_score_codex":0.02066228,"about_ca_topic_score_gemma":0.0250985,"teacher_disagreement_score":0.02066228,"about_ca_system_score_codex":0.00064774544,"about_ca_system_score_gemma":0.0014723109,"threshold_uncertainty_score":0.04108399},"labels":[],"label_agreement":null},{"id":"W4409202067","doi":"10.1016/j.eswa.2025.127553","title":"Feature Similarity Group-Class Activation Mapping (FSG-CAM): Clarity in deep learning models and Enhancement of visual explanations","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Shanxi Provincial Key Research and Development Project; National Natural Science Foundation of China","keywords":"CLARITY; Artificial intelligence; Class (philosophy); Similarity (geometry); Feature (linguistics); Computer science; Pattern recognition (psychology); Group (periodic table); Machine learning; Image (mathematics); Chemistry","score_opus":0.027184840549033456,"score_gpt":0.2890037241834583,"score_spread":0.26181888363442485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409202067","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04074952,0.00022221367,0.95470446,0.0005407612,0.00009043261,0.000053371827,0.00015291074,0.0011203338,0.0023659035],"genre_scores_gemma":[0.76936257,0.00020004557,0.22682986,0.00021030681,0.00005048928,0.00007628046,0.00023128146,0.00019937556,0.002839658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975103,0.0000859326,0.000011251273,0.00007263471,0.000054030323,0.000025203613],"domain_scores_gemma":[0.998701,0.0006576814,0.00010346282,0.0002989086,0.0001558437,0.00008306109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083434535,0.00056407304,0.0004182519,0.00040782284,0.00023246152,0.00076039846,0.0012194646,0.0011638969,0.005678024],"category_scores_gemma":[0.0059316535,0.00025119158,0.00051893626,0.0003695343,0.0005371947,0.0020344423,0.0016781284,0.0017819657,0.0004838857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005724402,0.00022751906,0.001611206,0.00027333706,0.00008666746,0.00020195672,0.0004996027,0.13831297,0.045311283,0.08580004,0.007490725,0.71961224],"study_design_scores_gemma":[0.000026627584,0.000073956486,0.00043056443,0.000024362991,0.000021717924,0.0000537853,0.000029680126,0.9249166,0.009883117,0.06281815,0.0017092607,0.000012150964],"about_ca_topic_score_codex":0.0014519043,"about_ca_topic_score_gemma":0.0016890471,"teacher_disagreement_score":0.005678024,"about_ca_system_score_codex":0.00036311086,"about_ca_system_score_gemma":0.00052487693,"threshold_uncertainty_score":0.018994868},"labels":[],"label_agreement":null},{"id":"W4409290269","doi":"10.1016/j.eswa.2025.127628","title":"Probabilistic accident risk based on productivity for prefabricated structural system at large-scale industrial project","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Occupational Health and Safety Research","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Research Foundation of Korea","keywords":"Computer science; Probabilistic logic; Scale (ratio); Productivity; Accident (philosophy); Risk analysis (engineering); Industrial engineering; Artificial intelligence; Business; Engineering","score_opus":0.06724474204632719,"score_gpt":0.4317353108583275,"score_spread":0.3644905688120003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409290269","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98288906,0.00022990128,0.014925185,0.00006339522,0.000012526527,0.000030119954,0.00061056,0.00015616426,0.0010831573],"genre_scores_gemma":[0.99856144,0.000041571835,0.0006528353,0.0000035148314,0.0000033092742,0.000008585733,0.00034266736,0.000006437645,0.00037957483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884605,0.00019917783,0.000055207976,0.0002952846,0.0004043787,0.00019988131],"domain_scores_gemma":[0.99553716,0.0023721731,0.00085224654,0.00025969674,0.0008012791,0.0001775031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016538342,0.00072227867,0.00051176036,0.0020046092,0.00032293086,0.0008575786,0.0009175503,0.0009096884,0.0023194586],"category_scores_gemma":[0.005319083,0.00038923835,0.0011757863,0.0009978632,0.00034658832,0.00085321354,0.0005933933,0.00049309473,0.0002918605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075737777,0.00017762226,0.26094776,0.00012699819,0.00024231801,0.0006060346,0.0001650495,0.7134336,0.0043200883,0.0010908982,0.0007200278,0.017412284],"study_design_scores_gemma":[0.000015572907,0.0003176652,0.17802116,0.000017947603,0.000163794,0.00025054353,0.00016074318,0.8173648,0.0023533287,0.0010673593,0.00022371828,0.000043298027],"about_ca_topic_score_codex":0.007817227,"about_ca_topic_score_gemma":0.006191523,"teacher_disagreement_score":0.007817227,"about_ca_system_score_codex":0.0007449669,"about_ca_system_score_gemma":0.0003814319,"threshold_uncertainty_score":0.015543461},"labels":[],"label_agreement":null},{"id":"W4409358257","doi":"10.1016/j.eswa.2025.127569","title":"Knowledge-enhanced prototypical network with graph structure and semantic information interaction for low-shot joint spoken language understanding","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Joint (building); Natural language processing; Graph; Spoken language; Artificial intelligence; Shot (pellet); Semantic network; Knowledge graph; Theoretical computer science","score_opus":0.0211581728894171,"score_gpt":0.2812500811196482,"score_spread":0.2600919082302311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409358257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0430896,0.00026029357,0.95271087,0.00014141259,0.000050480383,0.000041447554,0.00018944999,0.001519953,0.001996517],"genre_scores_gemma":[0.75110334,0.00021576401,0.24159418,0.00017674718,0.000046186786,0.00010074748,0.0010590586,0.00025754704,0.005446477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997309,0.00005923276,0.000010041274,0.00011986567,0.000045007837,0.000035002264],"domain_scores_gemma":[0.9993981,0.0003247531,0.00003012234,0.00008664828,0.00011174402,0.000048625912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046433986,0.00058845285,0.0006527705,0.0006788261,0.00046251167,0.00043910774,0.0012123625,0.0011169077,0.0027491355],"category_scores_gemma":[0.001773774,0.0003008532,0.00052220764,0.0006127832,0.0004203117,0.0018657838,0.0012207179,0.0010090527,0.0006711934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054747675,0.0003571097,0.0010479479,0.00021285134,0.00013733467,0.00046153375,0.0004626948,0.40198743,0.054526493,0.016775332,0.005264988,0.5182189],"study_design_scores_gemma":[0.000004007469,0.000018912468,0.00014174521,0.0000025942682,0.0000096152435,0.000027507824,0.00002213062,0.9916385,0.0024647322,0.0053125042,0.0003519685,0.0000057400625],"about_ca_topic_score_codex":0.008637247,"about_ca_topic_score_gemma":0.013405905,"teacher_disagreement_score":0.008637247,"about_ca_system_score_codex":0.00046622366,"about_ca_system_score_gemma":0.00063123833,"threshold_uncertainty_score":0.017173946},"labels":[],"label_agreement":null},{"id":"W4409543171","doi":"10.1016/j.eswa.2025.127487","title":"Predicting wind turbines faults using Multi-Objective Genetic Programming","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Genetic programming; Computer science; Wind power; Genetic algorithm; Machine learning; Electrical engineering","score_opus":0.01614303241012859,"score_gpt":0.2805800546306219,"score_spread":0.2644370222204933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409543171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45679727,0.000483422,0.5366845,0.00038686057,0.00012821214,0.00010105639,0.00016211567,0.00051135407,0.004745144],"genre_scores_gemma":[0.9671651,0.000087220964,0.03169661,0.00003602318,0.0000142966,0.000042482206,0.00007408089,0.00002032468,0.0008637672],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999772,0.00007362566,0.000012742119,0.000050601746,0.000058133817,0.000032824722],"domain_scores_gemma":[0.99894327,0.00076233095,0.00009659806,0.000025103356,0.00014343478,0.000029414758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072996825,0.0010912851,0.0010021133,0.0008997546,0.00036739773,0.0009905971,0.0007854021,0.0016430981,0.0008399136],"category_scores_gemma":[0.002577141,0.00059084245,0.0006246331,0.0007379375,0.00044709037,0.000705689,0.00038027842,0.0008602725,0.00013877012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013205996,0.000011423519,0.0003134869,0.0000050948183,0.000010605509,0.000015271831,0.0000032276846,0.9965346,0.00012863391,0.00009694622,0.00003754988,0.00282995],"study_design_scores_gemma":[0.000002069627,0.000005054839,0.0000726831,9.016628e-7,0.0000019491488,0.0000016071975,0.000001389874,0.99972373,0.000057354184,0.00012650061,0.0000060335215,7.982585e-7],"about_ca_topic_score_codex":0.011365952,"about_ca_topic_score_gemma":0.009221583,"teacher_disagreement_score":0.011365952,"about_ca_system_score_codex":0.0006803228,"about_ca_system_score_gemma":0.0006675699,"threshold_uncertainty_score":0.022599578},"labels":[],"label_agreement":null},{"id":"W4409572435","doi":"10.1016/j.eswa.2025.127612","title":"Fact retrieval from knowledge graphs through semantic and contextual attention","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Dalhousie University; Cape Breton University","funders":"Faculty of Graduate Studies and Research, University of Alberta; Natural Sciences and Engineering Research Council of Canada; Southern Methodist University; Saint Mary’s University","keywords":"Computer science; Knowledge graph; Semantic memory; Information retrieval; Natural language processing; Artificial intelligence; Cognition; Psychology","score_opus":0.02090410090579666,"score_gpt":0.2828838789322674,"score_spread":0.26197977802647077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409572435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07371933,0.0052207736,0.8909152,0.0008650264,0.00021917104,0.00034422305,0.004498389,0.01765089,0.00656695],"genre_scores_gemma":[0.4511371,0.0024141085,0.5218453,0.0005754375,0.00025556132,0.00020015247,0.018329876,0.00074736576,0.0044950168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989182,0.00022087566,0.000072202725,0.00040139852,0.00029656437,0.00009092684],"domain_scores_gemma":[0.99781775,0.00111337,0.00016662035,0.00048508923,0.0003250842,0.00009200008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009439155,0.00147254,0.001047641,0.006742939,0.0008340465,0.001915725,0.001511363,0.0010095835,0.0028591035],"category_scores_gemma":[0.00624625,0.0004734283,0.0011801893,0.0047319746,0.00071872293,0.0054623745,0.002624617,0.0011592535,0.0014299245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051632733,0.00030669116,0.0041825133,0.0010860538,0.00028638754,0.0005895783,0.0010478998,0.04067267,0.033392556,0.017741911,0.046303574,0.8538738],"study_design_scores_gemma":[0.00016530657,0.00041824413,0.009109776,0.00018134435,0.00058664393,0.0010330098,0.0011446428,0.74891865,0.04039369,0.1307901,0.06708404,0.00017459819],"about_ca_topic_score_codex":0.0144943,"about_ca_topic_score_gemma":0.029086443,"teacher_disagreement_score":0.0144943,"about_ca_system_score_codex":0.001119919,"about_ca_system_score_gemma":0.0014496689,"threshold_uncertainty_score":0.028819859},"labels":[],"label_agreement":null},{"id":"W4409738260","doi":"10.1016/j.eswa.2025.127717","title":"CCMA: A framework for cascading cooperative multi-agent in autonomous driving merging using Large Language Models","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs","funders":"Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation, Changsha University of Science and Technology; Tsinghua Shenzhen International Graduate School; Science, Technology and Innovation Commission of Shenzhen Municipality","keywords":"Computer science; Artificial intelligence","score_opus":0.028189147063689083,"score_gpt":0.32765445691081624,"score_spread":0.29946530984712716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409738260","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039139874,0.00010258169,0.99242204,0.000094049814,0.000044829412,0.00005666282,0.000061113,0.0018777326,0.0014269195],"genre_scores_gemma":[0.4080154,0.00020274073,0.5842418,0.0001589094,0.000057262347,0.00048319603,0.00030436207,0.0006107821,0.0059255036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944264,0.00017027537,0.000030189916,0.00013890595,0.00014463325,0.00007342348],"domain_scores_gemma":[0.99901223,0.00044243166,0.00007149688,0.00015338877,0.00019437535,0.00012614876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001310901,0.0009366762,0.0010876459,0.00069490855,0.0012566211,0.0013371649,0.0038857074,0.0016876694,0.0063470905],"category_scores_gemma":[0.0031224801,0.0008031119,0.0012701307,0.0005913393,0.001100979,0.0017695188,0.0037344655,0.0021610302,0.0011809483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009249812,0.00008580866,0.00040648514,0.00010697213,0.00009048492,0.00019742982,0.00016632475,0.89069873,0.0034934988,0.042734925,0.0032212406,0.05870553],"study_design_scores_gemma":[0.0000047341073,0.00000828243,0.000014490968,0.000002612984,0.0000044349017,0.000008997003,0.0000064586307,0.9924972,0.0002994673,0.006426905,0.0007218955,0.0000045547267],"about_ca_topic_score_codex":0.018333623,"about_ca_topic_score_gemma":0.023922576,"teacher_disagreement_score":0.018333623,"about_ca_system_score_codex":0.0011002488,"about_ca_system_score_gemma":0.002449516,"threshold_uncertainty_score":0.036453784},"labels":[],"label_agreement":null},{"id":"W4409753650","doi":"10.1016/j.eswa.2025.127818","title":"Reinforcement learning-based algorithm for the dynamic multi-depot crowdsourced delivery problem","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Depot; Artificial intelligence; Machine learning; Algorithm; Mathematical optimization; Mathematics","score_opus":0.00787518912783338,"score_gpt":0.23941390497177747,"score_spread":0.2315387158439441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409753650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02760812,0.00040140317,0.96516746,0.00061239285,0.00014568963,0.00015651935,0.00010938061,0.00052748004,0.0052715293],"genre_scores_gemma":[0.80197054,0.00024677868,0.18832989,0.00040715514,0.00011588122,0.00037357505,0.000288783,0.00013394866,0.008133451],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994017,0.00015457216,0.000023444,0.00014529492,0.00012625345,0.00014867257],"domain_scores_gemma":[0.9979481,0.0013741303,0.00015689402,0.00007193133,0.00026931235,0.00017959755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016147539,0.0010984679,0.0024576925,0.0007350294,0.0008078723,0.0010329915,0.002967557,0.0023410902,0.005099315],"category_scores_gemma":[0.0035034134,0.0007096065,0.0007535029,0.00080235,0.0011903832,0.0011190397,0.0021312241,0.001966157,0.00065173267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009200849,0.000065183965,0.00025929514,0.000045162204,0.000023850955,0.00003758051,0.000026113312,0.9750137,0.0002840141,0.0030097156,0.0011809884,0.01996239],"study_design_scores_gemma":[0.000022294686,0.000014699704,0.000027431275,0.0000034266097,0.0000031528205,0.0000048442175,0.0000037975333,0.9986386,0.000056222874,0.0010941064,0.00012882598,0.000002501874],"about_ca_topic_score_codex":0.016335161,"about_ca_topic_score_gemma":0.011044913,"teacher_disagreement_score":0.016335161,"about_ca_system_score_codex":0.0017541577,"about_ca_system_score_gemma":0.0032213356,"threshold_uncertainty_score":0.03248018},"labels":[],"label_agreement":null},{"id":"W4409880148","doi":"10.1016/j.eswa.2025.127928","title":"Natural language processing methods for assessing social determinants of health in the electronic health records: A narrative review","year":2025,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Mental Health via Writing","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"North York General Hospital; Toronto Western Hospital; University Health Network; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto","keywords":"Health records; Narrative; Computer science; Natural language processing; Natural (archaeology); Social determinants of health; Data science; Electronic health record; Artificial intelligence; Linguistics; Public health; History; Medicine; Health care; Nursing; Political science","score_opus":0.10119135776535619,"score_gpt":0.5857522439506605,"score_spread":0.4845608861853043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409880148","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00022406915,0.9965946,0.00089791237,0.00113116,0.00018704456,0.00011006893,0.00015474437,0.000011399274,0.0006890534],"genre_scores_gemma":[0.0023025943,0.99338186,0.0026206065,0.00087888254,0.00019303303,0.00030014242,0.00016495613,0.0000082759025,0.00014953282],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9907075,0.0036062347,0.0030383312,0.00066501624,0.0018497054,0.00013316913],"domain_scores_gemma":[0.91047645,0.07785616,0.0052246875,0.00093494315,0.0052493145,0.00025854915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013167668,0.0010630363,0.0020521374,0.0103580635,0.00059245975,0.003321427,0.0018739677,0.0017018685,0.0036769558],"category_scores_gemma":[0.05960231,0.00061337865,0.0030238316,0.0070931464,0.0014637595,0.0042420607,0.0016610294,0.0022852719,0.0009482196],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009957313,0.000068159774,0.001599102,0.3350757,0.0009216944,0.00023314674,0.0010450985,0.0003322783,0.00037104456,0.0072188387,0.016053937,0.63698137],"study_design_scores_gemma":[0.000038095877,0.00018748747,0.005485547,0.59453505,0.0032893075,0.0013617654,0.0011340494,0.00044600456,0.0006512043,0.004342534,0.38841957,0.000109391556],"about_ca_topic_score_codex":0.004591627,"about_ca_topic_score_gemma":0.007529724,"teacher_disagreement_score":0.013167668,"about_ca_system_score_codex":0.0022069812,"about_ca_system_score_gemma":0.009891089,"threshold_uncertainty_score":0.06963807},"labels":[],"label_agreement":null},{"id":"W4410050098","doi":"10.1016/j.eswa.2025.127872","title":"Sequential methods for error correction of probabilistic wind power forecasts","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Probabilistic logic; Wind power; Forecast error; Wind power forecasting; Power (physics); Artificial intelligence; Machine learning; Econometrics; Electric power system; Mathematics; Electrical engineering","score_opus":0.01932661802728094,"score_gpt":0.3107841474797758,"score_spread":0.29145752945249487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410050098","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003230309,0.00012623757,0.9954401,0.00006793582,0.00007852036,0.00001844004,0.00005883809,0.00064340624,0.00033606368],"genre_scores_gemma":[0.30488858,0.00047798883,0.6881127,0.00024074288,0.00031406654,0.00026876118,0.0006806142,0.00091962965,0.004097028],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850935,0.0005148212,0.00010163451,0.00028291025,0.00049281406,0.00009842782],"domain_scores_gemma":[0.9951762,0.0026641754,0.00038060054,0.0006346622,0.0010439969,0.000100414116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028939494,0.0009497092,0.00076358113,0.00070347346,0.00033988018,0.0006890659,0.0014068626,0.0007044496,0.0035912984],"category_scores_gemma":[0.013691783,0.0005349324,0.0008591768,0.0006859583,0.00051916455,0.001123665,0.0012429097,0.0017528248,0.0009430121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016699833,0.00006173167,0.0026585667,0.00017669502,0.0002065918,0.00008753813,0.000094649404,0.7376895,0.0045056776,0.019928338,0.0035407585,0.230883],"study_design_scores_gemma":[0.000007762963,0.0000125378765,0.00021776263,0.000008031798,0.0000065262693,0.000011342723,0.0000040776677,0.99242544,0.0010188995,0.0052939947,0.0009865671,0.000007094217],"about_ca_topic_score_codex":0.00783741,"about_ca_topic_score_gemma":0.007196707,"teacher_disagreement_score":0.00783741,"about_ca_system_score_codex":0.0005677944,"about_ca_system_score_gemma":0.0012829554,"threshold_uncertainty_score":0.015583575},"labels":[],"label_agreement":null},{"id":"W4410351582","doi":"10.1016/j.eswa.2025.128127","title":"High performance point-Voxel feature set abstraction with mamba for 3D object detection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Jilin Province Key R&D Plan Project; Education Department of Jilin Province; Ministry of Education; National Natural Science Foundation of China; Jiangsu University; Jilin Province Development and Reform Commission; Ontario Ministry of Natural Resources and Forestry","keywords":"Abstraction; Computer science; Feature (linguistics); Object (grammar); Set (abstract data type); Artificial intelligence; Point (geometry); Voxel; Pattern recognition (psychology); Computer vision; Mathematics; Programming language","score_opus":0.008977726881029904,"score_gpt":0.2536343444885778,"score_spread":0.2446566176075479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410351582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006992925,0.00021639778,0.98295456,0.000067176225,0.000043952892,0.00004096774,0.0002452541,0.008569136,0.0008696932],"genre_scores_gemma":[0.19344708,0.00019547409,0.8015979,0.00013507335,0.00003877878,0.00016655633,0.001194732,0.00056942087,0.0026549993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996505,0.00004173709,0.000018141258,0.00007436382,0.00016394301,0.00005135659],"domain_scores_gemma":[0.99967444,0.00009334772,0.000020577021,0.00009019564,0.00009969504,0.000021616092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041437853,0.0009625779,0.0012674454,0.00080264057,0.00047740858,0.0009799822,0.0021407737,0.00084765785,0.010957908],"category_scores_gemma":[0.001547716,0.0006530125,0.0008854531,0.0012621051,0.0002702634,0.001164062,0.0017473749,0.0014166117,0.003734031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067375414,0.00014610308,0.0011653287,0.00030065043,0.0002489505,0.00015455949,0.00011554773,0.089214765,0.07155803,0.008278403,0.016338404,0.8118054],"study_design_scores_gemma":[0.000022058623,0.000057028043,0.00057196023,0.000010636247,0.00002375864,0.00007117427,0.000031612464,0.97125745,0.017371496,0.0059666415,0.004598109,0.000018020299],"about_ca_topic_score_codex":0.0063460497,"about_ca_topic_score_gemma":0.011165857,"teacher_disagreement_score":0.010957908,"about_ca_system_score_codex":0.00054468546,"about_ca_system_score_gemma":0.0010798018,"threshold_uncertainty_score":0.03665781},"labels":[],"label_agreement":null},{"id":"W4410495852","doi":"10.1016/j.eswa.2025.128238","title":"Unmanned