{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":211,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":211,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"c388a008f909","filters":{"venue":"Applied Soft Computing"}},"results":[{"id":"W2139669429","doi":"10.1016/j.asoc.2009.06.019","title":"The use of computational intelligence in intrusion detection systems: A review","year":2009,"lang":"en","type":"review","venue":"Applied Soft Computing","topic":"Artificial Immune Systems Applications","field":"Engineering","cited_by":694,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Intrusion detection system; Computational intelligence; Soft computing; Artificial intelligence; Field (mathematics); Resilience (materials science); Artificial immune system; Adaptation (eye); Scope (computer science); Swarm intelligence; Evolutionary computation; Data science; Machine learning; Fuzzy logic; Artificial neural network; Particle swarm optimization","retraction":null,"screen_n_in":null,"score":{"opus":0.06006090240183988,"gpt":0.3021923552342986,"spread":0.2421314528324587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006571135,0.000309695,0.001102772,0.0001508803,0.0001577025,0.0000662225,0.0003386723,0.0001662062,9.426789e-7],"category_scores_gemma":[0.0000775581,0.0002528018,0.0001547909,0.0008512445,0.00004658126,0.000045674,0.00008591653,0.0004513188,0.00004807582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002118816,"about_ca_system_score_gemma":0.0000541207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003415489,"about_ca_topic_score_gemma":0.00001345281,"domain_scores_codex":[0.9974838,0.0001163729,0.001659336,0.0002639939,0.0002258043,0.0002506713],"domain_scores_gemma":[0.9977862,0.001192283,0.0004851404,0.0004292979,0.00007339647,0.00003374269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[3.818529e-7,0.00000685696,5.357512e-8,0.01279098,0.00002297115,3.224858e-7,0.00003004543,0.138425,0.000001224328,0.0009537623,0.0000746904,0.8476937],"study_design_scores_gemma":[0.00002172388,0.000006531928,0.000001654681,0.04135154,0.0001003731,0.00001853479,0.00001955873,0.1640072,0.000002153529,0.0001348265,0.7940546,0.0002813096],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000002051249,0.7727743,0.2252112,0.00000265962,0.0001770323,0.001576579,0.000003184612,0.0001421556,0.0001107942],"genre_scores_gemma":[0.002476466,0.9958999,0.001299638,0.00001042835,0.00007411091,0.0001483053,0.00003281612,0.0000535767,0.000004782703],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8474124,"threshold_uncertainty_score":0.9999924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4311543603","doi":"10.1016/j.asoc.2022.109924","title":"The choice of scaling technique matters for classification performance","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":342,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Scaling; Normalization (sociology); Preprocessor; Pipeline (software); Data mining; Data pre-processing; Range (aeronautics); Machine learning; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02762515347572305,"gpt":0.2739750754989949,"spread":0.2463499220232718,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001193235,0.0001049573,0.0001246899,0.00007355097,0.0008891475,0.0000578061,0.00163435,0.00002587678,8.808482e-7],"category_scores_gemma":[0.00003571409,0.00009613101,0.00004159959,0.0003530525,0.00005892666,0.00009622137,0.0008907455,0.0001792934,0.000001212547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007118226,"about_ca_system_score_gemma":0.00004474202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007976309,"about_ca_topic_score_gemma":3.386439e-7,"domain_scores_codex":[0.9988347,0.00003695211,0.0003158299,0.0003125432,0.0002457799,0.0002542036],"domain_scores_gemma":[0.9982278,0.0007558252,0.0002956638,0.0006488034,0.00004760211,0.00002433923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002523976,0.00008649071,0.0009688889,0.0001169543,0.00003192222,4.242522e-7,0.001149266,0.002334679,0.09248815,0.153286,0.00688312,0.7426288],"study_design_scores_gemma":[0.0003406462,0.0001445792,0.00249973,0.00004743952,0.0000118643,0.00001473234,0.0003088032,0.8704174,0.07944652,0.005032215,0.04134708,0.0003889468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01230407,0.00001416794,0.9855074,0.0005731006,0.0001127529,0.0005975346,0.000004902454,0.0004107136,0.0004753839],"genre_scores_gemma":[0.6887048,0.000001053063,0.3107503,0.0002418806,0.00002337879,0.0002515841,0.000008413526,0.00001068338,0.000007905507],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8680828,"threshold_uncertainty_score":0.6838693,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2009637664","doi":"10.1016/j.asoc.2012.08.033","title":"Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description","year":2012,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":303,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"SCADA; Adaptive neuro fuzzy inference system; Turbine; Computer science; Anomaly detection; Wind power; Fault (geology); Artificial neural network; Context (archaeology); Fuzzy logic; Data mining; Control theory (sociology); Control engineering; Fuzzy control system; Artificial intelligence; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05682776602067444,"gpt":0.2915319613914796,"spread":0.2347041953708052,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005654671,0.0002905667,0.0002688309,0.0001254495,0.0002419191,0.0000975579,0.0003526791,0.0001453066,0.00001038519],"category_scores_gemma":[0.00001096577,0.000340537,0.00004115689,0.0001600265,0.00002258727,0.0004872481,0.0001525503,0.000336943,0.00002334904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002758086,"about_ca_system_score_gemma":0.00001349011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006930754,"about_ca_topic_score_gemma":0.000001500362,"domain_scores_codex":[0.9984055,0.00003023644,0.0003723378,0.0003272078,0.0003296549,0.0005351144],"domain_scores_gemma":[0.998974,0.0001032481,0.0001017241,0.0006545059,0.0000316683,0.000134852],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001115366,0.0001299811,0.01513387,0.0002945939,0.00002806382,0.000005799945,0.0002517067,0.9583892,0.01262565,0.0003180463,0.0006531028,0.01215886],"study_design_scores_gemma":[0.0003013246,0.00001123716,0.003179861,0.0003252997,0.00007082596,0.000009144783,0.00008060441,0.9879754,0.007568427,0.000009076479,0.0001234413,0.0003453247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6714006,0.0001109359,0.3245539,0.000002746718,0.0007163944,0.0004362179,0.0000270684,0.001640631,0.001111552],"genre_scores_gemma":[0.9628741,0.000002177441,0.03589556,0.00002416387,0.0008374802,0.00002112842,0.0002557652,0.00008857639,0.000001082534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2914735,"threshold_uncertainty_score":0.9999047,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4378417908","doi":"10.1016/j.asoc.2023.110415","title":"A broad review on class imbalance learning techniques","year":2023,"lang":"en","type":"review","venue":"Applied Soft Computing","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":258,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Class (philosophy); Support vector machine; Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.05958989506665886,"gpt":0.3534783318788895,"spread":0.2938884368122306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001982061,0.0009204366,0.002565868,0.0004995829,0.0004089971,0.0003312755,0.00400414,0.0004801251,0.000005453289],"category_scores_gemma":[0.0003797013,0.0008456056,0.0004788668,0.002346359,0.00009316116,0.0001760077,0.001635634,0.001950201,0.001342878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003504432,"about_ca_system_score_gemma":0.0003085405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004151225,"about_ca_topic_score_gemma":3.394045e-7,"domain_scores_codex":[0.9946837,0.0003519537,0.001556454,0.001801302,0.0007499433,0.0008565895],"domain_scores_gemma":[0.9944912,0.001239717,0.001643485,0.002345433,0.0001249541,0.0001552606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[4.337615e-7,0.00002370887,1.894353e-7,0.02131077,0.00004102675,0.00001179603,0.00001705521,0.000001697626,0.000003351183,0.02505629,0.007811601,0.9457221],"study_design_scores_gemma":[0.00004410763,0.00004059418,5.350673e-7,0.07730808,0.00007920319,0.00002874291,0.000002098467,0.0006641617,0.00003052401,0.0004470755,0.9205841,0.0007707304],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.715452e-8,0.7219288,0.2639842,0.0001056191,0.0001777563,0.0017277,0.000008947967,0.007196222,0.004870714],"genre_scores_gemma":[0.000003103972,0.9328093,0.06471898,0.001010427,0.0002393488,0.0005255278,0.000205148,0.0001579387,0.0003302523],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9449514,"threshold_uncertainty_score":0.9994347,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2177188361","doi":"10.1016/j.asoc.2015.09.045","title":"Gray Wolf Optimizer for hyperspectral band selection","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":195,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Pattern recognition (psychology); Differential evolution; Curse of dimensionality; Pixel; Benchmark (surveying); Selection (genetic algorithm); Heuristic; Optimization problem; Spectral bands; Algorithm; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.02445991997740743,"gpt":0.2360742637030447,"spread":0.2116143437256372,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245112,0.0001413723,0.0001513632,0.00006689873,0.00009012868,0.00007190194,0.00008271536,0.00007940746,0.000001788459],"category_scores_gemma":[0.00004980063,0.000156184,0.00004057603,0.0001699326,0.00002261925,0.00005675509,0.00001117035,0.0001350282,0.00003254556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001638417,"about_ca_system_score_gemma":0.00002291272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003445095,"about_ca_topic_score_gemma":0.000001348908,"domain_scores_codex":[0.9991975,0.000008578182,0.0001886638,0.0002083528,0.0001174969,0.0002793818],"domain_scores_gemma":[0.9995637,0.00009655516,0.0000403374,0.0001365607,0.00007847994,0.00008435946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002871196,0.0000213039,0.0000817602,0.00004595264,0.00004663718,8.186933e-7,0.001106309,0.8184758,0.138501,0.0009886969,0.008068089,0.03263494],"study_design_scores_gemma":[0.0006581635,0.0000173323,0.0001830622,0.00001076782,0.00001822982,0.00001104076,0.0001565733,0.9643989,0.03100977,0.0006322058,0.002682942,0.0002210137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1352145,0.00004280666,0.8552396,0.00005990612,0.0003904308,0.0003022407,7.948066e-7,0.0008317936,0.007917905],"genre_scores_gemma":[0.8243211,8.387017e-7,0.1752615,0.0000382456,0.0002705966,0.000004466251,0.00001182502,0.00004671909,0.00004470374],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6891066,"threshold_uncertainty_score":0.6368997,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2069793359","doi":"10.1016/j.asoc.2015.01.059","title":"Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC)","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":170,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Queen's University","keywords":"Surface roughness; Materials science; Metallurgy; Surface finish; Environmental science; Composite material","retraction":null,"screen_n_in":null,"score":{"opus":0.01867411529532528,"gpt":0.2185943165352698,"spread":0.1999202012399446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001989995,0.0001442899,0.0002494323,0.00005348946,0.00006018347,0.00001377253,0.0001218697,0.00007320799,0.00000210506],"category_scores_gemma":[0.00004071005,0.0001615966,0.0000317767,0.0002453631,0.0000255596,0.0001171869,0.00006738829,0.0001582506,0.000001354999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004660188,"about_ca_system_score_gemma":0.00002024802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005153794,"about_ca_topic_score_gemma":4.517316e-7,"domain_scores_codex":[0.9990425,0.000008926523,0.0003780327,0.0001717574,0.0001959856,0.0002027876],"domain_scores_gemma":[0.999455,0.00007198542,0.0001574302,0.0001529848,0.000103964,0.00005868811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001609538,0.00001199584,0.001478237,0.0002923317,0.00002152494,4.210613e-7,0.001428106,0.9602339,0.03435133,0.0002095361,0.00001320178,0.001943352],"study_design_scores_gemma":[0.0008754266,0.00002712433,0.003420085,0.0001669392,0.00002496171,0.000003190652,0.0006483315,0.9381419,0.05625649,0.0001518549,0.00008694945,0.0001967025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6102828,0.0001654671,0.3877605,0.000002240025,0.0001651998,0.00008465473,0.000003351906,0.0002317919,0.001303935],"genre_scores_gemma":[0.9536981,0.000009273236,0.04614582,0.000003844138,0.00006888881,0.000001715959,0.00001617875,0.0000400059,0.00001616646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3434153,"threshold_uncertainty_score":0.6589718,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017067922","doi":"10.1016/j.asoc.2015.03.054","title":"A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Quality and Safety in Healthcare","field":"Health Professions","cited_by":130,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Prioritization; Risk analysis (engineering); Reliability engineering; Reliability (semiconductor); Failure mode, effects, and criticality analysis; Fuzzy logic; Criticality; Failure mode and effects analysis; Engineering; Process