{"meta":{"query_hash":"fa07bf585f57","filters":{"venue":"Artificial Intelligence Research"},"cohort_total":127,"direct_labels_cover":0,"predictions_cover":127,"exported":127,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/fa07bf585f57","api":"https://metacan.xera.ac/api/v1/cohort?venue=Artificial+Intelligence+Research"},"results":[{"id":"W1629730282","doi":"10.5430/air.v4n2p106","title":"Kinematic gait analysis of workers exposed to knee straining postures by Bayes decision rule","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université de Sherbrooke; Institut National de la Recherche Scientifique; Université TÉLUQ","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Réseau Provincial de Recherche en Adaptation-Réadaptation; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Sagittal plane; Kinematics; Gait; Physical medicine and rehabilitation; Gait analysis; Physical therapy; Medicine; Squatting position; Anatomy","score_opus":0.20838494820580475,"score_gpt":0.44174926876535164,"score_spread":0.2333643205595469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1629730282","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98978984,0.00043942375,0.0067687724,0.0005144528,0.000109283064,0.00069242093,0.000022779293,0.00003810878,0.0016249084],"genre_scores_gemma":[0.99353814,0.000015501064,0.005734751,0.000050834733,0.00006607777,0.000048755028,0.00005695784,0.000021575454,0.00046737937],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970785,0.00014641623,0.00054865115,0.00039637313,0.0012862631,0.00054383266],"domain_scores_gemma":[0.99772984,0.0005713092,0.000062959465,0.00045031117,0.00066589646,0.00051971193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017442001,0.00015756497,0.0004907746,0.0010319983,0.00010593636,0.00006492283,0.0001982142,0.000110881214,0.0004394291],"category_scores_gemma":[0.0015496736,0.00013185503,0.00016428594,0.0025685404,0.00013571174,0.00009088577,0.000105875406,0.00023676342,0.0002867339],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021122026,0.00079395506,0.006214361,0.00004424026,0.0005906202,0.00011907594,0.006267579,0.0006762169,0.21302345,0.002680418,0.0021009764,0.7653769],"study_design_scores_gemma":[0.0006056882,0.010832584,0.001130336,0.00080067915,0.0008543288,0.000011863877,0.050138447,0.00604117,0.9034123,0.025160944,0.0005531908,0.00045845768],"about_ca_topic_score_codex":0.00018862661,"about_ca_topic_score_gemma":0.00030108463,"teacher_disagreement_score":0.76491845,"about_ca_system_score_codex":0.00011286116,"about_ca_system_score_gemma":0.00019188852,"threshold_uncertainty_score":0.5376892},"labels":[],"label_agreement":null},{"id":"W1747560310","doi":"10.5430/air.v4n2p93","title":"Augmenting cost-SVM with gaussian mixture models for imbalanced classification","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Discriminative model; Artificial intelligence; Support vector machine; Machine learning; Computer science; Classifier (UML); Pattern recognition (psychology); Benchmark (surveying); Generative grammar; Mixture model; Generative model; Data mining","score_opus":0.3768862626796906,"score_gpt":0.4441573306332618,"score_spread":0.06727106795357118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1747560310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008559834,0.0000556672,0.98844045,0.0049013863,0.00014273192,0.0015047124,0.000018931863,0.00038618752,0.0036939296],"genre_scores_gemma":[0.82640404,0.00002961,0.17195445,0.00009766153,0.00015100787,0.00093565683,0.000056525558,0.00002832163,0.0003427013],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963743,0.00026139454,0.00050049,0.00086346635,0.0011246962,0.0008756339],"domain_scores_gemma":[0.9961563,0.00039740617,0.00016121003,0.0013012731,0.0016644867,0.00031933247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003492604,0.00020103659,0.00021989837,0.00038849586,0.00040740328,0.0006290794,0.0018620727,0.00015194886,0.000007842324],"category_scores_gemma":[0.00054892007,0.000172033,0.00005290402,0.0015099733,0.00029713433,0.0013760609,0.00027340717,0.0004495767,0.00015354973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009585849,0.00013582307,0.0000573492,0.000020348942,0.000012359352,0.0000040989084,0.0009294998,0.00051243784,0.010013626,0.78900576,0.0030800055,0.19613282],"study_design_scores_gemma":[0.000062774605,0.0002644099,0.00003833412,0.000045177487,0.0000031527682,0.00000677119,0.0009837906,0.5791242,0.15470363,0.2600284,0.0045070974,0.00023223118],"about_ca_topic_score_codex":0.00006652645,"about_ca_topic_score_gemma":0.00006442007,"teacher_disagreement_score":0.82554805,"about_ca_system_score_codex":0.00030733706,"about_ca_system_score_gemma":0.0005141593,"threshold_uncertainty_score":0.7015302},"labels":[],"label_agreement":null},{"id":"W1751574924","doi":"10.5430/air.v4n2p61","title":"Unsupervised analysis of similarities between musicians and musical genres using spectrograms","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Music and Audio Processing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Foundation","keywords":"Spectrogram; Musical; Speech recognition; Psychology; Computer science; Art; Visual arts","score_opus":0.5288765904362847,"score_gpt":0.46601802265122677,"score_spread":0.06285856778505788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1751574924","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6037573,0.0002065888,0.39475513,0.00065310794,0.00005244062,0.0001013907,0.0000037586594,0.00003707111,0.0004331893],"genre_scores_gemma":[0.9842179,0.000021311509,0.015469145,0.00007802229,0.0001817303,0.0000042587426,0.0000033704957,0.000009220823,0.000015037502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716157,0.00028543777,0.00046356436,0.00050539616,0.000984753,0.00059928076],"domain_scores_gemma":[0.99812245,0.00045718247,0.000076157296,0.00048244713,0.0005933983,0.00026838935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024622218,0.00013890395,0.00038724442,0.00082462444,0.00026101584,0.00038776745,0.0007946218,0.000096025935,0.000025931551],"category_scores_gemma":[0.0003712604,0.00012789747,0.00010244847,0.0038408483,0.0006370104,0.00042227434,0.0005165397,0.0003222035,0.00001092146],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038671486,0.00023425375,0.013533928,0.000069667505,0.0004898399,0.000049262086,0.01688965,0.002607154,0.010560644,0.1124762,0.0001416449,0.8429091],"study_design_scores_gemma":[0.000059031183,0.00033652634,0.0023195362,0.00007255766,0.00020324947,0.000004968847,0.008771026,0.8108533,0.07640395,0.100109234,0.00044360964,0.00042297787],"about_ca_topic_score_codex":0.0009349323,"about_ca_topic_score_gemma":0.00020041708,"teacher_disagreement_score":0.8424861,"about_ca_system_score_codex":0.000065589564,"about_ca_system_score_gemma":0.00025638097,"threshold_uncertainty_score":0.5215508},"labels":[],"label_agreement":null},{"id":"W1823609680","doi":"10.5430/air.v4n2p72","title":"Cross-language phoneme mapping for phonetic search keyword spotting in continuous speech of under-resourced languages","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Keyword spotting; Computer science; Spotting; Speech recognition; Natural language processing; Keyword search; Artificial intelligence; Information retrieval","score_opus":0.2760964527524231,"score_gpt":0.44965197702364745,"score_spread":0.17355552427122434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1823609680","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8000891,0.00026726007,0.19493166,0.0007322911,0.00014787899,0.0006733433,0.000008599118,0.00008409196,0.0030657654],"genre_scores_gemma":[0.9534855,0.000016074868,0.04558071,0.00005317191,0.00011660625,0.00005802016,0.000004976576,0.000022240074,0.0006627011],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962489,0.00045550527,0.0006913597,0.00059425173,0.0010792718,0.00093071925],"domain_scores_gemma":[0.9965125,0.0016934535,0.00009306209,0.00060015975,0.0008570498,0.00024380446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066928086,0.00016922328,0.00034219195,0.0008741119,0.00016426739,0.00034883778,0.0012470757,0.00013601675,0.00014967087],"category_scores_gemma":[0.002370806,0.00016791515,0.000111399284,0.001855029,0.00033972177,0.0002620077,0.00038788133,0.00043575713,0.00028704683],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013179997,0.00029725826,0.0010945344,0.000080387166,0.00002109974,0.00011731509,0.01476263,0.00041534237,0.06472067,0.009707202,0.000120703386,0.90853107],"study_design_scores_gemma":[0.00020097672,0.00025032798,0.0006907778,0.00016689589,0.000002435678,0.000023121442,0.04449277,0.10605109,0.8321275,0.01540062,0.0002947138,0.0002987669],"about_ca_topic_score_codex":0.0016794223,"about_ca_topic_score_gemma":0.00030764725,"teacher_disagreement_score":0.9082323,"about_ca_system_score_codex":0.00014931225,"about_ca_system_score_gemma":0.00027771818,"threshold_uncertainty_score":0.68473804},"labels":[],"label_agreement":null},{"id":"W1826701780","doi":"10.5430/air.v4n2p112","title":"Cascaded techniques for improving emphysema classification in computed tomography images","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Receiver operating characteristic; Artificial intelligence; Pattern recognition (psychology); Local binary patterns; Histogram; Computed tomography; Tomography; Fractal; Computer science; Mathematics; Image (mathematics); Medicine; Radiology; Statistics","score_opus":0.25903402760271943,"score_gpt":0.4149809644527375,"score_spread":0.1559469368500181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1826701780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008449647,0.00016341663,0.9884556,0.0013726666,0.00010056252,0.0005573802,0.0000031506856,0.00015731517,0.00074029627],"genre_scores_gemma":[0.9142287,0.000007723175,0.08539555,0.000026200789,0.00014421302,0.0001240704,0.000006563843,0.000012549481,0.000054424036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975261,0.00018616223,0.00050592754,0.0005744125,0.0005715109,0.000635877],"domain_scores_gemma":[0.9979577,0.00036070173,0.0000959291,0.0005229605,0.0008962149,0.00016651484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038086146,0.00013411549,0.00020761088,0.0006512671,0.00023941201,0.0004848986,0.0009924583,0.00009968485,0.000004682408],"category_scores_gemma":[0.00046144106,0.00012589397,0.00012672815,0.0026234144,0.00018873996,0.0006235948,0.00032452645,0.0003050827,0.000030109666],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032124364,0.00012031128,0.0002647604,0.000022813654,0.000010104449,0.000005568213,0.0012039974,0.00013028883,0.019414619,0.068877004,0.00037902652,0.9095394],"study_design_scores_gemma":[0.00002532083,0.00024081298,0.00018360782,0.000028884748,0.0000026619184,0.00000303857,0.0016750006,0.6950607,0.2555522,0.046448812,0.0006060214,0.00017296187],"about_ca_topic_score_codex":0.00073105574,"about_ca_topic_score_gemma":0.00022221719,"teacher_disagreement_score":0.9093664,"about_ca_system_score_codex":0.00012084317,"about_ca_system_score_gemma":0.00016389866,"threshold_uncertainty_score":0.5133807},"labels":[],"label_agreement":null},{"id":"W1843531989","doi":"10.5430/air.v4n2p83","title":"About the sensitivity of ordinal classifiers to non-monotone noise","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Noise (video); Monotone polygon; Mathematics; Statistics; Artificial intelligence; Ordinal Scale; Pattern recognition (psychology); Sensitivity (control systems); Econometrics; Machine learning; Computer science","score_opus":0.3022652006054436,"score_gpt":0.44374539109109423,"score_spread":0.14148019048565064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1843531989","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15145402,0.000038092985,0.83426565,0.0114636505,0.00017746243,0.0005047113,0.0000024102528,0.00003496915,0.0020590322],"genre_scores_gemma":[0.99682826,0.000015455389,0.0025687842,0.0001742597,0.00016589693,0.000057954476,7.269424e-7,0.0000066647162,0.00018201626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99781704,0.00032625388,0.00028814888,0.0003612588,0.0007243414,0.00048295822],"domain_scores_gemma":[0.9974134,0.0007160079,0.000046543406,0.00078590383,0.0006758307,0.00036231166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035128372,0.00009212558,0.00013468087,0.00012967287,0.000269948,0.0001714521,0.0009791787,0.00004785786,0.0000070583],"category_scores_gemma":[0.00047902775,0.00006587332,0.00005356909,0.0018612301,0.00032158245,0.00018193739,0.0005342082,0.00035608633,0.00046125933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007777378,0.00021523336,0.00028916853,0.000008348008,0.000013395483,0.000022911629,0.0032395846,0.0069394195,0.040045097,0.44033504,0.009421571,0.49939245],"study_design_scores_gemma":[0.000030751373,0.00044022838,0.0005265462,0.00004914079,0.0000035705273,0.000014677743,0.002706267,0.63250464,0.25594008,0.09639204,0.011132281,0.00025976688],"about_ca_topic_score_codex":0.00061270694,"about_ca_topic_score_gemma":0.00018667133,"teacher_disagreement_score":0.8453742,"about_ca_system_score_codex":0.000053435626,"about_ca_system_score_gemma":0.0002554041,"threshold_uncertainty_score":0.59287095},"labels":[],"label_agreement":null},{"id":"W1858246752","doi":"10.5430/air.v4n2p126","title":"K Nearest Gaussian-A model fusion based framework for imbalanced classification with noisy dataset","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Noise (video); Computer science; Artificial intelligence; Pattern recognition (psychology); Gaussian noise; Benchmark (surveying); Gaussian; Data mining; Noisy data; Machine learning; Geography","score_opus":0.4051996626847235,"score_gpt":0.47102421377542,"score_spread":0.06582455109069646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1858246752","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005578236,0.000024253846,0.9905378,0.0066687707,0.00010999164,0.0010853964,0.00035300755,0.00032649375,0.00033641313],"genre_scores_gemma":[0.5118448,0.000015087563,0.48657507,0.00021568866,0.00009969786,0.0004908766,0.00068804517,0.00002620904,0.00004453773],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960594,0.00027062648,0.0005191281,0.001001558,0.0013196297,0.0008296258],"domain_scores_gemma":[0.99527454,0.00077771314,0.00016615159,0.0021741465,0.0012283429,0.00037908845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032623091,0.00022840792,0.00023638472,0.00041959365,0.00043879088,0.0006255674,0.0023563346,0.00021228788,0.0000150047135],"category_scores_gemma":[0.001511262,0.0001960353,0.000046581525,0.0016294265,0.0003968753,0.00093955704,0.00032317475,0.0005879561,0.00027239925],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002808086,0.00030179974,0.00010779331,0.000031272146,0.000008070843,0.0000062628806,0.00037109698,0.0034162372,0.009387814,0.9079275,0.012966637,0.06519469],"study_design_scores_gemma":[0.00004890443,0.0002896176,0.00004429349,0.000053218395,0.0000026796151,0.0000019210092,0.00023210618,0.7222465,0.071863905,0.20174992,0.0032614637,0.00020544896],"about_ca_topic_score_codex":0.00007119286,"about_ca_topic_score_gemma":0.000055476907,"teacher_disagreement_score":0.7188303,"about_ca_system_score_codex":0.0002673113,"about_ca_system_score_gemma":0.0009170518,"threshold_uncertainty_score":0.79940873},"labels":[],"label_agreement":null},{"id":"W1902658720","doi":"10.5430/air.v4n2p143","title":"A