mining fleet Management: A Multi-Objective framework integrating deep reinforcement learning and Internet of Things","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Reinforcement learning; Computer science; Internet of Things; The Internet; Fleet management; Artificial intelligence; World Wide Web; Telecommunications","score_opus":0.007245315082915603,"score_gpt":0.2408449607261687,"score_spread":0.2335996456432531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410495852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07945799,0.00058458606,0.9131184,0.0004939272,0.000091914226,0.00006129146,0.00009179256,0.00032850492,0.00577163],"genre_scores_gemma":[0.956288,0.00018390262,0.041373085,0.000097513395,0.00002973931,0.00009404497,0.00008057467,0.00002402701,0.00182906],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979347,0.000057233767,0.000010145499,0.000047947004,0.000044897515,0.000046262627],"domain_scores_gemma":[0.9996934,0.00013077822,0.000056227007,0.000014124451,0.00007354676,0.000031951564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064146286,0.000775398,0.0006947536,0.0003010322,0.00029818123,0.0006731094,0.0009085269,0.00083169184,0.0008483402],"category_scores_gemma":[0.0007804727,0.0003992955,0.000647801,0.0002942311,0.00049905677,0.00054078706,0.0006905859,0.0008725983,0.00010042448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009151349,0.000015238588,0.0002746759,0.000011349397,0.00001723394,0.000026483362,0.000008163095,0.99286824,0.00031460854,0.0008644671,0.00013458957,0.0054557826],"study_design_scores_gemma":[0.0000017328214,0.000008802461,0.000048169768,0.0000012081445,0.0000026168914,0.0000019415065,0.0000021754047,0.9994155,0.000039991843,0.00040239314,0.00007429213,0.0000011408434],"about_ca_topic_score_codex":0.012614515,"about_ca_topic_score_gemma":0.010450159,"teacher_disagreement_score":0.012614515,"about_ca_system_score_codex":0.00071613415,"about_ca_system_score_gemma":0.0011580371,"threshold_uncertainty_score":0.025082171},"labels":[],"label_agreement":null},{"id":"W4410714749","doi":"10.1016/j.eswa.2025.128243","title":"Securing financial sector applications in the quantum era: a comprehensive evaluation of NIST’s recommended algorithms through use-case analysis","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nova Scotia Department of Agriculture; University of New Brunswick","funders":"","keywords":"NIST; Computer science; Algorithm; Natural language processing","score_opus":0.044722297583826405,"score_gpt":0.31513775727909743,"score_spread":0.27041545969527103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410714749","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7868206,0.0034844726,0.16290942,0.0047396375,0.00012880807,0.0018619085,0.0014704731,0.0021201759,0.036464516],"genre_scores_gemma":[0.82694525,0.0012924598,0.16782092,0.0002940521,0.000021493912,0.00032556255,0.0011556799,0.00015447188,0.001990106],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9513769,0.016824882,0.0027007856,0.001586798,0.026250238,0.0012604235],"domain_scores_gemma":[0.80097115,0.13076298,0.008570437,0.023580665,0.034979716,0.0011350131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.045724597,0.0007706885,0.00088946585,0.0064846217,0.0019709761,0.0038097315,0.0030334424,0.0026079228,0.0019857604],"category_scores_gemma":[0.13056748,0.00054148206,0.0009832564,0.0042465525,0.0019442138,0.006049895,0.0020611698,0.0017432161,0.00050878874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017973386,0.0035930034,0.07458829,0.0021262395,0.0006218347,0.00052693835,0.0018583499,0.17288618,0.008155094,0.09478548,0.014302131,0.6247591],"study_design_scores_gemma":[0.00036024716,0.0020559502,0.028601525,0.0011969948,0.00058985833,0.00082418823,0.0019244255,0.8610444,0.030685289,0.04115553,0.031324685,0.0002369391],"about_ca_topic_score_codex":0.009794118,"about_ca_topic_score_gemma":0.016810857,"teacher_disagreement_score":0.045724597,"about_ca_system_score_codex":0.0057552294,"about_ca_system_score_gemma":0.010014766,"threshold_uncertainty_score":0.24181771},"labels":[],"label_agreement":null},{"id":"W4410963786","doi":"10.1016/j.eswa.2025.128441","title":"Enhanced TARA model for heavy-duty vehicles using ISO/SAE 21434 and Fuzzy Analytic Hierarchy Process (FAHP)","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"FedDev Ontario; Mitacs; University of Windsor","keywords":"Heavy duty; Computer science; Fuzzy logic; Process (computing); Analytic hierarchy process; Operations research; Hierarchy; Industrial engineering; Artificial intelligence; Automotive engineering; Mathematics; Engineering; Economics; Operating system","score_opus":0.12642417738572131,"score_gpt":0.44389679054542824,"score_spread":0.31747261315970693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410963786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038143214,0.0002655435,0.9435049,0.00030505305,0.000059787784,0.00029158435,0.00038333726,0.00022449743,0.016822005],"genre_scores_gemma":[0.76762956,0.00041032227,0.22234447,0.00008262886,0.00003883151,0.0007636449,0.0004562831,0.00004311624,0.008231174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991937,0.00027055058,0.00004428063,0.00014397912,0.0002520724,0.00009543632],"domain_scores_gemma":[0.9992217,0.00038241604,0.00009132299,0.000027634906,0.00024368992,0.00003317327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015123455,0.000821524,0.0006977035,0.0012692617,0.0007740798,0.0020908893,0.001767877,0.0015197876,0.0049763066],"category_scores_gemma":[0.0017466182,0.0004171163,0.001624888,0.0008271939,0.00044872516,0.0011323559,0.0009817923,0.0011353663,0.0005446012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023822658,0.000033032313,0.00031602287,0.00005034439,0.000014747232,0.00009258515,0.00006569391,0.98100567,0.00080503296,0.009730344,0.00029777034,0.007564843],"study_design_scores_gemma":[0.0000029271282,0.000013234071,0.00006684425,0.000005538981,0.000004598673,0.000008526073,0.000015433174,0.99745864,0.000104407605,0.0019350616,0.00038085275,0.0000039837046],"about_ca_topic_score_codex":0.022664806,"about_ca_topic_score_gemma":0.017846957,"teacher_disagreement_score":0.022664806,"about_ca_system_score_codex":0.0017392798,"about_ca_system_score_gemma":0.002700692,"threshold_uncertainty_score":0.04506576},"labels":[],"label_agreement":null},{"id":"W4411005531","doi":"10.1016/j.eswa.2025.128384","title":"E-CARGO Based distributionally robust chance-constrained optimization under severe weather conditions","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nipissing University","funders":"National Natural Science Foundation of China","keywords":"Robust optimization; Computer science; Mathematical optimization; Operations research; Mathematics","score_opus":0.03485903491674016,"score_gpt":0.3284380142646913,"score_spread":0.2935789793479512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411005531","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07281433,0.00041401637,0.91986936,0.00041201757,0.00009595056,0.000047946123,0.00022914492,0.00026254638,0.005854689],"genre_scores_gemma":[0.9439087,0.00022814034,0.05081587,0.00012226551,0.000057123434,0.00006925898,0.00031313166,0.00012645534,0.0043591475],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999471,0.0001856823,0.000029558109,0.00011790893,0.0001105011,0.00008543718],"domain_scores_gemma":[0.99831736,0.0010857625,0.00022539,0.00008260443,0.00019520635,0.00009370704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017911163,0.00096173136,0.0017158516,0.00065166777,0.0003263132,0.0016268758,0.0010377232,0.0018610213,0.0020540112],"category_scores_gemma":[0.004703807,0.00061846134,0.0008334023,0.00077889056,0.0008813932,0.001457281,0.0016857466,0.0008820805,0.00022231905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032667296,0.0000080868085,0.00013258611,0.000016894703,0.000015397332,0.000026014503,0.0000048761726,0.99455774,0.00022968196,0.0023157666,0.00016345271,0.0024968325],"study_design_scores_gemma":[0.0000028878771,0.00000894687,0.00006870341,0.000002070718,0.0000026745558,0.000005162062,0.0000022535876,0.99859995,0.000093163835,0.001156926,0.000054714787,0.0000025808624],"about_ca_topic_score_codex":0.0057771974,"about_ca_topic_score_gemma":0.0029313192,"teacher_disagreement_score":0.0057771974,"about_ca_system_score_codex":0.0008531685,"about_ca_system_score_gemma":0.001060271,"threshold_uncertainty_score":0.011487186},"labels":[],"label_agreement":null},{"id":"W4411114699","doi":"10.1016/j.eswa.2025.128459","title":"MFEL-YOLO for small object detection in UAV aerial images","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Basic Research Program of Shaanxi Province; Key Research and Development Projects of Shaanxi Province; Alberta Innovates; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Computer vision; Artificial intelligence; Object detection; Object (grammar); Remote sensing; Aerial image; Pattern recognition (psychology); Image (mathematics); Geography","score_opus":0.020134188182562945,"score_gpt":0.2729607291042553,"score_spread":0.2528265409216923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411114699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039241064,0.00087881816,0.9298664,0.00023525312,0.00019703186,0.00014098929,0.001276963,0.020374903,0.007788519],"genre_scores_gemma":[0.19278789,0.00040313692,0.78070766,0.00022955461,0.000092671704,0.0004002175,0.0032348565,0.0008558722,0.02128816],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987197,0.000020375364,0.000006823057,0.000032114953,0.000040209437,0.00002856153],"domain_scores_gemma":[0.9998647,0.000044064684,0.000011452373,0.00001661339,0.000052236264,0.000010914318],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028229176,0.00048854237,0.00034615872,0.00049635,0.0002477754,0.0004755777,0.0004881644,0.00048128737,0.014895487],"category_scores_gemma":[0.00081961654,0.00013997134,0.0002948143,0.00027268394,0.00012811358,0.00039589038,0.00053476245,0.00038853032,0.005040763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007365202,0.00017350882,0.0009784742,0.00041823342,0.00007033748,0.00014297568,0.000104055216,0.01630747,0.077328384,0.0046243058,0.028472045,0.8706437],"study_design_scores_gemma":[0.00008962162,0.00022140922,0.0036897527,0.00008579947,0.000036905654,0.00021261591,0.00008181328,0.871991,0.08135035,0.002298754,0.03990954,0.00003235363],"about_ca_topic_score_codex":0.0038143508,"about_ca_topic_score_gemma":0.007722925,"teacher_disagreement_score":0.014895487,"about_ca_system_score_codex":0.00023380568,"about_ca_system_score_gemma":0.0005195106,"threshold_uncertainty_score":0.049830437},"labels":[],"label_agreement":null},{"id":"W4411140207","doi":"10.1016/j.eswa.2025.128482","title":"Leveraging social media and google trends to identify waves of avian influenza outbreaks in USA and Canada","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Food Inspection Agency; University of Guelph","funders":"Ontario Ministry of Agriculture, Food and Rural Affairs; University of Guelph","keywords":"Outbreak; Social media; Influenza A virus subtype H5N1; Computer science; Data science; Virology; World Wide Web; Biology; Virus","score_opus":0.01766921158636678,"score_gpt":0.31357207274542853,"score_spread":0.2959028611590617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411140207","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90114903,0.0017931769,0.002680622,0.0019114866,0.00017367747,0.0001462709,0.07754442,0.0005074624,0.014093873],"genre_scores_gemma":[0.9580295,0.0010041344,0.0043475064,0.0002451547,0.000081029146,0.000036353398,0.032146383,0.00004818965,0.0040618996],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99948114,0.000042010328,0.000039514915,0.00008922198,0.00023771437,0.00011040521],"domain_scores_gemma":[0.9967776,0.0004982618,0.0002938579,0.000113405156,0.0020225265,0.00029433187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070328277,0.00035687452,0.00025756648,0.004430773,0.00067516114,0.001707683,0.0006808985,0.0003760445,0.0008564415],"category_scores_gemma":[0.003890548,0.00018575758,0.00046864714,0.0049544815,0.00022837598,0.00062211254,0.000651692,0.00045792077,0.00033118832],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019352009,0.000107931846,0.89650345,0.00020462692,0.00035292422,0.00034499788,0.0008677114,0.0060863667,0.0015755497,0.00066844036,0.026995124,0.066099375],"study_design_scores_gemma":[0.000018629413,0.000043144457,0.9052757,0.000112273156,0.00020044194,0.00011125769,0.0030707184,0.061965592,0.0015409753,0.00048137843,0.02711592,0.00006399035],"about_ca_topic_score_codex":0.9772309,"about_ca_topic_score_gemma":0.98796576,"teacher_disagreement_score":0.022769094,"about_ca_system_score_codex":0.0058984677,"about_ca_system_score_gemma":0.009231582,"threshold_uncertainty_score":0.04580629},"labels":[],"label_agreement":null},{"id":"W4411263732","doi":"10.1016/j.eswa.2025.128601","title":"A novel method based on wavelet transform and prototypical network for gearbox detection in few-shot learning","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Manitoba","funders":"Tsinghua University","keywords":"Computer science; Artificial intelligence; Shot (pellet); Wavelet transform; Pattern recognition (psychology); Wavelet; Machine learning; Continuous wavelet transform; Discrete wavelet transform; Materials science","score_opus":0.009449258409089792,"score_gpt":0.255898810586471,"score_spread":0.24644955217738124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411263732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014914051,0.00025688956,0.98324996,0.000111510555,0.00006597337,0.000041662843,0.00006624302,0.00050286844,0.0007907761],"genre_scores_gemma":[0.62261754,0.00064756605,0.36960658,0.00030892028,0.00013431617,0.0002065633,0.00068173074,0.00016563245,0.005631111],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999648,0.000046031684,0.00001840264,0.00014390891,0.00010028886,0.00004344121],"domain_scores_gemma":[0.99962044,0.00012486546,0.000049853836,0.000050720213,0.00012378852,0.000030343404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006464857,0.00085988693,0.0006998876,0.00086133304,0.0003120587,0.0006308324,0.0012875563,0.00097381236,0.0013126958],"category_scores_gemma":[0.0020915093,0.00034362826,0.0005802153,0.0006989228,0.00046410973,0.0014848892,0.0010174855,0.0010896823,0.00040314472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002308488,0.00014629502,0.0021294204,0.00017845674,0.0001237862,0.00023994039,0.00014064185,0.22436751,0.03201061,0.008389307,0.0035903538,0.7284529],"study_design_scores_gemma":[0.0000043776067,0.000036741618,0.00027707606,0.000005176793,0.000011802541,0.00006280402,0.00000959012,0.9937691,0.003349091,0.0019162378,0.0005509754,0.0000068792947],"about_ca_topic_score_codex":0.0027740493,"about_ca_topic_score_gemma":0.0030097025,"teacher_disagreement_score":0.0027740493,"about_ca_system_score_codex":0.00047977237,"about_ca_system_score_gemma":0.00063741783,"threshold_uncertainty_score":0.005515814},"labels":[],"label_agreement":null},{"id":"W4411301909","doi":"10.1016/j.eswa.2025.128559","title":"Enhancing fruit disease classification with an advanced 3D shallow deep neural network for precise and efficient identification","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Identification (biology); Computer science; Artificial neural network; Artificial intelligence; Disease; Machine learning; Deep neural networks; Pattern recognition (psychology); Medicine; Pathology; Biology; Botany","score_opus":0.01149124709480628,"score_gpt":0.24012023438212254,"score_spread":0.22862898728731626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411301909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08364063,0.0007091778,0.9086407,0.0003293988,0.00016645173,0.000068520836,0.0008092282,0.0029817182,0.0026542416],"genre_scores_gemma":[0.7355255,0.0004376496,0.254131,0.00051157444,0.00007890448,0.00010321397,0.0017429403,0.000110938425,0.0073583024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983096,0.00001613471,0.00000925004,0.000052768744,0.00005315929,0.000037693742],"domain_scores_gemma":[0.9998085,0.000054200646,0.000020469579,0.000025972293,0.00007634409,0.000014502459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026654243,0.000785188,0.00060375367,0.0006054663,0.00020503289,0.000595612,0.0008642528,0.0009513099,0.0018635935],"category_scores_gemma":[0.0005171416,0.000383544,0.0008160667,0.00048075366,0.00020423839,0.0007368908,0.0008804263,0.0006937985,0.0009216444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025811951,0.000311377,0.006101421,0.00015664402,0.00016248158,0.00016507728,0.000069544716,0.2649797,0.08169672,0.001662574,0.00762418,0.6368122],"study_design_scores_gemma":[0.0000033549534,0.000022468214,0.00065496383,0.0000040078316,0.000012788604,0.00002334358,0.000005585281,0.9944739,0.0038773478,0.0004867538,0.00042925632,0.0000063102634],"about_ca_topic_score_codex":0.009322375,"about_ca_topic_score_gemma":0.015647762,"teacher_disagreement_score":0.009322375,"about_ca_system_score_codex":0.00045839546,"about_ca_system_score_gemma":0.00077142264,"threshold_uncertainty_score":0.01853621},"labels":[],"label_agreement":null},{"id":"W4411396075","doi":"10.1016/j.eswa.2025.128622","title":"Multi-channel and multi-scale weight adaptive neural network for intelligent rotating speed extraction","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; University of Pretoria; University of Manitoba","keywords":"Computer science; Artificial neural network; Extraction (chemistry); Scale (ratio); Artificial intelligence; Channel (broadcasting); Computer network; Chromatography","score_opus":0.025579763003291418,"score_gpt":0.27597259066508456,"score_spread":0.25039282766179316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411396075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038649593,0.0008409163,0.9576583,0.00010247858,0.0001410191,0.00003657176,0.000055050452,0.00049986027,0.0020161513],"genre_scores_gemma":[0.7971719,0.0006449032,0.1941948,0.000088400666,0.00009219537,0.00009260592,0.00019453962,0.000057767666,0.0074629234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979573,0.000026907335,0.000019692361,0.000064051135,0.00006254717,0.00003112786],"domain_scores_gemma":[0.9997708,0.00007073106,0.000023470198,0.000020792633,0.00010564638,0.000008582583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048281107,0.00057420327,0.0005756423,0.0005259379,0.00034897335,0.00049614115,0.00058068195,0.00080330507,0.0013042054],"category_scores_gemma":[0.0009428374,0.00035684524,0.0005240793,0.00074069935,0.0002084744,0.0010301071,0.00051531306,0.00060499704,0.00034727014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002599678,0.00014202697,0.0014557354,0.00015952691,0.00009806934,0.00007920039,0.000060717746,0.39108118,0.02640599,0.0025328961,0.002171487,0.5755532],"study_design_scores_gemma":[0.0000047609315,0.0000195268,0.0004838766,0.0000036938409,0.000012677427,0.000009766757,0.0000045650972,0.9966266,0.0021745947,0.00032354167,0.00033033063,0.000006083387],"about_ca_topic_score_codex":0.0062136236,"about_ca_topic_score_gemma":0.0063894405,"teacher_disagreement_score":0.0062136236,"about_ca_system_score_codex":0.00037386816,"about_ca_system_score_gemma":0.0005490619,"threshold_uncertainty_score":0.01235491},"labels":[],"label_agreement":null},{"id":"W4411613687","doi":"10.1016/j.eswa.2025.128731","title":"Contrastive and self-supervised learning for open-set damage classification in structural health monitoring with incomplete and imbalanced vibration data","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Jiangsu Province; KU Leuven","keywords":"Computer science; Structural health monitoring; Artificial intelligence; Set (abstract data type); Machine learning; Open set; Data set; Vibration; Supervised learning; Pattern recognition (psychology); Structural engineering; Mathematics; Artificial neural network; Engineering","score_opus":0.041661583334567216,"score_gpt":0.3453157598843263,"score_spread":0.30365417654975907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411613687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10500175,0.0008044302,0.8914203,0.00027702018,0.00009436488,0.00010212933,0.00022598475,0.0011403313,0.00093368767],"genre_scores_gemma":[0.7669323,0.0001898954,0.22919813,0.00021662054,0.00017588331,0.00017493231,0.00092700485,0.00016811353,0.0020169988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981711,0.0006577965,0.00013772878,0.0005517207,0.0003251928,0.00015652731],"domain_scores_gemma":[0.9914214,0.0062709996,0.00044529347,0.0007561078,0.0009008834,0.00020525864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049174516,0.000894458,0.0017465575,0.0014885971,0.0005746298,0.0010950639,0.0023703487,0.0019245443,0.0012132752],"category_scores_gemma":[0.0096537,0.0005057236,0.001063982,0.0008488294,0.0011451287,0.0020608725,0.0020562264,0.0018809438,0.00039656556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011227507,0.0009439661,0.0073611215,0.00031496625,0.00030861836,0.00020855851,0.00030945629,0.30167764,0.014091692,0.007098433,0.0040326794,0.6625301],"study_design_scores_gemma":[0.000012405299,0.00006426569,0.00061747385,0.000006776597,0.000012403819,0.00002345878,0.000009952501,0.9959544,0.0014079175,0.0017172437,0.00016757457,0.0000061116853],"about_ca_topic_score_codex":0.0024065787,"about_ca_topic_score_gemma":0.00385376,"teacher_disagreement_score":0.0049174516,"about_ca_system_score_codex":0.0007525718,"about_ca_system_score_gemma":0.000868472,"threshold_uncertainty_score":0.026006341},"labels":[],"label_agreement":null},{"id":"W4411729508","doi":"10.1016/j.eswa.2025.128800","title":"A