management; Business; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1461451858963026,"gpt":0.4669021126963455,"spread":0.3207569268000429,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002587262,0.0001612108,0.0004522622,0.00006332981,0.0006360548,0.000006894562,0.0003163666,0.0005209162,0.00001915846],"category_scores_gemma":[0.002255552,0.0001528664,0.0000697447,0.0002741123,0.0001106759,0.00003001379,0.0001288057,0.0008779208,0.00003455539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174072,"about_ca_system_score_gemma":0.0009545368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002757335,"about_ca_topic_score_gemma":0.00007681523,"domain_scores_codex":[0.9970859,0.0004801688,0.0009322379,0.0003161057,0.0006522759,0.000533328],"domain_scores_gemma":[0.9906774,0.007298421,0.0007398303,0.000298219,0.0007012184,0.0002848805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007454919,0.0001974959,0.07685921,0.006981954,0.00008382734,0.000004040305,0.03303034,0.005491152,0.000019526,0.820388,0.004244844,0.05195409],"study_design_scores_gemma":[0.01270134,0.0005497283,0.0505696,0.00882305,0.0001494287,0.000003710637,0.0872179,0.3111773,0.0001152529,0.442913,0.0844446,0.001335169],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1375795,0.0002610658,0.854034,0.00471466,0.0008096386,0.001507422,0.0000460534,0.0001963514,0.0008512725],"genre_scores_gemma":[0.8995379,0.00001148813,0.09506593,0.004739828,0.0004904396,0.00006417229,0.00005637586,0.00002734489,0.000006483318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7619584,"threshold_uncertainty_score":0.6233709,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3070581385","doi":"10.1016/j.asoc.2020.106630","title":"Cryptocurrency malware hunting: A deep Recurrent Neural Network approach","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University; University of Guelph","funders":"","keywords":"Cryptocurrency; Computer science; Malware; Opcode; Artificial intelligence; Deep learning; Machine learning; Exploit; Recurrent neural network; Perceptron; Artificial neural network; Computer security; Operating system","retraction":null,"screen_n_in":null,"score":{"opus":0.02145561154983816,"gpt":0.2444350806087735,"spread":0.2229794690589353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002938243,0.0003623582,0.0003854583,0.00007479521,0.000449461,0.0002370505,0.001392826,0.0001167716,0.000007530586],"category_scores_gemma":[0.00008769602,0.0003913553,0.0001278973,0.001181782,0.00005709031,0.0002854806,0.001217175,0.0006510546,0.00004703632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005795852,"about_ca_system_score_gemma":0.00003849675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002538342,"about_ca_topic_score_gemma":5.234526e-7,"domain_scores_codex":[0.9971883,0.00006870384,0.0005407798,0.001037288,0.0004051267,0.0007597745],"domain_scores_gemma":[0.9985863,0.0001571283,0.0003301372,0.0005698175,0.00009789993,0.0002587184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001633523,0.00004833928,0.0003625342,0.00009451515,0.00002120828,0.000009834964,0.001610995,0.04796353,0.0002933349,0.03958002,0.001079937,0.9089194],"study_design_scores_gemma":[0.0003272518,0.0001020378,0.0003004232,0.00002513774,0.000008716903,0.00001977924,0.00008252497,0.987406,0.0006859341,0.004607231,0.005869178,0.0005658033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001566999,0.0002059992,0.9898877,0.0004345962,0.0004377341,0.0005513633,6.915213e-7,0.003331569,0.0035834],"genre_scores_gemma":[0.5551688,0.000002501685,0.4432749,0.0009427557,0.000543957,0.00003195893,0.000005456554,0.00002664315,0.000003049173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9394425,"threshold_uncertainty_score":0.9998538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2216812937","doi":"10.1016/j.asoc.2015.11.005","title":"Evolutionary multi-objective optimization algorithms for fuzzy portfolio selection","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":129,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Portfolio; Computer science; Mathematical optimization; Crossover; Portfolio optimization; Cardinality (data modeling); Selection (genetic algorithm); Evolutionary algorithm; Genetic algorithm; Optimization problem; Skewness; Modern portfolio theory; Fuzzy logic; Algorithm; Mathematics; Econometrics; Data mining; Economics; Machine learning; Artificial intelligence; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.103236916506005,"gpt":0.3649082193145247,"spread":0.2616713028085197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002323036,0.0002266984,0.0003248848,0.0003926645,0.000483329,0.0002129584,0.0003515843,0.0001718425,0.000022257],"category_scores_gemma":[0.001038912,0.0002095881,0.0001113772,0.001334169,0.00006324472,0.0003333658,0.0001165646,0.0001549012,0.00008035683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001726906,"about_ca_system_score_gemma":0.0002575124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003671761,"about_ca_topic_score_gemma":0.000005017981,"domain_scores_codex":[0.99714,0.00009287123,0.0007727844,0.0007072639,0.0008885649,0.0003984734],"domain_scores_gemma":[0.9971729,0.0006307,0.000526151,0.0002874216,0.001180086,0.0002027379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000591731,0.00007083754,0.002388838,0.000001424308,0.000017063,6.478755e-7,0.000690858,0.9282689,0.0000250665,0.001516904,0.007314499,0.05964575],"study_design_scores_gemma":[0.00115757,0.0000698534,0.001616796,0.000005606468,0.00002021038,0.00001479745,0.0009119399,0.983228,0.0001143241,0.008305872,0.004293429,0.0002616467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004014346,0.00008960681,0.9872317,0.00007253727,0.0007163415,0.0007361267,0.000009619012,0.0002442138,0.006885502],"genre_scores_gemma":[0.4780825,0.000009208079,0.5207044,0.0001509642,0.0003898953,0.00003641816,0.00008203032,0.0000306238,0.0005139558],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4740682,"threshold_uncertainty_score":0.8546754,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983664186","doi":"10.1016/j.asoc.2011.07.001","title":"Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm","year":2011,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Algorithm; Overfitting; Computer science; Mean squared error; Monte Carlo method; Bayesian probability; Feedforward neural network; Support vector machine; Artificial intelligence; Machine learning; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02906903108889821,"gpt":0.2143056865226848,"spread":0.1852366554337866,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003366958,0.0004946104,0.0004508928,0.00008954418,0.0003904742,0.0001126534,0.0003575767,0.0001546243,0.00001804065],"category_scores_gemma":[0.00001442722,0.0005605287,0.0001358315,0.0002742569,0.00006466285,0.0001574797,0.0001868352,0.0006317188,0.000004115171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001504661,"about_ca_system_score_gemma":0.00001822372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001009687,"about_ca_topic_score_gemma":0.000009403532,"domain_scores_codex":[0.9975302,0.00002916988,0.000601357,0.000511258,0.0002820683,0.001045965],"domain_scores_gemma":[0.9991537,0.0001292083,0.0001087416,0.0003260133,0.00005352157,0.0002288353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007226351,0.00001512493,0.0008962195,0.00003151494,0.00006280101,0.00004441153,0.0007514546,0.5848009,0.000647021,0.00002112779,0.0000923417,0.4126298],"study_design_scores_gemma":[0.000291139,0.00002000561,0.00008848197,0.00008597563,0.00003985118,0.00009530009,0.0001009187,0.9971863,0.001345607,0.00004555979,0.0001034334,0.0005974538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4189955,0.00048155,0.5743665,0.000001604739,0.0006890713,0.0001592802,0.000005446145,0.000827461,0.004473635],"genre_scores_gemma":[0.9671782,0.00000664824,0.03202882,0.0000537597,0.0005223356,0.000006825539,0.00001973235,0.0001666409,0.00001701071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5481828,"threshold_uncertainty_score":0.9996846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2560547527","doi":"10.1016/j.asoc.2016.12.010","title":"Kernel-based learning and feature selection analysis for cancer diagnosis","year":2016,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":110,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Support vector machine; Feature selection; Computer science; Artificial intelligence; Pattern recognition (psychology); Context (archaeology); Kernel (algebra); Binary classification; Machine learning; Data mining; Mathematics; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.009541893351236438,"gpt":0.2705496081411798,"spread":0.2610077147899433,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001058382,0.00008830331,0.00009895299,0.00005425152,0.000151348,0.00001982555,0.00004938933,0.00009214662,0.00001171006],"category_scores_gemma":[0.00003094617,0.0000686396,0.00005709368,0.0001603905,0.00002143271,0.000001271256,0.00002672871,0.00004115954,6.876651e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001669378,"about_ca_system_score_gemma":0.00002956804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004304477,"about_ca_topic_score_gemma":0.00001477722,"domain_scores_codex":[0.9993912,0.00001686648,0.00008690663,0.0003100414,0.00005672252,0.0001382855],"domain_scores_gemma":[0.9996993,0.00004026087,0.00008651584,0.00007732792,0.00005442561,0.00004215179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005799393,0.00001517953,0.1666231,0.00001555788,0.0001293698,3.509908e-8,0.00002622051,0.002379736,0.7004417,0.00006326882,0.001689474,0.1285584],"study_design_scores_gemma":[0.001554962,0.0001062205,0.05479693,0.00003936081,0.0002529725,5.901363e-7,0.00007274946,0.0106166,0.6752532,0.0000681649,0.2568493,0.0003889783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4699633,0.0003531247,0.5288886,0.0004627534,0.00004813381,0.0001526502,0.000004760092,0.00003017184,0.00009653426],"genre_scores_gemma":[0.9960698,0.00006425037,0.003019009,0.0001784908,0.0001599619,0.0001695067,0.00003035956,0.00001301104,0.000295555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5261066,"threshold_uncertainty_score":0.2799042,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1986810629","doi":"10.1016/j.asoc.2007.09.002","title":"Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation","year":2008,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Water resources management and optimization","field":"Engineering","cited_by":109,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Simulated annealing; Genetic algorithm; Computer science; Sensitivity (control systems); Adaptive simulated annealing; Mathematical optimization; Algorithm; Set (abstract data type); Mathematics; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02489014173811523,"gpt":0.2540675419767874,"spread":0.2291774002386722,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001096827,0.0001572502,0.0003521433,0.000263203,0.0001992241,0.0000397565,0.00009685069,0.00006284251,0.000004975693],"category_scores_gemma":[0.000007227691,0.000166937,0.00005584437,0.0004921203,0.00003714764,0.00006495352,0.00004955147,0.00007772959,8.65878e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002469055,"about_ca_system_score_gemma":0.00000437269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002667036,"about_ca_topic_score_gemma":0.000003938854,"domain_scores_codex":[0.9990826,0.00001715276,0.0003604692,0.0002194865,0.0001191592,0.000201085],"domain_scores_gemma":[0.9995605,0.0001254007,0.00008306379,0.0001269984,0.0000625618,0.00004152739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001510438,0.00001026841,0.001009667,0.0000376974,0.000446934,0.000001396614,0.003723628,0.9932655,0.0009447193,0.00002636961,0.00002090967,0.000497781],"study_design_scores_gemma":[0.000520203,0.00002471481,0.003062539,0.00001089507,0.0002192734,5.686367e-7,0.000120354,0.9944585,0.001337675,0.00001066836,0.00006205359,0.0001725684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6058,0.00006911215,0.3936023,0.000002763556,0.00001934377,0.0002434866,0.000003166844,0.0001196893,0.0001401332],"genre_scores_gemma":[0.9556931,0.000009285571,0.04407416,0.00001239366,0.00003398604,0.000003581316,0.0001444505,0.00001875518,0.00001029781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3498931,"threshold_uncertainty_score":0.6807494,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999994061","doi":"10.1016/j.asoc.2011.04.005","title":"Design of interval type-2 fuzzy models through optimal granularity allocation","year":2011,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":108,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Granularity; Fuzzy logic; Computer science; Interval (graph theory); Data mining; Mathematical optimization; Membership function; Fuzzy set; Defuzzification; Fuzzy classification; Type (biology); Fuzzy number; Process (computing); Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.06838382500961226,"gpt":0.2398426756685281,"spread":0.1714588506589159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000600314,0.000164541,0.000292758,0.0000433476,0.0001169354,0.0000426812,0.0008593712,0.00009418818,0.00000173649],"category_scores_gemma":[0.00001328311,0.0001542152,0.00005888314,0.0002728508,0.00005757587,0.000261654,0.0002943271,0.0001435024,0.00002687108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002792339,"about_ca_system_score_gemma":0.00006053925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001179172,"about_ca_topic_score_gemma":7.520096e-7,"domain_scores_codex":[0.9985822,0.00008498584,0.000405688,0.0003883058,0.000244271,0.0002945315],"domain_scores_gemma":[0.9990209,0.0001207436,0.0002194679,0.0004620424,0.0001267071,0.00005011414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006039733,0.0001332061,0.00005997002,0.00003960003,0.00005569922,0.000004783451,0.01037992,0.0776822,0.00308695,0.8881375,0.0001111588,0.02024864],"study_design_scores_gemma":[0.0005224534,0.0001274301,0.0001659707,0.00003243925,0.00001411691,0.0000102367,0.000166134,0.8224038,0.001914238,0.1743762,0.00001670605,0.0002502392],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007536143,0.0001050712,0.9666069,0.00002545081,0.0002627937,0.0002963309,2.585692e-7,0.0001990797,0.02496798],"genre_scores_gemma":[0.7101198,0.000001672915,0.2897114,0.0001054589,0.00003890534,0.000005601713,0.000001161277,0.000007860355,0.000008149032],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7447216,"threshold_uncertainty_score":0.6288716,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2396310064","doi":"10.1016/j.asoc.2016.04.038","title":"Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation","year":2016,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":108,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"Data envelopment analysis; Computer science; Fuzzy logic; Supplier evaluation; Mathematical optimization; Fuzzy set; Operations research; Supply chain management; Supply chain; Mathematics; Artificial intelligence; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.1122171586024204,"gpt":0.4228131959430572,"spread":0.3105960373406368,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009139538,0.0003371412,0.000449264,0.0006013391,0.0005918735,0.000474978,0.00125437,0.0001818061,0.0001314942],"category_scores_gemma":[0.003273207,0.0002455875,0.00008783188,0.001451469,0.00007743653,0.0005145709,0.0007366405,0.0001687778,0.001312113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004349844,"about_ca_system_score_gemma":0.0002600616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003829901,"about_ca_topic_score_gemma":0.00003120665,"domain_scores_codex":[0.9940075,0.0002775256,0.001044728,0.00155168,0.002327999,0.0007905771],"domain_scores_gemma":[0.9938569,0.001824314,0.0004408855,0.001454862,0.002091726,0.0003312886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000167122,0.0001451247,0.0008825315,0.000005421859,0.00001579059,0.000002550862,0.00283816,0.1456755,0.05193742,0.02012482,0.001301505,0.7769041],"study_design_scores_gemma":[0.001343779,0.00005306035,0.003769251,0.00001989924,0.00002173124,0.000004142181,0.001966855,0.9115598,0.002161566,0.07593384,0.002694763,0.0004713011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.247015,0.00001325945,0.7483252,0.0002033317,0.0001707233,0.001329546,0.000005560066,0.000204743,0.002732662],"genre_scores_gemma":[0.88895,4.381634e-7,0.1096042,0.0004725632,0.0001724627,0.0001010394,0.000009339765,0.00005366533,0.0006362589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7764328,"threshold_uncertainty_score":0.9999996,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803519183","doi":"10.1016/j.asoc.2018.05.013","title":"Ensemble selector for attribute reduction","year":2018,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Rough set; Computer science; Reduction (mathematics); Viewpoints; Heuristic; Set (abstract data type); Artificial intelligence; Support vector machine; Ensemble learning; Data mining; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02336956307305835,"gpt":0.2565540896092258,"spread":0.2331845265361674,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003546939,0.0001349014,0.0001597577,0.00005580103,0.0004698437,0.0001545406,0.0005000658,0.00006770734,0.000003213314],"category_scores_gemma":[0.00002282199,0.0001276665,0.00005411947,0.0003498305,0.00004892222,0.0001037075,0.0001925322,0.00008970661,0.00009004496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004012306,"about_ca_system_score_gemma":0.00004395053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005986963,"about_ca_topic_score_gemma":0.000001147673,"domain_scores_codex":[0.9987508,0.00001479264,0.0002123501,0.0004533353,0.0001468194,0.0004218986],"domain_scores_gemma":[0.999245,0.0001112699,0.0001065193,0.0003496237,0.0001177111,0.00006983453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003428846,0.0001229925,0.0001418739,0.0000478081,0.0000575641,0.000002268668,0.002767844,0.0007949837,0.02464447,0.2250099,0.02461002,0.721766],"study_design_scores_gemma":[0.002577501,0.0009572777,0.002296504,0.00005660264,0.0000394908,0.000101985,0.0002219382,0.7557436,0.06653634,0.08271378,0.08718753,0.001567427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02882429,0.00002326167,0.9653103,0.000299988,0.000708493,0.0003199938,9.578115e-7,0.0003960049,0.00411676],"genre_scores_gemma":[0.7373593,5.53428e-7,0.2616348,0.0002557262,0.000692654,0.0000109315,0.000004408703,0.00000973016,0.00003188139],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7549486,"threshold_uncertainty_score":0.520609,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2112255588","doi":"10.1016/j.asoc.2009.08.011","title":"An informed genetic algorithm for the examination timetabling problem","year":2009,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":96,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Heuristics; Genetic algorithm; Domain (mathematical analysis); Process (computing); Set (abstract data type); Constraint (computer-aided design); Range (aeronautics); Evolutionary algorithm; Quality (philosophy); Mathematical optimization; Algorithm; Artificial intelligence; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06682508495774124,"gpt":0.3649826455882064,"spread":0.2981575606304651,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004479494,0.0001672454,0.0002341699,0.0002198095,0.001096803,0.0005952322,0.0008263479,0.00008534907,0.00001745368],"category_scores_gemma":[0.0007445361,0.0001143796,0.0001023186,0.0009450496,0.0000614618,0.0001771263,0.00005203467,0.0001764438,0.00008943262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003472316,"about_ca_system_score_gemma":0.0001119368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009853497,"about_ca_topic_score_gemma":0.000002558463,"domain_scores_codex":[0.9976247,0.00006827168,0.0006521283,0.0004621822,0.0007336884,0.0004590379],"domain_scores_gemma":[0.9950691,0.003634041,0.0003002525,0.0005706441,0.0003229044,0.0001030609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000380048,0.00003869501,0.00002143326,0.000001059911,0.0000104597,1.977699e-7,0.0006348235,0.04576768,0.0004605417,0.002457916,0.0001429649,0.9504604],"study_design_scores_gemma":[0.0004379926,0.00008651848,0.01574749,0.000008254669,0.00003459481,0.000006219914,0.0005962658,0.9553062,0.000261093,0.02442481,0.002909826,0.0001806896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02640713,0.0001382668,0.9706741,0.0002605118,0.0002055509,0.0005572266,0.00000332729,0.0001907539,0.001563149],"genre_scores_gemma":[0.5911811,0.00000131208,0.4081728,0.000317121,0.0002261892,0.00001285438,0.000007094343,0.000008530847,0.0000730495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9502797,"threshold_uncertainty_score":0.843583,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3131945045","doi":"10.1016/j.asoc.2021.107210","title":"Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":89,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Pandemic; Heuristics; Computer science; Internet of Things; Supply chain; Set (abstract data type); Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Operations research; Health care; Risk analysis (engineering); Computer security; Business; Medicine; Engineering; Economics; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.06756151905247089,"gpt":0.2733710713372726,"spread":0.2058095522848017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001464823,0.0002311832,0.0002399012,0.00007953402,0.0007753809,0.0002918107,0.0003920061,0.00006213212,0.00003698102],"category_scores_gemma":[0.0002008085,0.0002068754,0.0001079493,0.0008102906,0.00002886112,0.00008101076,0.0004342168,0.0001542234,0.0002673605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004488228,"about_ca_system_score_gemma":0.00003471661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002041942,"about_ca_topic_score_gemma":0.0002034354,"domain_scores_codex":[0.9982052,0.00001797774,0.0004357043,0.0005091439,0.0002493557,0.0005826273],"domain_scores_gemma":[0.9990008,0.0002987576,0.00009584749,0.0004290274,0.0001606635,0.00001487602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001405543,0.0001307,0.001635461,0.0003874144,0.0001863309,0.000006603932,0.0006576277,0.5379872,0.007278739,0.1252839,0.1532143,0.1730912],"study_design_scores_gemma":[0.0007071994,0.000009756846,0.001925033,0.00008364285,0.0001011061,0.000001612607,0.001147283,0.6263968,0.0005213547,0.004858212,0.3637437,0.0005043314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03973239,0.0002565613,0.9491688,0.003891718,0.001051308,0.001522869,0.000001482204,0.0004393934,0.003935467],"genre_scores_gemma":[0.9617052,0.000008391534,0.01733806,0.0191903,0.001394197,0.0001208378,0.00003093276,0.00004215806,0.0001699372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9318308,"threshold_uncertainty_score":0.8436133,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4323543432","doi":"10.1016/j.asoc.2023.110170","title":"Evaluation of agriculture-food 4.0 supply chain approaches using Fermatean probabilistic hesitant-fuzzy sets based decision making model","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Food Waste Reduction and Sustainability","field":"Agricultural and Biological Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Imperial College London","keywords":"Probabilistic logic; Computer science; Supply chain; Fuzzy logic; Agriculture; Group decision-making; Decision-making models; Operations research; Artificial intelligence; Mathematics; Business; Marketing","retraction":null,"screen_n_in":null,"score":{"opus":0.1031200578474457,"gpt":0.2843489368479948,"spread":0.1812288790005491,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002664233,0.0002132902,0.0002842527,0.00004774461,0.0003915144,0.00006436996,0.000233661,0.000135215,0.000009784233],"category_scores_gemma":[0.0002648892,0.0001000431,0.0001161669,0.0009874899,0.00006880953,0.00006819434,0.0001379774,0.0001447776,0.000003646718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001874506,"about_ca_system_score_gemma":0.00006885729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000133841,"about_ca_topic_score_gemma":0.00002738929,"domain_scores_codex":[0.9976091,0.0001931366,0.0004605437,0.0004883633,0.0008515036,0.0003973413],"domain_scores_gemma":[0.9989053,0.0003777173,0.0002237423,0.0001139891,0.0003056014,0.00007365887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003662769,0.00009962107,0.0002992379,0.00005998028,0.00001667585,4.375274e-7,0.0003640943,0.6920918,0.02905388,0.0008735353,0.00002764022,0.2770765],"study_design_scores_gemma":[0.0002388034,0.00005721271,0.009363497,0.00006854464,0.00005200044,0.000003108589,0.005057584,0.9693435,0.000924104,0.01467028,0.000003735958,0.000217661],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964802,0.00003747519,0.001848837,0.0001303018,0.00007389788,0.0008339015,0.00001618328,0.000210692,0.0003684685],"genre_scores_gemma":[0.9961202,4.965614e-7,0.003694411,0.00002691234,0.00007320913,0.00001836228,0.00006130081,0.000003164839,0.000001975094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2772517,"threshold_uncertainty_score":0.4079639,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4323545679","doi":"10.1016/j.asoc.2023.110173","title":"A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":83,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of New Brunswick","funders":"","keywords":"Adversarial system; Computer science; Artificial intelligence; Intrusion detection system; Deep learning; Machine learning; Transferability; Artificial neural network; Deep neural networks; Jacobian matrix and determinant; Data mining; Pattern recognition (psychology); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02341448391837548,"gpt":0.2629387199650703,"spread":0.2395242360466949,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001933376,0.0004086408,0.0004597848,0.0003379435,0.001432532,0.000317894,0.000937932,0.0002426166,0.000001578092],"category_scores_gemma":[0.0003266674,0.0004302438,0.0001859725,0.001620782,0.0000638147,0.0001197828,0.0003617971,0.0007429795,0.00005977755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000230841,"about_ca_system_score_gemma":0.00008718672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002345994,"about_ca_topic_score_gemma":0.000002808688,"domain_scores_codex":[0.9965039,0.0002770372,0.0005394287,0.001098246,0.000602365,0.0009790027],"domain_scores_gemma":[0.9969401,0.00172768,0.0004645327,0.0006005699,0.0001116409,0.0001555008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001372753,0.00004289727,0.0001542252,0.00009924088,0.0000240784,0.000004105634,0.0002355633,0.9517528,0.0001280719,0.004578278,0.000128188,0.04271523],"study_design_scores_gemma":[0.00197471,0.000218941,0.000177059,0.00006274952,0.00002180959,0.000002104643,0.00009926003,0.9950112,0.000123597,0.0001670225,0.001693182,0.0004483671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009173555,0.00001769229,0.9848363,0.00007208601,0.002015425,0.00111953,7.870037e-7,0.002211957,0.0005526207],"genre_scores_gemma":[0.8989295,3.427276e-7,0.0994852,0.0001998289,0.001099203,0.0001171227,0.0000758678,0.00007000309,0.00002288547],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.889756,"threshold_uncertainty_score":0.9998674,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3190908459","doi":"10.1016/j.asoc.2021.107739","title":"Predicting