text feature selection method based on category-distribution divergence","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Selection (genetic algorithm); Divergence (linguistics); Artificial intelligence; Computer science; Feature (linguistics); Natural language processing; Pattern recognition (psychology); Distribution (mathematics); Mathematics; Statistics; Linguistics; Philosophy","score_opus":0.24040444128353383,"score_gpt":0.445637965919208,"score_spread":0.20523352463567418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1902658720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016737827,0.00005077785,0.9833659,0.012198643,0.00026944946,0.00032535882,0.0000045976444,0.00047194614,0.0016395377],"genre_scores_gemma":[0.9748663,0.000015185191,0.024269316,0.00008245995,0.00007798951,0.000096705444,0.000016178623,0.000008733034,0.0005671544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969063,0.00046350269,0.00025811294,0.00063785905,0.001153569,0.00058066286],"domain_scores_gemma":[0.99777865,0.0004178565,0.00007295575,0.00067895954,0.0008571496,0.00019439729],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0031003626,0.00014965048,0.00013752996,0.0003793136,0.00040855454,0.0004190191,0.0013210911,0.00018421398,0.000052293784],"category_scores_gemma":[0.00147859,0.00013348836,0.00005974423,0.0028072493,0.0001864153,0.0004624847,0.0002602459,0.0006456966,0.0012057818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000496039,0.00016961139,0.0002047632,0.0000054651905,0.000004659455,0.000005961976,0.00021995464,0.0027049733,0.0022272612,0.58102053,0.013703673,0.39968356],"study_design_scores_gemma":[0.000028416,0.00040853466,0.00016032135,0.000012750867,0.0000017364914,0.000003022417,0.0005358858,0.5914442,0.2316163,0.16316654,0.012455077,0.00016719787],"about_ca_topic_score_codex":0.00019602937,"about_ca_topic_score_gemma":0.00004637824,"teacher_disagreement_score":0.9731925,"about_ca_system_score_codex":0.0003777277,"about_ca_system_score_gemma":0.00038317358,"threshold_uncertainty_score":0.9995719},"labels":[],"label_agreement":null},{"id":"W1964439841","doi":"10.5430/air.v1n2p198","title":"Role of soft computing techniques in predicting stock market direction","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Soft computing; Stock market; Computer science; Popularity; Chaotic; Financial market; Econometrics; Data mining; Finance; Artificial intelligence; Economics; Artificial neural network","score_opus":0.36928803085420103,"score_gpt":0.5345690801322399,"score_spread":0.16528104927803888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964439841","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8511201,0.0003724838,0.090868056,0.00016102297,0.0004602583,0.0007355065,0.0000044749445,0.000121980265,0.056156095],"genre_scores_gemma":[0.98230326,0.000008650518,0.016986575,0.0000069301886,0.00034932562,0.000027441458,5.6719534e-7,0.00002051017,0.00029674993],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9914136,0.003105983,0.001364009,0.00053213607,0.0025572027,0.0010270511],"domain_scores_gemma":[0.9825357,0.01542317,0.00027516772,0.000622616,0.00095191225,0.00019140841],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.06789365,0.00015175414,0.00035497267,0.0014301214,0.000292754,0.00014935773,0.000945678,0.00016132524,0.00054922525],"category_scores_gemma":[0.04961738,0.00013225515,0.000104413586,0.003920306,0.00038762196,0.0004881936,0.0005732068,0.00074194116,0.00009061579],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000093826515,0.00013258723,0.1474444,0.000007893326,0.0000042886777,8.5629193e-7,0.0016937266,0.00007148468,0.012728904,0.0013593022,0.00020384061,0.8362589],"study_design_scores_gemma":[0.000028700986,0.0002911576,0.087020494,0.00021605675,0.0000058460287,0.000019388639,0.013960292,0.23564324,0.4764875,0.18335481,0.0026369612,0.00033557366],"about_ca_topic_score_codex":0.000673433,"about_ca_topic_score_gemma":0.0002002964,"teacher_disagreement_score":0.8359233,"about_ca_system_score_codex":0.0001615137,"about_ca_system_score_gemma":0.000117711024,"threshold_uncertainty_score":0.95979965},"labels":[],"label_agreement":null},{"id":"W1979594949","doi":"10.5430/air.v2n1p107","title":"Noise-Robust environmental sound classification method based on combination of ICA and MP features","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Information and Communications Technology; Iran Telecommunication Research Center","keywords":"Mel-frequency cepstrum; Environmental noise; Noise (video); Computer science; Speech recognition; Feature extraction; Independent component analysis; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Background noise; Context (archaeology); Ambient noise level; Sound (geography); Acoustics; Telecommunications","score_opus":0.2145145657511374,"score_gpt":0.4207561876921068,"score_spread":0.20624162194096943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979594949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10809529,0.00023999569,0.88848156,0.0014511707,0.00012335154,0.00022816742,0.000002637031,0.000034210672,0.0013436148],"genre_scores_gemma":[0.95601344,0.000024830711,0.043766137,0.0000581762,0.000066116874,0.000015659987,0.000004242458,0.0000075184157,0.000043851836],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99802655,0.00031171666,0.00023499384,0.00030950445,0.0007090087,0.0004081972],"domain_scores_gemma":[0.9987156,0.00064827764,0.00007699713,0.00033892284,0.00008363727,0.00013659656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024060875,0.00009704313,0.00011474288,0.00027621194,0.00027603103,0.00015616624,0.0004140776,0.00007984269,0.000029345476],"category_scores_gemma":[0.00026556963,0.000088444845,0.000033020468,0.00048818823,0.00022749197,0.0004421868,0.00011879053,0.00028408103,0.00006811701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005339959,0.00062247424,0.003246211,0.000034315013,0.0000075297485,0.000001482414,0.0011859997,0.0005004897,0.15033576,0.080672935,0.0000883269,0.76325107],"study_design_scores_gemma":[0.000033181528,0.00018534798,0.009678013,0.000026715417,0.0000027656076,0.000003279514,0.0005759857,0.117009714,0.83552724,0.03674714,0.000094599105,0.000116037256],"about_ca_topic_score_codex":0.000021144066,"about_ca_topic_score_gemma":0.000004373629,"teacher_disagreement_score":0.84791815,"about_ca_system_score_codex":0.00007607752,"about_ca_system_score_gemma":0.000049791324,"threshold_uncertainty_score":0.36066762},"labels":[],"label_agreement":null},{"id":"W1981778103","doi":"10.5430/air.v4n2p1","title":"Heavy path based super-sequence frequent pattern mining on web log dataset","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Path (computing); Sequence (biology); Heuristic; Dynamic programming; Data mining; Graph; Algorithm; Theoretical computer science; Artificial intelligence; Biology","score_opus":0.44111280278568665,"score_gpt":0.4560858783839468,"score_spread":0.014973075598260166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981778103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041215595,0.000094682844,0.94329995,0.011346513,0.00049524446,0.00063082867,0.0013989983,0.0002476075,0.0012705612],"genre_scores_gemma":[0.96325296,0.000023362543,0.034969702,0.0007010628,0.00024069601,0.00016683803,0.000562213,0.000023043813,0.00006012656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961356,0.0003376168,0.00044738845,0.00091032905,0.0013159201,0.0008531625],"domain_scores_gemma":[0.9967143,0.0006310889,0.000060209422,0.0016902123,0.00042285325,0.00048131563],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0033979581,0.0001944628,0.00018812179,0.0003111298,0.0003918465,0.00059067854,0.002471665,0.00009120298,0.00007164688],"category_scores_gemma":[0.00061025185,0.00017846542,0.00004664987,0.0012750779,0.00039856133,0.00051981857,0.0005595878,0.000512497,0.0024313035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024765422,0.0005235113,0.00022445481,0.000014896037,0.00001092152,0.0001414838,0.0011157844,0.00064186036,0.0023316443,0.04638697,0.03704319,0.9115405],"study_design_scores_gemma":[0.00005811261,0.00060001254,0.000028878056,0.00009186089,0.000002851675,0.000011070568,0.0012007634,0.92283046,0.034672167,0.0108893765,0.029265797,0.00034867445],"about_ca_topic_score_codex":0.0010242738,"about_ca_topic_score_gemma":0.0001789538,"teacher_disagreement_score":0.9221886,"about_ca_system_score_codex":0.0001819065,"about_ca_system_score_gemma":0.0006792873,"threshold_uncertainty_score":0.99834543},"labels":[],"label_agreement":null},{"id":"W1993807130","doi":"10.5430/air.v1n2p185","title":"An ABC-Genetic method to solve resource constrained project scheduling problem","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Fredericton; University of New Brunswick","funders":"","keywords":"Computer science; Mathematical optimization; Metaheuristic; Genetic algorithm; Scheduling (production processes); Set (abstract data type); Algorithm; Mathematics","score_opus":0.49280051569781036,"score_gpt":0.5737288236780013,"score_spread":0.0809283079801909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993807130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18498853,0.00020943707,0.7936593,0.0019008133,0.00024259034,0.0024309908,0.000025461297,0.00021728338,0.01632555],"genre_scores_gemma":[0.57620496,0.0000047425983,0.42246047,0.00017754319,0.0007352313,0.00017323621,0.0000050368885,0.00005470488,0.0001840568],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.98389345,0.003524056,0.002195162,0.0017989192,0.0054808115,0.0031076078],"domain_scores_gemma":[0.98647946,0.007855517,0.00029689915,0.0020500228,0.0019069359,0.0014111561],"candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.055017143,0.0004839736,0.0007104573,0.0026404327,0.001290463,0.0017482021,0.0033283161,0.00036684968,0.0010083459],"category_scores_gemma":[0.014487801,0.00040255263,0.00027037464,0.007357828,0.000999418,0.0009882106,0.00073897856,0.001549414,0.0040281834],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031930694,0.00048440887,0.001807588,0.000018593322,0.000043352426,0.000033671255,0.018075692,0.013848269,0.087206684,0.047172956,0.00041569825,0.8305738],"study_design_scores_gemma":[0.0002051315,0.0020951943,0.0006438619,0.0002630049,0.00004889107,0.00029269443,0.1639681,0.14647134,0.46664962,0.17419276,0.043095883,0.0020735452],"about_ca_topic_score_codex":0.00053513004,"about_ca_topic_score_gemma":0.00011480122,"teacher_disagreement_score":0.8285002,"about_ca_system_score_codex":0.00023598052,"about_ca_system_score_gemma":0.0009360151,"threshold_uncertainty_score":0.9999049},"labels":[],"label_agreement":null},{"id":"W2002510000","doi":"10.5430/air.v1n2p131","title":"Prediction of weld quality using intelligent decision making tools","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Welding Techniques and Residual Stresses","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Particle swarm optimization; Taguchi methods; Artificial intelligence; Process (computing); Machine learning; Field (mathematics); Genetic algorithm; Predictive modelling; Data mining","score_opus":0.5156509725328996,"score_gpt":0.48932993510336553,"score_spread":0.026321037429534067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002510000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65956324,0.0007678408,0.3373554,0.000017607543,0.00041547744,0.00025090823,0.00001955131,0.00017558574,0.0014343633],"genre_scores_gemma":[0.99038076,0.00038649532,0.008863661,0.0000031548188,0.00031389238,0.000014801505,0.0000031409315,0.000027050859,0.00000702804],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99775434,0.00014166767,0.00062409794,0.00017697732,0.00068396196,0.00061893277],"domain_scores_gemma":[0.9984639,0.00080815156,0.000045722125,0.00033861445,0.00022664497,0.00011694946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026026883,0.00013038688,0.00019798864,0.00029342176,0.00016072438,0.00007509829,0.0003019206,0.00015496122,0.00024040208],"category_scores_gemma":[0.0006870002,0.00011913327,0.00008077531,0.00067485124,0.00016415142,0.00032507596,0.00014882302,0.00041112734,0.000057327805],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010390852,0.00024968327,0.0051875357,0.00026596285,0.000051610805,0.0000035652452,0.0012482131,0.0450198,0.093043074,0.018035721,0.0006429827,0.83614796],"study_design_scores_gemma":[0.0000080056225,0.0000669105,0.0011947497,0.000313172,0.000006712498,0.00000446249,0.0009114373,0.019019421,0.95939803,0.018091286,0.0008400019,0.00014583187],"about_ca_topic_score_codex":0.00016602087,"about_ca_topic_score_gemma":0.000022799766,"teacher_disagreement_score":0.86635494,"about_ca_system_score_codex":0.00015975165,"about_ca_system_score_gemma":0.000026540407,"threshold_uncertainty_score":0.48581138},"labels":[],"label_agreement":null},{"id":"W2011011346","doi":"10.5430/air.v1n2p38","title":"A enhanced algorithm for floorplan design using evolutionary technique","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"VLSI and FPGA Design Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Floorplan; Placement; Very-large-scale integration; Computer science; Genetic algorithm; Reliability (semiconductor); Physical design; Bounding overwatch; Domain (mathematical analysis); Chip; Scale (ratio); Mathematical optimization; Integrated circuit layout; Reliability engineering; Circuit design; Integrated circuit; Engineering; Mathematics; Embedded system; Artificial intelligence; Machine learning","score_opus":0.2746751030610162,"score_gpt":0.4215272742641248,"score_spread":0.14685217120310856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011011346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010861399,0.00057623116,0.99578196,0.000018674393,0.00020582772,0.0013713441,0.000015223766,0.00047150653,0.00047310613],"genre_scores_gemma":[0.63231176,0.00008271632,0.36637214,0.0000062319946,0.0004440992,0.0006823814,0.0000069269518,0.000048943406,0.000044771954],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979576,0.00015639771,0.00032912122,0.00021439942,0.0003680406,0.0009744416],"domain_scores_gemma":[0.9988389,0.00042896008,0.000019614386,0.00027672603,0.00025504557,0.00018075494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002326421,0.0001645648,0.00017410774,0.00036791095,0.00026710785,0.000055024848,0.0003011462,0.00019373286,0.00009713856],"category_scores_gemma":[0.00014775067,0.00016967577,0.00007473939,0.0006151458,0.00014537456,0.00031737186,0.000053687614,0.00037921395,0.00012824788],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017736147,0.000065137785,0.000003714638,0.000037681883,0.000020364305,0.0000020320733,0.00031045094,0.0019415698,0.57034147,0.003264968,0.0011047634,0.4228901],"study_design_scores_gemma":[0.000007179805,0.00007240241,0.0000017303533,0.000029519713,0.00000382083,0.000008341556,0.00015598867,0.3154464,0.66514164,0.01845894,0.0005295595,0.00014448303],"about_ca_topic_score_codex":0.000049132243,"about_ca_topic_score_gemma":0.0000022310342,"teacher_disagreement_score":0.63122565,"about_ca_system_score_codex":0.00033175712,"about_ca_system_score_gemma":0.00008200478,"threshold_uncertainty_score":0.6919177},"labels":[],"label_agreement":null},{"id":"W2012377985","doi":"10.5430/air.v1n2p99","title":"A