robust hyperchaotic system-controlled color image encryption with triangular fractals and alternating channel vectors","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Chaos-based Image/Signal Encryption","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Department of Science and Technology of Jilin Province","keywords":"Encryption; Computer science; Image (mathematics); Fractal; Channel (broadcasting); Artificial intelligence; Computer vision; Theoretical computer science; Mathematics; Computer security; Computer network; Mathematical analysis","score_opus":0.01068266099020381,"score_gpt":0.23268133116600992,"score_spread":0.2219986701758061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411729508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27648503,0.0008392019,0.7022547,0.0003365039,0.00026767183,0.00014653085,0.00010932045,0.000903271,0.018657802],"genre_scores_gemma":[0.94469976,0.00013977621,0.050941974,0.000042117626,0.000027374801,0.000028822482,0.000037025744,0.00002615078,0.004056996],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984765,0.000028771614,0.000010535591,0.00003692453,0.000060548344,0.000015581796],"domain_scores_gemma":[0.9998914,0.00002390105,0.000020337304,0.000020510477,0.000034316952,0.000009437353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015756843,0.0002503693,0.00032903004,0.00017684292,0.00023486424,0.0003943915,0.00039966166,0.00031398953,0.0012214116],"category_scores_gemma":[0.00023690479,0.00010227586,0.00025812155,0.00024117551,0.0003047655,0.00039614236,0.00023603183,0.00021831512,0.00027079784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073417753,0.00008635786,0.00073989347,0.0002866558,0.00009145849,0.00035465183,0.00021928058,0.028469544,0.80671144,0.06784931,0.0019685393,0.092488766],"study_design_scores_gemma":[0.00012795614,0.00064933544,0.0011812726,0.000019686851,0.00008676684,0.00081784406,0.000033072214,0.74626845,0.23644629,0.005479235,0.008799528,0.00009052265],"about_ca_topic_score_codex":0.00052784756,"about_ca_topic_score_gemma":0.0005419261,"teacher_disagreement_score":0.0012214116,"about_ca_system_score_codex":0.00029104494,"about_ca_system_score_gemma":0.00022381273,"threshold_uncertainty_score":0.004086077},"labels":[],"label_agreement":null},{"id":"W4411920999","doi":"10.1016/j.eswa.2025.128806","title":"Granular computing-based fuzzy deep neural network for long-tailed fault diagnosis: Design and analysis","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Key Research and Development Program of Liaoning Province; Key Project of Research and Development Plan of Hunan Province","keywords":"Computer science; Artificial neural network; Artificial intelligence; Fuzzy logic; Neuro-fuzzy; Fault (geology); Data mining; Granular computing; Machine learning; Fuzzy control system; Geology; Seismology; Rough set","score_opus":0.015858667249315547,"score_gpt":0.2638451972552217,"score_spread":0.24798653000590615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411920999","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049250584,0.0008629505,0.94692254,0.00028537126,0.000081459344,0.000044752996,0.00006087655,0.00031825172,0.0021731711],"genre_scores_gemma":[0.93803835,0.00024558572,0.060205817,0.00010544334,0.00002784758,0.0000410888,0.000048356942,0.000016643655,0.0012708526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997409,0.000048693088,0.000016460466,0.000060683327,0.000079870406,0.00005344912],"domain_scores_gemma":[0.9991358,0.00044584056,0.000089758774,0.000055393044,0.00023247153,0.000040691473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009969866,0.0004761971,0.0009235101,0.00046416526,0.00036559717,0.00092108117,0.0010640261,0.00091025466,0.0015773297],"category_scores_gemma":[0.0019736926,0.00031174853,0.0004885538,0.00048049644,0.000496558,0.0011093123,0.0005853499,0.0009844032,0.0001434678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027805465,0.00012171636,0.00105965,0.00012127745,0.000077461984,0.00007983709,0.000044076398,0.85549855,0.004339904,0.011931124,0.0011613581,0.12528703],"study_design_scores_gemma":[0.000002341109,0.00002023401,0.00008439728,0.0000038799835,0.000006763366,0.00000539275,0.0000024914393,0.9979488,0.00029090373,0.0015772879,0.000054920347,0.00000251258],"about_ca_topic_score_codex":0.007675991,"about_ca_topic_score_gemma":0.0089567285,"teacher_disagreement_score":0.007675991,"about_ca_system_score_codex":0.0010033322,"about_ca_system_score_gemma":0.0009094497,"threshold_uncertainty_score":0.015262663},"labels":[],"label_agreement":null},{"id":"W4412025916","doi":"10.1016/j.eswa.2025.128887","title":"Multiplex graph prompt collaboration for open-set social event detection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China","keywords":"Computer science; Graph; Event (particle physics); Set (abstract data type); Multiplex; Artificial intelligence; Theoretical computer science; Programming language","score_opus":0.016093999057527907,"score_gpt":0.3363110022090546,"score_spread":0.3202170031515267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412025916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15803531,0.0006508646,0.81147987,0.00083406584,0.0003045595,0.0004687937,0.007471479,0.010404185,0.010350912],"genre_scores_gemma":[0.80425733,0.00015626031,0.18542616,0.00014625347,0.00016264284,0.00023738625,0.0045705955,0.00018206763,0.004861445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853075,0.00033916847,0.000060556602,0.0004476482,0.0004606875,0.00016116473],"domain_scores_gemma":[0.99593216,0.0018851091,0.00045450803,0.0006892099,0.000519366,0.0005196249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013672073,0.0008173068,0.0007375934,0.0032595978,0.0010110697,0.0014651791,0.00129154,0.00128948,0.0058593024],"category_scores_gemma":[0.007867459,0.0002858088,0.0004948791,0.0019736013,0.00035819155,0.002391178,0.0029962456,0.0010799586,0.001972244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022009984,0.0014791496,0.068478465,0.0007905664,0.00046386354,0.0013532254,0.0014786755,0.06957028,0.0448169,0.06099989,0.050148867,0.698219],"study_design_scores_gemma":[0.000050130184,0.00019118117,0.0121604465,0.00004735154,0.00007095514,0.00038907822,0.00045970027,0.9035723,0.01312189,0.05617559,0.013708961,0.0000524835],"about_ca_topic_score_codex":0.0024197658,"about_ca_topic_score_gemma":0.006925449,"teacher_disagreement_score":0.0058593024,"about_ca_system_score_codex":0.0005819197,"about_ca_system_score_gemma":0.0008936023,"threshold_uncertainty_score":0.019601345},"labels":[],"label_agreement":null},{"id":"W4412049267","doi":"10.1016/j.eswa.2025.128920","title":"A comprehensive review on data-level methods for imbalanced data classification","year":2025,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Data classification; Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.3769644452128858,"score_gpt":0.5124121553384758,"score_spread":0.13544771012559004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412049267","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030013078,0.98247206,0.014457904,0.000683243,0.00053531094,0.000057170007,0.00025413124,0.00013646595,0.0011035473],"genre_scores_gemma":[0.002660471,0.9743241,0.019647837,0.000721676,0.0009907188,0.00010036094,0.00076694414,0.00005176668,0.00073610665],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777263,0.00040915806,0.00034310156,0.00046921973,0.00091957336,0.00008630522],"domain_scores_gemma":[0.9939599,0.0038339936,0.00041376773,0.000235021,0.0014430115,0.00011433622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004910303,0.0016740407,0.0032089972,0.006237285,0.00053895055,0.0024537737,0.002624422,0.0014356375,0.003762131],"category_scores_gemma":[0.011639898,0.000667889,0.0019713307,0.00881183,0.0007624559,0.0033595006,0.0013713908,0.0023849097,0.0025667811],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057897323,0.00006524984,0.00044058962,0.014439277,0.0002099206,0.00003308177,0.000040841463,0.0007204143,0.00061905093,0.0027931232,0.023429729,0.9571509],"study_design_scores_gemma":[0.00007694349,0.00032926,0.005230389,0.023861198,0.0017850907,0.0010472576,0.00019138299,0.007521174,0.0035749578,0.024386084,0.9318098,0.00018653963],"about_ca_topic_score_codex":0.0025694354,"about_ca_topic_score_gemma":0.0034898773,"teacher_disagreement_score":0.006237285,"about_ca_system_score_codex":0.0010785327,"about_ca_system_score_gemma":0.003474131,"threshold_uncertainty_score":0.025968492},"labels":[],"label_agreement":null},{"id":"W4412430915","doi":"10.1016/j.eswa.2025.128912","title":"An intelligent wireless sensing algorithm for complex cross-domain scenarios based on DB-FA-YoLov6","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"State Key Laboratory of Networking and Switching Technology; Guangxi Normal University; Fundamental Research Funds for the Central Universities; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Computer science; Wireless; Domain (mathematical analysis); Algorithm; Data mining; Artificial intelligence; Telecommunications; Mathematics","score_opus":0.013965486768502684,"score_gpt":0.2839655763082468,"score_spread":0.27000008953974414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412430915","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012449817,0.00015993872,0.9853512,0.000044835153,0.000057445643,0.000032571923,0.000032001168,0.00045058137,0.0014216185],"genre_scores_gemma":[0.25749788,0.00017869593,0.7388558,0.00007996236,0.00003607625,0.00010233215,0.0002743873,0.00008288296,0.0028919452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997217,0.000044771452,0.000017158372,0.00008072531,0.00008902704,0.00004665081],"domain_scores_gemma":[0.9997311,0.0000749682,0.000022411303,0.000034741115,0.00011891543,0.000017803488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004219448,0.0005632817,0.00070165267,0.0007474344,0.00045786487,0.0006462744,0.000938962,0.0006269923,0.0019168777],"category_scores_gemma":[0.0012628669,0.00026091578,0.00047043565,0.00053138303,0.00030358753,0.00069502706,0.0007868647,0.00054393266,0.0007558251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038014867,0.00015066886,0.0025846742,0.00012598377,0.00009903067,0.00011911216,0.00011895358,0.21400267,0.0581838,0.011673884,0.0036263177,0.7089347],"study_design_scores_gemma":[0.0000099274475,0.000048942544,0.00040012292,0.0000070942992,0.000011523874,0.00008907257,0.000015728392,0.9885357,0.00777185,0.0009386417,0.002161018,0.000010465155],"about_ca_topic_score_codex":0.0036354288,"about_ca_topic_score_gemma":0.004102923,"teacher_disagreement_score":0.0036354288,"about_ca_system_score_codex":0.00044170808,"about_ca_system_score_gemma":0.00072549406,"threshold_uncertainty_score":0.0072285533},"labels":[],"label_agreement":null},{"id":"W4412967627","doi":"10.1016/j.eswa.2025.128946","title":"Leveraging model explainability and fine-grained cutmix augmentation for robust detection of apricot diseases in UAV images","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Ottawa","funders":"Zayed University","keywords":"Computer science; Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.014552606735312949,"score_gpt":0.23482157701958145,"score_spread":0.2202689702842685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412967627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.121784724,0.0010155992,0.87141776,0.00056216796,0.00010892564,0.00010266163,0.00058381923,0.0030615404,0.0013628333],"genre_scores_gemma":[0.83840847,0.0005295953,0.15612343,0.00034885274,0.00011409041,0.00009802388,0.0020407236,0.00032273418,0.0020139962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949896,0.00007652373,0.000025664085,0.00021551836,0.000097484,0.000085890126],"domain_scores_gemma":[0.9990146,0.0004542367,0.00014844273,0.00020904056,0.00013356261,0.000040126026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010371949,0.0012338826,0.0010141472,0.0011319764,0.0003375291,0.0010655624,0.00089256366,0.0013865973,0.0012120971],"category_scores_gemma":[0.0025388412,0.0004811491,0.0014812133,0.00063152896,0.0007222735,0.0013126357,0.0011350969,0.001773698,0.00058999326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008146184,0.00033475104,0.006568941,0.00030728252,0.00036236388,0.0003513858,0.00023513289,0.49635124,0.11981819,0.004386395,0.0036666694,0.36680293],"study_design_scores_gemma":[0.000006725492,0.000032173957,0.0010150523,0.000009250718,0.000030400804,0.00004668648,0.000014754423,0.9923254,0.0043545836,0.0016288502,0.00052634365,0.000009792188],"about_ca_topic_score_codex":0.0052309427,"about_ca_topic_score_gemma":0.008226537,"teacher_disagreement_score":0.0052309427,"about_ca_system_score_codex":0.00046123177,"about_ca_system_score_gemma":0.00084325555,"threshold_uncertainty_score":0.010400951},"labels":[],"label_agreement":null},{"id":"W4413042544","doi":"10.1016/j.eswa.2025.129274","title":"Multi-representation space recommendation with graph contrastive learning","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Graph; Artificial intelligence; Representation (politics); Space (punctuation); Natural language processing; Machine learning; Theoretical computer science","score_opus":0.017695634313741545,"score_gpt":0.29486895874522545,"score_spread":0.2771733244314839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413042544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015280065,0.00056460814,0.98055226,0.0003350325,0.00009178988,0.000089926725,0.00021482399,0.001194132,0.0016773277],"genre_scores_gemma":[0.39142364,0.00047355163,0.5991129,0.00052435783,0.00019090457,0.0002138508,0.0012423049,0.00026477114,0.0065537333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868697,0.00044639103,0.000055838722,0.0004008088,0.00032192565,0.00008807506],"domain_scores_gemma":[0.9963322,0.0021818883,0.0001363969,0.0006534146,0.0005649856,0.00013110871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012469016,0.0010526172,0.00178854,0.0026201922,0.00079069927,0.0015430562,0.002619265,0.002250532,0.0047437632],"category_scores_gemma":[0.006804943,0.00065683905,0.00152294,0.002865451,0.0008640169,0.0030338764,0.0016400535,0.002439524,0.0014507938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071058125,0.0006893422,0.0027309267,0.00035469764,0.00043631886,0.00017558891,0.00019918285,0.2214816,0.0082261795,0.029852571,0.01934245,0.7158005],"study_design_scores_gemma":[0.00003111618,0.0000695133,0.00018269029,0.000010202373,0.000030768802,0.000034907378,0.000014302711,0.9881153,0.000995614,0.009685478,0.00081778446,0.000012301554],"about_ca_topic_score_codex":0.011068751,"about_ca_topic_score_gemma":0.01624966,"teacher_disagreement_score":0.011068751,"about_ca_system_score_codex":0.0009232949,"about_ca_system_score_gemma":0.0009572771,"threshold_uncertainty_score":0.022008657},"labels":[],"label_agreement":null},{"id":"W4413053856","doi":"10.1016/j.eswa.2025.129261","title":"Using external knowledge to enhance user preferences for better sequential recommendation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Key Laboratory of Software Engineering of Yunnan Province; Yunnan University; Yunnan Power Grid Company; National Key Research and Development Program of China; People's Government of Yunnan Province; Natural Science Foundation of Yunnan Province","keywords":"Computer science; Machine learning; Artificial intelligence; Information retrieval","score_opus":0.05091744191422129,"score_gpt":0.3741167245093967,"score_spread":0.3231992825951754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413053856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5131477,0.0016705571,0.4552978,0.00067418313,0.0002700055,0.00020814293,0.00075446296,0.0019314004,0.026045762],"genre_scores_gemma":[0.9286554,0.00030098445,0.06342588,0.00020346078,0.00010353517,0.00005139616,0.00065273687,0.00013137932,0.0064752297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990061,0.00029219448,0.00006612049,0.00022433006,0.00031407023,0.000097210206],"domain_scores_gemma":[0.99510694,0.0023480959,0.00023254874,0.0007616095,0.0013469476,0.0002039279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014125395,0.0007194679,0.00082441216,0.0010090749,0.0003683601,0.0016014316,0.0005766829,0.001127356,0.006220319],"category_scores_gemma":[0.008848003,0.00029015532,0.0006368522,0.0011203309,0.00024010178,0.0023770672,0.00066009036,0.0012103439,0.0016791552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022303937,0.0026856784,0.0375288,0.0005736234,0.00072310393,0.00032902622,0.00054475624,0.05565658,0.060446646,0.0045934143,0.008656287,0.8260317],"study_design_scores_gemma":[0.00021726287,0.0020371873,0.046191424,0.00016877982,0.0009366209,0.0007424011,0.00044581955,0.86586785,0.06045521,0.012028085,0.010718251,0.0001911565],"about_ca_topic_score_codex":0.0036700042,"about_ca_topic_score_gemma":0.013810026,"teacher_disagreement_score":0.006220319,"about_ca_system_score_codex":0.00045296803,"about_ca_system_score_gemma":0.0006883647,"threshold_uncertainty_score":0.020808995},"labels":[],"label_agreement":null},{"id":"W4413214380","doi":"10.1016/j.eswa.2025.129308","title":"The synergy of statistical and fuzzy logic approaches in mining patterns from the peer-to-peer lending data","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"H2020 Marie Skłodowska-Curie Actions; HORIZON EUROPE Framework Programme; European Commission; Nemzeti Kutatási Fejlesztési és Innovációs Hivatal; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap; Ministerstvo školstva, vedy, výskumu a športu Slovenskej republiky; European Cooperation in Science and Technology","keywords":"Computer science; Fuzzy logic; Peer-to-peer; Data mining; Data science; Machine learning; Artificial intelligence; World Wide Web","score_opus":0.05962620928861447,"score_gpt":0.2811118080085952,"score_spread":0.22148559871998072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413214380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14246789,0.00278816,0.8450397,0.0019144943,0.00012454156,0.00018889803,0.0013309644,0.0006715148,0.0054737786],"genre_scores_gemma":[0.72657144,0.0011798647,0.2698264,0.00027582812,0.0002866865,0.00011460785,0.0009083462,0.000039098104,0.0007977333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639136,0.0013920291,0.00042201567,0.00046799515,0.0012169506,0.00010967047],"domain_scores_gemma":[0.9755009,0.02031619,0.0011392784,0.00090129935,0.0018988246,0.00024353618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053368127,0.0004717568,0.00079760957,0.0077860896,0.0005966034,0.0028980332,0.00086190534,0.0007291183,0.0009914227],"category_scores_gemma":[0.023098055,0.0002508008,0.0007850836,0.005630735,0.00064699113,0.0032302851,0.0008416995,0.0008695356,0.00053280056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003211513,0.00057397486,0.07743066,0.0006296241,0.00083572045,0.00039746438,0.000636142,0.07792393,0.005625558,0.021843642,0.003182374,0.81059974],"study_design_scores_gemma":[0.00004050225,0.00022273966,0.022539824,0.0001286035,0.00025534185,0.0004986271,0.00080168084,0.8467358,0.004230621,0.1197378,0.0047265394,0.00008195033],"about_ca_topic_score_codex":0.0039008306,"about_ca_topic_score_gemma":0.0071999715,"teacher_disagreement_score":0.0077860896,"about_ca_system_score_codex":0.0005646801,"about_ca_system_score_gemma":0.001449364,"threshold_uncertainty_score":0.028224051},"labels":[],"label_agreement":null},{"id":"W4413231680","doi":"10.1016/j.eswa.2025.129321","title":"Imputation for incomplete data based on granulated single output neural network group","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Imputation (statistics); Computer science; Artificial neural network; Missing data; Artificial intelligence; Group (periodic table); Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.043599066828304084,"score_gpt":0.2841364386125429,"score_spread":0.2405373717842388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413231680","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023458455,0.0001751212,0.9751456,0.00022423115,0.00007554263,0.000061940875,0.00022427135,0.00026890496,0.00036607397],"genre_scores_gemma":[0.53402203,0.00032007418,0.46075797,0.00020968357,0.00020915322,0.00039377908,0.001770781,0.0001289501,0.0021875685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99445504,0.002671905,0.00044891203,0.0011807405,0.00089677627,0.00034657127],"domain_scores_gemma":[0.97486705,0.014050064,0.0020222203,0.006561749,0.0021674752,0.00033143244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013224377,0.00068044476,0.0038548235,0.0022166753,0.0011832145,0.0023332026,0.004398941,0.0024745609,0.0030721347],"category_scores_gemma":[0.03543196,0.0007765168,0.0024875754,0.0034064665,0.0017544191,0.0039338167,0.0032560923,0.0029869487,0.0005694371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014649414,0.00029480463,0.019598871,0.00045216075,0.0012144467,0.00096040044,0.00091935304,0.62219924,0.001345763,0.07692894,0.005524757,0.26909634],"study_design_scores_gemma":[0.000028149607,0.00004807513,0.001034666,0.00003593289,0.000059207843,0.00007360894,0.000038322658,0.93554425,0.00036452294,0.06225033,0.0005033207,0.000019613211],"about_ca_topic_score_codex":0.0022754462,"about_ca_topic_score_gemma":0.0019617418,"teacher_disagreement_score":0.013224377,"about_ca_system_score_codex":0.0009461859,"about_ca_system_score_gemma":0.0014344383,"threshold_uncertainty_score":0.069938004},"labels":[],"label_agreement":null},{"id":"W4413294059","doi":"10.1016/j.eswa.2025.129401","title":"Multi-UAV-aided power-up and data collection: Multi-agent DQL with genetic algorithm approach","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Korea Institute of Energy Technology Evaluation