load capacity of shear walls using SVR–RSM model","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Seismic Performance and Analysis","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"University of Zabol; Iran National Science Foundation","keywords":"Computer science; Response surface methodology; Support vector machine; Eurocode; Empirical modelling; Mean squared error; Predictive modelling; Empirical research; Machine learning; Data mining; Structural engineering; Mathematics; Engineering; Simulation; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.01922492211949597,"gpt":0.2160298115956424,"spread":0.1968048894761464,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002070014,0.0001535709,0.0003039612,0.00005750767,0.0001134744,0.00002427345,0.0001220382,0.00008100727,0.00001450795],"category_scores_gemma":[0.00001837112,0.0001757141,0.0000949383,0.0003233168,0.00002832444,0.00007126485,0.0001052276,0.0002161749,0.000007608192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009986542,"about_ca_system_score_gemma":0.00006068507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004389683,"about_ca_topic_score_gemma":0.000004147625,"domain_scores_codex":[0.9988879,0.000007588003,0.0003522563,0.0002071289,0.0002339438,0.0003111835],"domain_scores_gemma":[0.9995148,0.00005540606,0.00006521764,0.0002322121,0.00007598635,0.00005640955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001175299,0.00001100459,0.001072652,0.0001098531,0.0000705621,0.000001957346,0.0009364517,0.9165486,0.07598285,0.00009921624,0.00001922656,0.005146435],"study_design_scores_gemma":[0.0001778763,0.000002017341,0.0001516819,0.00005827728,0.00005795044,0.000006177792,0.000225671,0.9665557,0.03233315,0.0002481009,0.00002216301,0.0001612504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7901538,0.00008275729,0.206176,0.000004692803,0.00006965739,0.00003672458,0.000002583171,0.0001736951,0.003300081],"genre_scores_gemma":[0.9680285,0.0000107127,0.03172332,0.00007740989,0.0001131048,0.000001032864,0.000006257124,0.0000286353,0.00001105347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1778747,"threshold_uncertainty_score":0.7165413,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2793283161","doi":"10.1016/j.asoc.2018.01.041","title":"SCGOSR: Surrogate-based constrained global optimization using space reduction","year":2018,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Kriging; Mathematical optimization; Global optimization; Surrogate model; Computer science; Local optimum; Reduction (mathematics); Optimization problem; Local search (optimization); Constraint (computer-aided design); Constrained optimization; Penalty method; Algorithm; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.01656813940173784,"gpt":0.2762662904990873,"spread":0.2596981510973495,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003469931,0.0002766804,0.0002478105,0.0001292708,0.0006285922,0.0002212089,0.0004873622,0.0001175377,0.0000170768],"category_scores_gemma":[0.00008503774,0.0003192576,0.00005889836,0.001335067,0.0002802391,0.0003640929,0.0002345492,0.0001469277,0.00003303677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003587558,"about_ca_system_score_gemma":0.0002434765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002009129,"about_ca_topic_score_gemma":0.000001580364,"domain_scores_codex":[0.9979444,0.00007454513,0.000380332,0.0007616379,0.0003500853,0.0004889782],"domain_scores_gemma":[0.9984927,0.00009791187,0.0003386135,0.000484923,0.00044581,0.0001400259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001282692,0.00004795631,0.00008792748,0.000006813272,0.00001334212,0.000002687738,0.0001925735,0.9724853,0.001049331,0.01122797,0.00001806278,0.01485523],"study_design_scores_gemma":[0.0009511756,0.00004166089,0.00006402004,0.00002889704,0.000009774984,0.00003724219,0.00008830415,0.9945865,0.003195615,0.0006174925,0.00005075119,0.0003285945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003782803,0.00001371402,0.99178,0.0001453353,0.0007374891,0.0003981888,0.000003222247,0.0007967112,0.002342506],"genre_scores_gemma":[0.4475698,3.592665e-7,0.5521181,0.0001161045,0.0001641283,0.000002614531,0.000008167111,0.00001465664,0.000006097473],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.443787,"threshold_uncertainty_score":0.999926,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389080370","doi":"10.1016/j.asoc.2023.111107","title":"Input-parameter optimization using a SVR based ensemble model to predict landslide displacements in a reservoir area – A comparative study","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Key Scientific Instrument and Equipment Development Projects of China; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Landslide; Support vector machine; Computer science; Hilbert–Huang transform; Displacement (psychology); Ensemble forecasting; Particle swarm optimization; Algorithm; Artificial intelligence; Data mining; Machine learning; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.04423271274189602,"gpt":0.2919263229906631,"spread":0.247693610248767,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005517788,0.0002091611,0.0002936206,0.0001485848,0.0002319143,0.00006921635,0.0002332349,0.00008132076,0.0000422152],"category_scores_gemma":[0.0000299144,0.000181678,0.00003992516,0.001060306,0.00003276091,0.00008224894,0.000455394,0.0002054328,0.00008369892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002160147,"about_ca_system_score_gemma":0.00002517574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001506845,"about_ca_topic_score_gemma":0.0000809249,"domain_scores_codex":[0.9981671,0.00006397157,0.0003699549,0.0005198823,0.000412372,0.0004667155],"domain_scores_gemma":[0.9993372,0.0001690047,0.0001002042,0.0002611864,0.00001091571,0.0001214962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001090634,0.0001577796,0.04880451,0.000006960201,0.00001842603,0.0000112877,0.004906934,0.9447017,0.0006582986,0.000002501883,0.0002448777,0.0003776617],"study_design_scores_gemma":[0.001341366,0.00007767759,0.004804715,0.0000486458,0.0000235593,8.224715e-7,0.0008532741,0.9924549,0.00008259516,0.00006858731,0.00002210968,0.0002217821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7121133,0.000001440855,0.2858334,0.00002686765,0.00003928125,0.0007391309,0.000003448763,0.0001004636,0.001142692],"genre_scores_gemma":[0.965093,4.785293e-7,0.03457109,0.000172836,0.00001488193,0.00003779077,0.00002729921,0.00002419605,0.00005844435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2529797,"threshold_uncertainty_score":0.7408612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3164710970","doi":"10.1016/j.asoc.2021.107522","title":"A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"All-India Institute of Medical Sciences","keywords":"Convolutional neural network; Computer science; Deep learning; Transfer of learning; Artificial intelligence; Multilayer perceptron; Coronavirus disease 2019 (COVID-19); Software deployment; Breathing; Artificial neural network; Machine learning; Triage; Pattern recognition (psychology); Medicine; Pathology; Medical emergency","retraction":null,"screen_n_in":null,"score":{"opus":0.02987655868512174,"gpt":0.3133184787993515,"spread":0.2834419201142298,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001108756,0.0004005326,0.0007576377,0.0002222312,0.001031866,0.0002595038,0.0001340774,0.0002141001,0.00001689167],"category_scores_gemma":[0.002221516,0.000445244,0.0001889264,0.0004522337,0.0001237622,0.00007468252,0.0002373115,0.0004554112,0.0000051327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006820231,"about_ca_system_score_gemma":0.0006305004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002409162,"about_ca_topic_score_gemma":0.000009475903,"domain_scores_codex":[0.997257,0.0001241056,0.0005786809,0.0009856026,0.0003834194,0.0006711839],"domain_scores_gemma":[0.9931067,0.005509865,0.0003017813,0.0003990669,0.0002242727,0.0004582844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004087439,0.0007939742,0.1188837,0.01731944,0.0005929678,0.00102875,0.008957583,0.728637,0.05428945,0.0005490432,0.0008527843,0.0676866],"study_design_scores_gemma":[0.005581852,0.00008865781,0.003221372,0.001149639,0.0005547081,0.0002034248,0.003368092,0.9723997,0.006469848,0.00002957733,0.006322125,0.000610982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2327681,0.0007417633,0.7608418,0.003526767,0.0002224059,0.001022774,0.000008892445,0.0007850204,0.00008248768],"genre_scores_gemma":[0.8523623,0.000007250072,0.1389338,0.008068024,0.0003564917,0.00009764469,0.00005704709,0.000105473,0.00001188561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6219079,"threshold_uncertainty_score":0.9997999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2084083808","doi":"10.1016/j.asoc.2014.01.013","title":"Online identification of evolving Takagi–Sugeno–Kang fuzzy models for crane systems","year":2014,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Óbudai Egyetem; Autoritatea Natională pentru Cercetare Stiintifică","keywords":"Identification (biology); Computer science; Automatic summarization; Fuzzy logic; Data mining; Fuzzy rule; Set (abstract data type); Neuro-fuzzy; Fuzzy control system; Basis (linear algebra); Artificial intelligence; Machine learning; Ranking (information retrieval); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01765602491317936,"gpt":0.2304560867896465,"spread":0.2128000618764671,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001438744,0.0002085083,0.0004646641,0.0001174231,0.0002476207,0.0001958399,0.000981058,0.0001095107,2.960853e-7],"category_scores_gemma":[0.00008672886,0.0002020122,0.0001121867,0.0002912309,0.00003833523,0.0002155783,0.0002007361,0.000115963,0.00001053743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005205477,"about_ca_system_score_gemma":0.00004625327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000586214,"about_ca_topic_score_gemma":0.000002595015,"domain_scores_codex":[0.9977215,0.00007698552,0.0008556046,0.0005643212,0.0003686495,0.0004129525],"domain_scores_gemma":[0.9977608,0.0005874408,0.0006040737,0.0006925001,0.0002673939,0.00008784296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001215109,0.0001240605,0.00005923204,0.0003946533,0.0000570035,6.03386e-7,0.001074636,0.2065619,0.02199014,0.7248513,0.0003428602,0.04453153],"study_design_scores_gemma":[0.0005987066,0.00003773778,0.0002216783,0.00006419302,0.00001508469,0.000004816124,0.0001382045,0.9664073,0.0002418968,0.03191834,0.0001412419,0.0002108084],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0111747,0.0004171192,0.9812021,0.00008890683,0.0006706847,0.0007078726,0.00000614919,0.0002788077,0.005453672],"genre_scores_gemma":[0.9704698,0.000001743957,0.02891846,0.00009557026,0.0003695838,0.00004037999,0.0000164135,0.00002193284,0.00006613254],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9592951,"threshold_uncertainty_score":0.8237818,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2980588314","doi":"10.1016/j.asoc.2019.105830","title":"Web service API recommendation for automated mashup creation using multi-objective evolutionary search","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada; United Arab Emirates University","keywords":"Mashup; Computer science; Web service; World Wide Web; Service (business); Software; Web application; Web modeling; Software engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02060192056652095,"gpt":0.2862690552023032,"spread":0.2656671346357822,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005183002,0.0002777532,0.0002805234,0.0002650176,0.0005028168,0.0001800685,0.0007271801,0.0001421302,0.00001680383],"category_scores_gemma":[0.000008028206,0.0002909557,0.00007626323,0.001067053,0.00001752391,0.0003925917,0.0005092336,0.0002368222,0.0001179821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001562274,"about_ca_system_score_gemma":0.0001840441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002270144,"about_ca_topic_score_gemma":0.00005067743,"domain_scores_codex":[0.9978863,0.00009796376,0.0004077778,0.0007959682,0.0002610129,0.0005509557],"domain_scores_gemma":[0.9983081,0.0005246689,0.0002295499,0.0004692832,0.0003666696,0.0001017544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003565919,0.0008455691,0.01122866,0.001513906,0.0005563239,0.000005233298,0.0358203,0.5752547,0.1701233,0.04665728,0.0003624601,0.1572756],"study_design_scores_gemma":[0.0013177,0.00004681078,0.003578013,0.00006164408,0.00001911758,0.00001123304,0.0006229757,0.9910745,0.001597861,0.0005449919,0.0007939477,0.0003311832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3010404,0.00002912304,0.6946709,0.0005190159,0.0004815599,0.001033192,0.000009157021,0.001161224,0.001055451],"genre_scores_gemma":[0.7152995,8.814435e-7,0.2828307,0.001569353,0.0001209951,0.00001875804,0.0001183789,0.00002833205,0.00001307261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4158198,"threshold_uncertainty_score":0.9999543,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2809158771","doi":"10.1016/j.asoc.2018.06.019","title":"A recursive PSO scheme for gene selection in microarray data","year":2018,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":62,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Computer science; Particle swarm optimization; Benchmark (surveying); Feature selection; Gene selection; Ranking (information retrieval); Generalization; Selection (genetic algorithm); Data mining; Support