hybrid agent based virtual organization for studying knowledge evolution in social systems","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Macro; Evolutionary algorithm; Artificial intelligence; Process (computing); Social system; Genetic algorithm; Multi-agent system; Cultural algorithm; Swarm intelligence; Machine learning; Particle swarm optimization; Meta-optimization","score_opus":0.2515909848228119,"score_gpt":0.4318724132390408,"score_spread":0.1802814284162289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012377985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01524764,0.0001990496,0.98185796,0.0004085457,0.0007189387,0.0012229831,0.0000067892643,0.00009813571,0.00023996364],"genre_scores_gemma":[0.9916825,0.000009635488,0.007323398,0.00001119663,0.0005467931,0.00019832159,0.000018570458,0.000030282541,0.00017924944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955234,0.0011034107,0.00061894965,0.0005113239,0.001114554,0.0011283944],"domain_scores_gemma":[0.99649096,0.0010933161,0.000083282524,0.00042468111,0.0016719057,0.00023584024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007946567,0.00015925363,0.00023454231,0.0010209916,0.0007897964,0.00050781795,0.0010646665,0.0000982873,0.00006204072],"category_scores_gemma":[0.0025243245,0.00016482463,0.000053900916,0.0030926196,0.00015870508,0.0006957118,0.00039736912,0.00039321437,0.0004873977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004872882,0.001545031,0.0023291889,0.00011744779,0.000023466031,0.000008049957,0.0039096563,0.0178202,0.0019080398,0.8802815,0.0010677022,0.09094097],"study_design_scores_gemma":[0.00008878979,0.00014753126,0.0007807428,0.000028854345,0.0000029706107,0.0000031560555,0.0010159564,0.9854117,0.008744242,0.0031419138,0.00043054338,0.00020361232],"about_ca_topic_score_codex":0.00010642777,"about_ca_topic_score_gemma":0.000011917387,"teacher_disagreement_score":0.9764349,"about_ca_system_score_codex":0.00076597166,"about_ca_system_score_gemma":0.00050191703,"threshold_uncertainty_score":0.6721353},"labels":[],"label_agreement":null},{"id":"W2016890367","doi":"10.5430/air.v2n2p96","title":"Use of biclustering for missing value imputation in gene expression data","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hong Kong Polytechnic University","keywords":"Biclustering; Imputation (statistics); Missing data; Data mining; Computer science; Pattern recognition (psychology); Statistics; Artificial intelligence; Cluster analysis; Mathematics; Machine learning","score_opus":0.4163733186004739,"score_gpt":0.47138163711107517,"score_spread":0.055008318510601284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016890367","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7987354,0.00016935039,0.19994174,0.00043540157,0.00009048384,0.0005682295,0.000013799471,0.000005420279,0.0000402096],"genre_scores_gemma":[0.9867738,0.00011072444,0.012645514,0.000019969544,0.000109288834,0.00008977102,0.00014704025,0.000014287547,0.00008962048],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986458,0.00015113066,0.00032581628,0.00039255896,0.00022442454,0.00026028967],"domain_scores_gemma":[0.99890816,0.000101931946,0.00006497587,0.0005735327,0.00028833578,0.000063074585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079957873,0.000072840434,0.0000879866,0.0001623221,0.00008648853,0.00007448988,0.0003499387,0.00009959481,0.000026087451],"category_scores_gemma":[0.00067849085,0.000068466055,0.000026787699,0.00025293618,0.00008841335,0.000032403692,0.00027453632,0.000092224036,0.000011415852],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000066918205,0.000051305124,0.0002913697,0.000020179497,0.0000028749835,1.276159e-7,0.000086831125,0.0009518512,0.89869314,0.00015535948,0.0005889589,0.09909108],"study_design_scores_gemma":[0.000027267824,0.00008152468,0.0004434864,0.00004242575,0.0000012070788,6.149028e-7,0.00035976298,0.04568242,0.949404,0.002714681,0.0011713306,0.00007124955],"about_ca_topic_score_codex":0.0002674191,"about_ca_topic_score_gemma":0.000031790478,"teacher_disagreement_score":0.18803842,"about_ca_system_score_codex":0.000020350135,"about_ca_system_score_gemma":0.0000997463,"threshold_uncertainty_score":0.27919647},"labels":[],"label_agreement":null},{"id":"W2017893815","doi":"10.5430/air.v4n1p53","title":"An effect of initial distribution covariance for annealing Gaussian restricted Boltzmann machines","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Covariance; Mathematics; Estimation of covariance matrices; Covariance matrix; Gaussian; Rational quadratic covariance function; CMA-ES; Law of total covariance; Covariance intersection; Applied mathematics; Matérn covariance function; Statistics; Statistical physics; Physics","score_opus":0.4103755150543119,"score_gpt":0.5721556670266622,"score_spread":0.1617801519723503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017893815","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40874428,0.000097263786,0.5885156,0.00027037796,0.00033500974,0.0011347231,0.0001569343,0.00006955254,0.0006762594],"genre_scores_gemma":[0.98192286,0.000014466456,0.017192738,0.0000064740093,0.00049786014,0.00015013009,0.00011357625,0.00003279381,0.000069075606],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99629176,0.0014243247,0.0006096325,0.00039226885,0.00069060404,0.00059141434],"domain_scores_gemma":[0.99485606,0.0031157513,0.00013723232,0.00054057303,0.0010597613,0.00029062352],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009131362,0.0001770947,0.00038582017,0.0001892579,0.00022463503,0.00009116135,0.0004318429,0.00017116909,0.0000121881185],"category_scores_gemma":[0.012385681,0.00014756019,0.000110698275,0.0007607967,0.0002672042,0.00018227329,0.0000866197,0.00034700535,0.0000010384935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0047099055,0.00057388627,0.0004984089,0.00072611653,0.000078660094,0.000037374273,0.0024340944,0.0006609505,0.03346396,0.5355871,0.0032376186,0.41799194],"study_design_scores_gemma":[0.00027309847,0.0039055466,0.000023989529,0.00016772748,0.00004359142,0.00000862983,0.0014021093,0.16849874,0.5727441,0.25133613,0.0012581319,0.00033823054],"about_ca_topic_score_codex":0.00038419786,"about_ca_topic_score_gemma":0.00015573879,"teacher_disagreement_score":0.5731786,"about_ca_system_score_codex":0.00011315131,"about_ca_system_score_gemma":0.00018652796,"threshold_uncertainty_score":0.9959334},"labels":[],"label_agreement":null},{"id":"W2023875268","doi":"10.5430/air.v4n2p13","title":"Reproduce stylized facts of artificial financial market and comparison with real data","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Stylized fact; Volatility clustering; Financial market; Computational finance; Volatility (finance); Big data; Explication; Market data; Cluster analysis; Order (exchange); Finance; Economics; Financial economics; Computer science; Artificial intelligence; Autoregressive conditional heteroskedasticity; Data mining; Macroeconomics","score_opus":0.47617858798363694,"score_gpt":0.4068856807643041,"score_spread":0.06929290721933284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023875268","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95811886,0.001846088,0.011713911,0.00130055,0.00029214207,0.000745247,0.0003663915,0.000050227598,0.025566576],"genre_scores_gemma":[0.9978383,0.00011920114,0.0011058277,0.000007731796,0.00023248319,0.000014733193,0.00003841061,0.000021899825,0.0006213952],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969881,0.00014498782,0.0011305085,0.0009218929,0.00028442944,0.0005300341],"domain_scores_gemma":[0.99745405,0.00023510674,0.00032179314,0.0013584138,0.0003937602,0.00023686358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005848107,0.00016672746,0.00066851184,0.00042706222,0.0002032748,0.0001773768,0.0006841136,0.00010336693,0.00048892025],"category_scores_gemma":[0.0015952622,0.00016223619,0.000050671995,0.0011170594,0.00046692873,0.00033014503,0.0005148446,0.00032235094,0.00023327133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021823207,0.0009518684,0.074797615,0.00018382573,0.00026832035,0.000042153766,0.0051055322,0.00048697813,0.0006392743,0.78790325,0.012335292,0.115103565],"study_design_scores_gemma":[0.00055413385,0.0028196238,0.021621423,0.00023038714,0.00007837112,0.000030539988,0.022715747,0.36090717,0.014546596,0.4820197,0.09263211,0.0018441911],"about_ca_topic_score_codex":0.0111942245,"about_ca_topic_score_gemma":0.0045238766,"teacher_disagreement_score":0.3604202,"about_ca_system_score_codex":0.00006989729,"about_ca_system_score_gemma":0.00017388901,"threshold_uncertainty_score":0.9953903},"labels":[],"label_agreement":null},{"id":"W2032722848","doi":"10.5430/air.v3n3p1","title":"A hierarchical target recognition method based on image processing","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Zhengzhou University of Light Industry; China Postdoctoral Science Foundation","keywords":"Wavelet packet decomposition; Computer science; Artificial intelligence; Pattern recognition (psychology); Wavelet; Feature extraction; Feature (linguistics); Fuzzy logic; Transformation (genetics); Wavelet transform; Image processing; Process (computing); Stationary wavelet transform; Signal processing; Matching (statistics); Computer vision; Image (mathematics); Mathematics; Digital signal processing","score_opus":0.1686362379318915,"score_gpt":0.4320528284783743,"score_spread":0.2634165905464828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032722848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030598186,0.000010011086,0.98206604,0.0047201705,0.00014559447,0.00026948174,0.0000031008565,0.00017030923,0.0095554525],"genre_scores_gemma":[0.5827943,0.0000063764605,0.41618678,0.0005708418,0.00023259262,0.00010319036,0.00001653657,0.000018438785,0.000070944196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959074,0.0012368489,0.00036897778,0.0007057062,0.0010850413,0.00069601496],"domain_scores_gemma":[0.9973169,0.001278012,0.00006111533,0.00051934633,0.0005882919,0.00023635298],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004972778,0.00015691844,0.0001751571,0.00053773203,0.0005526166,0.00057661015,0.0008254063,0.00012912611,0.000254233],"category_scores_gemma":[0.00146423,0.00013713534,0.00008238878,0.0012263984,0.00018039318,0.00055454986,0.00018084998,0.0007457868,0.002514714],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006228842,0.00020971349,0.0000053723597,0.000027020977,0.0000019434963,0.000008700677,0.00027006905,0.00045364272,0.015182543,0.009112567,0.00051027513,0.97415584],"study_design_scores_gemma":[0.000021742155,0.00021258726,0.000009240317,0.000084195875,8.9272663e-7,0.0000018501113,0.00006925625,0.57804036,0.21717022,0.2032438,0.0010300174,0.00011584302],"about_ca_topic_score_codex":0.000044794397,"about_ca_topic_score_gemma":0.0000074000195,"teacher_disagreement_score":0.97404003,"about_ca_system_score_codex":0.000058640744,"about_ca_system_score_gemma":0.0001651281,"threshold_uncertainty_score":0.9982619},"labels":[],"label_agreement":null},{"id":"W2036101886","doi":"10.5430/air.v2n1p44","title":"Exploiting web scraping in a collaborative filtering- based approach to web advertising","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":132,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; World Wide Web; Web page; Scraper site; Copying; Web mining; Web development; Static web page; Web modeling; Web analytics; Web navigation; Information retrieval; Data Web; Web intelligence","score_opus":0.21157754419777228,"score_gpt":0.41026095760821296,"score_spread":0.19868341341044068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036101886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1924749,0.00019375558,0.80062646,0.0010741681,0.00016170723,0.0003460555,0.000009640809,0.00012352466,0.004989779],"genre_scores_gemma":[0.9295261,0.000013481851,0.07000553,0.00012259572,0.00017403546,0.00009555577,0.0000060423295,0.000015843721,0.000040816416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960811,0.0006150031,0.0005048226,0.0006446879,0.00088977825,0.00126462],"domain_scores_gemma":[0.9979173,0.00060064445,0.00005931632,0.0007178503,0.00034817384,0.00035672844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00520404,0.00017643925,0.00025766075,0.0011657483,0.00039955552,0.0005499252,0.0012934143,0.00007991378,0.00003163405],"category_scores_gemma":[0.0012467293,0.0001768634,0.00006322897,0.006046857,0.00012159431,0.0011152253,0.0006332644,0.00049175444,0.00040283307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011274658,0.0015938664,0.0076795206,0.00013345249,0.00006523605,0.000060042436,0.027571881,0.021745896,0.24601807,0.122825444,0.0011861278,0.5710077],"study_design_scores_gemma":[0.000041116666,0.000091577895,0.00014918571,0.00019886982,0.0000031953896,0.0000029552477,0.0073666372,0.8940472,0.09546468,0.00091530813,0.0013810311,0.000338229],"about_ca_topic_score_codex":0.00020628997,"about_ca_topic_score_gemma":0.00011713178,"teacher_disagreement_score":0.87230134,"about_ca_system_score_codex":0.00018978641,"about_ca_system_score_gemma":0.00037414563,"threshold_uncertainty_score":0.721228},"labels":[],"label_agreement":null},{"id":"W2044611148","doi":"10.5430/air.v2n4p13","title":"Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Artificial Intelligence and Decision Support Systems","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Spillage; Adaptive neuro fuzzy inference system; Computer science; Analytics; MATLAB; Inference engine; Data mining; Software; Knowledge extraction; Artificial neural network; Inference; Fuzzy logic; Database; Machine learning; Artificial intelligence; Engineering; Operating system; Fuzzy control system","score_opus":0.21178524982136754,"score_gpt":0.4454563822907867,"score_spread":0.23367113246941917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044611148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4039377,0.00026201556,0.5909986,0.0001991286,0.000731125,0.0017726314,0.000005117396,0.000095824646,0.0019978567],"genre_scores_gemma":[0.96418035,0.00008174471,0.034263436,0.0000545203,0.00018369134,0.0009129501,0.0000013255404,0.000032418033,0.00028954056],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9939936,0.00058258837,0.0013981379,0.0011062637,0.0019014014,0.001018027],"domain_scores_gemma":[0.99472576,0.002000338,0.0002645834,0.0017598527,0.00093341613,0.0003160305],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0025546122,0.00028542534,0.00043382694,0.0006694556,0.0006510479,0.0006069965,0.0027577104,0.00014386195,0.0003944813],"category_scores_gemma":[0.0006994681,0.00025452944,0.00019288731,0.001770831,0.0002642475,0.00052497827,0.0010134842,0.000441562,0.002089417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084906496,0.0031117254,0.00059404963,0.000047597747,0.000090466645,0.00005207693,0.008469602,0.00060493307,0.0026013996,0.19765353,0.0017120348,0.7849777],"study_design_scores_gemma":[0.00008791522,0.0040553687,0.00040462008,0.00009613921,0.000019897763,0.0000081812605,0.058497235,0.032387946,0.26289234,0.63987535,0.0011040476,0.0005709718],"about_ca_topic_score_codex":0.0012857586,"about_ca_topic_score_gemma":0.000050789084,"teacher_disagreement_score":0.7844067,"about_ca_system_score_codex":0.00006769891,"about_ca_system_score_gemma":0.000073499155,"threshold_uncertainty_score":0.9999907},"labels":[],"label_agreement":null},{"id":"W2051542317","doi":"10.5430/air.v1n2p149","title":"Fuzzy