and Planning; Information Technology Research Centre; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Trade, Industry and Energy","keywords":"Computer science; Genetic algorithm; Power (physics); Data collection; Data mining; Algorithm; Artificial intelligence; Machine learning; Mathematics","score_opus":0.02210736225022162,"score_gpt":0.25815380543085537,"score_spread":0.23604644318063375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413294059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016525345,0.00016521648,0.9796674,0.00012222995,0.000037721802,0.00005461277,0.000020832833,0.00020864894,0.0031979706],"genre_scores_gemma":[0.7854852,0.00013276536,0.21162823,0.00012075381,0.000026803067,0.00020064873,0.000056586832,0.000045048146,0.0023040313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996619,0.00010109816,0.00001786888,0.00006459868,0.00010643191,0.000048088597],"domain_scores_gemma":[0.9994783,0.0002630681,0.000067995985,0.000034380988,0.00012840077,0.00002784828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007587716,0.000532001,0.0011087904,0.0005579144,0.00053056085,0.0009608352,0.0010894362,0.001057428,0.0015977945],"category_scores_gemma":[0.0015222419,0.00041965127,0.00057985564,0.0006191228,0.00047052422,0.0007297015,0.0010086851,0.00066890183,0.00027138894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000622366,0.00006933103,0.0004946167,0.000068393834,0.00004328809,0.00007846725,0.000067368426,0.9403608,0.0029327506,0.0032985408,0.00051402964,0.052010145],"study_design_scores_gemma":[0.0000065211625,0.00001960354,0.000050935894,0.000002929219,0.0000042536035,0.000008508631,0.0000075287458,0.999012,0.0003122628,0.0004237671,0.00014939359,0.0000023028042],"about_ca_topic_score_codex":0.007096912,"about_ca_topic_score_gemma":0.0055553145,"teacher_disagreement_score":0.007096912,"about_ca_system_score_codex":0.0006358393,"about_ca_system_score_gemma":0.0009831032,"threshold_uncertainty_score":0.014111221},"labels":[],"label_agreement":null},{"id":"W4413325339","doi":"10.1016/j.eswa.2025.129306","title":"Corrigendum to: “Multi-view neutrosophic <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.svg\"><mml:mi>c</mml:mi></mml:math>-means clustering algorithms” [Expert Syst. Appl. 260 (2025) 126763]","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Algorithm; Cluster analysis; Computer science; Mathematics; Algebra over a field; Artificial intelligence; Pure mathematics","score_opus":0.02688683925754727,"score_gpt":0.2685451494034896,"score_spread":0.24165831014594236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413325339","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040153624,0.0027853167,0.004051812,0.042619675,0.88336897,0.00012340386,0.0027278461,0.0014564858,0.062464844],"genre_scores_gemma":[0.010294841,0.0047483775,0.004743519,0.015708571,0.101016924,0.00016908148,0.0053656283,0.0017046313,0.8562484],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844724,0.00022643953,0.00015480186,0.00030351657,0.0007186954,0.00014931938],"domain_scores_gemma":[0.98751605,0.0020247435,0.00022613755,0.00060441816,0.009080351,0.0005484193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013334557,0.0020304227,0.002110255,0.002999214,0.002525993,0.0031819753,0.0024591836,0.003071384,0.475174],"category_scores_gemma":[0.017508367,0.00077645906,0.0018944326,0.0021622097,0.0010587056,0.0023154456,0.0020574117,0.0029198576,0.25997522],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016178692,0.000007901575,0.00002753984,0.000055786604,0.0000064087853,0.000058867805,0.000014577587,0.000052869003,0.00008439512,0.0006691023,0.99388015,0.005126224],"study_design_scores_gemma":[0.000019734129,0.0000251515,0.00061952864,0.00009264794,0.000022061331,0.0001971089,0.000061980616,0.0005680427,0.0004865592,0.0015668008,0.9963098,0.000030469415],"about_ca_topic_score_codex":0.018259156,"about_ca_topic_score_gemma":0.026474753,"teacher_disagreement_score":0.475174,"about_ca_system_score_codex":0.0032395981,"about_ca_system_score_gemma":0.0017407874,"threshold_uncertainty_score":0.7486006},"labels":[],"label_agreement":null},{"id":"W4413476775","doi":"10.1016/j.eswa.2025.129481","title":"Clean-label backdoor attack via sample-customized feature alignment","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China-Shandong Joint Fund; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Backdoor; Computer science; Sample (material); Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Data mining; Computer security; Chromatography","score_opus":0.015064438993322509,"score_gpt":0.2946220818503113,"score_spread":0.2795576428569888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413476775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02527059,0.0003089643,0.9675656,0.00079027645,0.00020807136,0.00008246072,0.0002974181,0.0028758724,0.0026007555],"genre_scores_gemma":[0.7472788,0.00027516286,0.24353655,0.00088706973,0.00020619511,0.0001905346,0.0009124818,0.0005386986,0.00617447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99638414,0.00085741235,0.00012920311,0.0007711955,0.0013957722,0.00046225262],"domain_scores_gemma":[0.99550205,0.0018875017,0.0002977946,0.0018712865,0.0003135491,0.00012792593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020113105,0.0016637389,0.0017145436,0.00077939726,0.00092416065,0.001417593,0.0013802266,0.0024941012,0.0033634575],"category_scores_gemma":[0.010550121,0.0006315344,0.0013156546,0.0008970244,0.0018363293,0.0028626116,0.0052594226,0.004137778,0.0016768649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026694073,0.0005631396,0.0036322845,0.0005033541,0.000528034,0.001001678,0.00036761136,0.26275197,0.08943509,0.15139572,0.028116994,0.45903468],"study_design_scores_gemma":[0.000072954106,0.00014238147,0.00076452084,0.000031097385,0.000049382244,0.00043834693,0.00004352211,0.88125616,0.027598476,0.08657973,0.0029739768,0.000049340488],"about_ca_topic_score_codex":0.000789627,"about_ca_topic_score_gemma":0.0012147756,"teacher_disagreement_score":0.0033634575,"about_ca_system_score_codex":0.0006914167,"about_ca_system_score_gemma":0.001584114,"threshold_uncertainty_score":0.011251867},"labels":[],"label_agreement":null},{"id":"W4413825808","doi":"10.1016/j.eswa.2025.129483","title":"LGTime: Leveraging LLMs with feature-aware processing and multi-granularity fusion for zero-shot time series forecasting","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Key Research and Development Project of Hainan Province","keywords":"Granularity; Computer science; Shot (pellet); Series (stratigraphy); Feature (linguistics); Fusion; Zero (linguistics); Data mining; Artificial intelligence; Pattern recognition (psychology); Materials science","score_opus":0.02309501030211214,"score_gpt":0.25344059369140776,"score_spread":0.23034558338929562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413825808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027450342,0.0009199977,0.96228015,0.00024996372,0.00030930346,0.000040887884,0.00044381773,0.0068961643,0.0014094475],"genre_scores_gemma":[0.60985214,0.0004087586,0.38435268,0.0004350335,0.00031066537,0.00009510101,0.0013652764,0.00041215235,0.00276822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995721,0.00007405249,0.000028893422,0.00012429921,0.00014104763,0.00005962401],"domain_scores_gemma":[0.9994968,0.00018805928,0.000041960175,0.000103659586,0.00012226938,0.00004722269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078086066,0.0010381582,0.0012136677,0.0011954142,0.00046358156,0.0012299833,0.001064913,0.0008694846,0.0025742522],"category_scores_gemma":[0.0028013159,0.0003216217,0.0007463344,0.0011490367,0.00031133005,0.0017939056,0.0014835924,0.0012223974,0.0010512933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011383338,0.00038986595,0.002565468,0.00018197818,0.00028685294,0.00024955752,0.00017284497,0.14311038,0.044131912,0.0049801143,0.011268533,0.791524],"study_design_scores_gemma":[0.000013934387,0.000049482274,0.0004877163,0.000008050105,0.000021825348,0.000025943616,0.000017932456,0.9900323,0.004096982,0.0037907118,0.0014386507,0.000016443055],"about_ca_topic_score_codex":0.005720567,"about_ca_topic_score_gemma":0.009553618,"teacher_disagreement_score":0.005720567,"about_ca_system_score_codex":0.0003928218,"about_ca_system_score_gemma":0.0007102496,"threshold_uncertainty_score":0.011374533},"labels":[],"label_agreement":null},{"id":"W4413836684","doi":"10.1016/j.eswa.2025.129468","title":"Optimal non-exclusive trade-in rebates design for a profit-maximizing company","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cape Breton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Profit (economics); Computer science; Industrial organization; Operations research; Business; Not for profit; Microeconomics; Economics; Mathematics; Accounting","score_opus":0.025052075032891093,"score_gpt":0.26160783453384356,"score_spread":0.23655575950095248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413836684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25134918,0.0009079784,0.71155626,0.00073415355,0.00008324259,0.00070755114,0.00012610415,0.0005314932,0.03400408],"genre_scores_gemma":[0.96590614,0.0001994489,0.03001134,0.00007782262,0.000011528183,0.00016129667,0.000032710705,0.00004758272,0.003552072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987515,0.0004940946,0.00006382104,0.00023262136,0.00019165777,0.00026627525],"domain_scores_gemma":[0.9983285,0.0007910004,0.00029733405,0.00010482205,0.00027033745,0.00020803159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017972976,0.0013325182,0.0017689082,0.0007771179,0.00059772347,0.0026528286,0.0015832032,0.001585088,0.0071049137],"category_scores_gemma":[0.0039244224,0.0009597894,0.00070611754,0.0004128144,0.0008606592,0.0023798645,0.0013396397,0.0011919576,0.00073914806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093933387,0.000469108,0.0009701539,0.00035597236,0.00009848994,0.00023347978,0.00018268522,0.88634664,0.014452805,0.037197262,0.0015242081,0.05722989],"study_design_scores_gemma":[0.000072153074,0.00038953184,0.00043059225,0.000034300843,0.0000533419,0.00007950959,0.000078591824,0.9826726,0.0027566694,0.012278557,0.0011269317,0.000027336324],"about_ca_topic_score_codex":0.0015424349,"about_ca_topic_score_gemma":0.001390301,"teacher_disagreement_score":0.0071049137,"about_ca_system_score_codex":0.002116087,"about_ca_system_score_gemma":0.0015980912,"threshold_uncertainty_score":0.023768246},"labels":[],"label_agreement":null},{"id":"W4413854296","doi":"10.1016/j.eswa.2025.129529","title":"A biologically inspired separable learning vision model for real-time traffic object perception in Dark","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus","funders":"Chongqing Jiaotong University; National Natural Science Foundation of China","keywords":"Object (grammar); Computer science; Artificial intelligence; Perception; Separable space; Computer vision; Object detection; Pattern recognition (psychology); Mathematics; Psychology","score_opus":0.00810138743306262,"score_gpt":0.25746154236608865,"score_spread":0.24936015493302602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413854296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076008156,0.00034113106,0.9164873,0.0005488421,0.000088046974,0.000039059803,0.00013814504,0.00051768066,0.0058316584],"genre_scores_gemma":[0.9464694,0.00022714514,0.044830002,0.00020914957,0.000038607028,0.000058626203,0.000114213675,0.00006481821,0.00798796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999149,0.000010745472,0.0000030862432,0.00003288613,0.000017810258,0.000020589743],"domain_scores_gemma":[0.99985266,0.00004771487,0.0000218662,0.000015036247,0.000042443062,0.000020343556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025102854,0.00034391438,0.00046777524,0.0002807426,0.00026258733,0.0007026708,0.0013325975,0.0011072494,0.002239941],"category_scores_gemma":[0.0006558007,0.00025897942,0.0005368712,0.00032488373,0.00050791295,0.0008498159,0.0005311787,0.0009114896,0.00039909573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009972366,0.000117418385,0.000723214,0.000080215,0.000046971407,0.00007874276,0.00009142495,0.908437,0.019924305,0.025350759,0.0012379892,0.043812282],"study_design_scores_gemma":[0.000002314455,0.000008737654,0.00009464308,0.0000012270148,0.000003659495,0.000009933878,0.00000215573,0.9973992,0.00046705597,0.0018949847,0.000112844675,0.0000031774632],"about_ca_topic_score_codex":0.0098115485,"about_ca_topic_score_gemma":0.0076499414,"teacher_disagreement_score":0.0098115485,"about_ca_system_score_codex":0.0010900394,"about_ca_system_score_gemma":0.0007336927,"threshold_uncertainty_score":0.019508898},"labels":[],"label_agreement":null},{"id":"W4414093545","doi":"10.1016/j.eswa.2025.129650","title":"Two-stage feature selection utilizing three-way adaptive neighborhood characteristic measure and optimal combination search","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Measure (data warehouse); Feature selection; Fitness function; Feature (linguistics); Partition (number theory); Fitness approximation; Pattern recognition (psychology)","score_opus":0.0212431758320869,"score_gpt":0.26737480775761363,"score_spread":0.24613163192552673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414093545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034935802,0.0003356824,0.9627475,0.00006931896,0.000069010006,0.000115178525,0.00004253992,0.00043375307,0.0012511068],"genre_scores_gemma":[0.43229657,0.0002914804,0.5625381,0.000115940726,0.00008316104,0.00047074613,0.0004196431,0.00014588062,0.0036384251],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985429,0.00019552548,0.00016416385,0.00036380885,0.00058554363,0.00014812523],"domain_scores_gemma":[0.9991824,0.0002789227,0.00005604284,0.00007538074,0.00035576386,0.000051416766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015902756,0.0011095258,0.00327719,0.0022725933,0.0011759513,0.001463025,0.00203093,0.0012954286,0.0019175494],"category_scores_gemma":[0.0024531693,0.00070927653,0.0023206307,0.0028957054,0.0005033142,0.0018592699,0.0013186553,0.0007168227,0.0003799615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086474896,0.000557755,0.004420536,0.00039008973,0.000684031,0.00030906746,0.00032690246,0.19105397,0.030989232,0.007391143,0.0051971455,0.7578155],"study_design_scores_gemma":[0.00005630597,0.0001692082,0.0016717149,0.000009277544,0.00013781045,0.00015026469,0.000039920436,0.9921895,0.0032654295,0.0015077057,0.00076288555,0.000039998027],"about_ca_topic_score_codex":0.005231829,"about_ca_topic_score_gemma":0.0050235065,"teacher_disagreement_score":0.005231829,"about_ca_system_score_codex":0.00057764427,"about_ca_system_score_gemma":0.0019011109,"threshold_uncertainty_score":0.010402739},"labels":[],"label_agreement":null},{"id":"W4414137051","doi":"10.1016/j.eswa.2025.129666","title":"Three-way large-scale group decision-making under incomplete multi-scale information systems: A perspective of quantum social networks","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Sichuan Province Science and Technology Support Program; Chongqing Municipal Education Commission; Shanxi University; Science and Technology Innovation Group of Shanxi Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Perspective (graphical); Core (optical fiber); Complete information; Quantum; Dual (grammatical number); Bounded rationality; Bounded function","score_opus":0.010364172519812009,"score_gpt":0.2918430355028095,"score_spread":0.28147886298299746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414137051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16981733,0.00066501496,0.8179861,0.0037569173,0.00010562996,0.00015379682,0.0003042894,0.00009501588,0.007115954],"genre_scores_gemma":[0.95261127,0.0006556504,0.042561293,0.0002562423,0.00018610158,0.00023146386,0.00016881674,0.000034776593,0.0032944034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99376327,0.0034572217,0.00023599947,0.0012377688,0.0006793178,0.0006265443],"domain_scores_gemma":[0.96038187,0.031850144,0.0035948192,0.0011775767,0.0013922943,0.0016032486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009205947,0.00097353774,0.0036613788,0.001605417,0.0020643785,0.0050563794,0.0038375496,0.0036028652,0.0036103688],"category_scores_gemma":[0.027980298,0.0010044015,0.0018884958,0.0021346705,0.004965724,0.008022765,0.003157773,0.0026775175,0.0002523106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012687386,0.000112358764,0.0012315838,0.00016776378,0.00020483245,0.0002958552,0.00039031846,0.58952,0.00044824104,0.4004662,0.000925258,0.006110702],"study_design_scores_gemma":[0.000018979877,0.000024374012,0.00023657146,0.000010746102,0.000019572635,0.000018802439,0.000094145165,0.77107286,0.00007655276,0.22821808,0.0001878954,0.000021432754],"about_ca_topic_score_codex":0.0072897673,"about_ca_topic_score_gemma":0.0040937075,"teacher_disagreement_score":0.009205947,"about_ca_system_score_codex":0.0031011235,"about_ca_system_score_gemma":0.0023448807,"threshold_uncertainty_score":0.048686326},"labels":[],"label_agreement":null},{"id":"W4414140393","doi":"10.1016/j.eswa.2025.129460","title":"Regularity model-driven large-scale multi-objective evolutionary algorithm based on dual-information offspring reproduction strategy","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Dalian Science and Technology Innovation Fund; Higher Education Discipline Innovation Project; China Academy of Space Technology; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Evolutionary algorithm; Benchmark (surveying); Population; Exploit; Set (abstract data type); Estimation of distribution algorithm; Pareto principle; Optimization problem","score_opus":0.011455052258397037,"score_gpt":0.266928922922486,"score_spread":0.25547387066408894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414140393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05155669,0.00023028861,0.94026244,0.00023426425,0.00007203708,0.000060473347,0.000035084675,0.00017236621,0.0073763547],"genre_scores_gemma":[0.8425604,0.00017160203,0.15106627,0.00011186086,0.00003762869,0.0002318618,0.0001045043,0.00007454895,0.005641242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999798,0.0000563926,0.000008093309,0.000039419927,0.00007215448,0.000025982477],"domain_scores_gemma":[0.9995715,0.00020350961,0.000050721304,0.000038333987,0.000098400866,0.000037503447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061798503,0.00044838185,0.0010936481,0.00047032896,0.00051940215,0.0007285949,0.0016330362,0.0010320771,0.0017787456],"category_scores_gemma":[0.0016753833,0.00032668514,0.00065617176,0.00043724565,0.00059977156,0.00071803556,0.0012482693,0.000689652,0.00023755779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003708936,0.000034164408,0.0005531727,0.000039183436,0.000029692406,0.00007833052,0.00003879658,0.96300536,0.002805468,0.01858999,0.00064156647,0.014147109],"study_design_scores_gemma":[0.000005033363,0.000008911956,0.000038973143,0.0000012028833,0.0000030168871,0.000010046513,0.0000017556313,0.99884075,0.000086739565,0.00091270905,0.00008893518,0.0000019978208],"about_ca_topic_score_codex":0.0022986268,"about_ca_topic_score_gemma":0.0015661325,"teacher_disagreement_score":0.0022986268,"about_ca_system_score_codex":0.0006163489,"about_ca_system_score_gemma":0.00080632523,"threshold_uncertainty_score":0.0059505105},"labels":[],"label_agreement":null},{"id":"W4414153271","doi":"10.1016/j.eswa.2025.129655","title":"The power of text similarity in identifying AI-LLM paraphrased documents: The case of BBC news articles and ChatGPT","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Similarity (geometry); Task (project management); Benchmark (surveying); Generative grammar; Power (physics); Revenue","score_opus":0.016926619472284115,"score_gpt":0.29931495088042326,"score_spread":0.2823883314081391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414153271","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80364007,0.0052229036,0.10819483,0.004864914,0.00027201232,0.0005000415,0.0019819224,0.0017284559,0.07359492],"genre_scores_gemma":[0.9546155,0.00056429807,0.039622147,0.00022137673,0.00020256943,0.00006708888,0.0010875418,0.00026474916,0.0033547946],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9941332,0.003259786,0.0004464957,0.0006202885,0.0011642511,0.00037598063],"domain_scores_gemma":[0.9056955,0.078072175,0.004010832,0.0045287814,0.0068525784,0.0008401277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007110476,0.0005407254,0.0007092745,0.011401982,0.0033447442,0.00597668,0.0013429641,0.003148895,0.0055076075],"category_scores_gemma":[0.068942346,0.0004275022,0.00047387645,0.008124547,0.002157419,0.009066674,0.0030239352,0.0017238756,0.002694533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005071802,0.00093377545,0.09735958,0.0031447175,0.00046235486,0.016409164,0.046002317,0.016153103,0.06638074,0.052310295,0.02426023,0.671512],"study_design_scores_gemma":[0.0003554186,0.0010639495,0.12545839,0.0009810438,0.00076467474,0.029965913,0.04423346,0.52203393,0.06491574,0.12567522,0.08407681,0.00047546934],"about_ca_topic_score_codex":0.008955238,"about_ca_topic_score_gemma":0.009777203,"teacher_disagreement_score":0.011401982,"about_ca_system_score_codex":0.0012161887,"about_ca_system_score_gemma":0.0011266979,"threshold_uncertainty_score":0.037604272},"labels":[],"label_agreement":null},{"id":"W4414222720","doi":"10.1016/j.eswa.2025.129739","title":"Clustering-based brain functional segmentation via deep collapsed nonparametric von Mises-Fisher mixture models","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Mixture model; Autoencoder; Generative model; Pattern recognition (psychology); Segmentation; Functional magnetic resonance imaging; Bayes' theorem; Inference; Prior