vector machine; Artificial intelligence; Microarray analysis techniques; Machine learning; Gene; Mathematics; Biology; Genetics","retraction":null,"screen_n_in":null,"score":{"opus":0.03126037132482766,"gpt":0.3084914535260862,"spread":0.2772310822012585,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002320071,0.00009191214,0.0000840521,0.00004091578,0.0001111543,0.00001961899,0.000250937,0.00009951444,0.000004752621],"category_scores_gemma":[0.0000451588,0.00009836246,0.00001970805,0.0001283914,0.00003885911,0.000002448182,0.0001444144,0.00005644983,0.000007793178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001792682,"about_ca_system_score_gemma":0.00006412349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005898712,"about_ca_topic_score_gemma":0.00002104176,"domain_scores_codex":[0.9991121,0.00001570588,0.0001612384,0.0004632153,0.00005757876,0.0001901483],"domain_scores_gemma":[0.9995001,0.00001231569,0.00008694108,0.0002979148,0.0000688509,0.00003389396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007026453,0.00002386652,0.0003619826,0.00000749312,0.000009943662,4.455519e-8,0.00007981277,0.00002593271,0.9798959,0.0001220712,0.006669871,0.01273285],"study_design_scores_gemma":[0.0008784183,0.00009457827,0.001278494,0.00001631328,0.000007007558,0.000003767336,0.0001617172,0.00626521,0.8999355,0.0002376955,0.09091602,0.0002052286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6289112,0.0001584853,0.3692908,0.0001364639,0.0001836269,0.0003901031,0.00001102845,0.00002624069,0.0008920173],"genre_scores_gemma":[0.9495817,0.000008305875,0.04906261,0.0003361109,0.0006026899,0.00002112943,0.0002837274,0.00001705237,0.00008664303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3206705,"threshold_uncertainty_score":0.4011104,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2984747650","doi":"10.1016/j.asoc.2019.105930","title":"Estimating incomplete information in group decision making: A framework of granular computing","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":59,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"King Abdulaziz University","keywords":"Granular computing; Granularity; Consistency (knowledge bases); Preference; Missing data; Fuzzy logic; Computer science; Complete information; Group decision-making; Data mining; Flexibility (engineering); Mathematics; Artificial intelligence; Machine learning; Rough set; Statistics; Mathematical economics; Social psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.04415738569989185,"gpt":0.3668752219627582,"spread":0.3227178362628664,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.008980496,0.0004068137,0.001044789,0.001339249,0.0002825324,0.0006132287,0.001718643,0.0002776079,0.0001983903],"category_scores_gemma":[0.006162666,0.0003746374,0.0002025868,0.002507191,0.00009917937,0.0007250799,0.001519831,0.000696215,0.0006098695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299125,"about_ca_system_score_gemma":0.00006368761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002933447,"about_ca_topic_score_gemma":0.000005954749,"domain_scores_codex":[0.9921741,0.0002476033,0.003344027,0.000858305,0.002656722,0.0007192711],"domain_scores_gemma":[0.9824013,0.01402952,0.001830729,0.00124051,0.0003821774,0.0001158395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001278373,0.00005976227,0.03960874,0.00005857336,0.000009653761,0.00000547191,0.004505663,0.2715844,0.0009608956,0.01050639,0.00009685329,0.6724757],"study_design_scores_gemma":[0.0009766321,0.00003873564,0.03626781,0.0008528344,0.000005631835,0.00001231319,0.001076336,0.8863665,0.00004536724,0.07352841,0.0004609718,0.0003684417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4862433,0.00001898439,0.5117832,0.00001775027,0.0006520199,0.0004171972,0.000003244527,0.00007191719,0.0007923528],"genre_scores_gemma":[0.576043,1.709538e-7,0.423562,0.0002950936,0.00007008102,0.000002243778,0.000005932638,0.00002031984,0.000001151493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6721073,"threshold_uncertainty_score":0.9998705,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3201468878","doi":"10.1016/j.asoc.2021.107902","title":"Systems failure analysis using Z-number theory-based combined compromise solution and full consistency method","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":57,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Failure mode and effects analysis; Computer science; Ranking (information retrieval); Consistency (knowledge bases); Reliability (semiconductor); Prioritization; Reliability engineering; Process (computing); Compromise; Risk analysis (engineering); Automotive industry; Machine learning; Engineering; Management science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.09582894413593868,"gpt":0.3980041777284429,"spread":0.3021752335925042,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007858264,0.0003864579,0.001181609,0.0005309511,0.000870839,0.001305306,0.0005807204,0.0002176251,0.0007984905],"category_scores_gemma":[0.002467765,0.00034682,0.0003191714,0.003014386,0.0001782865,0.0001501217,0.0006349404,0.0003316626,0.0001008928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009897207,"about_ca_system_score_gemma":0.0001909028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000293368,"about_ca_topic_score_gemma":0.00003539845,"domain_scores_codex":[0.9934853,0.001440836,0.001565249,0.001395637,0.001541564,0.0005714459],"domain_scores_gemma":[0.9853308,0.01163818,0.0008563819,0.00114337,0.00078986,0.000241371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006962006,0.0005633614,0.01697371,0.0002033076,0.001893099,0.000312243,0.004058962,0.6215109,0.1426114,0.08040926,0.003171603,0.1275959],"study_design_scores_gemma":[0.001219876,0.00001232341,0.001045559,0.00006781367,0.000357706,0.0000578438,0.002143863,0.9869528,0.000336012,0.006380272,0.001006937,0.0004189996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2094808,0.0001056051,0.7888528,0.00008896917,0.0003018156,0.0002918205,0.0000391551,0.0001247917,0.0007141966],"genre_scores_gemma":[0.6746794,2.705239e-7,0.324921,0.0002025098,0.00006449676,0.000006295888,0.00003494457,0.00002439436,0.00006663422],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4651986,"threshold_uncertainty_score":0.9998984,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2768896610","doi":"10.1016/j.asoc.2017.11.024","title":"Parallel deep solutions for image retrieval from imbalanced medical imaging archives","year":2017,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Semantic gap; Computer science; Deep learning; Convolutional neural network; Artificial intelligence; Image retrieval; Scheme (mathematics); Task (project management); Key (lock); Bridge (graph theory); Feature (linguistics); Similarity (geometry); Semantics (computer science); Machine learning; Pattern recognition (psychology); Information retrieval; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01950741243215443,"gpt":0.2824617520525718,"spread":0.2629543396204174,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.000605015,0.0002091482,0.0002764045,0.00006774467,0.00181034,0.0006834487,0.002307916,0.00008510655,0.00001126177],"category_scores_gemma":[0.0004569724,0.0002048106,0.0001346621,0.0000938932,0.0003134907,0.0003240074,0.001058341,0.0002754396,0.00002948088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000328145,"about_ca_system_score_gemma":0.0001217684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001741705,"about_ca_topic_score_gemma":0.000002031846,"domain_scores_codex":[0.9979057,0.0000348953,0.000394736,0.0006394329,0.000411204,0.0006140162],"domain_scores_gemma":[0.9976715,0.0006363618,0.0003599659,0.001066978,0.00008129693,0.0001838979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007981137,0.0001563652,0.0004651744,0.00004401481,0.0000653327,0.00002425367,0.00124478,0.0000180941,0.07283768,0.2260024,0.0006903136,0.6983718],"study_design_scores_gemma":[0.0009459861,0.0000148946,0.004719633,0.00005479133,0.0000129715,0.00001000183,0.00007333074,0.9031978,0.01323993,0.0761019,0.001246772,0.0003819469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008643009,0.0001267238,0.9913878,0.002784797,0.0002619461,0.0003517072,0.000005950222,0.0006742832,0.00354252],"genre_scores_gemma":[0.6067731,0.00001399329,0.3925271,0.0003497454,0.000251786,0.00001720254,0.0000156474,0.00001654682,0.00003493273],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9031798,"threshold_uncertainty_score":0.9994892,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3109504448","doi":"10.1016/j.asoc.2020.106908","title":"WhoReview: A multi-objective search-based approach for code reviewers recommendation in modern code review","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Software Engineering Research","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Code review; Computer science; Context (archaeology); Code (set theory); Software development; Set (abstract data type); Software; Workload; Software quality; Source code; Software evolution; Software engineering; Quality (philosophy); Empirical research; Software construction; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.07410835405611745,"gpt":0.3230320341263064,"spread":0.2489236800701889,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00251774,0.0003158927,0.0006906428,0.0001426414,0.0001534078,0.0001148478,0.001185587,0.00008841966,0.000004056231],"category_scores_gemma":[0.001631039,0.0003256485,0.0001528068,0.001319727,0.00004757061,0.0001651029,0.0004253286,0.0005542443,0.00002344163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002141265,"about_ca_system_score_gemma":0.0001943034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007630066,"about_ca_topic_score_gemma":0.000001875499,"domain_scores_codex":[0.9969679,0.0001970677,0.0006820285,0.001051919,0.0004267592,0.0006743241],"domain_scores_gemma":[0.997376,0.001462405,0.0001698351,0.0005570416,0.0002042121,0.0002305014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003183594,0.0002100616,0.0006535025,0.0112828,0.00004985274,0.000005162351,0.001751319,0.1048679,0.0003212309,0.001191205,0.003240383,0.8763947],"study_design_scores_gemma":[0.0008913802,0.00004972361,0.0001726404,0.0009316406,0.00001150052,0.000001844819,0.00002387227,0.9931725,0.0001644143,0.00005233821,0.004191293,0.0003368394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001413498,0.006852846,0.9873299,0.002181211,0.00006554961,0.002825469,0.00001115777,0.0004810205,0.0001114994],"genre_scores_gemma":[0.1732118,0.0009898741,0.8196368,0.005478323,0.0001013349,0.0004033736,0.0001040412,0.00006918581,0.000005258288],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8883046,"threshold_uncertainty_score":0.9999195,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2050013525","doi":"10.1016/j.asoc.2006.06.002","title":"Buffer allocation and performance modeling in asynchronous assembly system operations: An artificial neural network metamodeling approach","year":2006,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metamodeling; Computer science; Artificial neural network; Asynchronous communication; Context (archaeology); Domain (mathematical analysis); Artificial intelligence; Systems engineering; Distributed computing; Engineering; Software engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01347537931249474,"gpt":0.2003372814042379,"spread":0.1868619020917432,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005597018,0.000235656,0.0002815909,0.0001137012,0.0002890183,0.0001590175,0.0001166949,0.0001202423,3.116865e-7],"category_scores_gemma":[0.000005675495,0.0002712307,0.00002145977,0.0003027491,0.00001219966,0.0003557005,0.00004768678,0.0002333619,0.000003114304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001530827,"about_ca_system_score_gemma":0.00002181009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009156213,"about_ca_topic_score_gemma":0.00005604493,"domain_scores_codex":[0.9984433,0.00003546282,0.0005639653,0.0003525849,0.0001679794,0.0004367144],"domain_scores_gemma":[0.9996188,0.00003752359,0.0000457614,0.0001964879,0.00005423519,0.00004718006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006215963,0.00001552835,0.0003063682,0.0001052878,0.000007307562,4.615989e-7,0.0002343238,0.9881595,0.00131944,0.002290618,0.000001533963,0.007553407],"study_design_scores_gemma":[0.0002066296,0.00001138471,0.0003906505,0.00004909057,0.00001750819,0.000008554468,0.0002448344,0.9986308,0.0001340992,0.0000315284,5.322794e-7,0.0002743798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4942102,0.0001040208,0.5047265,0.000002864345,0.0000752132,0.0002030231,2.675426e-7,0.0003353009,0.0003426545],"genre_scores_gemma":[0.8963177,0.000003232639,0.1028941,0.00001216815,0.000591634,0.00002994838,0.00009165606,0.00005871117,9.135616e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4021075,"threshold_uncertainty_score":0.999974,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388191662","doi":"10.1016/j.asoc.2023.110975","title":"Ensemble reinforcement learning: A survey","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Science and Technology Innovation Group of Shanxi Province; National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Popularity; Generalization; Field (mathematics); Ensemble learning; Artificial intelligence; Machine learning; Open research; Selection (genetic algorithm); Data