adaptive catfish particle swarm optimization","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council","keywords":"Catfish; Particle swarm optimization; Benchmark (surveying); Fuzzy logic; Inertia; Swarm intelligence; Multi-swarm optimization; Swarm behaviour; Mathematical optimization; Computer science; Artificial intelligence; Mathematics; Fish <Actinopterygii>; Biology; Physics; Fishery; Geography","score_opus":0.28251493352013757,"score_gpt":0.4290115289024241,"score_spread":0.14649659538228654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051542317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020700018,0.00028061288,0.98613596,0.0017158815,0.00046456986,0.00052684435,0.0000027132505,0.00018090288,0.008622503],"genre_scores_gemma":[0.80557126,0.00012751635,0.19308552,0.000077654884,0.00037220362,0.00011173865,0.000005214244,0.000025935487,0.0006229524],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99444187,0.0009488988,0.00056388567,0.00058621756,0.0018607943,0.0015983232],"domain_scores_gemma":[0.99605674,0.0009316,0.00007854676,0.0009832084,0.0012876769,0.00066224707],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005855088,0.00018674134,0.00021360262,0.00041347812,0.00061022124,0.0005455503,0.0015573711,0.00012410995,0.00040404144],"category_scores_gemma":[0.0019039087,0.00018054555,0.00007516125,0.0032842318,0.00037255362,0.0015351452,0.00076456653,0.000626208,0.002845183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037769634,0.00058871077,0.00027300345,0.000012045108,0.000027643988,0.000014721311,0.0026318699,0.096930444,0.00043360385,0.7367186,0.0009123731,0.16141921],"study_design_scores_gemma":[0.000030503406,0.0001475468,0.00006519952,0.000011740705,0.000003117517,0.000009606389,0.0006991589,0.9244534,0.05415109,0.01961513,0.00058904546,0.00022448212],"about_ca_topic_score_codex":0.00016954982,"about_ca_topic_score_gemma":0.000013408503,"teacher_disagreement_score":0.82752293,"about_ca_system_score_codex":0.0002076332,"about_ca_system_score_gemma":0.00026492836,"threshold_uncertainty_score":0.99793124},"labels":[],"label_agreement":null},{"id":"W2056418479","doi":"10.5430/air.v1n2p67","title":"Detection of damaged seeds in laboratory evaluation of precision planter using impact acoustics and artificial neural networks","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Fast Fourier transform; Artificial neural network; Metering mode; Acoustics; Mean squared error; Feature (linguistics); Computer science; Pattern recognition (psychology); Point (geometry); Artificial intelligence; Engineering; Mathematics; Statistics; Algorithm","score_opus":0.16062142974571011,"score_gpt":0.4137018668726264,"score_spread":0.2530804371269163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056418479","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9520468,0.0003392195,0.047051754,0.0000022436138,0.00027435078,0.0002485433,0.000010775453,0.000013885524,0.000012437209],"genre_scores_gemma":[0.99968714,0.00007085483,0.00008534141,7.2057236e-7,0.0001249336,0.0000072161088,0.000003722941,0.000019830153,2.3366674e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982433,0.00028660244,0.00041738487,0.000121922596,0.00055002404,0.00038082205],"domain_scores_gemma":[0.9990043,0.0002547673,0.00006106905,0.00016890079,0.00043112258,0.00007982957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044740927,0.0000987667,0.00016301764,0.00036375524,0.000055385568,0.000025958358,0.00009732301,0.00013869714,0.000015177832],"category_scores_gemma":[0.00042777992,0.00009805246,0.00003086625,0.00077221316,0.000077331206,0.00020351756,0.000051687093,0.00036682718,0.0000015669668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007286745,0.000053441272,0.0026319944,0.000027120426,0.000010489768,4.849966e-7,0.0004980555,0.4298723,0.34180665,0.00015121321,5.62604e-7,0.22487482],"study_design_scores_gemma":[0.00002370225,0.00007111537,0.00476172,0.000037539536,0.0000125099095,0.0000014282167,0.0006261357,0.8928155,0.10073004,0.0008471694,2.1503833e-7,0.00007293023],"about_ca_topic_score_codex":0.00015668101,"about_ca_topic_score_gemma":0.00013626613,"teacher_disagreement_score":0.4629432,"about_ca_system_score_codex":0.00015506826,"about_ca_system_score_gemma":0.000041749467,"threshold_uncertainty_score":0.39984632},"labels":[],"label_agreement":null},{"id":"W2058602204","doi":"10.5430/air.v1n1p96","title":"Parallel hybrid enhanced inherited GA based scuc in a distributed cluster","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Electric Power System Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Schedule; Economic dispatch; Power system simulation; Computer science; Genetic algorithm; Operator (biology); Mathematical optimization; Power (physics); Electric power system; Mathematics; Operating system","score_opus":0.0907725757499153,"score_gpt":0.3547781529685037,"score_spread":0.26400557721858836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058602204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17328395,0.00039039325,0.82345825,0.00018128238,0.000314701,0.00064166926,0.000012584375,0.00021215958,0.0015049903],"genre_scores_gemma":[0.9980204,0.00004899666,0.0014469802,0.0000213191,0.0001427014,0.00017679288,0.000058279773,0.000044546854,0.000039967956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971497,0.00033709983,0.00053246913,0.00025438907,0.0005674463,0.0011588632],"domain_scores_gemma":[0.99878395,0.00040164107,0.00002813565,0.00037045308,0.00020743208,0.00020839997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019972837,0.00017586205,0.00021718844,0.00047709895,0.000098368655,0.00009391462,0.00031846346,0.00011677858,0.00025052257],"category_scores_gemma":[0.0005102397,0.0001849631,0.000051107203,0.0017005029,0.00008882326,0.0003341866,0.000053604097,0.0005499639,0.00076902215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003116499,0.0006671835,0.0025073222,0.00026238023,0.000056134762,0.000031694744,0.0020164587,0.8798476,0.045322526,0.0034071933,0.0031284103,0.06244147],"study_design_scores_gemma":[0.000073005176,0.00005393079,0.00037050882,0.00007626931,0.000002891755,0.0000031878444,0.00022847527,0.80770737,0.18992776,0.0007342393,0.0005715199,0.00025087132],"about_ca_topic_score_codex":0.00011302444,"about_ca_topic_score_gemma":0.000107454456,"teacher_disagreement_score":0.8247365,"about_ca_system_score_codex":0.00038509854,"about_ca_system_score_gemma":0.000068568035,"threshold_uncertainty_score":0.988448},"labels":[],"label_agreement":null},{"id":"W2064344480","doi":"10.5430/air.v1n2p75","title":"A Bayesian Network approach to diagnosing the root cause of failure from Trouble Tickets","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Software System Performance and Reliability","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian network; Scope (computer science); Hierarchy; Computer science; Root cause; Context (archaeology); Root cause analysis; Element (criminal law); Root (linguistics); Network element; Network monitoring; Distributed computing; Computer security; Computer network; Artificial intelligence; Reliability engineering; Engineering","score_opus":0.14744883045305152,"score_gpt":0.37923639586671715,"score_spread":0.23178756541366563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064344480","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21564972,0.00042731033,0.7800309,0.0016946655,0.00054670783,0.0007126651,0.0000025625886,0.000073726296,0.0008617128],"genre_scores_gemma":[0.98495865,0.000013502151,0.014110462,0.00005579401,0.0006977721,0.00011566872,0.0000022580082,0.000013095779,0.000032780536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643916,0.0005665473,0.0005145258,0.00044399282,0.0009701479,0.0010656319],"domain_scores_gemma":[0.9966627,0.0013916331,0.00007500862,0.0012439571,0.00034466022,0.0002820669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047037834,0.00015415117,0.00024487238,0.00013448426,0.00049975724,0.0002168505,0.0016595499,0.00011961438,0.00003296767],"category_scores_gemma":[0.0005342267,0.0001027102,0.00009323381,0.0018841872,0.00024721771,0.00055737764,0.0006257281,0.0004778653,0.0003594556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019007639,0.002134603,0.23757106,0.00020151856,0.00021675059,0.000014333719,0.058484722,0.0171126,0.0026762628,0.22546242,0.016074516,0.43986112],"study_design_scores_gemma":[0.00019681192,0.0010403048,0.048936833,0.0007574465,0.00006502302,0.000056860234,0.013019532,0.5046406,0.25010505,0.16303504,0.016319823,0.001826684],"about_ca_topic_score_codex":0.0011504027,"about_ca_topic_score_gemma":0.00023407539,"teacher_disagreement_score":0.7693089,"about_ca_system_score_codex":0.00009221475,"about_ca_system_score_gemma":0.00014051072,"threshold_uncertainty_score":0.4620194},"labels":[],"label_agreement":null},{"id":"W2065858755","doi":"10.5430/air.v2n4p63","title":"Impact of the characteristics of data sets on incremental learning","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Stability (learning theory); Computer science; Task (project management); Set (abstract data type); Artificial neural network; Incremental learning; Machine learning; Artificial intelligence; Plasticity; Data set; Boundary (topology); Class (philosophy); Data mining; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.2720874982054512,"score_gpt":0.4543232648875551,"score_spread":0.1822357666821039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065858755","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99596614,0.000010763024,0.0012687012,0.000024260742,0.000048698752,0.00031385067,0.000028767758,0.00008052714,0.0022583066],"genre_scores_gemma":[0.994394,0.00001985713,0.0055011166,0.0000011604312,0.000034399316,0.000012198494,0.000010275659,0.000022689444,0.000004319481],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985597,0.000163815,0.0003403226,0.00017130487,0.00045969783,0.0003051575],"domain_scores_gemma":[0.998455,0.00048261197,0.000057790017,0.0006889206,0.00026563535,0.000050079427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010310863,0.00009896785,0.00015046581,0.00014210085,0.00008081599,0.000033843375,0.00091065146,0.00005586714,0.00023138728],"category_scores_gemma":[0.001070368,0.00007308942,0.000046913832,0.00048285667,0.00029819194,0.00018020009,0.0004049342,0.0005506809,0.00008518391],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000323616,0.00014123209,0.067048885,0.00009862006,0.00007290589,0.000002024985,0.00051358953,0.00031536,0.7398967,0.009978663,0.00052762474,0.18137205],"study_design_scores_gemma":[0.000022820215,0.00070342113,0.16319215,0.00033486373,0.000008806384,0.0000053118224,0.00045374467,0.06058195,0.6423447,0.13208479,0.000013200561,0.000254244],"about_ca_topic_score_codex":0.0011331344,"about_ca_topic_score_gemma":0.000014241921,"teacher_disagreement_score":0.1811178,"about_ca_system_score_codex":0.0001092595,"about_ca_system_score_gemma":0.00005588525,"threshold_uncertainty_score":0.29805},"labels":[],"label_agreement":null},{"id":"W2066502489","doi":"10.5430/air.v2n4p87","title":"Effective classification of Chinese tea samples in hyperspectral imaging","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Classifier (UML); Feature extraction; Computer science; Robustness (evolution); Pixel; Imaging spectroscopy; Contextual image classification; Computer vision; Image (mathematics); Chemistry","score_opus":0.1107078367755154,"score_gpt":0.43108876330174806,"score_spread":0.3203809265262326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066502489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9810194,0.0003578448,0.0009856607,0.00050402136,0.000018735376,0.00019247137,0.000004609543,0.000030199355,0.016887052],"genre_scores_gemma":[0.99920136,0.00006111531,0.0003365746,0.0000068682925,0.00011025565,0.00011986023,0.000008161581,0.000014970135,0.00014086122],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99816775,0.00008744292,0.00040298994,0.00036341377,0.0004720171,0.00050637167],"domain_scores_gemma":[0.9981841,0.0009619685,0.00006938187,0.0003410549,0.00035504744,0.000088448025],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00067751866,0.00012943872,0.00023641369,0.0005488584,0.00010310357,0.00007329314,0.0003803816,0.00008034853,0.0027860212],"category_scores_gemma":[0.001385309,0.00011242757,0.00007987992,0.0019098547,0.00037746318,0.00020241708,0.0000801756,0.00049525744,0.0002962029],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003171919,0.00024095623,0.06877165,0.00004661896,0.000019139077,0.0000028241325,0.0006004283,0.000029154618,0.88654524,0.00776205,0.00004350804,0.035906687],"study_design_scores_gemma":[0.000027280437,0.00002727142,0.01861748,0.000021127404,0.00000605486,0.0000019927563,0.008846063,0.006637884,0.90534425,0.06033495,0.00001296304,0.00012270731],"about_ca_topic_score_codex":0.0038985726,"about_ca_topic_score_gemma":0.00017924112,"teacher_disagreement_score":0.0525729,"about_ca_system_score_codex":0.00020566309,"about_ca_system_score_gemma":0.0000659323,"threshold_uncertainty_score":0.99812555},"labels":[],"label_agreement":null},{"id":"W2068878641","doi":"10.5430/air.v2n2p87","title":"Discontinuous fuzzy Fredholm integral equations and strong fuzzy Henstock integrals","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Mathematics; Fredholm integral equation; Fuzzy logic; Fredholm theory; Fuzzy measure theory; Integral equation; Mathematical analysis; Applied mathematics; Pure mathematics; Fuzzy number; Fuzzy set; Computer science; Artificial intelligence","score_opus":0.28398683665392516,"score_gpt":0.436402979917066,"score_spread":0.15241614326314085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068878641","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8161819,0.0004929058,0.1397378,0.0045051607,0.00065607036,0.0036882649,0.000056890713,0.00030850898,0.03437253],"genre_scores_gemma":[0.9901538,0.000046574056,0.007018171,0.000020802436,0.00028944697,0.00028115598,0.000018002205,0.000047746347,0.0021242944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99668044,0.00042872835,0.00078453904,0.00052226585,0.0007690881,0.0008149695],"domain_scores_gemma":[0.9966184,0.0015807588,0.00012240079,0.0005478319,0.00086110865,0.00026951687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018819636,0.00024271742,0.00035473853,0.0004065776,0.0005022047,0.0005915207,0.00040188592,0.00018345499,0.00049787085],"category_scores_gemma":[0.0030778598,0.00019802079,0.00009739093,0.0007417274,0.00045671812,0.00056589325,0.00023632187,0.0006864735,0.0006624015],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019246729,0.00017502198,0.00027194954,0.000064158645,0.00003425076,0.000005448578,0.002125723,0.00010619809,0.0016608428,0.9134107,0.0032128866,0.07891358],"study_design_scores_gemma":[0.000046648103,0.00022686149,0.000112836824,0.00016365222,0.00001379403,0.000008124295,0.015334908,0.024834039,0.003931234,0.9548294,0.00020556388,0.00029294164],"about_ca_topic_score_codex":0.0035193095,"about_ca_topic_score_gemma":0.0013252839,"teacher_disagreement_score":0.17397194,"about_ca_system_score_codex":0.00012705631,"about_ca_system_score_gemma":0.00012046619,"threshold_uncertainty_score":0.8514052},"labels":[],"label_agreement":null},{"id":"W2070691548","doi":"10.5430/air.v3n1p18","title":"A