probability","score_opus":0.014589231231754207,"score_gpt":0.26399298747321814,"score_spread":0.24940375624146394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414222720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004405929,0.00010892633,0.9947497,0.000070624934,0.000008407936,0.00001205873,0.000040994033,0.0003179418,0.00028532185],"genre_scores_gemma":[0.36940795,0.00050982664,0.624408,0.0002362431,0.00006774988,0.00020052597,0.0007159271,0.00061806216,0.003835727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994836,0.00016035295,0.000026793196,0.0001333797,0.000123359,0.00007249198],"domain_scores_gemma":[0.99886215,0.0006307811,0.000120606455,0.00012810164,0.00018886644,0.00006948068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001472062,0.00092029216,0.0015551723,0.0015272537,0.0006794546,0.0015916085,0.0022905793,0.002169636,0.0018546458],"category_scores_gemma":[0.0045306874,0.0013228946,0.0019833993,0.0014505147,0.0010312761,0.0018686232,0.002194005,0.0021050822,0.00089373434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020361623,0.00006100924,0.00068480836,0.00012119397,0.00013508243,0.000080460726,0.00020005781,0.8297677,0.007691977,0.032408617,0.001881131,0.1267643],"study_design_scores_gemma":[0.000002123134,0.0000051999,0.00007846379,0.0000054243324,0.0000050504195,0.000017247274,0.000004430214,0.9896116,0.0005621632,0.009508364,0.00019268203,0.000007207748],"about_ca_topic_score_codex":0.009052851,"about_ca_topic_score_gemma":0.012838353,"teacher_disagreement_score":0.009052851,"about_ca_system_score_codex":0.0012965965,"about_ca_system_score_gemma":0.0016356433,"threshold_uncertainty_score":0.018000305},"labels":[],"label_agreement":null},{"id":"W4414521063","doi":"10.1016/j.eswa.2025.129649","title":"GE-adapter: A general and efficient adapter for enhanced video editing with pretrained text-to-image diffusion models","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Research Chairs","funders":"Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation, Changsha University of Science and Technology; Shenzhen Fundamental Research Program; Science, Technology and Innovation Commission of Shenzhen Municipality; Hunan Provincial Science and Technology Department","keywords":"Adapter (computing); Consistency (knowledge bases); Fidelity; Key (lock); Coherence (philosophical gambling strategy); Consistency model; Temporal database; Flexibility (engineering)","score_opus":0.008610764350851122,"score_gpt":0.23893384635608278,"score_spread":0.23032308200523166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414521063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029917434,0.00020775244,0.7607003,0.00007388402,0.00014090224,0.00015677117,0.0027677245,0.23128293,0.0016779288],"genre_scores_gemma":[0.070117794,0.0005788644,0.86406165,0.00031363984,0.00012780222,0.0007061343,0.012705776,0.03531562,0.01607271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996412,0.000051316412,0.00004301256,0.00010924889,0.000107735,0.00004747717],"domain_scores_gemma":[0.99891555,0.00044452012,0.000060583174,0.00028557476,0.00022000936,0.00007372124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082859345,0.0025279748,0.000991862,0.0012830085,0.00032361722,0.0014113024,0.0031565889,0.0017795119,0.041789267],"category_scores_gemma":[0.0057139094,0.00087358366,0.0012767507,0.000841444,0.00023037972,0.0019523314,0.0019720758,0.0015028099,0.0193691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015031067,0.00026698105,0.00096350873,0.0009721437,0.00034230115,0.00078477233,0.0002838282,0.021528943,0.07130278,0.0074111028,0.13149458,0.763146],"study_design_scores_gemma":[0.00025416748,0.00019045617,0.0011618866,0.00011230316,0.00010892864,0.00087622297,0.00010659015,0.7219271,0.15891175,0.01169929,0.10448314,0.00016814559],"about_ca_topic_score_codex":0.0032735565,"about_ca_topic_score_gemma":0.0049008434,"teacher_disagreement_score":0.041789267,"about_ca_system_score_codex":0.00042674286,"about_ca_system_score_gemma":0.0005573483,"threshold_uncertainty_score":0.139799},"labels":[],"label_agreement":null},{"id":"W4415045106","doi":"10.1016/j.eswa.2025.129828","title":"KAN-MoDTI: Drug target interaction prediction based on Kolmogorov-Arnold network and multimodal feature fusion","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Feature (linguistics); Drug target; Task (project management); Graph; Encoding (memory); ENCODE; Pattern recognition (psychology); Fusion; Identification (biology)","score_opus":0.007705087769185061,"score_gpt":0.27744864538882974,"score_spread":0.2697435576196447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415045106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092137836,0.0010341754,0.8871747,0.0005002238,0.00021611646,0.00020551594,0.0016328698,0.012404775,0.00469378],"genre_scores_gemma":[0.677905,0.00044191713,0.31181738,0.00018267562,0.000107319014,0.0002721438,0.0030131377,0.00028294002,0.0059774886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997621,0.000037035847,0.000015492406,0.000056524863,0.000097584365,0.000031180054],"domain_scores_gemma":[0.9997123,0.00010328796,0.00002640408,0.000046976187,0.000086289125,0.000024734252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005796686,0.0006735297,0.0009935909,0.0012370675,0.0003891304,0.0006247962,0.0010450627,0.00068946433,0.0025765318],"category_scores_gemma":[0.0013920087,0.0002424031,0.0006745588,0.00078203576,0.0002524179,0.0011428081,0.001061678,0.00075921824,0.0008410392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078465434,0.0003566512,0.0051704952,0.00027561787,0.00028678836,0.00033353138,0.00007694983,0.2943596,0.020380728,0.009224597,0.018406454,0.6503439],"study_design_scores_gemma":[0.0000103455695,0.000031512285,0.00037699603,0.0000020461353,0.000011977344,0.000033706336,0.0000039153556,0.9954188,0.0021046891,0.0014457663,0.00055208395,0.00000822363],"about_ca_topic_score_codex":0.0047269687,"about_ca_topic_score_gemma":0.0048501277,"teacher_disagreement_score":0.0047269687,"about_ca_system_score_codex":0.00054895884,"about_ca_system_score_gemma":0.0009597382,"threshold_uncertainty_score":0.009398937},"labels":[],"label_agreement":null},{"id":"W4415070379","doi":"10.1016/j.eswa.2025.129996","title":"Generalizing inverse data envelopment analysis through directional distance function","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Data envelopment analysis; Inverse; Context (archaeology); Function (biology); Focus (optics); Ideal (ethics); Inverse function; Perturbation (astronomy)","score_opus":0.10396545803081385,"score_gpt":0.39211749071415164,"score_spread":0.2881520326833378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415070379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021221642,0.000058160254,0.99714273,0.000023185088,0.000005158699,0.000007924333,0.000012657771,0.00001946856,0.0006085649],"genre_scores_gemma":[0.33537233,0.0012253925,0.6591301,0.00011533502,0.00005575934,0.00024801446,0.00025093212,0.00016787485,0.0034342164],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998454,0.00070961553,0.00011402461,0.0002355304,0.0004104293,0.00007640561],"domain_scores_gemma":[0.99781656,0.0012166052,0.00017255294,0.00034468618,0.00042343,0.00002611533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043579726,0.001055006,0.0011186406,0.0016767891,0.00034506677,0.0017943094,0.0007535909,0.00061065925,0.0014379916],"category_scores_gemma":[0.009987091,0.0004994362,0.0013799393,0.0021168645,0.000879615,0.00202508,0.0017955926,0.0014204382,0.0005060597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035840745,0.000054626013,0.00087806315,0.00022050699,0.000092926326,0.00006915234,0.00014730525,0.67724735,0.00614538,0.23638602,0.00061823253,0.07810457],"study_design_scores_gemma":[0.0000030820538,0.000021320777,0.0002711225,0.000020685786,0.000017658966,0.000035695914,0.000030759304,0.93976116,0.0014487888,0.056180928,0.0021973262,0.000011513813],"about_ca_topic_score_codex":0.002708937,"about_ca_topic_score_gemma":0.0013937544,"teacher_disagreement_score":0.0043579726,"about_ca_system_score_codex":0.00090391224,"about_ca_system_score_gemma":0.0014567523,"threshold_uncertainty_score":0.023047447},"labels":[],"label_agreement":null},{"id":"W4415354881","doi":"10.1016/j.eswa.2025.130050","title":"A multi-user game-based system for planning modular construction activities","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"BIM and Construction Integration","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Modular design; Key (lock); Usability; Limiting; Protocol (science); Mode (computer interface); Modular construction","score_opus":0.009885647539409665,"score_gpt":0.23777627739242801,"score_spread":0.22789062985301836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415354881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12568086,0.000140556,0.8420138,0.00018571307,0.0000852564,0.0017919854,0.0006230313,0.016269414,0.013209418],"genre_scores_gemma":[0.5235862,0.00011341993,0.46761653,0.00014903494,0.000017256143,0.0011497077,0.0008599916,0.00022656246,0.0062813195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947363,0.00018257197,0.000052369236,0.00011598407,0.000121469384,0.000054021206],"domain_scores_gemma":[0.9990926,0.0004099708,0.00006748409,0.00014910445,0.00012144484,0.00015936077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009410643,0.0010739384,0.0004591238,0.00079059636,0.00037488996,0.0010290772,0.0018042626,0.00078140927,0.008087037],"category_scores_gemma":[0.0023323381,0.0003580263,0.000497927,0.00031076986,0.00044177525,0.000989741,0.001817489,0.00053741055,0.0011221252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003909358,0.002879666,0.019254053,0.0014047527,0.00043066303,0.0026165976,0.0043317676,0.20260519,0.13960777,0.033753723,0.017289773,0.57191664],"study_design_scores_gemma":[0.00034520088,0.000813603,0.005182336,0.00008850852,0.00013029738,0.00053700386,0.0003334094,0.92642105,0.019114977,0.007606759,0.039288495,0.00013828216],"about_ca_topic_score_codex":0.0028709099,"about_ca_topic_score_gemma":0.004093966,"teacher_disagreement_score":0.008087037,"about_ca_system_score_codex":0.0005826284,"about_ca_system_score_gemma":0.0007830946,"threshold_uncertainty_score":0.027053833},"labels":[],"label_agreement":null},{"id":"W4415378794","doi":"10.1016/j.eswa.2025.130041","title":"Prediction of iron ore inventory at ports: A decomposition-integration hybrid approach incorporating key influencing factors","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Iron ore; Raw data; Supply chain; Key (lock); Entropy (arrow of time); Raw material; Reduction (mathematics); Port (circuit theory)","score_opus":0.011123701758969581,"score_gpt":0.22115693829775818,"score_spread":0.21003323653878858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415378794","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7344988,0.00032533603,0.26159322,0.00013934587,0.00003663767,0.00004757885,0.0003893673,0.0005038696,0.0024658588],"genre_scores_gemma":[0.9826596,0.00007873092,0.0162997,0.000014377016,0.0000100910975,0.0000267318,0.00022952167,0.000022919345,0.00065832195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998573,0.000031535015,0.000009000559,0.000040906754,0.000028861345,0.00003228177],"domain_scores_gemma":[0.9996213,0.00020742873,0.000038296723,0.000017516719,0.00008335785,0.000032078588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004789004,0.0008952709,0.001089043,0.000778,0.0003613044,0.0009402685,0.00071625627,0.0011414395,0.00081864937],"category_scores_gemma":[0.0008263265,0.0007054254,0.0010352737,0.0006839937,0.00028192386,0.00068597525,0.0005173521,0.0005454217,0.00019672963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059549937,0.000050355593,0.0035560504,0.00002322401,0.000043588818,0.000049017995,0.000010130843,0.9873733,0.0014441925,0.00013308534,0.00009539714,0.0071621146],"study_design_scores_gemma":[0.0000015925416,0.0000041412977,0.0004439542,9.548431e-7,0.000004797035,0.000002038287,0.0000028040056,0.9993369,0.00013807585,0.000051729632,0.000011197799,0.0000018294712],"about_ca_topic_score_codex":0.028140916,"about_ca_topic_score_gemma":0.01758091,"teacher_disagreement_score":0.028140916,"about_ca_system_score_codex":0.000524937,"about_ca_system_score_gemma":0.0010448846,"threshold_uncertainty_score":0.055954218},"labels":[],"label_agreement":null},{"id":"W4415422573","doi":"10.1016/j.eswa.2025.130088","title":"DGSEP: Dual-stage generative model with sequence-oriented labeling and element-to-tuple prompting improves aspect sentiment triplet extraction","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Sichuan Province Science and Technology Support Program; Key Research and Development Program of Sichuan Province; Chengdu Science and Technology Bureau; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Fuse (electrical); Generative grammar; Generative model; Sequence (biology); Task (project management); Sentiment analysis","score_opus":0.018136075905115134,"score_gpt":0.32024835108470256,"score_spread":0.30211227517958744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415422573","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01786525,0.00028933483,0.95478874,0.00034246512,0.0002394179,0.00020948304,0.0016924912,0.021426477,0.0031462114],"genre_scores_gemma":[0.30761775,0.00026304633,0.6644517,0.0006992757,0.00019038646,0.00036336918,0.010623958,0.0040194574,0.011770983],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923146,0.00018585497,0.000042484135,0.0003083419,0.00014703446,0.00008484006],"domain_scores_gemma":[0.9986131,0.00065214565,0.000058139798,0.00032378055,0.00027924648,0.000073581505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010692817,0.0015775168,0.00095123105,0.0012231977,0.0007370836,0.001517908,0.00201975,0.0017297461,0.008648429],"category_scores_gemma":[0.0028896497,0.00096006325,0.0019464382,0.0011280844,0.0005535012,0.0023430798,0.002244402,0.0029227575,0.0060673202],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011349309,0.0005043329,0.0057381596,0.0005623579,0.00025485523,0.00082826184,0.00084848714,0.09650544,0.05852775,0.021240566,0.05593546,0.75791955],"study_design_scores_gemma":[0.00004192774,0.000057110592,0.000635336,0.000023515875,0.00006366312,0.000117860494,0.0000638951,0.96502584,0.010751009,0.015424658,0.0077646747,0.000030430989],"about_ca_topic_score_codex":0.007068081,"about_ca_topic_score_gemma":0.016884714,"teacher_disagreement_score":0.008648429,"about_ca_system_score_codex":0.00065910345,"about_ca_system_score_gemma":0.001874743,"threshold_uncertainty_score":0.028931856},"labels":[],"label_agreement":null},{"id":"W4415423296","doi":"10.1016/j.eswa.2025.130091","title":"Consciousness-ECG transformer for conscious state estimation system with real-time monitoring","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Information Technology Research Centre; Institute for Information and Communications Technology Promotion; Korea University","keywords":"Transformer; Electroencephalography; Heart rate variability; Sleep (system call); Noise (video); Consciousness; Electrocardiography; State (computer science)","score_opus":0.014650346226659727,"score_gpt":0.2836405009802866,"score_spread":0.26899015475362686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415423296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06341554,0.0013708577,0.91742927,0.0004221935,0.00026872783,0.00026632857,0.00071073195,0.013035107,0.0030812076],"genre_scores_gemma":[0.8916498,0.000804734,0.10273586,0.00051627343,0.00013724444,0.00017952739,0.0011369009,0.00015260022,0.0026870465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997365,0.000045638757,0.000026263126,0.000077027784,0.00008681735,0.000027818654],"domain_scores_gemma":[0.99973506,0.000065707434,0.00003735202,0.000038654893,0.00009358452,0.000029713414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004345955,0.00054495217,0.00046680536,0.00062463456,0.00011955376,0.0005826159,0.0005571866,0.0004980585,0.0017546596],"category_scores_gemma":[0.0015861916,0.00017325513,0.00044305518,0.0003034015,0.00014287025,0.0006884988,0.0006056836,0.00045188252,0.0007926479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009918789,0.00036693853,0.016976632,0.00040087765,0.00026273227,0.0006740625,0.00020335669,0.01295939,0.09647414,0.002515752,0.0126988655,0.8554753],"study_design_scores_gemma":[0.00020987663,0.00080511585,0.044359293,0.00007285587,0.00035719952,0.0026080827,0.00012301402,0.84239346,0.086832516,0.006556406,0.015537205,0.0001449422],"about_ca_topic_score_codex":0.0014303622,"about_ca_topic_score_gemma":0.0019443656,"teacher_disagreement_score":0.0017546596,"about_ca_system_score_codex":0.0001903086,"about_ca_system_score_gemma":0.00034620988,"threshold_uncertainty_score":0.0058699846},"labels":[],"label_agreement":null},{"id":"W4415624224","doi":"10.1016/j.eswa.2025.130178","title":"R2D-EQ: a two-stage workflow for risk reasoning and decision-making in earthquake emergency scenarios","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China; Wuhan University; Ministry of Natural Resources","keywords":"Workflow; Geospatial analysis; Scalability; Risk management; Event (particle physics); Emergency management; Focus (optics); Risk assessment","score_opus":0.012223168840670552,"score_gpt":0.2896451143439583,"score_spread":0.27742194550328775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415624224","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023664604,0.00015056324,0.9087599,0.0004026888,0.0001019167,0.0006617787,0.0062287753,0.075053774,0.0062741805],"genre_scores_gemma":[0.04488146,0.00031616635,0.9244717,0.0005373675,0.000044875636,0.0009758626,0.012187667,0.009943011,0.0066420063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970132,0.0008848437,0.00040763526,0.0005537904,0.000807735,0.0003327309],"domain_scores_gemma":[0.9919287,0.005153375,0.00024097416,0.0010958554,0.0011381755,0.00044293786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058515808,0.0041450094,0.0022277208,0.004028929,0.0017833028,0.008102678,0.0063040266,0.0030328843,0.067550056],"category_scores_gemma":[0.019341309,0.0021845023,0.0049140323,0.0019374331,0.0010963462,0.0042885826,0.008331109,0.0030413205,0.02591144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018778773,0.0009855328,0.007829329,0.0036215882,0.0009816929,0.0029540972,0.004926377,0.12143255,0.013280861,0.071039595,0.24953356,0.5215369],"study_design_scores_gemma":[0.0005910884,0.000121995654,0.001942838,0.0005863146,0.00022525442,0.000522286,0.0009631302,0.69289595,0.018295068,0.113345005,0.17009415,0.00041688487],"about_ca_topic_score_codex":0.020963114,"about_ca_topic_score_gemma":0.028270658,"teacher_disagreement_score":0.067550056,"about_ca_system_score_codex":0.0023438917,"about_ca_system_score_gemma":0.005497189,"threshold_uncertainty_score":0.22597748},"labels":[],"label_agreement":null},{"id":"W4415626332","doi":"10.1016/j.eswa.2026.132374","title":"Modeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification","year":2025,"lang":"en","type":"preprint","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Multiplex; Homophily; Node (physics); Graph; Class (philosophy); Directed graph","score_opus":0.07061930209504859,"score_gpt":0.3108450335913657,"score_spread":0.24022573149631715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415626332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11429203,0.0003094819,0.8832685,0.00043913216,0.00005361438,0.000064714724,0.00020075629,0.0003178503,0.0010539241],"genre_scores_gemma":[0.92602015,0.00026735797,0.07027064,0.00014032752,0.00013385869,0.00011901914,0.00025903666,0.000068378955,0.0027211104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992669,0.0002583441,0.000030238776,0.00024891002,0.00010061728,0.000094931864],"domain_scores_gemma":[0.9959997,0.0027175623,0.00043150724,0.00034068184,0.0003299167,0.00018069068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002153337,0.00075411954,0.0013217625,0.0015465916,0.0006647979,0.0013473731,0.0026663,0.001869221,0.0016685938],"category_scores_gemma":[0.008738752,0.0007322563,0.0010149799,0.0012859061,0.0009702025,0.0025820178,0.0012913635,0.0017550389,0.0003262267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012988021,0.00011056196,0.0036803323,0.000052613854,0.00008367092,0.0000654255,0.00010423845,0.93955415,0.0013590798,0.019462563,0.0011509707,0.034246422],"study_design_scores_gemma":[0.0000013591887,0.0000030556878,0.00009746783,0.0000014191687,0.0000034140096,0.000002761603,0.0000033355284,0.9963319,0.000049324393,0.0034758665,0.000028843382,0.0000013592055],"about_ca_topic_score_codex":0.0065231635,"about_ca_topic_score_gemma":0.007842443,"teacher_disagreement_score":0.0065231635,"about_ca_system_score_codex":0.0011512318,"about_ca_system_score_gemma":0.00060328346,"threshold_uncertainty_score":0.012970388},"labels":[],"label_agreement":null},{"id":"W4415951164","doi":"10.1016/j.eswa.2025.130307","title":"A large language model-based chatbot system framework for urban planners","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"AI in Service Interactions","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cosine similarity; Chatbot; Mean reciprocal rank; Scalability; Baseline (sea); Rank (graph theory); Similarity (geometry); Personalization; Preprocessor; The