science","retraction":null,"screen_n_in":null,"score":{"opus":0.03247530673738754,"gpt":0.2652782368121937,"spread":0.2328029300748062,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001897115,0.0002481542,0.0002698866,0.0002373892,0.0005180044,0.0003192042,0.001125598,0.00009335207,0.00001779605],"category_scores_gemma":[0.0002607606,0.0002655759,0.00007129212,0.001529371,0.00004766952,0.000151799,0.001199054,0.0004373861,0.001734178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007569789,"about_ca_system_score_gemma":0.00008675491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004250084,"about_ca_topic_score_gemma":0.000003078113,"domain_scores_codex":[0.9974181,0.0001090369,0.0004776124,0.0005644055,0.0006407648,0.0007900738],"domain_scores_gemma":[0.998013,0.0008615177,0.0002421489,0.0006475452,0.00009679159,0.0001389937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003770723,0.000005998801,0.001776562,0.00001584367,0.00002255494,0.000008424397,0.0009975538,0.9639947,0.0004003336,0.0175137,0.001179397,0.01408119],"study_design_scores_gemma":[0.000341124,0.00005958553,0.008140815,0.00001656651,0.000003512279,0.000003696325,0.00006727902,0.9863053,0.0003814666,0.0002800034,0.004084138,0.0003165381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01381373,0.000006925399,0.9646641,0.0001052415,0.0003915356,0.0002606582,1.075776e-7,0.001925114,0.01883258],"genre_scores_gemma":[0.9833896,0.000005195251,0.01413075,0.0002982714,0.00007735335,0.00001009498,0.00004512463,0.00003167538,0.002011942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9695759,"threshold_uncertainty_score":0.9999796,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3003586727","doi":"10.1016/j.asoc.2020.106143","title":"Adaptive repair method for constraint handling in multi-objective genetic algorithm based on relationship between constraints and variables","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Institute for Computing, Information and Cognitive Systems","keywords":"Mathematical optimization; Constraint (computer-aided design); Feasible region; Computer science; Evolutionary algorithm; Genetic algorithm; Metric (unit); Multi-objective optimization; Convergence (economics); Variable (mathematics); Optimization problem; Algorithm; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.05105968804820702,"gpt":0.3041225723608524,"spread":0.2530628843126453,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007734982,0.0003344832,0.0004730465,0.0002109803,0.0003531744,0.00009808612,0.0003260889,0.0001551124,0.000001688195],"category_scores_gemma":[0.0008219116,0.000376187,0.00008518701,0.0006835072,0.000192193,0.0001451216,0.0001999787,0.0004214266,0.00000397357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429166,"about_ca_system_score_gemma":0.0001940788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001061857,"about_ca_topic_score_gemma":0.00000142741,"domain_scores_codex":[0.997415,0.0002004677,0.0005661375,0.001107446,0.0002440889,0.000466826],"domain_scores_gemma":[0.9935333,0.00552078,0.0002924843,0.0002728319,0.0001708124,0.0002098634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000325723,0.00007542913,0.004944837,0.0000397885,0.00004651809,0.00001358946,0.002821442,0.7180817,0.00005504381,0.01160095,0.000005124845,0.262283],"study_design_scores_gemma":[0.002764388,0.0001366746,0.01138021,0.00006589302,0.0000186448,0.000004603029,0.0004238119,0.9824107,0.0002710559,0.002126192,0.00001125542,0.0003865769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002683761,0.00002747895,0.9974645,0.0001679293,0.00007674478,0.001288435,0.00003047396,0.0004438588,0.0002321762],"genre_scores_gemma":[0.391816,3.617086e-7,0.6076077,0.0004512487,0.00005251883,0.00003926274,0.000008566672,0.00002287078,0.000001468591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3915476,"threshold_uncertainty_score":0.999869,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3154289000","doi":"10.1016/j.asoc.2021.107416","title":"Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inconel; Materials science; Machining; Nanofluid; Lubrication; Coolant; Soft computing; Response surface methodology; Tool wear; Nanoparticle; Surface roughness; Energy consumption; Nano-; Computer science; Artificial neural network; Process engineering; Mechanical engineering; Composite material; Metallurgy; Nanotechnology; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.008877700037328781,"gpt":0.2253756384881698,"spread":0.216497938450841,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000127642,0.0001831223,0.0003341002,0.0001386755,0.0001426984,0.00004046141,0.00006406942,0.00006691488,0.000006892759],"category_scores_gemma":[0.00004158497,0.0001868707,0.00003847479,0.0005888498,0.00003685171,0.00009288102,0.00004735377,0.0001449135,2.828766e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002720315,"about_ca_system_score_gemma":0.00004659361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002308998,"about_ca_topic_score_gemma":0.0000374771,"domain_scores_codex":[0.9990723,0.00002098471,0.0002829401,0.0003030909,0.0001327623,0.0001879977],"domain_scores_gemma":[0.9993952,0.0001460588,0.0001191254,0.0001382363,0.0001513624,0.00005002496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002371345,0.00002922619,0.00181308,0.0001080586,0.0001794641,0.000002198723,0.0006794556,0.9951141,0.0005871276,0.000171885,6.412012e-7,0.001291041],"study_design_scores_gemma":[0.0006852136,0.00001737072,0.0002756059,0.00006761002,0.0001649846,0.000002150866,0.0004590995,0.9963868,0.001705289,0.00001582345,0.000001865748,0.0002181361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08915127,0.000246755,0.9100655,0.000003730893,0.00003087505,0.00009673039,0.00001248273,0.0001678088,0.0002248339],"genre_scores_gemma":[0.6790581,0.00001932754,0.3207522,0.00002382587,0.00001227509,0.000003410036,0.0001048748,0.00002426368,0.000001702736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5899068,"threshold_uncertainty_score":0.7620364,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W834673113","doi":"10.1016/j.asoc.2015.06.035","title":"Customer satisfaction in dynamic vehicle routing problem with time windows","year":2015,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Customer satisfaction; Vehicle routing problem; Mathematical optimization; Genetic algorithm; Scheduling (production processes); Plan (archaeology); Routing (electronic design automation); Operations research; Machine learning; Engineering; Embedded system; Marketing; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.009919678541106057,"gpt":0.2320866207892608,"spread":0.2221669422481547,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008348523,0.0002271994,0.0002726685,0.0001577864,0.00008508949,0.00006814755,0.0001268687,0.0001104704,0.000009528872],"category_scores_gemma":[0.00002982111,0.0002386873,0.00002347002,0.0006189041,0.00002964724,0.0001260287,0.00006182967,0.0003600827,0.0001547876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00030277,"about_ca_system_score_gemma":0.00004397616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003364166,"about_ca_topic_score_gemma":0.00002031421,"domain_scores_codex":[0.9985616,0.00005934418,0.0003789709,0.0002970974,0.0002547815,0.0004482639],"domain_scores_gemma":[0.9993857,0.0001469379,0.00009012817,0.0002089694,0.00005344975,0.0001148529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001203328,0.000009692766,0.01087985,0.00002376717,0.00001612183,0.000003621553,0.001040941,0.9461444,0.002797162,0.0001306539,0.00003514791,0.03890663],"study_design_scores_gemma":[0.0009814205,0.00001591428,0.006638117,0.00006212035,0.00001031413,0.00001130889,0.000210801,0.9912272,0.0003626932,0.00009870619,0.00007334338,0.000308037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5917758,0.00002854833,0.3933677,0.00002588726,0.00009142711,0.0004071631,9.342107e-7,0.001084557,0.01321803],"genre_scores_gemma":[0.8140664,6.502829e-7,0.1857368,0.00003446,0.00004189023,0.00001067005,0.00000740231,0.00007278827,0.00002897006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2222906,"threshold_uncertainty_score":0.9733385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4308799544","doi":"10.1016/j.asoc.2022.109791","title":"Mathematical modeling of Vehicle Routing Problem in Omni-Channel retailing","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"University of Tehran","keywords":"Routing (electronic design automation); Computer science; Heuristic; Channel (broadcasting); Pareto principle; Product (mathematics); Function (biology); Vehicle routing problem; Distribution (mathematics); Operations research; Mathematical optimization; Computer network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02272546618253651,"gpt":0.1905539200416689,"spread":0.1678284538591324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005136066,0.0001550445,0.0002972049,0.000104482,0.0001282817,0.00001246164,0.0002022774,0.00005037837,0.00004678707],"category_scores_gemma":[0.00000957284,0.0001845944,0.00005351102,0.0003032917,0.00002404706,0.00002702267,0.00009904063,0.0004488351,0.000008478124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008383863,"about_ca_system_score_gemma":0.00001641794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009303315,"about_ca_topic_score_gemma":0.000001549294,"domain_scores_codex":[0.998615,0.0000148459,0.000572384,0.0002059121,0.0002310593,0.0003608375],"domain_scores_gemma":[0.9996014,0.0001132992,0.00005457915,0.0001695552,0.00001619592,0.0000449716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007381844,0.0000369211,0.0001794603,0.0001497879,0.00001466306,0.00000774353,0.001507125,0.9849824,0.001126865,0.01081498,0.000006354503,0.001166321],"study_design_scores_gemma":[0.0003212941,0.00001359145,0.0000213264,0.00004137777,0.00001123009,0.000003849778,0.0005992276,0.9923332,0.0002179866,0.00623415,0.00001764734,0.0001851254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3098657,0.0001428744,0.6673007,0.000008531336,0.00007852547,0.0002634944,0.000004976599,0.0003632639,0.02197189],"genre_scores_gemma":[0.9845565,0.00000134643,0.01530532,0.00001724573,0.00003817619,0.00001316503,0.00001037235,0.00004755832,0.00001034505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6746907,"threshold_uncertainty_score":0.7527542,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2520621294","doi":"10.1016/j.asoc.2016.09.022","title":"Distributionally robust fuzzy project portfolio optimization problem with interactive returns","year":2016,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Environment and Climate Change Canada; National Natural Science Foundation of China","keywords":"Mathematical optimization; Computer science; Portfolio; Parametric statistics; Fuzzy logic; Project portfolio management; Selection (genetic algorithm); Portfolio optimization; Credibility theory; Variable (mathematics); Mathematics; Project management; Economics; Machine learning; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02296823501680164,"gpt":0.2575551861070122,"spread":0.2345869510902105,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003595073,0.0002296299,0.0002747829,0.00009341187,0.0001987571,0.00007766348,0.0001547304,0.0001004157,0.00003081778],"category_scores_gemma":[0.000107648,0.000148701,0.0000479611,0.0003120005,0.00005035394,0.0001749624,0.00008863331,0.0001259288,0.000008737169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001759603,"about_ca_system_score_gemma":0.0001206062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001293371,"about_ca_topic_score_gemma":0.000008040144,"domain_scores_codex":[0.9984903,0.00004356906,0.0004372113,0.0003986521,0.0003128758,0.0003173828],"domain_scores_gemma":[0.9985946,0.0004101304,0.0004398024,0.0002474869,0.0002496276,0.00005834142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006424175,0.0006664352,0.002909434,0.0005853582,0.0005235787,0.00003402572,0.004078834,0.6012578,0.001139243,0.3577658,0.01459511,0.01580201],"study_design_scores_gemma":[0.02136434,0.001477835,0.002301109,0.008560286,0.0008399747,0.000696191,0.006939331,0.8100528,0.004903292,0.1307437,0.005211581,0.00690953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01100987,0.00000749198,0.9686818,0.0001396686,0.00007877998,0.0009015626,0.00002651899,0.0003092257,0.01884507],"genre_scores_gemma":[0.7099064,0.000002167446,0.2895088,0.00003307232,0.0001524487,0.00003914232,0.00006612635,0.00004500498,0.0002469093],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6988965,"threshold_uncertainty_score":0.606385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3037624695","doi":"10.1016/j.asoc.2020.106508","title":"A phase change material selection using the interval-valued target-based BWM-CoCoMULTIMOORA approach: A case-study on interior building applications","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Phase Change Materials Research","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"European Regional Development Fund","keywords":"Computer science; Selection (genetic algorithm); Interval (graph theory); Rank (graph theory); Mathematical optimization; Operations research; Industrial engineering; Machine