multi-view image rectification algorithm for matrix camera arrangement","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Rectification; Image rectification; Matrix (chemical analysis); Computer vision; Essential matrix; Image (mathematics); TRACE (psycholinguistics); Camera matrix; Algorithm; Ideal (ethics); Computer science; Artificial intelligence; Fundamental matrix (linear differential equation); Rotation (mathematics); Projection (relational algebra); Mathematics; Camera auto-calibration; Camera resectioning; State-transition matrix; Symmetric matrix; Pinhole camera model; Physics; Mathematical analysis","score_opus":0.2075417322219517,"score_gpt":0.48484279643914874,"score_spread":0.277301064217197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070691548","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030349902,0.00028112988,0.99257475,0.0048577734,0.0002945291,0.0013377263,0.0000032637988,0.00012881204,0.00021850257],"genre_scores_gemma":[0.024349716,0.00022584539,0.9734581,0.00014304236,0.00015312184,0.000706469,0.000005728862,0.00002125083,0.00093670795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974065,0.00019834323,0.00044344107,0.00064566126,0.00057707913,0.00072896865],"domain_scores_gemma":[0.9974556,0.00042139742,0.000071993105,0.0007140589,0.0011456256,0.00019131339],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017312923,0.0001449865,0.00016839677,0.00030107936,0.0004920797,0.00068391877,0.0010573149,0.00005171124,0.0001976774],"category_scores_gemma":[0.0004392998,0.0001319253,0.000086541535,0.0010027499,0.00017906963,0.0011005603,0.0002807412,0.00031108322,0.0027463092],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024454296,0.000112556954,0.000002269153,0.000013576422,0.000004965412,0.0000016026503,0.00040731375,0.000013601618,0.04445604,0.0070056454,0.00096551917,0.94701445],"study_design_scores_gemma":[0.0000352047,0.000097164186,0.000028262642,0.00003356079,0.0000014513711,0.0000028797203,0.0006982769,0.84911877,0.10968536,0.031397037,0.008735319,0.0001667012],"about_ca_topic_score_codex":0.00027065538,"about_ca_topic_score_gemma":0.000017113985,"teacher_disagreement_score":0.94684774,"about_ca_system_score_codex":0.00013562065,"about_ca_system_score_gemma":0.00010450429,"threshold_uncertainty_score":0.9980302},"labels":[],"label_agreement":null},{"id":"W2073654225","doi":"10.5430/air.v4n1p1","title":"Using a predefined passphrase to evaluate a speaker verification system","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Speaker verification; Computer science; Biometrics; Variety (cybernetics); Process (computing); Feature (linguistics); Speaker recognition; Focus (optics); Speech recognition; Artificial intelligence; Natural language processing; Programming language; Linguistics","score_opus":0.32271136736696715,"score_gpt":0.4872202259271605,"score_spread":0.16450885856019337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073654225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0408391,0.000019535806,0.9554945,0.0007602272,0.00010169532,0.00052285945,9.741013e-7,0.00037901814,0.0018820479],"genre_scores_gemma":[0.85300106,0.000004097932,0.14666294,0.000046773494,0.000100675294,0.0000926092,0.0000015455862,0.000017373402,0.00007294782],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959285,0.00076343707,0.00054008904,0.00081126805,0.0012340194,0.00072266854],"domain_scores_gemma":[0.9969169,0.0003910734,0.0000917971,0.0014313946,0.0009027053,0.00026613177],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0051947227,0.0001702867,0.00024213022,0.0006320496,0.00041190657,0.000425825,0.0014935099,0.00008716393,0.000031071962],"category_scores_gemma":[0.0011467645,0.00016267144,0.00008775863,0.0027025715,0.00012298995,0.0004673873,0.00051444204,0.00030803285,0.001121125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002969708,0.00008753379,0.000074646865,0.000026597994,0.000015426389,0.0000072936864,0.0007207774,0.004441837,0.08555941,0.5798951,0.00014706323,0.32899463],"study_design_scores_gemma":[0.000012070049,0.00013052771,0.0000270601,0.00006317164,0.0000057653992,0.0000062520053,0.00021475031,0.74860215,0.19594483,0.053876087,0.00093967875,0.00017767394],"about_ca_topic_score_codex":0.00037770547,"about_ca_topic_score_gemma":0.00010156068,"teacher_disagreement_score":0.8121619,"about_ca_system_score_codex":0.00039136628,"about_ca_system_score_gemma":0.00013654341,"threshold_uncertainty_score":0.9996566},"labels":[],"label_agreement":null},{"id":"W2082663547","doi":"10.5430/air.v1n2p117","title":"Adaboost and SVM based cybercrime detection and prevention model","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; AdaBoost; Cybercrime; Machine learning; Executable; Artificial intelligence; Classifier (UML); Data mining; Software; Pattern recognition (psychology); The Internet; Operating system","score_opus":0.2550317435761351,"score_gpt":0.41731110525537196,"score_spread":0.16227936167923684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082663547","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21354774,0.00025635504,0.784795,0.00031266877,0.00017184862,0.00017983739,5.304876e-7,0.00006780015,0.0006682313],"genre_scores_gemma":[0.99328566,0.00004757226,0.006353771,0.000030851847,0.00013387039,0.00002697395,5.892308e-7,0.000007909161,0.00011278913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984534,0.00022189318,0.00018205654,0.00030514208,0.00040475608,0.0004328027],"domain_scores_gemma":[0.9991444,0.00022507156,0.000027304974,0.00026882938,0.00015326365,0.0001811696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022798462,0.00008920734,0.00008436236,0.00024874517,0.00039894605,0.0002793374,0.00021246976,0.00008865485,0.000013622409],"category_scores_gemma":[0.00023155179,0.00008715445,0.00002493856,0.0005120146,0.00013236883,0.00087348063,0.00015744557,0.000290059,0.00006547768],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003856213,0.00008932934,0.00027206514,0.000020281253,0.000005783009,0.0000010250186,0.0010732069,0.00031275113,0.055158053,0.04553632,0.00004157985,0.89745104],"study_design_scores_gemma":[0.000020340973,0.00016369426,0.0007443492,0.000018899687,0.000003242397,0.000008320236,0.0001310367,0.63816047,0.27572787,0.08466962,0.00022917385,0.00012298174],"about_ca_topic_score_codex":0.00034481028,"about_ca_topic_score_gemma":0.00023286694,"teacher_disagreement_score":0.8973281,"about_ca_system_score_codex":0.000051938307,"about_ca_system_score_gemma":0.000052014537,"threshold_uncertainty_score":0.3554055},"labels":[],"label_agreement":null},{"id":"W2087462978","doi":"10.5430/air.v2n1p55","title":"Yager ranking index in fuzzy bilevel optimization","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Fuzzy Systems and Optimization","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bilevel optimization; Mathematical optimization; Ranking (information retrieval); Fuzzy logic; Optimization problem; Selection (genetic algorithm); Mathematics; Computer science; Pessimism; Artificial intelligence","score_opus":0.3967703050662592,"score_gpt":0.47476941660496075,"score_spread":0.07799911153870154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087462978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16253825,0.00039758548,0.81271225,0.00067598256,0.0008609707,0.0015780762,0.000008331654,0.00015487192,0.021073703],"genre_scores_gemma":[0.9852022,0.00004944215,0.013901715,0.000017148162,0.0003928344,0.00007629773,0.0000069386588,0.000036756148,0.00031669278],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970998,0.00044018493,0.0006012008,0.00026740305,0.00077387766,0.00081754435],"domain_scores_gemma":[0.9982746,0.0008184901,0.00008005921,0.0003478067,0.0003364844,0.00014252769],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050920746,0.00013752544,0.000210401,0.0005854308,0.00020959396,0.000119687094,0.00026275092,0.0001735743,0.00043284413],"category_scores_gemma":[0.0017941607,0.00012744087,0.00005190373,0.0014062467,0.000115832256,0.0004884199,0.00011545486,0.0004364842,0.00020058326],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020387585,0.0013386447,0.020779282,0.00028551125,0.00003917388,0.000019643945,0.01242256,0.18140334,0.0015584284,0.6835067,0.0014195661,0.09702323],"study_design_scores_gemma":[0.000128339,0.00010064168,0.0008302566,0.0003270097,0.000010191162,0.000012062274,0.0060131196,0.53563243,0.015405065,0.44041243,0.0005551094,0.0005733287],"about_ca_topic_score_codex":0.00034808496,"about_ca_topic_score_gemma":0.00017432478,"teacher_disagreement_score":0.8226639,"about_ca_system_score_codex":0.00018375715,"about_ca_system_score_gemma":0.000078561476,"threshold_uncertainty_score":0.5196888},"labels":[],"label_agreement":null},{"id":"W2093275257","doi":"10.5430/air.v1n2p11","title":"Interpretable support vector regression","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; European Commission","keywords":"Support vector machine; Interpretability; Data mining; Fuzzy rule; Kernel (algebra); Identification (biology); Computer science; Artificial intelligence; Reduction (mathematics); Least squares support vector machine; Fuzzy logic; Kernel method; Relevance vector machine; Mathematics; Pattern recognition (psychology); Machine learning; Fuzzy set","score_opus":0.24615392452537876,"score_gpt":0.4550938221825369,"score_spread":0.20893989765715815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093275257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03710278,0.00041627712,0.9335234,0.0072392086,0.0010557905,0.0005819623,0.0000029159448,0.0002880874,0.019789537],"genre_scores_gemma":[0.99408334,0.000061247745,0.0042859293,0.000106335065,0.00041028598,0.000071294424,0.0000023475038,0.000009722519,0.00096947816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977807,0.0001587115,0.00027445043,0.00033099143,0.0005656034,0.0008895387],"domain_scores_gemma":[0.9984322,0.00033956382,0.000039388957,0.00069940666,0.00019858521,0.00029083632],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016849412,0.00010607484,0.000115244184,0.00014891819,0.00039495516,0.000242632,0.0011353985,0.00006691166,0.00040378547],"category_scores_gemma":[0.00012768655,0.000084190324,0.000057757876,0.001143204,0.00014420887,0.0007585134,0.0005468925,0.0004188086,0.0024086677],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007921883,0.00017940493,0.00022946038,0.0000048196935,0.0000045983616,0.0000038185976,0.0007213324,0.000030509214,0.013157784,0.62954235,0.008358221,0.34775978],"study_design_scores_gemma":[0.00002635032,0.0003228377,0.0005107309,0.00008541893,0.000004759536,0.00003962215,0.0006389922,0.09847603,0.629278,0.16635233,0.10372491,0.0005400056],"about_ca_topic_score_codex":0.000058812617,"about_ca_topic_score_gemma":0.000011024221,"teacher_disagreement_score":0.9569806,"about_ca_system_score_codex":0.00005662956,"about_ca_system_score_gemma":0.000067419656,"threshold_uncertainty_score":0.9983681},"labels":[],"label_agreement":null},{"id":"W2094656673","doi":"10.5430/air.v4n1p22","title":"Diagnostic with incomplete nominal/discrete data","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Missing data; Computer science; Imputation (statistics); Data mining; Decision tree; Cartesian product; Machine learning; Bayesian probability; Naive Bayes classifier; Outcome (game theory); Artificial intelligence; Mathematics; Support vector machine","score_opus":0.5670274782417085,"score_gpt":0.48427376241003667,"score_spread":0.08275371583167185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094656673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002608278,0.00009440047,0.98936033,0.004620686,0.000121119745,0.0004774461,0.00003571852,0.00032080177,0.002361219],"genre_scores_gemma":[0.88408893,0.000040188894,0.115308434,0.00009601397,0.00016075098,0.00009633878,0.00008101486,0.000020334632,0.00010799237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9962105,0.00040644288,0.00040457142,0.00094179047,0.001317092,0.00071958825],"domain_scores_gemma":[0.9937494,0.0013323767,0.00008517733,0.0036359017,0.00084143155,0.00035570946],"candidate_categories":["open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0043168645,0.00016293068,0.00018691932,0.00034593942,0.00025680097,0.0006022323,0.0057982816,0.00007374032,0.000027791375],"category_scores_gemma":[0.0033510888,0.00013504842,0.000019283949,0.001701476,0.00052271027,0.0015433123,0.0023804114,0.0005044767,0.0014824332],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007401031,0.00020903694,0.0008668441,0.000020905585,0.000022203338,0.00014197765,0.0010676028,0.00010284426,0.001963144,0.6338836,0.01565101,0.3459968],"study_design_scores_gemma":[0.00010780252,0.0013515363,0.00097517617,0.00018719153,0.000011286424,0.00010546796,0.002094513,0.38446936,0.14801697,0.3900749,0.07167052,0.0009352731],"about_ca_topic_score_codex":0.00064150215,"about_ca_topic_score_gemma":0.000418679,"teacher_disagreement_score":0.88148063,"about_ca_system_score_codex":0.00014594242,"about_ca_system_score_gemma":0.00058206596,"threshold_uncertainty_score":0.9995808},"labels":[],"label_agreement":null},{"id":"W2098352464","doi":"10.5430/air.v2n2p109","title":"Interactive Fuzzy Programming for Stochastic Two-level Linear Programming Problems through Probability Maximization","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Linear programming; Vagueness; Mathematical optimization; Stochastic programming; Simplex algorithm; Fuzzy logic; Computer science; Linear-fractional programming; Mathematics; Artificial intelligence","score_opus":0.2473736253788599,"score_gpt":0.4067232692812456,"score_spread":0.15934964390238568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098352464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047012446,0.000075656695,0.9875934,0.0003380754,0.00022938244,0.005767466,0.000006585905,0.0005499526,0.0007382347],"genre_scores_gemma":[0.6458656,0.000012889786,0.35052484,0.000012919568,0.00019009005,0.0031744447,0.00003906982,0.00008070469,0.00009944877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691445,0.00012681045,0.00077196496,0.0005153217,0.0005783288,0.0010931435],"domain_scores_gemma":[0.99738336,0.00076302123,0.00007159864,0.0003832,0.0011883939,0.00021044823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013614062,0.00027822857,0.00030598123,0.00017110423,0.0003833061,0.00049209106,0.00037834613,0.00014881125,0.00017071459],"category_scores_gemma":[0.0015678777,0.00026052078,0.000121866025,0.0010260749,0.0003131149,0.00081170653,0.00012065336,0.00058155175,0.00040027816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030730364,0.00038854152,0.000009026189,0.0005597762,0.000055782377,9.1930673e-7,0.0031649873,0.24919395,0.00095920236,0.033796567,0.000072514515,0.71176803],"study_design_scores_gemma":[0.00007834283,0.00021379982,0.0000021846377,0.00015529123,0.00001331482,0.000003438087,0.0020825225,0.7992594,0.00640576,0.1905627,0.00090347015,0.00031976064],"about_ca_topic_score_codex":0.00014184482,"about_ca_topic_score_gemma":0.00009022182,"teacher_disagreement_score":0.71144825,"about_ca_system_score_codex":0.00023048802,"about_ca_system_score_gemma":0.00006998202,"threshold_uncertainty_score":0.9999847},"labels":[],"label_agreement":null},{"id":"W2100072750","doi":"10.5430/air.v1n1p31","title":"Performance