Internet","score_opus":0.010187936222814841,"score_gpt":0.2959912247844134,"score_spread":0.2858032885615986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415951164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016198229,0.00036161402,0.87759626,0.0010099475,0.00022336951,0.00090635807,0.0027442905,0.093333155,0.007626789],"genre_scores_gemma":[0.37410575,0.00023571099,0.5969441,0.0009108869,0.00011513103,0.0017556491,0.010028471,0.0015547714,0.014349504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988136,0.00048382653,0.000091170165,0.00028784448,0.00022989025,0.000093641574],"domain_scores_gemma":[0.9982815,0.0008245299,0.00008395562,0.0002459837,0.00037398867,0.00019011414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021146627,0.00118076,0.00088533113,0.0011348737,0.0008595269,0.0016516242,0.0028379369,0.0015600913,0.011201636],"category_scores_gemma":[0.0059424946,0.0005184544,0.00082986197,0.0005474996,0.00066826784,0.0030152402,0.003179224,0.001563665,0.005349976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019710204,0.0013848445,0.0069384384,0.00214629,0.00031648402,0.0030760386,0.0048211296,0.18852867,0.040939726,0.069530874,0.20933418,0.47101226],"study_design_scores_gemma":[0.000064023756,0.00011055194,0.0003494627,0.000044883644,0.00003130782,0.00017996022,0.00035965265,0.953253,0.0039641275,0.013608073,0.027980294,0.00005476054],"about_ca_topic_score_codex":0.01285767,"about_ca_topic_score_gemma":0.01752611,"teacher_disagreement_score":0.01285767,"about_ca_system_score_codex":0.0013487822,"about_ca_system_score_gemma":0.0020973044,"threshold_uncertainty_score":0.03747326},"labels":[],"label_agreement":null},{"id":"W4415978147","doi":"10.1016/j.eswa.2025.130227","title":"DMtes:A dynamic multimodal framework with environmental temporal-awareness for road surface snow condition monitoring","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Transportation; Natural Science Foundation of Shandong Province; Key Technology Research and Development Program of Shandong; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Feature (linguistics); Snow; Sensor fusion; Feature extraction; Road surface; Representation (politics); Dependency (UML); Condition monitoring","score_opus":0.006489141367452751,"score_gpt":0.26004962464499776,"score_spread":0.253560483277545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415978147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006695274,0.00021936391,0.9817499,0.000089929206,0.000051344945,0.00006192698,0.0012758665,0.008819504,0.001036832],"genre_scores_gemma":[0.21274926,0.0005281277,0.7764342,0.0002865619,0.00009464203,0.0003990269,0.0042510564,0.0010601128,0.0041971626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965274,0.00007448153,0.000018757262,0.00011758392,0.00009761092,0.000038891496],"domain_scores_gemma":[0.9997819,0.00008129486,0.000020395166,0.000032295866,0.00005934214,0.000024780622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006199704,0.0012029856,0.00096852117,0.0010783739,0.00034950173,0.0011623285,0.0014219596,0.000888526,0.004518445],"category_scores_gemma":[0.0015410887,0.00044843697,0.001140142,0.00079957483,0.00034310055,0.0013885556,0.0024265978,0.0009197899,0.0012463697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073991745,0.00033322047,0.0037214295,0.00051426,0.0005495575,0.00053598493,0.000507039,0.28861994,0.03961576,0.012578381,0.024426077,0.6278584],"study_design_scores_gemma":[0.000019660769,0.000041929092,0.0008210641,0.000027607948,0.000050352304,0.00009932981,0.000067604815,0.9801311,0.0046231877,0.006353711,0.00772766,0.000036760346],"about_ca_topic_score_codex":0.0105697205,"about_ca_topic_score_gemma":0.020604733,"teacher_disagreement_score":0.0105697205,"about_ca_system_score_codex":0.00037506118,"about_ca_system_score_gemma":0.0007049083,"threshold_uncertainty_score":0.021016419},"labels":[],"label_agreement":null},{"id":"W4416028955","doi":"10.1016/j.eswa.2025.130317","title":"Signal-aware synthesis of tissue polarization uniformity from OCT images guided by an SNR-based heuristic","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; BC Children's Hospital; Vancouver Coastal Health; Vancouver Coastal Health Research Institute; Spinal Cord Injury BC; University of British Columbia","funders":"Shenzhen Science and Technology Innovation Program; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Shenzhen University; University of British Columbia; National Natural Science Foundation of China","keywords":"Optical coherence tomography; Generalizability theory; Robustness (evolution); Polarization (electrochemistry); Pattern recognition (psychology); Generative grammar; Coherence (philosophical gambling strategy); Medical imaging","score_opus":0.007619032174109835,"score_gpt":0.24732378221860166,"score_spread":0.23970475004449182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416028955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.067623176,0.00049442897,0.92615896,0.0003221225,0.00005588806,0.000055068846,0.00016834088,0.0010724942,0.0040494544],"genre_scores_gemma":[0.70361924,0.0003551909,0.2913712,0.0005040408,0.00006202912,0.00011477402,0.0004900379,0.00037240481,0.0031110498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998294,0.000039706487,0.0000061538476,0.00005496489,0.00004330803,0.000026448355],"domain_scores_gemma":[0.9996488,0.00018076664,0.00003303005,0.0000402478,0.00007198063,0.000025148172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004949069,0.0008027676,0.00039595074,0.00045098143,0.00015055147,0.0005708179,0.000554384,0.0005212106,0.0010087135],"category_scores_gemma":[0.0012932171,0.00032522276,0.00055083784,0.00023839939,0.0004928981,0.00045954392,0.000552692,0.0007498651,0.0003555847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030379556,0.000112194764,0.0019746316,0.00020653257,0.000085350744,0.00022937573,0.00013294544,0.62136656,0.15422787,0.00690007,0.003992108,0.2104685],"study_design_scores_gemma":[0.000009681493,0.00003728007,0.00023253162,0.0000068380823,0.00001214556,0.00005838164,0.000008529753,0.98647517,0.010834561,0.0017338976,0.00058477285,0.000006270999],"about_ca_topic_score_codex":0.0012534406,"about_ca_topic_score_gemma":0.0027077429,"teacher_disagreement_score":0.0012534406,"about_ca_system_score_codex":0.00048770272,"about_ca_system_score_gemma":0.00053083425,"threshold_uncertainty_score":0.003538549},"labels":[],"label_agreement":null},{"id":"W4416273494","doi":"10.1016/j.eswa.2025.130167","title":"Preserving data and model privacy during inference and training","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Research and Productivity Council; University of Victoria","funders":"National Research Council Canada; Alliance de recherche numérique du Canada; National Research Council","keywords":"Inference; Homomorphic encryption; Federated learning; Differential privacy; Information privacy; Table (database); Software deployment; Training (meteorology); Encryption","score_opus":0.059999868060799794,"score_gpt":0.321658714042964,"score_spread":0.26165884598216416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416273494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010086885,0.00020091946,0.9871178,0.00090801617,0.00005151262,0.0000475638,0.00030135995,0.00046021945,0.000825755],"genre_scores_gemma":[0.6648569,0.0005287546,0.3282197,0.00082082296,0.00022979497,0.00020811985,0.0011810927,0.0004169767,0.0035378013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9862271,0.006402924,0.0009447966,0.00251526,0.0029390682,0.00097088],"domain_scores_gemma":[0.9392683,0.026276035,0.0015592455,0.030892711,0.0015249628,0.0004787593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012271299,0.0012007807,0.002178529,0.0010864928,0.0016736359,0.006513002,0.0038572906,0.0024734547,0.002169643],"category_scores_gemma":[0.07351232,0.0014582361,0.0024710651,0.0014480933,0.0040151984,0.011317579,0.008011084,0.0070383456,0.0010056851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002089287,0.0004080629,0.009162197,0.00056169636,0.000594593,0.0011043356,0.0021941413,0.23922151,0.020703835,0.3892307,0.0085591,0.3261706],"study_design_scores_gemma":[0.000046797042,0.00010013701,0.0006061116,0.00007119452,0.000104900966,0.00062354247,0.00022108859,0.49250573,0.033567235,0.46747872,0.004636623,0.000037870555],"about_ca_topic_score_codex":0.0015006699,"about_ca_topic_score_gemma":0.0016848191,"teacher_disagreement_score":0.012271299,"about_ca_system_score_codex":0.0013455221,"about_ca_system_score_gemma":0.00377715,"threshold_uncertainty_score":0.0648976},"labels":[],"label_agreement":null},{"id":"W4416400492","doi":"10.1016/j.eswa.2025.130457","title":"Industrial steel slag flow data loading method for deep learning applications","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Deep learning; Slag (welding); Flow (mathematics); Data modeling","score_opus":0.040906114584819846,"score_gpt":0.3491765939039998,"score_spread":0.30827047931917995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416400492","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1991791,0.0006827289,0.7842871,0.0003135422,0.00017516896,0.00016919656,0.001630243,0.008941066,0.004621763],"genre_scores_gemma":[0.81281394,0.00028312122,0.17557713,0.00017281932,0.00003765931,0.00018348145,0.0037746613,0.00016457887,0.0069924803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978477,0.000019368275,0.000014347505,0.00006846457,0.000073676194,0.000039468607],"domain_scores_gemma":[0.9997439,0.000042118707,0.000036055186,0.000044337303,0.00011970524,0.000013873309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037962044,0.0009369311,0.00032376414,0.00089115516,0.00020126441,0.00037509625,0.0008100036,0.0005538521,0.0028549656],"category_scores_gemma":[0.0008791538,0.00020045908,0.00038712795,0.00071340904,0.00018890855,0.0006740129,0.0005257071,0.0006381797,0.0011070896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028073165,0.0003126497,0.007154378,0.00016297455,0.000081473874,0.00014526174,0.00007045491,0.07412799,0.08648204,0.0010009757,0.008053716,0.8221273],"study_design_scores_gemma":[0.000017332703,0.0001426294,0.005798234,0.000019908764,0.000020379452,0.000076987715,0.00006392788,0.92432153,0.06388734,0.001339701,0.0042944355,0.000017710398],"about_ca_topic_score_codex":0.0045505567,"about_ca_topic_score_gemma":0.008144834,"teacher_disagreement_score":0.0045505567,"about_ca_system_score_codex":0.00043739672,"about_ca_system_score_gemma":0.0007462135,"threshold_uncertainty_score":0.00955081},"labels":[],"label_agreement":null},{"id":"W4416418940","doi":"10.1016/j.eswa.2025.130520","title":"Integrating industry 4.0 into hospital waste management (HWM4.0): a framework and application of the novel interval CoCoSo method","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Healthcare and Environmental Waste Management","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Control (management); Empirical research; Interval (graph theory); Originality; Compromise; Risk management","score_opus":0.008835849134792252,"score_gpt":0.3134162820060274,"score_spread":0.30458043287123515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416418940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0129037965,0.00022295747,0.9731092,0.00053048483,0.00003384948,0.00023846868,0.00006787509,0.00008321966,0.012810139],"genre_scores_gemma":[0.20187691,0.0002346658,0.7961681,0.000116281386,0.000031504835,0.00040836676,0.00009497356,0.0000313457,0.0010377686],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98981327,0.0070643597,0.0005010329,0.0008594423,0.0014706479,0.0002912759],"domain_scores_gemma":[0.99115705,0.006088808,0.0009326098,0.0005949528,0.0010200792,0.00020657462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008616261,0.00097347936,0.00056432147,0.0036477593,0.0008609573,0.0035407785,0.0017921218,0.0014002376,0.0029991572],"category_scores_gemma":[0.015997868,0.0004144587,0.0011345905,0.0032195002,0.002825504,0.002434148,0.00344102,0.0015707787,0.00026799054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010656728,0.00023849722,0.005079686,0.000607507,0.00011530485,0.00033741334,0.0019276188,0.14870925,0.0029922815,0.6521968,0.0016119034,0.18607701],"study_design_scores_gemma":[0.00004681179,0.00030273912,0.0025582926,0.00044904742,0.00008701592,0.0002108799,0.0021944346,0.6726976,0.0033421172,0.29687554,0.021143131,0.00009239778],"about_ca_topic_score_codex":0.005352113,"about_ca_topic_score_gemma":0.005526649,"teacher_disagreement_score":0.008616261,"about_ca_system_score_codex":0.0033031222,"about_ca_system_score_gemma":0.004166175,"threshold_uncertainty_score":0.04556763},"labels":[],"label_agreement":null},{"id":"W4416509201","doi":"10.1016/j.eswa.2025.130460","title":"Explainable resource-Aware IoT security model via knowledge distillation and adaptive loss function optimization","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Divergence (linguistics); Convergence (economics); Artificial neural network; Convolutional neural network; Feature (linguistics); Distillation; Memory footprint; Compiler; Internet of Things","score_opus":0.008705002824890227,"score_gpt":0.22809275236125254,"score_spread":0.2193877495363623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416509201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022451917,0.00018878501,0.97432387,0.00041604636,0.000031564334,0.000034928475,0.000106786894,0.0005698275,0.0018762858],"genre_scores_gemma":[0.82630014,0.00027484898,0.16670427,0.00026281553,0.000060110993,0.00018733671,0.00045558717,0.00012873561,0.0056261458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996092,0.000119949524,0.000019913059,0.00009380647,0.00010366846,0.000053504704],"domain_scores_gemma":[0.9991615,0.00045120763,0.00010082605,0.00009595415,0.00014078774,0.000049755203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008131965,0.0008127975,0.0006789338,0.00044328332,0.00033520535,0.0010442043,0.001240624,0.0011213751,0.0017736305],"category_scores_gemma":[0.0026555753,0.00036789052,0.0008008838,0.0003145093,0.00075940747,0.0014132828,0.0018347882,0.00218964,0.0003828185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032072345,0.000027391625,0.00054800883,0.000031089556,0.000021762933,0.000052014584,0.000033386114,0.9663708,0.00096368336,0.009963992,0.0006457594,0.021309974],"study_design_scores_gemma":[0.000001445627,0.0000051457187,0.000019817193,0.0000014804117,0.0000015312553,0.0000039005477,0.0000015734528,0.9977222,0.000121246696,0.0020120577,0.00010800311,0.0000016439914],"about_ca_topic_score_codex":0.0045526978,"about_ca_topic_score_gemma":0.004769359,"teacher_disagreement_score":0.0045526978,"about_ca_system_score_codex":0.0010896039,"about_ca_system_score_gemma":0.0014014149,"threshold_uncertainty_score":0.009052396},"labels":[],"label_agreement":null},{"id":"W4416809770","doi":"10.1016/j.eswa.2025.130549","title":"Hybrid non-dominated sorting cuckoo search for parallel machine scheduling in additive manufacturing with two-dimensional packing constraints","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Hebei Province Science and Technology Support Program; National Natural Science Foundation of China","keywords":"Tardiness; Cuckoo search; Job shop scheduling; Sorting; Benchmark (surveying); Scheduling (production processes); Integer programming; Heuristics","score_opus":0.009752299265783958,"score_gpt":0.25820076220773125,"score_spread":0.24844846294194728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416809770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2067586,0.0016138925,0.77532774,0.00037874677,0.00021474468,0.00026508883,0.00016221144,0.0006242948,0.014654675],"genre_scores_gemma":[0.85818255,0.000332409,0.13444747,0.00017414833,0.000060714538,0.00030921432,0.00015211754,0.000112695416,0.0062287054],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949694,0.00020411678,0.00001954084,0.000050589897,0.00014376825,0.000084983265],"domain_scores_gemma":[0.9990528,0.0006475081,0.00006931147,0.00003374062,0.00015048831,0.000046056215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010200688,0.0008539842,0.0015923111,0.001140357,0.0007135318,0.0009551229,0.0012225658,0.0011441192,0.002399592],"category_scores_gemma":[0.001668926,0.0006775651,0.0006127628,0.0016138224,0.00061500364,0.00071613037,0.0006545001,0.0005060546,0.000207763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010921136,0.000049044964,0.00016252208,0.00008120073,0.000029294382,0.000025731357,0.000019549749,0.9811694,0.0007390939,0.002240392,0.0005453392,0.014829215],"study_design_scores_gemma":[0.000011408781,0.00002483153,0.000045119545,0.0000029096425,0.0000035726468,0.0000034981767,0.0000031976776,0.999146,0.00014722966,0.0004982825,0.0001117705,0.0000022494376],"about_ca_topic_score_codex":0.013271408,"about_ca_topic_score_gemma":0.011274023,"teacher_disagreement_score":0.013271408,"about_ca_system_score_codex":0.0011174105,"about_ca_system_score_gemma":0.001734819,"threshold_uncertainty_score":0.026388347},"labels":[],"label_agreement":null},{"id":"W4416866884","doi":"10.1016/j.eswa.2025.130664","title":"Time-frequency aware feature disentanglement learning for intelligent bearing fault diagnosis under variable speed conditions","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Adaptability; Convolution (computer science); Fault (geology); Bearing (navigation); Feature (linguistics); Reliability (semiconductor); Subspace topology; Pattern recognition (psychology); Convolutional neural network","score_opus":0.009472573700859282,"score_gpt":0.2895205041573827,"score_spread":0.2800479304565234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416866884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12636313,0.0009154598,0.8703844,0.0001413432,0.00010462635,0.000039337763,0.000110159584,0.00073716085,0.0012044032],"genre_scores_gemma":[0.92409796,0.0002554959,0.07382989,0.000048202095,0.000044584285,0.000028218,0.0001908642,0.000036045247,0.0014687909],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998448,0.0000209583,0.000012015098,0.000041113446,0.00005189139,0.000029266508],"domain_scores_gemma":[0.9996037,0.00021582069,0.00005179242,0.000037589984,0.000073162475,0.000017919543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033181853,0.00053575524,0.00065057067,0.00053211796,0.00020792613,0.00038489228,0.00043565134,0.0003981491,0.0011580747],"category_scores_gemma":[0.0013378272,0.00016169436,0.00034830146,0.00048322938,0.00017657332,0.00068075705,0.0005414743,0.0007011804,0.00030594977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005967742,0.00020746804,0.001908342,0.00011935188,0.00005437965,0.000095910706,0.000075565586,0.08512811,0.05292075,0.0013345187,0.0012990655,0.8562598],"study_design_scores_gemma":[0.000012097009,0.00016728336,0.002285956,0.000007124702,0.000026387193,0.000081107,0.000017882516,0.98537886,0.01019043,0.0010516982,0.0007716269,0.000009566],"about_ca_topic_score_codex":0.0018035732,"about_ca_topic_score_gemma":0.0025915354,"teacher_disagreement_score":0.0018035732,"about_ca_system_score_codex":0.00013824538,"about_ca_system_score_gemma":0.00032886476,"threshold_uncertainty_score":0.0038741827},"labels":[],"label_agreement":null},{"id":"W4417124880","doi":"10.1016/j.eswa.2025.130721","title":"A robust dual-pronged proactive defense framework against deepfakes via adversarial semi-fragile watermarking","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Social Science Fund Youth Project; National Natural Science Foundation of China","keywords":"Adversarial system; Robustness (evolution); Lossy compression; Digital watermarking; Identification (biology); Focus (optics); Generator (circuit theory); Backdoor","score_opus":0.01282736345966247,"score_gpt":0.2520024130151556,"score_spread":0.23917504955549312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417124880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073378333,0.00033973067,0.98962796,0.0001630959,0.00008709339,0.000027152337,0.000025996931,0.0006982301,0.0016929526],"genre_scores_gemma":[0.75913155,0.0003804518,0.23055035,0.000407145,0.00014330064,0.00008688241,0.000095669246,0.00012350154,0.009081057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994081,0.00008547704,0.000026199725,0.0001296892,0.00023960516,0.000110861154],"domain_scores_gemma":[0.99942434,0.00021891974,0.00007344314,0.00012830374,0.000120683886,0.0000343714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090406724,0.0010164769,0.00088302227,0.00045503103,0.0004262477,0.0009859045,0.0013927772,0.0019255988,0.0027314813],"category_scores_gemma":[0.0017103635,0.0003631337,0.00060790207,0.0002536473,0.00087589823,0.0017719867,0.0022572714,0.0019536447,0.00096525496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049795554,0.00013837685,0.00049191766,0.00026538965,0.00014258001,0.00037444025,0.00014110892,0.4400148,0.10933378,0.08885593,0.0058192005,0.3539244],"study_design_scores_gemma":[0.000011774083,0.00009253918,0.00006742836,0.000015318767,0.000017818376,0.000111941124,0.000009601684,0.97617537,0.010142827,0.011895418,0.0014424017,0.000017465774],"about_ca_topic_score_codex":0.0005566213,"about_ca_topic_score_gemma":0.0010254312,"teacher_disagreement_score":0.0027314813,"about_ca_system_score_codex":0.00035153513,"about_ca_system_score_gemma":0.0007993421,"threshold_uncertainty_score":0.00913769},"labels":[],"label_agreement":null},{"id":"W4417168797","doi":"10.1016/j.eswa.2025.130732","title":"An uncertain boundary region-aware network for multi-scale liver tumor segmentation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Boundary (topology); Segmentation; Feature (linguistics); Context (archaeology); Key (lock); Reinforcement learning; Focus (optics)","score_opus":0.029757990334915246,"score_gpt":0.31708520237489907,"score_spread":0.2873272120399838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417168797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017990824,0.00065329275,0.9792489,0.0001535022,0.000056046076,0.00004611043,0.00007915146,0.00066670426,0.0011054794],"genre_scores_gemma":[0.66609156,0.000600473,0.327552,0.00032821205,0.00013256024,0.0001696168,0.00035518355,0.00017135846,0.004599039],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.000047149275,0.000017145916,0.00013002686,0.00008543935,0.00004567141],"domain_scores_gemma":[0.99961424,0.00016011794,0.00004665514,0.000034338274,0.00012033879,0.000024172674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000763049,0.00082731136,0.0011826616,0.0007447082,0.00052399863,0.0007933949,0.0017602411,0.0018304078,0.0016713198],"category_scores_gemma":[0.0016297696,0.00066034455,0.00074170483,0.0007276387,0.00044782594,0.0010861317,0.001271037,0.00087519037,0.00039423688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003715436,0.000077585246,0.0009894563,0.00011873221,0.00010314698,0.00018405954,0.0000954954,0.6216803,0.015806302,0.0029672526,0.0027954527,0.35481063],"study_design_scores_gemma":[0.000003357986,0.000015407439,0.00009725968,0.000004019654,0.000011928096,0.000020237445,0.000003529094,0.998061,0.0010133088,0.00058039586,0.0001856987,0.0000038319645],"about_ca_topic_score_codex":0.008774181,"about_ca_topic_score_gemma":0.009279755,"teacher_disagreement_score":0.008774181,"about_ca_system_score_codex":0.00076235423,"about_ca_system_score_gemma":0.00079383876,"threshold_uncertainty_score":0.01744622},"labels":[],"label_agreement":null},{"id":"W4417188199","doi":"10.1016/j.eswa.2025.130568","title":"A