learning; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1330325185069894,"gpt":0.3507882057504811,"spread":0.2177556872434917,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005314295,0.0004015828,0.0004300472,0.0001769692,0.0004986949,0.0003152354,0.0004279035,0.0001059419,0.00005078129],"category_scores_gemma":[0.00002686875,0.000360605,0.00008804441,0.000672237,0.00006203639,0.00009120727,0.0002319897,0.0004264813,0.00002128172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224909,"about_ca_system_score_gemma":0.00002779345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009571541,"about_ca_topic_score_gemma":0.000002722163,"domain_scores_codex":[0.9978593,0.0001474376,0.0005067163,0.0005673346,0.0003263965,0.000592812],"domain_scores_gemma":[0.9991725,0.0001300241,0.0001224062,0.0003393747,0.00005991978,0.0001757649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000389151,0.0007265524,0.00003802624,0.0004557816,0.0001904966,0.0001231417,0.01343942,0.05038346,0.921625,0.00007245665,0.00009260246,0.01246388],"study_design_scores_gemma":[0.002055805,0.0002187458,0.000006055343,0.00003897216,0.00004350694,0.00007705773,0.002483103,0.9330512,0.06148705,0.000007412924,0.0001825719,0.0003485623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6191902,0.00001165619,0.3777626,0.00003368453,0.0001474277,0.002199845,0.00002681893,0.0005606953,0.00006703598],"genre_scores_gemma":[0.9775664,2.732699e-7,0.01996588,0.0002844711,0.001208456,0.0007970575,0.00004597012,0.0001310844,3.600801e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8826677,"threshold_uncertainty_score":0.9998846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2586924533","doi":"10.1016/j.asoc.2017.01.048","title":"Hybrid control of the three phase induction machine using artificial neural networks and fuzzy logic","year":2017,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Sensorless Control of Electric Motors","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Artificial neural network; Fuzzy logic; Artificial intelligence; Neuro-fuzzy; Simple (philosophy); Control engineering; Controller (irrigation); Fuzzy control system; Adaptive neuro fuzzy inference system; Intelligent control; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02107353728916055,"gpt":0.2413353930084719,"spread":0.2202618557193113,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002185098,0.0001740123,0.0002777695,0.00004446454,0.0004358049,0.00008516722,0.0002472823,0.00006368897,0.000001836722],"category_scores_gemma":[0.00005188195,0.0001460765,0.00005718627,0.00005476613,0.0001009054,0.00005945734,0.00006918477,0.0003008131,4.94435e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000315836,"about_ca_system_score_gemma":0.000007434469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003892642,"about_ca_topic_score_gemma":0.000009413002,"domain_scores_codex":[0.9990756,0.0000183529,0.0002947663,0.0001869365,0.0001326629,0.0002917197],"domain_scores_gemma":[0.9992322,0.0001206063,0.0002129665,0.0003610231,0.00002718652,0.00004598089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004244995,0.00002092815,0.001088317,0.00001648449,0.00006537584,0.000003102053,0.00002962344,0.7747129,0.02893449,0.002314863,0.000005199416,0.1927663],"study_design_scores_gemma":[0.001112176,0.00001684733,0.002864657,0.00001177226,0.00005068751,0.00001576847,0.000005511156,0.9928896,0.00128667,0.001602703,0.000005408466,0.0001381521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.676631,0.0001988619,0.3222215,0.00003442529,0.0004099064,0.0002459456,0.000001739855,0.00007956072,0.000176982],"genre_scores_gemma":[0.9990098,0.000001391124,0.0004952964,0.00005006085,0.0004098758,0.000002310684,0.000001259598,0.00002946071,4.991472e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3223788,"threshold_uncertainty_score":0.5956829,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014835763","doi":"10.1016/j.asoc.2007.02.002","title":"Prediction of laser solid freeform fabrication using neuro-fuzzy method","year":2007,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Laser Material Processing Techniques","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Computer science; Fabrication; Laser; Traverse; Process (computing); Adaptive neuro fuzzy inference system; Fuzzy logic; Pulse (music); Energy (signal processing); Artificial intelligence; Fuzzy control system; Optics; Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02152649642985898,"gpt":0.2682219087710887,"spread":0.2466954123412297,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006929156,0.0001430059,0.0001902286,0.0001205113,0.00008909297,0.00002906978,0.0001504884,0.0001005598,0.000004207102],"category_scores_gemma":[0.00003377592,0.0001546541,0.00003167566,0.0002503983,0.00002464462,0.0000940399,0.00007661001,0.0001343604,0.000003363416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008014499,"about_ca_system_score_gemma":0.00001168678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002440231,"about_ca_topic_score_gemma":9.913854e-7,"domain_scores_codex":[0.9989606,0.0000105168,0.0004120069,0.0001768925,0.0001578521,0.0002821582],"domain_scores_gemma":[0.9994596,0.0001119906,0.0001183698,0.000199255,0.00006669256,0.00004412352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001731782,0.00002851361,0.0004772771,0.0002850865,0.00003122203,0.000001715036,0.00052621,0.1262224,0.8099912,0.0005355537,0.0002224195,0.06166111],"study_design_scores_gemma":[0.0001439158,0.00001239284,0.001278009,0.00004299049,0.00001949704,0.000005846479,0.00003362562,0.3126349,0.6834447,0.002118489,0.00013382,0.0001318105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.252301,0.000008745273,0.743984,0.000002825608,0.0002225345,0.0001272552,0.000003162163,0.000940375,0.00241005],"genre_scores_gemma":[0.7820959,8.924142e-7,0.2176511,0.00002939457,0.0001718592,0.000001738507,0.000009520297,0.00003809926,0.000001500913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5297949,"threshold_uncertainty_score":0.6306612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391578830","doi":"10.1016/j.asoc.2024.111354","title":"An automated machine-learning-assisted stochastic-fuzzy multi-criteria decision making tool: Addressing record-to-record variability in seismic design","year":2024,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Drilling and Well Engineering","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Fuzzy logic; Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.02512455906924188,"gpt":0.2905771193124477,"spread":0.2654525602432058,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001978436,0.0005071178,0.0005386619,0.0005123593,0.0002088244,0.000488475,0.0003608972,0.0002366973,0.00001765095],"category_scores_gemma":[0.0002749456,0.0005705446,0.00008882669,0.001001723,0.00002326152,0.0001809408,0.0001306043,0.0008697388,0.00007832088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003486412,"about_ca_system_score_gemma":0.00004565169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004625376,"about_ca_topic_score_gemma":0.00001285069,"domain_scores_codex":[0.9971567,0.0001548661,0.0008350561,0.0008006001,0.0002635918,0.0007891225],"domain_scores_gemma":[0.9978269,0.001482328,0.00005940269,0.0004223037,0.00004192824,0.0001670829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002397248,0.00002899854,0.0001052858,0.0001649704,0.00002702684,0.00002334226,0.0006349864,0.7131609,0.006960993,0.0000226728,0.00003928714,0.2788076],"study_design_scores_gemma":[0.0003854462,0.0000334883,0.001207709,0.001271845,0.00002637574,0.00001681411,0.0000446266,0.9957854,0.0001918043,0.0002261641,0.0001651178,0.0006452667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2173478,0.00008861599,0.7745959,0.000005124509,0.001102932,0.0003843514,0.000004945688,0.006324107,0.0001461838],"genre_scores_gemma":[0.7315444,0.000001970246,0.2680798,0.00003009533,0.0001397694,0.00002317933,0.0000180577,0.0001592051,0.000003553881],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5141966,"threshold_uncertainty_score":0.9996746,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2995090400","doi":"10.1016/j.asoc.2019.106005","title":"Distribution linguistic preference relations with incomplete symbolic proportions for group decision making","year":2019,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Preference; Pairwise comparison; Context (archaeology); Consistency (knowledge bases); Group decision-making; Meaning (existential); Computer science; Mathematics; Linguistics; Natural language processing; Theoretical computer science; Artificial intelligence; Statistics; Social psychology; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.09389562549528573,"gpt":0.3714625945668729,"spread":0.2775669690715872,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002784749,0.0003413814,0.0005727509,0.0003397433,0.0009063573,0.0006850632,0.0009906966,0.0001523628,0.000513594],"category_scores_gemma":[0.005518517,0.0002676928,0.0001464452,0.001317451,0.0001062577,0.0002131581,0.0004879877,0.0003390321,0.0007280349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000165537,"about_ca_system_score_gemma":0.0001303398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006563965,"about_ca_topic_score_gemma":0.0000253225,"domain_scores_codex":[0.9950251,0.0001025682,0.001415388,0.001241017,0.001594973,0.0006209447],"domain_scores_gemma":[0.9864092,0.01066189,0.0008994405,0.001104054,0.0007827545,0.0001425916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008629662,0.0003669898,0.03508315,0.00008365711,0.00009742837,0.00001350622,0.002976796,0.08456914,0.002305433,0.1686695,0.00821403,0.6967574],"study_design_scores_gemma":[0.002039083,0.0002119393,0.08455394,0.0007347113,0.00006130577,0.00003902392,0.0006933743,0.7289984,0.00004289725,0.139926,0.04179232,0.0009070523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2602347,0.00001634314,0.7357894,0.00004171592,0.0004113245,0.001251074,0.0001425461,0.0001827797,0.001930088],"genre_scores_gemma":[0.8213848,2.672201e-7,0.1779523,0.00009657022,0.0001692729,0.00006718843,0.0001796885,0.00003913056,0.0001108175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6958504,"threshold_uncertainty_score":0.9999775,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3176224216","doi":"10.1016/j.asoc.2021.107656","title":"AFLN-DGCL: Adaptive Feature Learning Network with Difficulty-Guided Curriculum Learning for skin lesion segmentation","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Overfitting; Segmentation; Computer science; Artificial intelligence; Feature (linguistics); Convolutional neural network; Pattern recognition (psychology); Machine learning; Deep learning; Feature learning; Task (project management); Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.0143410365380731,"gpt":0.2572232696820712,"spread":0.2428822331439981,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002375568,0.0002330429,0.0003339837,0.00006155611,0.0005874527,0.00007283581,0.00005283895,0.0001050386,0.00002157207],"category_scores_gemma":[0.0000593093,0.0002074883,0.0001005968,0.0003737592,0.00002628457,0.00003057623,0.00009654012,0.0004966181,0.00001707738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389177,"about_ca_system_score_gemma":0.0000515157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001086386,"about_ca_topic_score_gemma":0.0000140611,"domain_scores_codex":[0.9984952,0.00006226564,0.0002488969,0.0004953531,0.0002930345,0.0004052563],"domain_scores_gemma":[0.9991829,0.0001725745,0.0002155381,0.0001430002,0.0001784167,0.0001075844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008488502,0.0003703353,0.006352623,0.0004577534,0.0006171874,0.0003086769,0.00318554,0.6642628,0.02797218,0.002442027,0.01048238,0.2826997],"study_design_scores_gemma":[0.01706042,0.00261609,0.0150099,0.001686762,0.001307094,0.001812804,0.04342657,0.7588171,0.01944121,0.0002519878,0.1365433,0.002026766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4582926,0.0001452625,0.532795,0.0004148075,0.0003167346,0.001337025,5.722293e-7,0.0005592531,0.006138787],"genre_scores_gemma":[0.9520205,0.00001207627,0.04425082,0.0003553371,0.000578858,0.00003898229,0.0001622801,0.00005427593,0.002526886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4937279,"threshold_uncertainty_score":0.8461127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4317754025","doi":"10.1016/j.asoc.2023.110042","title":"Multi-source fuzzy comprehensive evaluation","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Fuzzy logic; Data mining; Data source; Quality (philosophy); Credibility; Object (grammar); Process (computing); Fuse (electrical); Limit (mathematics); Function (biology); Multi-source; Factor (programming language); Fuzzy set; Mathematical optimization; Artificial intelligence; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.3137052210231857,"gpt":0.462824857014768,"spread":0.1491196359915823,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00782176,0.000300773,0.0005108141,0.0007417586,0.0006596317,0.0006296633,0.001215924,0.0001459234,0.0002442452],"category_scores_gemma":[0.00370174,0.0002660682,0.0001636008,0.00268288,0.0001114528,0.0001606031,0.001078457,0.0003058453,0.007706255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009429297,"about_ca_system_score_gemma":0.00008826512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001495389,"about_ca_topic_score_gemma":0.000006691993,"domain_scores_codex":[0.9931312,0.0004169345,0.001215391,0.001164566,0.003420381,0.0006515193],"domain_scores_gemma":[0.9907711,0.006568346,0.0005238304,0.00107616,0.0008806094,0.0001799264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002773064,0.00004495099,0.001282425,0.000006876635,0.00002291829,0.00001054841,0.003041837,0.1263394,0.02134923,0.0006771301,0.008902569,0.8382943],"study_design_scores_gemma":[0.001268862,0.00001224741,0.03115101,0.00002812965,0.00001556704,0.00000818492,0.002980098,0.9383525,0.0002591301,0.009628328,0.01596011,0.0003358132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.702687,0.00007294519,0.2901834,0.0002301883,0.00120217,0.0007924893,0.000004797681,0.0007373994,0.004089625],"genre_scores_gemma":[0.9570406,0.000001249126,0.04158759,0.0006336865,0.0002485552,0.00001983903,0.0000210467,0.0000459204,0.0004015148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8379585,"threshold_uncertainty_score":0.9999791,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3196026788","doi":"10.1016/j.asoc.2021.107827","title":"Ensemble-learning