analysis of neuro swarm optimization algorithm applied on detecting proportion of components in manhole gas mixture","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Methane; Component (thermodynamics); Swarm behaviour; Hydrogen sulfide; Sensitivity (control systems); Carbon monoxide; Computer science; Algorithm; Process engineering; Materials science; Engineering; Chemistry; Artificial intelligence; Electronic engineering; Organic chemistry","score_opus":0.10663714486991313,"score_gpt":0.34171884937090274,"score_spread":0.2350817045009896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100072750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93905944,0.000038024293,0.06031621,0.000013520061,0.000038842605,0.00020447555,0.0000030064004,0.00007684272,0.0002496221],"genre_scores_gemma":[0.9948351,0.00011223035,0.004979422,0.0000014748708,0.00002147207,0.000021547228,0.000010929065,0.000016039123,0.0000017910834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985427,0.000029749106,0.00042612405,0.00016067452,0.00044325678,0.00039747756],"domain_scores_gemma":[0.9993346,0.0001990901,0.000065265536,0.0002382779,0.00011994739,0.000042857897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038400106,0.00010018728,0.00022200879,0.0007034935,0.00004026396,0.0000070926103,0.00019889728,0.00011134581,0.000018741068],"category_scores_gemma":[0.00024284539,0.0000988701,0.00004303942,0.0022809017,0.00013791624,0.00010950712,0.000054143893,0.00040181968,0.000009531421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020233558,0.00006635573,0.0008717142,0.000033575703,0.000021766298,4.1543183e-7,0.00012264538,0.61060685,0.24986567,0.00014627904,5.8519765e-7,0.13824394],"study_design_scores_gemma":[0.000007845811,0.000022910472,0.00036759852,0.000012333882,0.000007942609,1.4644077e-7,0.00018061015,0.4583015,0.5408301,0.00022126872,0.0000025646368,0.00004519232],"about_ca_topic_score_codex":0.000021373591,"about_ca_topic_score_gemma":0.000006819307,"teacher_disagreement_score":0.29096442,"about_ca_system_score_codex":0.000102259175,"about_ca_system_score_gemma":0.0000032021296,"threshold_uncertainty_score":0.40318054},"labels":[],"label_agreement":null},{"id":"W2100111509","doi":"10.5430/air.v2n3p70","title":"A new web based data mining exploration and reporting tool for decision makers","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Web mining; Computer science; Association rule learning; Data science; Decision tree; Data mining; Cluster analysis; Scalability; Decision support system; Data stream mining; World Wide Web; Database; Machine learning; Web service","score_opus":0.5174205066787242,"score_gpt":0.48821820317275366,"score_spread":0.029202303505970528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100111509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012069492,0.000039917195,0.98246205,0.004548266,0.00009324319,0.0006201181,0.000011422686,0.000068297355,0.00008719973],"genre_scores_gemma":[0.16609254,0.000024049654,0.8333191,0.000060267434,0.0001505645,0.00019146276,0.0000418083,0.00001094773,0.00010921267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766964,0.00006168181,0.0006965384,0.00069623993,0.00046981356,0.00040607413],"domain_scores_gemma":[0.9964699,0.001531776,0.00019694355,0.0012748151,0.0003725588,0.00015398061],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003789553,0.0000923316,0.00012744336,0.00018637112,0.00043278505,0.001070901,0.0011693356,0.000053447096,0.000039669845],"category_scores_gemma":[0.0047760513,0.000085305066,0.000023237391,0.0007317806,0.000072651834,0.0016121642,0.0006741449,0.00014834166,0.00013013132],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047525523,0.00002093393,0.00004315162,0.0000059249824,0.0000026054506,0.0000014405356,0.0002164249,0.00005381408,0.0019202243,0.008570006,0.008132363,0.9810284],"study_design_scores_gemma":[0.000023220366,0.000069990274,0.000058512444,0.000048473587,0.0000017378454,0.00000272508,0.00052470417,0.95000094,0.005163921,0.04022636,0.0037743847,0.0001050058],"about_ca_topic_score_codex":0.00032267984,"about_ca_topic_score_gemma":0.000079913305,"teacher_disagreement_score":0.98092335,"about_ca_system_score_codex":0.00002797425,"about_ca_system_score_gemma":0.00033134926,"threshold_uncertainty_score":0.9999661},"labels":[],"label_agreement":null},{"id":"W2100118769","doi":"10.5430/air.v2n1p36","title":"Towards a pragmatic modeling of learner's complex system by reflecting Boulding's typology at the affective computing space","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Cognitive Science and Education Research","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Typology; Structuring; Perspective (graphical); Cognition; Space (punctuation); Psychology; Cognitive psychology; Valence (chemistry); Computer science; Cognitive science; Artificial intelligence; Sociology","score_opus":0.4975235756899331,"score_gpt":0.5357029334137281,"score_spread":0.03817935772379499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100118769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9552776,0.000176782,0.020811582,0.0023392127,0.00034984868,0.0008019492,0.000010452206,0.00006016904,0.020172408],"genre_scores_gemma":[0.99921554,0.000020147892,0.00017027034,0.00006386059,0.00020329704,0.000042875854,0.000002731111,0.000020911624,0.00026034217],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9940666,0.0021612444,0.0004878117,0.0005328347,0.0013712534,0.0013802823],"domain_scores_gemma":[0.99487346,0.0037366098,0.00013927945,0.0004087708,0.0005999133,0.00024193527],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008744559,0.0001599499,0.0002485686,0.00033201007,0.0016729208,0.00015354267,0.0007596938,0.00009107112,0.00029101977],"category_scores_gemma":[0.00805102,0.0001199829,0.00008602098,0.0021627436,0.0012466899,0.0002814905,0.00067085185,0.0007874488,0.0006020964],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119227625,0.00030970198,0.0005893768,0.0001435695,0.0000142005165,0.0000039376105,0.016009234,0.002770007,0.83213687,0.07188342,0.00071889,0.075301535],"study_design_scores_gemma":[0.000023660206,0.00017049607,0.0000361604,0.000079643636,0.0000051446973,0.000030332072,0.05351272,0.2639898,0.67982054,0.0019886047,0.00019941978,0.00014348746],"about_ca_topic_score_codex":0.0013056728,"about_ca_topic_score_gemma":0.00009853499,"teacher_disagreement_score":0.2612198,"about_ca_system_score_codex":0.0004177047,"about_ca_system_score_gemma":0.00026073252,"threshold_uncertainty_score":0.99962676},"labels":[],"label_agreement":null},{"id":"W2101821333","doi":"10.5430/air.v3n1p38","title":"A statistical approach for clustering in streaming data","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Computer science; Data stream mining; Data stream clustering; Component (thermodynamics); Data mining; Context (archaeology); Data stream; Concept drift; Streaming data; Focus (optics); Unsupervised learning; Machine learning; CURE data clustering algorithm; Correlation clustering","score_opus":0.4264192062234075,"score_gpt":0.4872538406245796,"score_spread":0.060834634401172094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101821333","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011624484,0.000012101246,0.996157,0.00031301152,0.000058945996,0.00044837946,0.000046032077,0.00013940892,0.0016626553],"genre_scores_gemma":[0.4841844,0.0000058327573,0.51555544,0.000013953334,0.00006847708,0.00007684041,0.000068985646,0.000009967319,0.000016107497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99714625,0.00032269052,0.00035641133,0.0008866997,0.00054646767,0.0007414538],"domain_scores_gemma":[0.99607086,0.0017660013,0.000038694547,0.0018535963,0.00015160895,0.000119211574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006205014,0.0001204353,0.00018167558,0.00037110024,0.00017428178,0.00043137383,0.0037922722,0.00007789703,0.000008366314],"category_scores_gemma":[0.0028703655,0.00011905074,0.000018558376,0.00076479773,0.00019698185,0.00063603907,0.0023883078,0.00036620142,0.000040188217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013731582,0.0001257447,0.00008349563,0.000027844693,0.0000026324053,0.0000035456505,0.00022555693,0.00011390309,0.00030830156,0.29821745,0.0005818435,0.700296],"study_design_scores_gemma":[0.0000169635,0.00013829481,0.00006631983,0.000027756954,9.1756004e-7,0.0000033121541,0.00021756746,0.9247802,0.0047080778,0.06888731,0.0010282113,0.00012508068],"about_ca_topic_score_codex":0.0004919489,"about_ca_topic_score_gemma":0.00025937345,"teacher_disagreement_score":0.9246663,"about_ca_system_score_codex":0.00007455908,"about_ca_system_score_gemma":0.000106338935,"threshold_uncertainty_score":0.7047048},"labels":[],"label_agreement":null},{"id":"W2103706669","doi":"10.5430/air.v2n3p35","title":"The role of statistical and semantic features in single-document extractive summarization","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Automatic summarization; Anaphora (linguistics); Sentence; Context (archaeology); Artificial intelligence; Word (group theory); Feature (linguistics); Term (time); Representation (politics); Resolution (logic); Information retrieval; Linguistics","score_opus":0.04453799708544237,"score_gpt":0.37893299108909595,"score_spread":0.3343949940036536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103706669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058786612,0.0053602816,0.9306856,0.0031782945,0.000088093366,0.0009795944,0.0000017867707,0.0001032156,0.00081653264],"genre_scores_gemma":[0.9646508,0.000085989996,0.03515798,0.000009121426,0.00001710461,0.000038710856,8.6764817e-7,0.000004787188,0.000034643792],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984725,0.00026081924,0.00024930423,0.00024854412,0.00044373845,0.00032511124],"domain_scores_gemma":[0.99802667,0.0012889414,0.00004807863,0.0002531195,0.00032873073,0.000054457632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011796446,0.000070587426,0.000091746144,0.00016428303,0.00016551456,0.00041185835,0.0005536236,0.00005479875,0.000012983636],"category_scores_gemma":[0.00094153365,0.00004907325,0.000012026627,0.00057623716,0.00032093332,0.0004354219,0.0002983041,0.00033805822,0.000018445091],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071436225,0.000035881098,0.00017459018,0.000006445476,0.0000019461465,0.000002511178,0.00062356197,0.0000029001528,0.026950572,0.45088464,0.000024597599,0.52128524],"study_design_scores_gemma":[0.000004543886,0.00007061001,0.0003730377,0.000024280123,4.6627187e-7,0.0000021938258,0.00057052786,0.0101263095,0.3228974,0.6658608,0.000026780897,0.00004309011],"about_ca_topic_score_codex":0.001034743,"about_ca_topic_score_gemma":0.00034662557,"teacher_disagreement_score":0.9058642,"about_ca_system_score_codex":0.000056116212,"about_ca_system_score_gemma":0.000058441976,"threshold_uncertainty_score":0.39715597},"labels":[],"label_agreement":null},{"id":"W2105012074","doi":"10.5430/air.v3n2p16","title":"Non-invasive blood pressure measurement algorithm using neural networks","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Blood pressure; Algorithm; Artificial neural network; Computer science; Medical instrumentation; Pressure sensor; Cuff; Pressure measurement; Gold standard (test); Software; Medicine; Artificial intelligence; Cardiology; Internal medicine; Engineering; Surgery","score_opus":0.13279443106586516,"score_gpt":0.3400204300141485,"score_spread":0.20722599894828334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105012074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1462037,0.0019498324,0.8466027,0.00006887463,0.0020556585,0.0009733403,0.0000074619234,0.00039539777,0.001743046],"genre_scores_gemma":[0.9947729,0.000090287525,0.0030627484,0.000010905891,0.001886179,0.00006201977,0.000002840209,0.000092274095,0.000019858797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99618924,0.00023575488,0.0005157057,0.0005181802,0.0012469734,0.0012941327],"domain_scores_gemma":[0.99781805,0.0004461183,0.000054995275,0.00058523327,0.000764185,0.00033138768],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002508357,0.0003074477,0.00030445695,0.0003297477,0.00040865535,0.0002740338,0.0006384413,0.00020083037,0.000078453864],"category_scores_gemma":[0.000666517,0.0003208083,0.000118964104,0.0009889621,0.00023486845,0.00029554256,0.00021423228,0.0009844536,0.0001509208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016698792,0.00010350539,0.00066163205,0.000114878894,0.00020218042,0.00003884069,0.00034215665,0.539821,0.3152011,0.0006964394,0.00012225879,0.14267927],"study_design_scores_gemma":[0.000027577105,0.00010203579,0.000028154194,0.00008514663,0.000038460093,0.0000071432323,0.00019632802,0.5959762,0.40080988,0.0023991417,0.0000948928,0.00023505796],"about_ca_topic_score_codex":0.00033169874,"about_ca_topic_score_gemma":0.00012613756,"teacher_disagreement_score":0.8485692,"about_ca_system_score_codex":0.00014527379,"about_ca_system_score_gemma":0.00007112095,"threshold_uncertainty_score":0.9999244},"labels":[],"label_agreement":null},{"id":"W2105482673","doi":"10.5430/air.v3n3p49","title":"A unified approach to content-based indexing and retrieval of digital videos from television archives","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Pró-Reitoria de Pesquisa, Universidade Federal do Rio Grande do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Search engine indexing; Information retrieval; Metadata; Key frame; Key (lock); Precision and recall; Video content analysis; Segmentation; Hash function; Histogram; Image retrieval; Frame (networking); Artificial intelligence; Computer vision; Video tracking; Video processing; Image (mathematics); World Wide Web","score_opus":0.18684833448235447,"score_gpt":0.35224792435034696,"score_spread":0.1653995898679925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105482673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1667426,0.0000294308,0.83135337,0.00050749304,0.00003182429,0.00019143955,0.0000041838475,0.000026355781,0.0011133322],"genre_scores_gemma":[0.98381925,0.00000806239,0.015999492,0.00004220089,0.000057086738,0.0000055738483,0.0000123938835,0.000007929657,0.000048035876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758893,0.00032211596,0.00044956998,0.0005337041,0.0007547663,0.00035093428],"domain_scores_gemma":[0.9977656,0.0011671111,0.000073214425,0.0005224456,0.0002756569,0.00019596043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014275636,0.00011239686,0.0002356728,0.0004793942,0.00019129811,0.0004927433,0.0007246774,0.00006357229,0.000006357214],"category_scores_gemma":[0.0016552901,0.00009724048,0.00006598714,0.0012140851,0.00021374569,0.00035445672,0.00036459818,0.00022634362,0.000027480797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019857894,0.0002776681,0.0037828241,0.000028522501,0.000028783574,0.0000021071035,0.001956131,0.0024119332,0.07891868,0.19263406,0.000016660373,0.719744],"study_design_scores_gemma":[0.000039843126,0.00022462662,0.00066202594,0.00005390144,0.000003913406,3.561417e-7,0.0006220738,0.7866092,0.16811198,0.043386817,0.00015230874,0.00013297789],"about_ca_topic_score_codex":0.00021995758,"about_ca_topic_score_gemma":0.000018784762,"teacher_disagreement_score":0.8170766,"about_ca_system_score_codex":0.000019581074,"about_ca_system_score_gemma":0.000081182734,"threshold_uncertainty_score":0.47515348},"labels":[],"label_agreement":null},{"id":"W2108269192","doi":"10.5430/air.v4n1p36","title":"A