multidimensional ensemble generalized three-way decision approach under mixed-normal hesitant fuzzy sets for evaluating IoT–blockchain integration in supply chain performance","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Weighting; Probabilistic logic; Supply chain; Ranking (information retrieval); Fuzzy logic; Bayesian probability; Ensemble forecasting; Dimension (graph theory)","score_opus":0.10393811200998508,"score_gpt":0.4016220762133183,"score_spread":0.2976839642033332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417188199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07977375,0.00034276213,0.91775566,0.00012677761,0.000059185055,0.000062099156,0.00005969068,0.00008279085,0.0017372386],"genre_scores_gemma":[0.89890975,0.00017059673,0.09979225,0.000041248946,0.000036996447,0.00010715103,0.00010234159,0.000015840646,0.00082385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732214,0.001249499,0.00019540214,0.00037397665,0.0006398917,0.00021902334],"domain_scores_gemma":[0.9968917,0.0017878173,0.0002281137,0.00012638814,0.0008311871,0.00013482025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046548224,0.0011684719,0.001986925,0.0021041878,0.0007610654,0.0023130819,0.0013214571,0.0014368102,0.0013771448],"category_scores_gemma":[0.0055327425,0.00050239824,0.0018291518,0.0015690842,0.0007405625,0.0021468827,0.001356478,0.00093368243,0.00012947938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019883041,0.00008499246,0.0022365192,0.000108182045,0.00030761672,0.00012278979,0.00015454167,0.9469947,0.0015070977,0.009916041,0.00036186117,0.038006794],"study_design_scores_gemma":[0.000002484759,0.000025863183,0.00016683046,0.0000056494396,0.000020136948,0.0000071442855,0.000017747421,0.9974814,0.00018575501,0.0020194731,0.000059709226,0.000007829398],"about_ca_topic_score_codex":0.0052237525,"about_ca_topic_score_gemma":0.004263183,"teacher_disagreement_score":0.0052237525,"about_ca_system_score_codex":0.0013062111,"about_ca_system_score_gemma":0.001066917,"threshold_uncertainty_score":0.024617374},"labels":[],"label_agreement":null},{"id":"W4417302552","doi":"10.1016/j.eswa.2025.130834","title":"Trust risk-aware multiple decision-makers consensus seeking under dynamic social network: towards sustainable post-disaster emergency recovery plan selection","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China; Social Science Planning Project of Shandong Province; Youth Innovation Technology Project of Higher School in Shandong Province; Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Credibility; Ranking (information retrieval); Obedience; Perception; Selection (genetic algorithm); Trust management (information system); Plan (archaeology); Risk perception","score_opus":0.03630038065654139,"score_gpt":0.3626192529710231,"score_spread":0.3263188723144817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417302552","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10048896,0.00035357964,0.8957206,0.00065802754,0.000088017834,0.00010731847,0.000082167244,0.000118312804,0.0023829371],"genre_scores_gemma":[0.96617436,0.00011168115,0.032401916,0.00007085892,0.000048279806,0.000070244496,0.0000720359,0.000015151965,0.0010355453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974891,0.0010785689,0.00012276018,0.0005228883,0.0004620072,0.00032481106],"domain_scores_gemma":[0.9919342,0.0056812516,0.0007889634,0.00026527056,0.00096179184,0.0003685257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043367594,0.00089381984,0.0020629321,0.001017738,0.00090674026,0.0021145048,0.0021070985,0.0018299043,0.0014628544],"category_scores_gemma":[0.012494106,0.0006267178,0.0010357255,0.0010029433,0.0009354001,0.0025763775,0.002301854,0.0013684299,0.00016644997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031654767,0.00016133001,0.002141739,0.00015319382,0.00020545708,0.00025695487,0.00025403828,0.94879407,0.0013413114,0.015250258,0.0010024783,0.030122608],"study_design_scores_gemma":[0.0000068802283,0.000036780897,0.00012395134,0.000004650621,0.000016823502,0.0000137099805,0.000028875273,0.99593544,0.00013669267,0.003609537,0.00008100233,0.000005696908],"about_ca_topic_score_codex":0.0044794213,"about_ca_topic_score_gemma":0.0034498412,"teacher_disagreement_score":0.0044794213,"about_ca_system_score_codex":0.0013993317,"about_ca_system_score_gemma":0.0019084939,"threshold_uncertainty_score":0.022935271},"labels":[],"label_agreement":null},{"id":"W4417482640","doi":"10.1016/j.eswa.2025.130870","title":"Brain compensation mechanisms of large language models in clinical decision-making in acupuncture: A fusion study using fNIRS and eye-tracking","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Acupuncture Treatment Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Fund for Distinguished Young Scholars; Natural Science Foundation of Sichuan Province; Chengdu University of Traditional Chinese Medicine; National Natural Science Foundation of China","keywords":"Interpretability; Neurocognitive; Cognition; Neuroimaging; Artificial neural network; Brain activity and meditation","score_opus":0.03342507256075702,"score_gpt":0.4333932929346722,"score_spread":0.3999682203739152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417482640","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9432343,0.0006200981,0.051808417,0.00042712342,0.0000804288,0.0001184919,0.00020497358,0.00015599905,0.0033501186],"genre_scores_gemma":[0.9956899,0.000088776644,0.0037128723,0.000033423134,0.000012015219,0.000019628369,0.00004835184,0.00001717053,0.00037784732],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995396,0.00014518408,0.000030286601,0.00011179929,0.000093256385,0.000079844045],"domain_scores_gemma":[0.9977933,0.0015821066,0.0001990516,0.00014781994,0.00019155444,0.00008632281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017010228,0.00050513167,0.00041189758,0.0004270365,0.00029851406,0.0013745825,0.00047620322,0.00069046253,0.0019343125],"category_scores_gemma":[0.010378304,0.0002792349,0.00049255876,0.00040401114,0.00047531596,0.0014682709,0.00051842973,0.00088167004,0.00025587587],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0055404357,0.0017389852,0.07768406,0.0006605449,0.0012327172,0.0014807311,0.0062115216,0.083822586,0.3100041,0.013711196,0.003684496,0.49422857],"study_design_scores_gemma":[0.00018697044,0.0007519884,0.28845188,0.000077655604,0.00048549895,0.000957076,0.0014508344,0.6521273,0.028644193,0.025546743,0.0011824016,0.00013745036],"about_ca_topic_score_codex":0.004745934,"about_ca_topic_score_gemma":0.0035524603,"teacher_disagreement_score":0.004745934,"about_ca_system_score_codex":0.0004546493,"about_ca_system_score_gemma":0.00078128604,"threshold_uncertainty_score":0.009436607},"labels":[],"label_agreement":null},{"id":"W4417526804","doi":"10.1016/j.eswa.2025.130906","title":"APENet: Task-aware adaptation prototype evolution network for few-shot semantic segmentation","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Leverage (statistics); Feature (linguistics); Focus (optics); Adaptation (eye); Ambiguity; Metric (unit); Query expansion; Semantic feature","score_opus":0.021938525889235966,"score_gpt":0.2911263125417948,"score_spread":0.2691877866525588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417526804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05366746,0.0015265285,0.893232,0.0004278751,0.0005370719,0.00042956142,0.0019689389,0.042943478,0.0052670683],"genre_scores_gemma":[0.478297,0.000550916,0.4968512,0.00084264635,0.00016541641,0.00064332667,0.008005857,0.0023295095,0.012314251],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952114,0.00005443373,0.000018253944,0.00026217397,0.00008489493,0.000059187023],"domain_scores_gemma":[0.9994085,0.00017833685,0.00002695253,0.0001636937,0.00016791729,0.00005466183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089219894,0.0017120106,0.0014669633,0.0009995655,0.0006969832,0.0011042609,0.004223618,0.0023252445,0.0074111046],"category_scores_gemma":[0.0027330949,0.00091100176,0.00096784777,0.0010615375,0.0004413884,0.0023244536,0.001912551,0.002503911,0.0023588694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006005484,0.00052274123,0.0014759878,0.00024965274,0.00032892183,0.00030726354,0.00014872073,0.08159681,0.030940544,0.0024521437,0.032624416,0.84875226],"study_design_scores_gemma":[0.00003293061,0.00008778766,0.00048274762,0.000012513648,0.000045023255,0.00010080389,0.000025752703,0.9854996,0.008189017,0.0026518009,0.0028519514,0.000020030095],"about_ca_topic_score_codex":0.009252347,"about_ca_topic_score_gemma":0.016479671,"teacher_disagreement_score":0.009252347,"about_ca_system_score_codex":0.00086745305,"about_ca_system_score_gemma":0.0009794538,"threshold_uncertainty_score":0.024792552},"labels":[],"label_agreement":null},{"id":"W626466576","doi":"10.1016/j.eswa.2015.05.036","title":"A combined interactive procedure using preference-based evolutionary multiobjective optimization. Application to the efficiency improvement of the auxiliary services of power plants","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Multi-objective optimization; Computer science; Mathematical optimization; Evolutionary algorithm; Profitability index; Set (abstract data type); Pareto principle; Preference; Decision maker; Optimization problem; Operations research; Mathematics; Artificial intelligence; Machine learning; Economics","score_opus":0.01707482874664799,"score_gpt":0.2587027076591576,"score_spread":0.2416278789125096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W626466576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009545129,0.000048916587,0.9873191,0.000027770902,0.000018661347,0.00010357399,0.000047019206,0.0004479533,0.0024418917],"genre_scores_gemma":[0.23477137,0.00006391599,0.7617756,0.00006176493,0.000018496037,0.0003860899,0.00014063176,0.00021495503,0.0025671115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929583,0.0002694036,0.000033731805,0.00007226364,0.0002551007,0.00007370087],"domain_scores_gemma":[0.9988463,0.00057615066,0.000063061954,0.00013957708,0.00031938936,0.00005555854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001585291,0.0009903876,0.0011358021,0.0014807753,0.0007074617,0.0006755618,0.001669878,0.0010993854,0.008700339],"category_scores_gemma":[0.0032942083,0.00045993662,0.0010667773,0.0017125435,0.00033507627,0.00075040443,0.0014243807,0.00078953826,0.0008349924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000276786,0.00047664606,0.0011935036,0.00033183134,0.00019298652,0.00021945663,0.00016205314,0.48161387,0.017106974,0.010462971,0.0026122348,0.48535073],"study_design_scores_gemma":[0.000032976477,0.00011913252,0.00033742675,0.000010886753,0.000036194513,0.000059819162,0.000021819373,0.99153835,0.0037581178,0.0027923859,0.0012751627,0.000017681921],"about_ca_topic_score_codex":0.0025973308,"about_ca_topic_score_gemma":0.0049301824,"teacher_disagreement_score":0.008700339,"about_ca_system_score_codex":0.0003572667,"about_ca_system_score_gemma":0.000899668,"threshold_uncertainty_score":0.029105544},"labels":[],"label_agreement":null},{"id":"W6921910029","doi":"10.1016/j.eswa.2025.129026","title":"DECTUIL: Cross-social network user identity linkage via dynamic embedding and clustering model driven by three-way decision","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"Science and Technology Service Network Plan; National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; Organization Department of Sichuan Provincial Party Committee; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Cluster analysis; Embedding; Robustness (evolution); Entropy (arrow of time); Smoothing; Linkage (software); Word embedding; Constrained clustering","score_opus":0.008314776252196229,"score_gpt":0.3051760184642313,"score_spread":0.2968612422120351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6921910029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03978003,0.0005862072,0.9465473,0.0007937603,0.00029962786,0.00023610919,0.0013018494,0.008129043,0.002326133],"genre_scores_gemma":[0.559707,0.0003634437,0.41229388,0.0006505897,0.00023744308,0.000437821,0.005628404,0.0010914083,0.019589981],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871004,0.00040485378,0.000042444193,0.0004180254,0.00024691471,0.00017768663],"domain_scores_gemma":[0.9970933,0.001444818,0.00016298106,0.00055894663,0.0004944664,0.00024544133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025524744,0.0013322092,0.0024388526,0.0018400318,0.0013851804,0.0021653334,0.00426959,0.0024605258,0.0045050792],"category_scores_gemma":[0.005903816,0.0008933243,0.0014249458,0.0018925294,0.0010361035,0.0037316564,0.0048968173,0.0030842184,0.0023740986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008589756,0.00084036676,0.0046072984,0.00021871564,0.00048746332,0.0003081099,0.00036016136,0.58848816,0.0019721547,0.02115588,0.026600957,0.35410175],"study_design_scores_gemma":[0.000007731204,0.00002014753,0.000097218486,0.0000029304208,0.000005735374,0.000011561624,0.000011500635,0.99690884,0.00020885054,0.0023673454,0.00035121164,0.0000069748357],"about_ca_topic_score_codex":0.021410596,"about_ca_topic_score_gemma":0.027221553,"teacher_disagreement_score":0.021410596,"about_ca_system_score_codex":0.0016588824,"about_ca_system_score_gemma":0.0018558457,"threshold_uncertainty_score":0.042571902},"labels":[],"label_agreement":null},{"id":"W6940488526","doi":"10.1016/j.eswa.2025.129041","title":"BoostCount: Diffusion-based position-sensitive adversarial purification for crowd counting","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Adversarial system; Generative grammar; Robustness (evolution); Key (lock); Generative adversarial network; Security domain","score_opus":0.007233739789542571,"score_gpt":0.2249351208934734,"score_spread":0.21770138110393084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6940488526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003294366,0.00018957756,0.9927753,0.0002017396,0.00010910502,0.000088522094,0.000121481535,0.002117062,0.0011028736],"genre_scores_gemma":[0.35548618,0.00042403667,0.62374675,0.0010435489,0.0003319936,0.0006206596,0.0011987825,0.0010792664,0.01606876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985251,0.00037622137,0.00006048619,0.00030017845,0.00054922985,0.00018884485],"domain_scores_gemma":[0.99646866,0.0020411438,0.00020411098,0.00043087915,0.0006209413,0.00023437465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024828352,0.0018225028,0.0024250513,0.001274245,0.0009835023,0.0012322213,0.004154947,0.0032131914,0.005378383],"category_scores_gemma":[0.008418493,0.00089206034,0.0012438507,0.0008947401,0.0016900508,0.002049811,0.006119419,0.0036957348,0.002190041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028560145,0.00013948444,0.00068667775,0.00021610284,0.00010559679,0.00014835458,0.00012227523,0.75167406,0.0080968775,0.02491311,0.013036797,0.20057508],"study_design_scores_gemma":[0.0000073697433,0.000016370916,0.00003178279,0.0000065059285,0.0000035121761,0.000018676908,0.0000042046754,0.9927689,0.0012599335,0.005267115,0.00060903106,0.000006649784],"about_ca_topic_score_codex":0.005229258,"about_ca_topic_score_gemma":0.005149612,"teacher_disagreement_score":0.005378383,"about_ca_system_score_codex":0.0013827223,"about_ca_system_score_gemma":0.0018087287,"threshold_uncertainty_score":0.017992496},"labels":[],"label_agreement":null},{"id":"W7081946321","doi":"10.1016/j.eswa.2025.129687","title":"Era splitting - Invariant learning for decision trees","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Decision tree; Disjoint sets; Boosting (machine learning); Generalization; Invariant (physics); Gradient boosting; Synthetic data; Decision theory","score_opus":0.012301940234533966,"score_gpt":0.25909006392521355,"score_spread":0.2467881236906796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7081946321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008566687,0.0007989529,0.9880107,0.00027320432,0.00009474043,0.000039733048,0.0001396315,0.00065179414,0.001424585],"genre_scores_gemma":[0.49796975,0.0008875758,0.48907742,0.0004715687,0.00037546357,0.00022650749,0.0017444582,0.0005193339,0.008727996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977437,0.0008784997,0.00015559561,0.00052946823,0.0005002648,0.00019244595],"domain_scores_gemma":[0.9944944,0.0033990024,0.00032264998,0.0008166755,0.00077129435,0.00019594679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038144959,0.00090203056,0.0014661393,0.0013568436,0.0006382463,0.0013271499,0.0020415229,0.0015432708,0.004932204],"category_scores_gemma":[0.011305329,0.0006797959,0.0012540879,0.0013849266,0.0012635407,0.002570253,0.0020957324,0.00307531,0.0014882336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029617464,0.0002348286,0.001520402,0.00023802216,0.00017334773,0.00008684741,0.00012261975,0.27766484,0.003364331,0.079017766,0.010345317,0.6269355],"study_design_scores_gemma":[0.000014630035,0.000051075767,0.00019312024,0.000019278341,0.000014672134,0.000022225744,0.000007907092,0.9293695,0.00081881747,0.068127275,0.0013541188,0.0000073814613],"about_ca_topic_score_codex":0.0027076618,"about_ca_topic_score_gemma":0.0026121258,"teacher_disagreement_score":0.004932204,"about_ca_system_score_codex":0.0011947204,"about_ca_system_score_gemma":0.0012898269,"threshold_uncertainty_score":0.020173192},"labels":[],"label_agreement":null},{"id":"W7081966312","doi":"10.1016/j.eswa.2025.129686","title":"Towards intelligent online cross-selling","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Innovation and Technology Commission","keywords":"Compatibility (geochemistry); Leverage (statistics); Backward compatibility; Encapsulation (networking); Reliability (semiconductor); Protocol (science)","score_opus":0.0207388395731493,"score_gpt":0.2995856829311429,"score_spread":0.2788468433579936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7081966312","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06476209,0.0013928843,0.8458377,0.0023754712,0.00038441952,0.00023658795,0.00030885206,0.004011262,0.08069078],"genre_scores_gemma":[0.6117832,0.0009998964,0.32406977,0.00077355735,0.00022557328,0.0001273476,0.0010105802,0.00055892684,0.060451165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971323,0.0008465428,0.00014535552,0.0005767489,0.0009710716,0.0003279926],"domain_scores_gemma":[0.9945686,0.0021908495,0.00024334126,0.0019414637,0.0007611518,0.0002947104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004253712,0.000769682,0.0013293428,0.0012850368,0.0015281878,0.005967438,0.0022732855,0.0027387321,0.026666552],"category_scores_gemma":[0.0102173155,0.00084490445,0.00093366596,0.0020514464,0.001275585,0.013306742,0.005344029,0.0030350897,0.0068781534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067651516,0.001732851,0.0041418723,0.0002865588,0.00013552354,0.00048796408,0.00067166163,0.060290184,0.0071471827,0.2549455,0.0355882,0.633896],"study_design_scores_gemma":[0.0000558599,0.00012594779,0.0005836633,0.00006947284,0.000052396506,0.00037620467,0.0004668529,0.6075321,0.0065724207,0.33827275,0.04585738,0.000034940633],"about_ca_topic_score_codex":0.0012740988,"about_ca_topic_score_gemma":0.0017893849,"teacher_disagreement_score":0.026666552,"about_ca_system_score_codex":0.0008908695,"about_ca_system_score_gemma":0.0011462545,"threshold_uncertainty_score":0.08920848},"labels":[],"label_agreement":null},{"id":"W7104584205","doi":"10.1016/j.eswa.2025.130311","title":"A novel neural network-based fuzzy ranking for decision problems in sustainable energy","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Science Basic Research Program of Shaanxi Province; Canada First Research Excellence Fund; University of Alberta","keywords":"Ranking (information retrieval); Fuzzy logic; Energy (signal processing); Decision problem; Artificial neural network; Sustainable energy","score_opus":0.0625539880705815,"score_gpt":0.3745762262368942,"score_spread":0.3120222381663127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104584205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01747536,0.0007839519,0.9759787,0.00040701078,0.00012988021,0.000085856955,0.00009957823,0.00025976796,0.004779881],"genre_scores_gemma":[0.6648414,0.0007966763,0.32517835,0.000335384,0.00022763823,0.00034679766,0.00030870247,0.00004915907,0.00791587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991493,0.0002764731,0.000049348106,0.00017718204,0.00026597388,0.000081748134],"domain_scores_gemma":[0.9995074,0.00022360045,0.000057126224,0.000018801436,0.00016945793,0.00002361321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013544107,0.00085677,0.0011067395,0.0009185191,0.0005531518,0.0012651435,0.001265616,0.0014958071,0.002108325],"category_scores_gemma":[0.0022791983,0.00030314387,0.0006423934,0.0011716182,0.00048301546,0.0013922786,0.0009333361,0.0010041054,0.00038253414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011979361,0.00013235307,0.000794323,0.00017666299,0.00008898927,0.00011734692,0.00005546228,0.8283125,0.0030239865,0.01234475,0.0021619457,0.15267195],"study_design_scores_gemma":[0.000005088615,0.000031873897,0.00009765222,0.000006932267,0.0000067224514,0.000012499853,0.0000037493803,0.99739456,0.00030006145,0.0017802552,0.00035475052,0.0000058986816],"about_ca_topic_score_codex":0.0056373626,"about_ca_topic_score_gemma":0.006005602,"teacher_disagreement_score":0.0056373626,"about_ca_system_score_codex":0.0011705279,"about_ca_system_score_gemma":0.0010574894,"threshold_uncertainty_score":0.011209071},"labels":[],"label_agreement":null},{"id":"W7116117035","doi":"10.1016/j.eswa.2025.130920","title":"Multilayer