regression to estimate sleep apnea severity using at-home oximetry in adults","year":2021,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Interreg; National Institute on Aging; National Institutes of Health; Sociedad Española Del Sueño; National Heart, Lung, and Blood Institute; Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; European Commission; Ministerio de Ciencia e Innovación; University of Missouri; York University; Ministerio de Ciencia, Innovación y Universidades; Instituto de Salud Carlos III; Sociedad Española de Neumología y Cirugía Torácica; European Social Fund; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina; University of Minnesota; Case Western Reserve University; Boston University; European Regional Development Fund; University of Washington; Johns Hopkins University; University of Arizona; Agencia Estatal de Investigación; University of California, Davis; Ministerio de Educación, Cultura y Deporte; New York University","keywords":"Obstructive sleep apnea; Sleep apnea; Regression analysis; Medicine; Regression; Ensemble learning; Apnea; Audiology; Psychology; Internal medicine; Computer science; Statistics; Artificial intelligence; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01812252125483625,"gpt":0.3203815393366152,"spread":0.302259018081779,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061584,0.0002973226,0.00057083,0.0003070921,0.0003518004,0.00005018316,0.000162702,0.0001806666,0.00006943781],"category_scores_gemma":[0.0005323102,0.0003058877,0.00009185084,0.001230773,0.00006421359,0.00005951195,0.0009673102,0.0008855137,0.0001168952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005851098,"about_ca_system_score_gemma":0.00005733678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004343149,"about_ca_topic_score_gemma":0.00001790664,"domain_scores_codex":[0.9970426,0.0001337481,0.0004940508,0.0008350893,0.0006667352,0.0008278082],"domain_scores_gemma":[0.9984862,0.000382186,0.0001379143,0.0004367664,0.0002147595,0.0003421576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001078862,0.0002882108,0.2221906,0.0007050478,0.0001021989,0.001069791,0.002900453,0.01789958,0.3519727,0.0002274461,0.0000178937,0.4015472],"study_design_scores_gemma":[0.004373474,0.000115769,0.1203031,0.0007710739,0.00005119259,0.0003660964,0.001415373,0.7981434,0.07350865,0.0001411295,0.0001827344,0.0006280091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9587407,0.0001140354,0.03786768,0.000152487,0.0001711024,0.0004984889,0.000001339927,0.0001787642,0.002275426],"genre_scores_gemma":[0.9349608,7.796871e-7,0.06453968,0.0001812235,0.0001613204,0.000007734207,0.00003194065,0.00007035032,0.00004612063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7802438,"threshold_uncertainty_score":0.9999393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4380758510","doi":"10.1016/j.asoc.2023.110495","title":"A customized multi-neighborhood search algorithm using the tabu list for a sustainable closed-loop supply chain network under uncertainty","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Metaheuristic; Tabu search; Computer science; Supply chain; Simulated annealing; Supply chain network; Mathematical optimization; Solver; Sustainability; Supply chain management; Algorithm; Operations research; Engineering; Business; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02612384555091139,"gpt":0.2662176532804113,"spread":0.2400938077294999,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004899284,0.0006301521,0.0006758238,0.000605966,0.002755045,0.001387461,0.001107823,0.0001911932,0.00006916757],"category_scores_gemma":[0.0002716588,0.0005522622,0.0002886837,0.003779741,0.0002052096,0.0004563396,0.002087457,0.0005252955,0.0001063134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004693707,"about_ca_system_score_gemma":0.000183644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00183262,"about_ca_topic_score_gemma":0.00005601377,"domain_scores_codex":[0.9947366,0.00007986437,0.0007704876,0.001052041,0.000725879,0.002635136],"domain_scores_gemma":[0.9969604,0.001114886,0.0004220577,0.0007935499,0.0006502604,0.00005882797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001676372,0.0001184346,0.0008124178,0.0007386026,0.0002432744,0.00007471994,0.0004803186,0.8605972,0.00006207444,0.09783024,0.01196157,0.0269135],"study_design_scores_gemma":[0.003850097,0.00001115859,0.0003368279,0.00006476836,0.0001544853,0.00000190622,0.03108611,0.9333016,0.000008145505,0.01256887,0.01796636,0.0006496937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07115803,0.0001863911,0.9117516,0.002922727,0.0008187593,0.008212971,0.0000082358,0.002012305,0.002929026],"genre_scores_gemma":[0.9753256,0.000005903787,0.01321941,0.004019634,0.004009661,0.0003950429,0.0002625619,0.0002585817,0.002503574],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9041676,"threshold_uncertainty_score":0.9996929,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3030155800","doi":"10.1016/j.asoc.2020.106429","title":"Kriging-assisted Discrete Global Optimization (KDGO) for black-box problems with costly objective and constraints","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for Central Universities of the Central South University; Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Kriging; Mathematical optimization; Computer science; Robustness (evolution); Black box; Underwater glider; Benchmark (surveying); Sampling (signal processing); Algorithm; Mathematics; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.01456537642046512,"gpt":0.2493201298439894,"spread":0.2347547534235243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001706313,0.0003238028,0.0003502677,0.00004959678,0.0003678498,0.0002661509,0.0003924289,0.00008554587,0.000003391175],"category_scores_gemma":[0.00009796111,0.0003091817,0.00004584326,0.0007348428,0.0003240401,0.0003428417,0.0002849995,0.0001694442,0.000004566797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001114836,"about_ca_system_score_gemma":0.0001270725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004259207,"about_ca_topic_score_gemma":0.000002390821,"domain_scores_codex":[0.9979429,0.00004271249,0.0003527937,0.0009338678,0.0002766882,0.0004510218],"domain_scores_gemma":[0.9986658,0.0002529639,0.0003250267,0.0002432527,0.0002834448,0.0002295443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000496545,0.00003240356,0.0002210337,0.00005387992,0.00006114505,0.000004128688,0.001742185,0.9452683,0.0001290119,0.01296259,0.00002333599,0.03945236],"study_design_scores_gemma":[0.002230779,0.0001586328,0.0002752699,0.00004851608,0.00002164105,0.00002319934,0.0003748943,0.9955695,0.0002390055,0.0005924443,0.0000638429,0.0004023248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000600118,0.00002453034,0.9949993,0.0004533752,0.00007481761,0.001408715,0.00002009388,0.0005480479,0.001870979],"genre_scores_gemma":[0.4416029,0.000002087336,0.5577892,0.0004931159,0.00004478313,0.00002646346,0.00001756137,0.00002026304,0.000003577799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4410028,"threshold_uncertainty_score":0.999936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389326698","doi":"10.1016/j.asoc.2023.111131","title":"Three-way clustering: Foundations, survey and challenges","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Consensus clustering; GRASP; Fuzzy clustering; Data mining; Constrained clustering; Correlation clustering; Cluster (spacecraft); Set (abstract data type); Conceptual clustering; Artificial intelligence; CURE data clustering algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.09446223775692422,"gpt":0.2758367361530555,"spread":0.1813744983961313,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009841485,0.0001417957,0.0001723314,0.00009929207,0.0003452038,0.000232654,0.0004859659,0.00005894443,0.00000189495],"category_scores_gemma":[0.00003971846,0.0001341398,0.00002430758,0.0004298301,0.00004310231,0.0001025073,0.0008346959,0.0001179175,0.0001265341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001471905,"about_ca_system_score_gemma":0.00001997072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004114891,"about_ca_topic_score_gemma":0.0001300987,"domain_scores_codex":[0.9987624,0.00003427701,0.000204558,0.0004654767,0.0001847406,0.0003485128],"domain_scores_gemma":[0.9989091,0.0005256976,0.00007516926,0.0003807792,0.00003243001,0.00007679263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003364157,0.00002098177,0.001894351,0.00004766261,0.00002080838,0.000008693215,0.001275921,0.001564492,0.00003632011,0.07858235,0.0005690306,0.915976],"study_design_scores_gemma":[0.0003026177,0.00003178208,0.2631599,0.00001445578,0.000002463191,0.000009677868,0.00009142952,0.716153,0.000006937684,0.01650937,0.003418563,0.0002998127],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02626805,0.0005354348,0.9635923,0.001330183,0.0004171094,0.0002224451,0.000001602283,0.000983733,0.006649132],"genre_scores_gemma":[0.9601537,0.0001694132,0.0393558,0.000185993,0.00009408464,0.000006493735,0.00001183343,0.00001369117,0.000009023142],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9338856,"threshold_uncertainty_score":0.5470061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3024415211","doi":"10.1016/j.asoc.2020.106400","title":"Neuro-fuzzy system dynamics technique for modeling construction systems","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"BIM and Construction Integration","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Probabilistic logic; System dynamics; Fuzzy logic; Neuro-fuzzy; Complex system; Industrial engineering; Fuzzy control system; Reliability engineering; Artificial intelligence; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01081686977004623,"gpt":0.1908803323796832,"spread":0.180063462609637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001033515,0.0001629187,0.0002051564,0.00005408093,0.0001529577,0.00008042822,0.0001038708,0.0001227947,7.017354e-7],"category_scores_gemma":[0.00001166001,0.0001832106,0.00005429373,0.0001721254,0.00001921077,0.00005496099,0.00002067006,0.0001918997,0.000007916369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001202138,"about_ca_system_score_gemma":0.00001551047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007017587,"about_ca_topic_score_gemma":7.203566e-7,"domain_scores_codex":[0.9991078,0.00001049529,0.0003550148,0.0002236611,0.0001033704,0.0001996167],"domain_scores_gemma":[0.9996322,0.00006235114,0.00006145449,0.0001091178,0.00006487275,0.00007003495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007595388,0.000001553848,0.00002206002,0.0003782517,0.00002082182,4.619303e-7,0.0001023487,0.8153331,0.007036618,0.1626964,0.00008143271,0.01431941],"study_design_scores_gemma":[0.0001831132,0.00001091581,7.603442e-7,0.00005245792,0.00001689317,0.00004069277,0.001191373,0.9968373,0.001113488,0.0002543376,0.0001248496,0.0001737849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006479683,0.00005399233,0.9866632,0.00003306068,0.0008068118,0.0006672621,0.00001023757,0.001454784,0.003830992],"genre_scores_gemma":[0.9727253,0.000001280673,0.02673983,0.00003751864,0.0003321074,0.00009268966,0.00002555133,0.00004455245,0.00000111506],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9662457,"threshold_uncertainty_score":0.7471114,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2997678919","doi":"10.1016/j.asoc.2019.106049","title":"Unified model for interpreting multi-view echocardiographic sequences without temporal information","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; Guangdong Science and Technology Department; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Pyramid (geometry); Segmentation; Pattern recognition (psychology); Fuse (electrical); Data mining; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.03241499583350681,"gpt":0.3282338755857637,"spread":0.2958188797522568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001513004,0.000182983,0.0003733541,0.00006732097,0.0001468247,0.00005435435,0.00007400689,0.00006214582,0.000002566574],"category_scores_gemma":[0.00006442097,0.0001656215,0.0005356013,0.0002062326,0.00003495783,0.0001062004,0.00006430242,0.000115479,0.00001948219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003622384,"about_ca_system_score_gemma":0.00007914488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001021652,"about_ca_topic_score_gemma":3.221465e-7,"domain_scores_codex":[0.9989874,0.00001239529,0.0003450848,0.0002312946,0.000179725,0.000244105],"domain_scores_gemma":[0.9993722,0.00005996721,0.0001501554,0.0001419057,0.00009100501,0.0001848107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001292847,0.0002243485,0.7236137,0.001765345,0.002660681,0.000008030984,0.009857413,0.07912166,0.0006564481,0.001603051,0.0005227873,0.1786737],"study_design_scores_gemma":[0.002494912,0.00007069358,0.006421566,0.0001364631,0.0004080015,0.00000249801,0.0004847225,0.9894092,0.00005718564,0.0001289385,0.0001930006,0.0001927843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2823969,0.0001933946,0.7146295,0.0002639683,0.00007521226,0.001278745,0.0000295088,0.0002321123,0.0009006001],"genre_scores_gemma":[0.966178,0.000008673305,0.03180357,0.001663807,0.00007757724,0.0000374153,0.000207142,0.00002019203,0.000003608469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9102876,"threshold_uncertainty_score":0.6753848,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}