fuzzy method for the selection of customized equipment suppliers in the public sector","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rank (graph theory); Selection (genetic algorithm); Process (computing); Computer science; Fuzzy logic; Legislation; Variable (mathematics); Risk analysis (engineering); Simple (philosophy); Public sector; Operations research; Industrial engineering; Artificial intelligence; Engineering; Business; Mathematics; Law; Political science","score_opus":0.782315716405534,"score_gpt":0.6155236739752322,"score_spread":0.16679204243030177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108269192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09201727,0.00021946889,0.882746,0.018616939,0.000991697,0.003138735,0.00002209019,0.000028715182,0.0022191042],"genre_scores_gemma":[0.9847763,0.000012017579,0.014328889,0.000116254945,0.0002018126,0.00034672592,0.0000015898179,0.000015290043,0.00020110022],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9892459,0.0035188717,0.0013151943,0.00063556293,0.004491996,0.0007924999],"domain_scores_gemma":[0.9635433,0.032527722,0.00022303019,0.00095484185,0.0025871191,0.00016399745],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.09473492,0.00014749994,0.00033998035,0.0009910319,0.00041166064,0.0008642597,0.002642443,0.000114070164,0.00041943858],"category_scores_gemma":[0.045224324,0.00007560859,0.00016732015,0.004463002,0.0003905298,0.0003210283,0.0003599281,0.00052509725,0.0003062176],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001378432,0.00031716286,0.0005639857,0.000008112907,0.000027427352,0.0000053690565,0.009330232,0.0021825328,0.01419214,0.12951946,0.014142176,0.82833296],"study_design_scores_gemma":[0.00023203716,0.00029803478,0.0001891168,0.000017737704,0.000007381385,0.000011338305,0.047521275,0.31515962,0.028740164,0.5760917,0.031574268,0.00015730955],"about_ca_topic_score_codex":0.0004787965,"about_ca_topic_score_gemma":0.0016213981,"teacher_disagreement_score":0.892759,"about_ca_system_score_codex":0.00020350543,"about_ca_system_score_gemma":0.0005551853,"threshold_uncertainty_score":0.96281815},"labels":[],"label_agreement":null},{"id":"W2108508218","doi":"10.5430/air.v1n1p55","title":"Anomalous pattern based clustering of mental tasks with subject independent learning – some preliminary results","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Subject (documents); Cluster analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Electroencephalography; Class (philosophy); Task (project management); Machine learning; Psychology; Engineering","score_opus":0.15134609492755205,"score_gpt":0.3828638795828137,"score_spread":0.23151778465526165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108508218","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9839452,0.00008859199,0.013722242,0.00039329645,0.0003030989,0.00047280898,0.000021224307,0.00007399519,0.0009795517],"genre_scores_gemma":[0.9992113,0.000020604797,0.00023841855,0.00007284212,0.0002182181,0.00002879516,0.000006224048,0.000032824828,0.00017077879],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958933,0.0007826565,0.00053828483,0.0005648543,0.0012029796,0.0010179116],"domain_scores_gemma":[0.99781936,0.0012785012,0.0001453237,0.00039775946,0.00014105249,0.00021797484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022996434,0.00020406487,0.00023134606,0.0003863142,0.00037372665,0.00013599487,0.0006452304,0.0000988473,0.000090145775],"category_scores_gemma":[0.00060560415,0.00016871095,0.00007028411,0.00057933974,0.00046921973,0.0005019482,0.00039887152,0.00079073367,0.00017875434],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006105388,0.0017974294,0.010298278,0.00023875623,0.000037267142,0.00019614621,0.013532384,0.01317949,0.64627,0.0017273743,0.00015914194,0.30645835],"study_design_scores_gemma":[0.000097285985,0.002031678,0.0006908722,0.00015320333,0.000004035826,0.000032887827,0.001471194,0.06729009,0.9276591,0.00019967144,0.00016853216,0.00020143202],"about_ca_topic_score_codex":0.00029840376,"about_ca_topic_score_gemma":0.00005041524,"teacher_disagreement_score":0.30625692,"about_ca_system_score_codex":0.000099344936,"about_ca_system_score_gemma":0.000102957274,"threshold_uncertainty_score":0.6879833},"labels":[],"label_agreement":null},{"id":"W2113414203","doi":"10.5430/air.v1n2p22","title":"Integrated ANN model for earthfill dams seepage analysis: Sattarkhan Dam in Iran","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Piezometer; Artificial neural network; Backpropagation; Radial basis function; Multilayer perceptron; Engineering; Finite element method; Artificial intelligence; Computer science; Structural engineering; Geotechnical engineering; Groundwater; Aquifer","score_opus":0.23428253314448014,"score_gpt":0.4086283935201503,"score_spread":0.17434586037567015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113414203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29209483,0.00048990385,0.7044518,0.00009590283,0.00028393324,0.000498412,0.00009378687,0.00028630547,0.0017051138],"genre_scores_gemma":[0.9962526,0.00009657497,0.0029958687,0.00000946628,0.00015535424,0.00012857874,0.00007752992,0.000049633894,0.00023440848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975179,0.00008845389,0.00049350667,0.0002879082,0.00041646234,0.0011957776],"domain_scores_gemma":[0.9988432,0.0003063546,0.000015830276,0.00043566158,0.0001393887,0.0002595884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025353606,0.0002082095,0.00031988646,0.00093095924,0.00011426277,0.00008197251,0.00036526885,0.00015977953,0.00007983038],"category_scores_gemma":[0.00027162145,0.00020551975,0.0001524319,0.0024624877,0.00009405353,0.00029756298,0.000054953474,0.0006111204,0.00018906126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041422227,0.00010675661,0.001342102,0.00005238852,0.00012733616,0.000002906808,0.00219314,0.9523366,0.0050585633,0.003813894,0.00026351886,0.03466139],"study_design_scores_gemma":[0.000027558106,0.000032715103,0.00062859687,0.000023862955,0.00002479153,6.975679e-7,0.00085432606,0.9833814,0.012133545,0.0018413336,0.000822238,0.00022893935],"about_ca_topic_score_codex":0.00016607955,"about_ca_topic_score_gemma":0.000895922,"teacher_disagreement_score":0.70415777,"about_ca_system_score_codex":0.00017312348,"about_ca_system_score_gemma":0.000032570057,"threshold_uncertainty_score":0.83808523},"labels":[],"label_agreement":null},{"id":"W2114191719","doi":"10.5430/air.v1n1p84","title":"Predicting the rates of cross-pollination between GM and non-GM crops using RBFNN with SVM and bootstrap approach","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Viral Infectious Diseases and Gene Expression in Insects","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pollination; Support vector machine; Mean squared error; Cross-validation; Agriculture; Computer science; Statistics; Machine learning; Biotechnology; Mathematics; Artificial intelligence; Biology; Pollen; Botany; Ecology","score_opus":0.18616843124228546,"score_gpt":0.4585223595847237,"score_spread":0.2723539283424382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114191719","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9904823,0.00094784086,0.0077809957,0.000037066548,0.000043062748,0.00027717973,0.00001339436,0.000004806868,0.00041337905],"genre_scores_gemma":[0.99905264,0.00013187366,0.0003191136,0.000010163832,0.00039676335,0.00002086629,0.000013990928,0.000014981485,0.000039612318],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987442,0.00015149194,0.00022672332,0.0002548692,0.00026042655,0.00036228885],"domain_scores_gemma":[0.9992293,0.00009518224,0.00007408104,0.00021463085,0.000263877,0.00012293718],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010128081,0.00010867091,0.0001124464,0.00006603087,0.00044049363,0.000113855895,0.00013040254,0.000106404885,0.000008741364],"category_scores_gemma":[0.00018635637,0.00007391159,0.000030777774,0.00020858586,0.00069096265,0.00002559071,0.00016800733,0.0001821069,0.000001582236],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019528739,0.0001507148,0.63232493,0.00010846573,0.00007270502,5.9195685e-7,0.0008601506,0.00056397484,0.35041842,0.0007201807,0.00004567285,0.01453892],"study_design_scores_gemma":[0.000067972025,0.00047619583,0.06816439,0.00004841319,0.000022385797,0.0000111290165,0.0017719074,0.004822109,0.9232464,0.0010893736,0.0001174273,0.00016229073],"about_ca_topic_score_codex":0.0005061588,"about_ca_topic_score_gemma":0.00002852061,"teacher_disagreement_score":0.572828,"about_ca_system_score_codex":0.000010590493,"about_ca_system_score_gemma":0.00006462658,"threshold_uncertainty_score":0.33879656},"labels":[],"label_agreement":null},{"id":"W2114623428","doi":"10.5430/air.v2n2p47","title":"Prediction of exchange rates using averaging intrinsic mode function and multiclass support vector regression","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Imperial College London","keywords":"Support vector machine; Hilbert–Huang transform; Filter (signal processing); Regression; Computer science; Mode (computer interface); Function (biology); Nonlinear system; Series (stratigraphy); Multiclass classification; Relevance vector machine; Regression analysis; Artificial intelligence; Algorithm; Mathematics; Machine learning; Statistics","score_opus":0.5693082736622433,"score_gpt":0.5277332758791052,"score_spread":0.04157499778313811,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114623428","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88424397,0.00009340965,0.11374879,0.00026460804,0.0005407922,0.0005556697,0.000009563789,0.000031544583,0.0005116826],"genre_scores_gemma":[0.993244,0.000032971435,0.0061492743,0.000012432667,0.00021143332,0.00003919397,0.0000032483772,0.00001957019,0.0002879112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943962,0.0014693603,0.0009246083,0.00065883744,0.0019905518,0.00056040834],"domain_scores_gemma":[0.99252707,0.0047368016,0.00023429861,0.0005539895,0.0017428735,0.00020495357],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011745212,0.00016129769,0.00030438296,0.0009598419,0.00042597883,0.00034156832,0.00043401477,0.00013963686,0.0013326361],"category_scores_gemma":[0.013145022,0.0001224319,0.000070631584,0.001798359,0.00050241995,0.0007299294,0.0004142587,0.00043415534,0.00021265078],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016454572,0.00007528125,0.014725746,0.000027224016,0.000012190783,0.0000028460433,0.0012150466,0.000329221,0.14202282,0.00084345235,0.00050206506,0.84007955],"study_design_scores_gemma":[0.00006303779,0.0003954733,0.021743497,0.0001244354,0.000011074392,0.000013995674,0.004457188,0.6438737,0.17048196,0.15848099,0.00019123856,0.00016340689],"about_ca_topic_score_codex":0.001353799,"about_ca_topic_score_gemma":0.000061224724,"teacher_disagreement_score":0.83991617,"about_ca_system_score_codex":0.000113721566,"about_ca_system_score_gemma":0.00013141605,"threshold_uncertainty_score":0.99958026},"labels":[],"label_agreement":null},{"id":"W2119512015","doi":"10.5430/air.v1n2p86","title":"The application of Gaussian processes in the predictions of permeability across mammalian and polydimethylsiloxane membranes","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advancements in Transdermal Drug Delivery","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Polydimethylsiloxane; Membrane; Covariance; Biological system; Permeability (electromagnetism); Gaussian process; Quantitative structure–activity relationship; Applicability domain; Membrane permeability; Gaussian; Computer science; Biochemical engineering; Mathematics; Materials science; Chemistry; Statistics; Machine learning; Nanotechnology; Engineering; Biology; Computational chemistry","score_opus":0.3121519440946197,"score_gpt":0.5591621568442808,"score_spread":0.24701021274966112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119512015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9884038,0.0035222482,0.004205285,0.00195333,0.0001715986,0.00096106104,0.0000508657,0.000011512543,0.0007203094],"genre_scores_gemma":[0.9979008,0.0016919624,0.00005565797,0.000035101435,0.000095708005,0.0001681542,0.0000042152956,0.000007402201,0.000041000818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99742746,0.0010056347,0.0004683001,0.00018378904,0.00037381786,0.0005409812],"domain_scores_gemma":[0.9963099,0.0029646663,0.00008603636,0.00027579098,0.00028304177,0.00008060032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052253846,0.00009358534,0.00013225312,0.00006997671,0.0005140065,0.000016223508,0.00045187224,0.00012005276,0.000060834474],"category_scores_gemma":[0.0007014073,0.000060534596,0.00003058664,0.0007668445,0.0019702832,0.0001653751,0.000086019536,0.0006720457,0.000015738695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001548313,0.0028858196,0.130474,0.0013391776,0.00012031069,0.000003232886,0.0736569,0.0025727383,0.13118005,0.050890256,0.00006356715,0.6052656],"study_design_scores_gemma":[0.00011062479,0.0002215699,0.015231431,0.000030518248,0.000036775593,0.000010905895,0.037602074,0.0057606865,0.9110229,0.015862186,0.013946953,0.00016335689],"about_ca_topic_score_codex":0.000297659,"about_ca_topic_score_gemma":0.0006822055,"teacher_disagreement_score":0.77984285,"about_ca_system_score_codex":0.000031219624,"about_ca_system_score_gemma":0.00007446465,"threshold_uncertainty_score":0.72595906},"labels":[],"label_agreement":null},{"id":"W2123135681","doi":"10.5430/air.v1n2p56","title":"Optimal location and capacity of multi-distributed generation for loss reduction and voltage profile improvement using imperialist competitive algorithm","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Optimal Power Flow Distribution","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Sizing; Voltage; Imperialist competitive algorithm; Particle swarm optimization; Reduction (mathematics); Power (physics); Algorithm; Mathematical optimization; Variable (mathematics); Integer (computer science); Computer science; Control theory (sociology); Mathematics; Engineering; Multi-swarm optimization; Electrical engineering; Artificial