artificial benchmark for community detection (mABCD)","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"HORIZON EUROPE Framework Programme; European Research Executive Agency; Ministerstwo Edukacji i Nauki; Narodowe Centrum Nauki","keywords":"Flexibility (engineering); Graph; Benchmark (surveying); Uniqueness; Network science; Complex network; Community structure; Random graph","score_opus":0.01815432120541413,"score_gpt":0.30615584080439784,"score_spread":0.2880015195989837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116117035","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5552575,0.0058677057,0.36207476,0.0033591778,0.0017295952,0.0007512668,0.02862462,0.0128821675,0.029453194],"genre_scores_gemma":[0.7603752,0.0005731448,0.20954537,0.00044975543,0.00027428626,0.000364605,0.021445254,0.0004578124,0.0065145968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968292,0.0013399033,0.00017551413,0.00062391243,0.0006844095,0.00034699868],"domain_scores_gemma":[0.99099874,0.0041267774,0.0005118794,0.0020163823,0.0017750148,0.00057122414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004263948,0.0013265951,0.0012650791,0.003091395,0.0015018199,0.002132902,0.00222028,0.002689278,0.0052253134],"category_scores_gemma":[0.023291826,0.00037090937,0.0011859349,0.002236765,0.0006415078,0.002110949,0.002386251,0.001534389,0.0017640028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002829089,0.0016275797,0.020241972,0.0016935137,0.0011002482,0.00035960352,0.00023483763,0.46513823,0.0087344935,0.020679597,0.10992352,0.36743727],"study_design_scores_gemma":[0.00011927324,0.00025300935,0.0031582117,0.00004785217,0.000064542946,0.000117468655,0.0000684525,0.9749517,0.0037359064,0.011930954,0.0055290707,0.000023602783],"about_ca_topic_score_codex":0.009878274,"about_ca_topic_score_gemma":0.013052904,"teacher_disagreement_score":0.009878274,"about_ca_system_score_codex":0.0012533846,"about_ca_system_score_gemma":0.0018195793,"threshold_uncertainty_score":0.022550225},"labels":[],"label_agreement":null},{"id":"W7116357590","doi":"10.1016/j.eswa.2025.130869","title":"In-context learning enhanced by multi-perspective sequential retrieval and predictive feedback for few-shot aspect-based sentiment analysis","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal","funders":"National Key Research and Development Program of China; Sichuan Province Science and Technology Support Program; National Natural Science Foundation of China; Department of Science and Technology of Sichuan Province; Organization Department of Sichuan Provincial Party Committee; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences","keywords":"Sentiment analysis; Benchmark (surveying); Parsing; Language model; Labeled data; Training set","score_opus":0.015722407220630865,"score_gpt":0.30492235914652605,"score_spread":0.2891999519258952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116357590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15066147,0.0032179197,0.82928395,0.00058363564,0.00031066855,0.00035738162,0.001110229,0.009449202,0.005025459],"genre_scores_gemma":[0.71271247,0.0006693873,0.27670386,0.00067594985,0.00027395782,0.00028035452,0.0036430492,0.00028087985,0.0047601713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991431,0.00019500169,0.00005082694,0.00031511544,0.00020035439,0.00009554914],"domain_scores_gemma":[0.99897194,0.0004462766,0.00008248976,0.00015749686,0.00027574343,0.00006612656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095761276,0.0017507876,0.0011388735,0.0015480047,0.00058608316,0.0008299043,0.0014486051,0.0011107827,0.0025878083],"category_scores_gemma":[0.0038312948,0.0003359823,0.0010645696,0.0009881406,0.0003848042,0.002377809,0.0012648682,0.0014623391,0.0015677611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061152515,0.0007196981,0.00567023,0.00042377957,0.00020801522,0.00048069362,0.00045180245,0.04003762,0.064734794,0.0027667943,0.015292635,0.86860245],"study_design_scores_gemma":[0.000045178997,0.00025098136,0.0016362632,0.00002761152,0.00008113605,0.00017053368,0.00015111272,0.976143,0.012469089,0.005644242,0.0033478725,0.000032886135],"about_ca_topic_score_codex":0.005142357,"about_ca_topic_score_gemma":0.010786557,"teacher_disagreement_score":0.005142357,"about_ca_system_score_codex":0.00058759504,"about_ca_system_score_gemma":0.0009065493,"threshold_uncertainty_score":0.010224879},"labels":[],"label_agreement":null},{"id":"W7116936213","doi":"10.1016/j.eswa.2025.130890","title":"Game-theoretic DEA optimization for sustainable agricultural carbon trading: Evidence from Türkiye’s maize production","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Data envelopment analysis; Nash equilibrium; Agriculture; Particle swarm optimization; Genetic algorithm; Production (economics); Welfare; Carbon fibers; Nonlinear programming; Bargaining problem","score_opus":0.03576305656460928,"score_gpt":0.3366895788686478,"score_spread":0.30092652230403855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116936213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96154654,0.00064154493,0.026603296,0.000453928,0.000020026848,0.000076772216,0.00018287743,0.000056330453,0.010418677],"genre_scores_gemma":[0.9952728,0.00014195827,0.0038434977,0.000014460477,0.0000021713452,0.000021562493,0.000069215224,0.0000069776947,0.0006274487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958414,0.0002425541,0.000016638107,0.000046983023,0.000051028877,0.000058646616],"domain_scores_gemma":[0.9970396,0.0024788352,0.0001699285,0.00010649554,0.00014722701,0.000057757068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016035143,0.0005668244,0.00056405313,0.00042591887,0.0005173055,0.0010946727,0.0005730128,0.0007757025,0.0015365164],"category_scores_gemma":[0.0034723997,0.00020252664,0.0005371862,0.00062378054,0.0006611645,0.0008990699,0.000510866,0.0010337146,0.00010724725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019907772,0.00016877889,0.0034453704,0.00008414574,0.00007152138,0.00020536539,0.00007164427,0.9740713,0.0007282672,0.012604745,0.00057447376,0.0077751614],"study_design_scores_gemma":[0.00005006765,0.00010648358,0.0021157293,0.0000087720955,0.00001760546,0.000016555241,0.00013005026,0.9898065,0.00047692377,0.0063717864,0.0008865867,0.000012912776],"about_ca_topic_score_codex":0.022671841,"about_ca_topic_score_gemma":0.01732504,"teacher_disagreement_score":0.022671841,"about_ca_system_score_codex":0.0016741618,"about_ca_system_score_gemma":0.0010177221,"threshold_uncertainty_score":0.045079768},"labels":[],"label_agreement":null},{"id":"W7116947360","doi":"10.1016/j.eswa.2025.130889","title":"Skyline operators in multi-criteria decision making: A review of characterization, comparison, and perspectives","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Saint Mary's University","funders":"","keywords":"Skyline; Robustness (evolution); Preference; Operator (biology); Decision support system","score_opus":0.027474986559130412,"score_gpt":0.34044039518064284,"score_spread":0.31296540862151245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116947360","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028733606,0.93006206,0.06053158,0.0021828262,0.00026755055,0.000045183355,0.00008298912,0.00003167595,0.0039228103],"genre_scores_gemma":[0.06966092,0.840705,0.08625048,0.00092706986,0.0014122706,0.00012941081,0.00017416968,0.000040437448,0.0007002975],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99456304,0.0023992418,0.0005815783,0.0007036393,0.0015935557,0.00015900756],"domain_scores_gemma":[0.9767553,0.019398406,0.0010360747,0.0004035039,0.0021838297,0.000222907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013895314,0.0016272569,0.0036111614,0.005045536,0.0006291047,0.004786087,0.0028404116,0.00209697,0.00196171],"category_scores_gemma":[0.015132561,0.000667007,0.001982764,0.010639397,0.0024904595,0.0062898872,0.0015408928,0.0031791723,0.00039214402],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002801734,0.00024290601,0.0017504435,0.018956432,0.0006301128,0.00012371737,0.00045687833,0.0279597,0.00063033996,0.15421318,0.0073067704,0.78744924],"study_design_scores_gemma":[0.00022013634,0.00142294,0.005391397,0.025773508,0.0013436557,0.0012367746,0.0018069394,0.13695359,0.0027831953,0.5404886,0.28212744,0.00045179512],"about_ca_topic_score_codex":0.0034946585,"about_ca_topic_score_gemma":0.003760339,"teacher_disagreement_score":0.013895314,"about_ca_system_score_codex":0.0023510756,"about_ca_system_score_gemma":0.0033551066,"threshold_uncertainty_score":0.07348633},"labels":[],"label_agreement":null},{"id":"W7117158241","doi":"10.1016/j.eswa.2025.130847","title":"Motion sickness prediction in intelligent electric vehicles using collaborative subjective-objective data fusion","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Noise and Vibration Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Major Science and Technology Projects in Anhui Province; Natural Science Foundation of Henan Province; Key Research and Development Project of Hainan Province; National Natural Science Foundation of China","keywords":"Motion (physics); Convolutional neural network; Fusion mechanism; Sensor fusion; Key (lock); Fusion; Artificial neural network; Motion sickness","score_opus":0.015720031110986406,"score_gpt":0.2652489865794129,"score_spread":0.24952895546842652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117158241","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62296605,0.00056131295,0.3741051,0.00013746045,0.00008952249,0.000049787533,0.00026225907,0.00024042054,0.0015880989],"genre_scores_gemma":[0.99261004,0.00006161727,0.0068238126,0.00001506939,0.000017036038,0.000011346424,0.000140363,0.000006078158,0.00031457804],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997726,0.00004402165,0.000016741496,0.000054297412,0.00006996877,0.000042458854],"domain_scores_gemma":[0.99951565,0.00015527461,0.00007986696,0.000029533168,0.00017844954,0.00004123536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047786275,0.00053237414,0.0004954658,0.00060525915,0.00017887985,0.00045530178,0.00026507658,0.00041631347,0.00037859206],"category_scores_gemma":[0.0013722393,0.00016549301,0.00039671623,0.00035061184,0.00017300552,0.000494319,0.0004922337,0.0003532988,0.00013745618],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014948134,0.0008360689,0.056005087,0.00030060642,0.00040332778,0.0003387749,0.00037584692,0.5550295,0.05994221,0.0010641118,0.0021405364,0.3220691],"study_design_scores_gemma":[0.000012624708,0.00015208343,0.01680877,0.000007711348,0.000032910903,0.000031135292,0.000052066076,0.9791595,0.0032207225,0.00036428298,0.00014339961,0.000014798921],"about_ca_topic_score_codex":0.00461011,"about_ca_topic_score_gemma":0.0064170295,"teacher_disagreement_score":0.00461011,"about_ca_system_score_codex":0.00020666332,"about_ca_system_score_gemma":0.00037700107,"threshold_uncertainty_score":0.009166598},"labels":[],"label_agreement":null},{"id":"W7117291504","doi":"10.1016/j.eswa.2025.130928","title":"Adaptive control via deep neural networks with an event-triggered mechanism","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Guangdong Provincial Applied Science and Technology Research and Development Program; National Natural Science Foundation of China","keywords":"Artificial neural network; Layer (electronics); Computational complexity theory; Nonlinear system; Control theory (sociology); Adaptive control; Control (management); Tracking error","score_opus":0.0064808753697874755,"score_gpt":0.22209998033758088,"score_spread":0.2156191049677934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117291504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022825547,0.00039535767,0.9717557,0.00027988813,0.00021914559,0.00003176026,0.00004656481,0.0004022122,0.0040438413],"genre_scores_gemma":[0.95393646,0.00018407042,0.04218766,0.00014399078,0.00005613638,0.000053518732,0.000045321412,0.000029486477,0.0033633136],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997631,0.000048125672,0.000016405447,0.00006798079,0.000064617416,0.000039796578],"domain_scores_gemma":[0.999548,0.00021969457,0.000050752442,0.00005844668,0.00009367897,0.000029342154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075198355,0.0005849102,0.00053939014,0.00017096454,0.0002510405,0.00074305997,0.0011626004,0.0011136079,0.002151457],"category_scores_gemma":[0.0016410641,0.00035114455,0.00039616067,0.0002308538,0.0006707077,0.00097567204,0.001188301,0.0015723963,0.00021952367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031942726,0.00013443921,0.00043761995,0.00013290056,0.00009264763,0.00010342197,0.000063303116,0.8230294,0.016519956,0.056700867,0.0018290742,0.100636944],"study_design_scores_gemma":[0.000004672029,0.00001569689,0.00003203155,0.0000025236031,0.0000032386554,0.0000039342503,8.6299883e-7,0.99639124,0.0005106833,0.0029068594,0.00012566491,0.0000025913373],"about_ca_topic_score_codex":0.0024558182,"about_ca_topic_score_gemma":0.0033146825,"teacher_disagreement_score":0.0024558182,"about_ca_system_score_codex":0.00054348394,"about_ca_system_score_gemma":0.00053706364,"threshold_uncertainty_score":0.0071973205},"labels":[],"label_agreement":null},{"id":"W7117484981","doi":"10.1016/j.eswa.2025.130958","title":"Topic modeling and alignment with large language models for multi-labeled text corpora","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Interpretability; Topic model; Language model; Probabilistic logic; Coherence (philosophical gambling strategy); Semantics (computer science); Latent Dirichlet allocation","score_opus":0.03005171954682459,"score_gpt":0.28740508879336835,"score_spread":0.25735336924654373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117484981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069574653,0.0009961647,0.98617315,0.00039189428,0.00018224203,0.00013386089,0.0009011235,0.003718505,0.00054555445],"genre_scores_gemma":[0.17280562,0.0014668189,0.80311036,0.00032028966,0.00096263684,0.001142686,0.014348002,0.0018221876,0.004021417],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99107224,0.0047263103,0.00067101774,0.0023213825,0.00084384566,0.0003652488],"domain_scores_gemma":[0.9786129,0.01610253,0.00083223975,0.0024049364,0.0016842886,0.00036309232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008410086,0.0019815264,0.0027723417,0.004875924,0.0022971751,0.0040465146,0.0033520544,0.0028450976,0.0038397338],"category_scores_gemma":[0.028687429,0.0018559756,0.0033729062,0.006107728,0.0009185768,0.006576587,0.0029114238,0.005273844,0.005154175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011528942,0.0006599505,0.004152684,0.0009898784,0.0009967373,0.0005628041,0.001311676,0.1466841,0.013194799,0.029173302,0.03202434,0.76909673],"study_design_scores_gemma":[0.00006713939,0.000077854915,0.0010732252,0.000049876013,0.00016782696,0.00016846131,0.00019338643,0.94695824,0.0041518165,0.040558994,0.0064706793,0.00006239522],"about_ca_topic_score_codex":0.0069727805,"about_ca_topic_score_gemma":0.012629913,"teacher_disagreement_score":0.008410086,"about_ca_system_score_codex":0.0013860473,"about_ca_system_score_gemma":0.0028858904,"threshold_uncertainty_score":0.044477284},"labels":[],"label_agreement":null},{"id":"W7117555463","doi":"10.1016/j.eswa.2025.131015","title":"Deep learning-based image outpainting of finger-vein image","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea","keywords":"Image (mathematics); Image processing; Feature detection (computer vision); Pattern recognition (psychology); Image restoration; Image segmentation","score_opus":0.006220728873907623,"score_gpt":0.23824038668543232,"score_spread":0.2320196578115247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117555463","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20991105,0.00072265125,0.7837588,0.00017611998,0.000113635506,0.00011594375,0.00018695043,0.0025147602,0.0025001112],"genre_scores_gemma":[0.82562226,0.0004134915,0.16604199,0.00019788388,0.00006068916,0.000060111182,0.00052656326,0.00011350388,0.00696348],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998679,0.0000131956795,0.000006433858,0.00003753654,0.000047869245,0.000026925965],"domain_scores_gemma":[0.9998031,0.000063999105,0.000025331781,0.00003602735,0.000056405912,0.000015154106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027369687,0.0004961265,0.00054600555,0.0003235204,0.00013628133,0.00026711123,0.00064592005,0.00030393884,0.001265211],"category_scores_gemma":[0.00062675664,0.00018044579,0.0004455791,0.0002702322,0.00019816984,0.0004913215,0.00036992237,0.0007024043,0.00023091478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044778074,0.00023585938,0.0018572082,0.00017231354,0.00009009776,0.00030008313,0.00010921165,0.15253279,0.13706152,0.0017454594,0.0043192212,0.7011284],"study_design_scores_gemma":[0.00001142701,0.00009400879,0.000775968,0.0000038847156,0.0000152384755,0.00009151486,0.000008379782,0.96964854,0.028280564,0.0003958435,0.00066970947,0.0000048464476],"about_ca_topic_score_codex":0.0030333586,"about_ca_topic_score_gemma":0.0049137897,"teacher_disagreement_score":0.0030333586,"about_ca_system_score_codex":0.00032937306,"about_ca_system_score_gemma":0.00033825779,"threshold_uncertainty_score":0.0060314536},"labels":[],"label_agreement":null},{"id":"W7117785157","doi":"10.1016/j.eswa.2025.130945","title":"SSVA: Self-scanned visual attention for enhanced mask-free shadow removal","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Alfaisal University; Prince Sultan University","keywords":"Shadow (psychology); Benchmark (surveying); Channel (broadcasting); Mean squared error; Feature (linguistics); Pattern recognition (psychology); Shadow mask; Feature extraction","score_opus":0.00870717423581578,"score_gpt":0.2810863656059223,"score_spread":0.2723791913701065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117785157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08857081,0.001772792,0.8789837,0.0002720463,0.00048657556,0.00024342832,0.0011893627,0.018017713,0.010463508],"genre_scores_gemma":[0.50481653,0.0009281919,0.46840888,0.000801157,0.00030079024,0.00028236426,0.0018628933,0.001572884,0.021026293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997725,0.000033272598,0.000006986291,0.00004839191,0.000100672696,0.000038056594],"domain_scores_gemma":[0.99961424,0.00013732757,0.000021554393,0.00006681957,0.00011675999,0.000043218548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031493598,0.0005684453,0.00046270326,0.00059838896,0.00021648232,0.0005286562,0.00080590276,0.00053968694,0.01176034],"category_scores_gemma":[0.0011260858,0.00021349473,0.00037060256,0.00036029573,0.00018809598,0.0005373865,0.0010489933,0.00047642298,0.0022585758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094466715,0.00014782485,0.0006921755,0.00022741372,0.000112295456,0.00015189953,0.0000970037,0.0045222174,0.30502477,0.0016238899,0.016024036,0.67043185],"study_design_scores_gemma":[0.00020659534,0.00069016195,0.010775874,0.000067466994,0.00019739315,0.0014724025,0.00008798424,0.51695514,0.43179822,0.0051430017,0.03248647,0.000119234],"about_ca_topic_score_codex":0.0022690475,"about_ca_topic_score_gemma":0.0044037364,"teacher_disagreement_score":0.01176034,"about_ca_system_score_codex":0.0002650723,"about_ca_system_score_gemma":0.00050899445,"threshold_uncertainty_score":0.039342284},"labels":[],"label_agreement":null},{"id":"W815473107","doi":"10.1016/j.eswa.2015.06.044","title":"An enhanced noise resilient K-associated graph classifier","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Classifier (UML); Graph; Algorithm; Pattern recognition (psychology); Parametric statistics; Training set; Artificial intelligence; Support vector machine; Decision tree; Mathematics; Theoretical computer science","score_opus":0.025092311759180325,"score_gpt":0.27529213597713986,"score_spread":0.25019982421795955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W815473107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038234424,0.0009974354,0.953479,0.00033510578,0.00045692277,0.00011085162,0.00041274907,0.003084448,0.0028892139],"genre_scores_gemma":[0.40016288,0.0008118093,0.57955605,0.0005519281,0.0003204083,0.00013215467,0.0020608811,0.0003825416,0.016021345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990835,0.00013413398,0.000045653334,0.00022339904,0.00041673915,0.00009658637],"domain_scores_gemma":[0.99897003,0.00024362693,0.000049352286,0.0002258081,0.0004447123,0.00006655233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059355877,0.0007834545,0.0014264956,0.0013713242,0.00075210945,0.0012223258,0.0020340637,0.0016640115,0.003251927],"category_scores_gemma":[0.002242755,0.0003476108,0.0008572731,0.0011935727,0.00039157135,0.0015722908,0.0014520551,0.001219712,0.0031883523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007336678,0.0003265141,0.0013862696,0.00014882951,0.00013023177,0.0001912657,0.000053845444,0.05601699,0.04689824,0.006171068,0.010905805,0.8770373],"study_design_scores_gemma":[0.000018085126,0.00007751949,0.0005848837,0.0000091964575,0.000044899636,0.00015294066,0.000016016344,0.9832691,0.009681854,0.0028255668,0.0033017434,0.000018212251],"about_ca_topic_score_codex":0.0067242123,"about_ca_topic_score_gemma":0.010352229,"teacher_disagreement_score":0.0067242123,"about_ca_system_score_codex":0.00047076494,"about_ca_system_score_gemma":0.0013502326,"threshold_uncertainty_score":0.013370156},"labels":[],"label_agreement":null}]}