intelligence; Physics","score_opus":0.14820558316492208,"score_gpt":0.3734391937443392,"score_spread":0.22523361057941713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123135681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5005248,0.00011926964,0.49840233,0.0000114603445,0.00018609656,0.00048208458,0.00024978849,0.000020909945,0.0000032100368],"genre_scores_gemma":[0.9766975,0.000046806308,0.022593273,6.887797e-7,0.00030528838,0.00007619306,0.00026045137,0.000016707563,0.0000030725064],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880606,0.00005528872,0.00032288727,0.00020333967,0.00022024284,0.00039218197],"domain_scores_gemma":[0.9990955,0.0000569363,0.00004449576,0.00012354442,0.0005712756,0.00010827478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010812039,0.00011727647,0.0001416444,0.00010890709,0.0001961717,0.000067747154,0.00005795133,0.00009875999,0.000013827674],"category_scores_gemma":[0.00016801672,0.00012877998,0.000022591748,0.00028458485,0.000283707,0.00036637433,0.000044773176,0.0001550737,0.0000036872386],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035396577,0.000106861226,0.00004368769,0.00012818088,0.000026243439,1.9910601e-7,0.00066267804,0.0049936436,0.9572954,0.005313144,0.000024839774,0.031369727],"study_design_scores_gemma":[0.00003459855,0.000089585716,0.00006314012,0.000014017669,0.000007930684,0.0000030580727,0.0007137646,0.5008221,0.49807495,0.00008185255,0.000023016972,0.000071977556],"about_ca_topic_score_codex":0.00024398178,"about_ca_topic_score_gemma":0.000016337563,"teacher_disagreement_score":0.49582848,"about_ca_system_score_codex":0.00022487086,"about_ca_system_score_gemma":0.000032301214,"threshold_uncertainty_score":0.5251495},"labels":[],"label_agreement":null},{"id":"W2125602612","doi":"10.5430/air.v3n2p41","title":"Partitioning trees: A global multiclass classification technique for SVMs","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Directed acyclic graph; Multiclass classification; Computer science; Support vector machine; Machine learning; Classifier (UML); Artificial intelligence; Decision tree; Node (physics); Binary classification; Binary decision diagram; Graph; Binary number; Pattern recognition (psychology); Data mining; Theoretical computer science; Mathematics; Algorithm","score_opus":0.28185344579650184,"score_gpt":0.4575787784853236,"score_spread":0.17572533268882173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125602612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004035137,0.000023603176,0.98999393,0.0026478812,0.00018077505,0.0007182115,0.0000057733046,0.00014203003,0.002252676],"genre_scores_gemma":[0.9474936,0.000021279799,0.05135818,0.00008297132,0.00016176142,0.0007885913,0.000011838526,0.000008551717,0.00007319484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976808,0.00032662787,0.00036040207,0.0005248054,0.00053379673,0.00057359674],"domain_scores_gemma":[0.9980206,0.0006388239,0.00006508645,0.0005135178,0.00060879864,0.00015319155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027038974,0.000114135306,0.00012978225,0.00015896629,0.00052181754,0.00036764142,0.00075410336,0.00012770329,0.000035783312],"category_scores_gemma":[0.00093258393,0.00010661388,0.000078621284,0.00086751324,0.00016406187,0.00048032138,0.00015627644,0.00021948249,0.0005515929],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027888094,0.00012795802,0.00011866542,0.000016832037,0.0000046751106,8.9206424e-7,0.00022702872,0.0001478292,0.063103706,0.3978909,0.0008428425,0.5374908],"study_design_scores_gemma":[0.000027485587,0.00018516407,0.00010315395,0.000056548994,0.0000017270885,0.0000027921371,0.00035178047,0.41671753,0.26922068,0.30918145,0.004024047,0.00012765803],"about_ca_topic_score_codex":0.000080695696,"about_ca_topic_score_gemma":0.00013972382,"teacher_disagreement_score":0.9434585,"about_ca_system_score_codex":0.00011628113,"about_ca_system_score_gemma":0.000093394614,"threshold_uncertainty_score":0.7089795},"labels":[],"label_agreement":null},{"id":"W2125875816","doi":"10.5430/air.v1n1p1","title":"Two-Strategy reinforcement group cooperation based symbiotic evolution for TSK-type fuzzy controller design","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Science Council","keywords":"Crossover; Controller (irrigation); Set (abstract data type); Reinforcement; Computer science; Population; Fuzzy logic; SIGNAL (programming language); Reinforcement learning; Group (periodic table); Compensation (psychology); Control (management); Artificial intelligence; Base (topology); Engineering; Mathematics; Biology; Psychology","score_opus":0.2209426353081047,"score_gpt":0.4114545148173586,"score_spread":0.1905118795092539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125875816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011464511,0.0002685417,0.9951657,0.0010201836,0.00026689348,0.0014518623,0.000002945893,0.00009303713,0.00058439624],"genre_scores_gemma":[0.94116265,0.000016718997,0.057744794,0.00006842691,0.00037599902,0.0003942597,0.000022292608,0.0000126785335,0.0002021705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973807,0.00031978122,0.0004528453,0.00041172872,0.00058559433,0.000849392],"domain_scores_gemma":[0.9975178,0.0008116272,0.00007230676,0.00046103733,0.0009095762,0.0002276187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003557712,0.00015055816,0.00015437928,0.00024265643,0.0008382701,0.00025374177,0.0006389918,0.00009318478,0.000049179398],"category_scores_gemma":[0.00030612442,0.00014102011,0.00006983678,0.0011902411,0.00016297399,0.00075659674,0.00010206281,0.00024715258,0.00049893535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059185073,0.00025936338,0.000033961172,0.000009638981,0.000011346064,5.151733e-7,0.00012281095,0.047343813,0.01707791,0.91651624,0.00044935933,0.018115858],"study_design_scores_gemma":[0.00010127146,0.00042234908,0.000092644106,0.0000143801935,0.0000057149728,0.000002936338,0.00014650996,0.9121759,0.022616103,0.0637949,0.00045468597,0.00017262617],"about_ca_topic_score_codex":0.00013008581,"about_ca_topic_score_gemma":0.000019418747,"teacher_disagreement_score":0.9400162,"about_ca_system_score_codex":0.00028839606,"about_ca_system_score_gemma":0.00029434427,"threshold_uncertainty_score":0.6447381},"labels":[],"label_agreement":null},{"id":"W2130323377","doi":"10.5430/air.v1n1p18","title":"Swine influenza inspired optimization algorithm and its application to multimodal function optimization and noise removal","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Algorithm; Optimization algorithm; Population; Computer science; Frame (networking); Convergence (economics); Noise (video); Optimization problem; Mathematical optimization; Mathematics; Artificial intelligence; Medicine","score_opus":0.1023294243914588,"score_gpt":0.3938904633862804,"score_spread":0.2915610389948216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130323377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012436576,0.00022182653,0.9848998,0.001122492,0.00008469398,0.0008470419,0.0000021926767,0.0002129961,0.00017235274],"genre_scores_gemma":[0.57279587,0.00009271459,0.42647368,0.00029214442,0.00015151083,0.00014077009,0.0000107829865,0.00001641621,0.000026151416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789995,0.00028560488,0.000370029,0.00046268638,0.00054446905,0.0004372315],"domain_scores_gemma":[0.9984786,0.00016794617,0.00007229994,0.00034310896,0.0006586771,0.00027934767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002332136,0.00014077795,0.00013309876,0.0004943513,0.0003525677,0.00030627046,0.00029990994,0.00013751972,0.000015902917],"category_scores_gemma":[0.00036942633,0.00014667331,0.000019961533,0.001258372,0.000076937926,0.0015055937,0.00033322145,0.0002521762,0.00006689353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049683353,0.00016689344,0.00011771547,0.000015457816,0.000009360683,0.0000010042098,0.0026172628,0.25116152,0.007834087,0.07881217,0.00003126505,0.65918356],"study_design_scores_gemma":[0.00003175662,0.00014871826,0.00016805244,0.000012536437,0.0000031514185,0.00000822098,0.00011577028,0.9605293,0.035991114,0.002277849,0.0005626327,0.00015088852],"about_ca_topic_score_codex":0.000112011934,"about_ca_topic_score_gemma":0.000008999776,"teacher_disagreement_score":0.7093678,"about_ca_system_score_codex":0.00007762089,"about_ca_system_score_gemma":0.000056915094,"threshold_uncertainty_score":0.5981164},"labels":[],"label_agreement":null},{"id":"W2132816823","doi":"10.5430/air.v1n1p63","title":"Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jadavpur University; Purdue University","keywords":"Hyperspectral imaging; Imaging spectrometer; Support vector machine; Benchmark (surveying); Artificial intelligence; Computer science; Kernel (algebra); Pattern recognition (psychology); VNIR; Weighting; Pixel; Remote sensing; Spectrometer; Mathematics; Geography","score_opus":0.15859631677193092,"score_gpt":0.37395208129258334,"score_spread":0.21535576452065242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132816823","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71442544,0.00015984582,0.26570898,0.00080893317,0.0014812596,0.0006961223,0.000007634369,0.0007585499,0.015953237],"genre_scores_gemma":[0.934541,0.000043214073,0.063489124,0.0000075008406,0.001686564,0.000027711885,0.00004024207,0.00008817174,0.00007651111],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965529,0.0003130274,0.0007085274,0.00040617477,0.00072678237,0.0012926013],"domain_scores_gemma":[0.99822265,0.00042626853,0.000092577146,0.0005690926,0.00038679826,0.0003026154],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002863778,0.00026737744,0.0002702563,0.00044961282,0.00043114982,0.00044460723,0.00039575552,0.00017877843,0.00022296538],"category_scores_gemma":[0.00076177984,0.00028244746,0.00010755592,0.001051673,0.00038645175,0.0010682825,0.00008059181,0.0008797701,0.0012875433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060302515,0.000085106185,0.00015245518,0.000035462243,0.000021921835,0.0000058772084,0.0010749496,0.00097148033,0.9407429,0.01317315,0.00031667974,0.043359723],"study_design_scores_gemma":[0.000049862167,0.000046974194,0.00054565334,0.000040616953,0.000010778866,0.00001793017,0.0022092937,0.43476638,0.5557166,0.0058613876,0.00040687577,0.00032769615],"about_ca_topic_score_codex":0.0000622037,"about_ca_topic_score_gemma":0.00001688159,"teacher_disagreement_score":0.43379492,"about_ca_system_score_codex":0.0005689817,"about_ca_system_score_gemma":0.00008438083,"threshold_uncertainty_score":0.99996275},"labels":[],"label_agreement":null},{"id":"W2138089115","doi":"10.5430/air.v2n4p75","title":"An interactive fuzzy satisficing method for random fuzzy multiobjective linear programming problems through fractile criteria optimization with possibility","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Optimization and Mathematical Programming","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Satisficing; Mathematical optimization; Goal programming; Fuzzy logic; Linear programming; Pareto principle; Preference; Mathematics; Computer science; Artificial intelligence; Statistics","score_opus":0.09963363422740967,"score_gpt":0.4310779230352048,"score_spread":0.33144428880779514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138089115","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006986981,0.00003743316,0.9870921,0.00013203094,0.00013479119,0.003957785,0.0000080399195,0.0004060935,0.0012447431],"genre_scores_gemma":[0.47375488,0.000014362615,0.5250101,0.000012826681,0.00010663698,0.0009974957,0.000029045304,0.000056655506,0.000018009818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715376,0.00037102,0.00064366014,0.00056636095,0.0004804606,0.0007847095],"domain_scores_gemma":[0.9962339,0.0017154202,0.00008545117,0.00041115173,0.001352414,0.00020170165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018516103,0.00027249518,0.00036629508,0.00020978916,0.0004280662,0.00057179114,0.00028006342,0.00016341876,0.00028036558],"category_scores_gemma":[0.0011879769,0.00023052315,0.00008590052,0.0008016377,0.00020047792,0.0015323965,0.000052932468,0.00056391594,0.000074884134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023233719,0.00034789732,0.000017904418,0.00026178578,0.00007319219,0.0000015321773,0.008714809,0.72914845,0.0040477137,0.0023080918,0.00003125087,0.25481504],"study_design_scores_gemma":[0.00017000435,0.00043228077,0.000005106946,0.00012008326,0.00001726876,0.000003837776,0.008138616,0.93901736,0.029512191,0.022210332,0.000099631354,0.0002733081],"about_ca_topic_score_codex":0.00072195637,"about_ca_topic_score_gemma":0.00017877406,"teacher_disagreement_score":0.4667679,"about_ca_system_score_codex":0.00019565377,"about_ca_system_score_gemma":0.000065840206,"threshold_uncertainty_score":0.94004613},"labels":[],"label_agreement":null},{"id":"W2138601625","doi":"10.5430/air.v4n2p45","title":"Automated selection of a software effort estimation model based on accuracy and uncertainty","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Software Engineering Research","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Machine learning; Software; Selection (genetic algorithm); Estimation; Data mining; Process (computing); Model selection; Bayesian probability; Artificial intelligence; Software development; Engineering; Systems engineering","score_opus":0.14942728062778057,"score_gpt":0.4149915128714236,"score_spread":0.265564232243643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138601625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14027768,0.000018346143,0.8583958,0.0003521948,0.000066519184,0.00033700286,0.0000024147412,0.0004986926,0.000051344723],"genre_scores_gemma":[0.92362416,0.000004578777,0.07624211,0.000016273549,0.000022847462,0.000051708223,0.0000042759425,0.000014114744,0.000019907586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716055,0.00017202951,0.00033388997,0.00045281768,0.0013638195,0.0005169031],"domain_scores_gemma":[0.9959023,0.0022530432,0.000052682408,0.0004912304,0.00104501,0.0002557576],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0031793478,0.0001323071,0.00016675529,0.0006426874,0.000153705,0.00018978918,0.00066002534,0.00010493343,0.000007917416],"category_scores_gemma":[0.010080861,0.00012572022,0.000035066583,0.0017520644,0.00017879091,0.00039739808,0.00021969376,0.00041631388,0.00006869051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060565497,0.000102453814,0.00074519665,0.000036726688,0.0000054745383,0.000004000083,0.00040463623,0.87629515,0.0006440276,0.0076824888,0.00026950275,0.11374977],"study_design_scores_gemma":[0.000040657487,0.00034857242,0.00031014453,0.000052541323,0.0000012753218,0.0000028394286,0.000041468247,0.9506994,0.032272525,0.016110547,0.00001187422,0.00010815118],"about_ca_topic_score_codex":0.00026024348,"about_ca_topic_score_gemma":0.000021865406,"teacher_disagreement_score":0.7833465,"about_ca_system_score_codex":0.00022993505,"about_ca_system_score_gemma":0.000670718,"threshold_uncertainty_score":0.99825764},"labels":[],"label_agreement":null},{"id":"W2139610220","doi":"10.5430/air.v2n1p12","title":"Worm-like robotic systems: Generation, analysis and shift of gaits using adaptive control","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Modular Robots and Swarm Intelligence","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Gait; Actuator; Controller (irrigation); Trajectory; Ground reaction force; Contact force; Computer science; Crawling; Point (geometry); Simulation; Kinematics; Engineering; Control engineering; Artificial intelligence; Mathematics; Control (management); Physics","score_opus":0.21437000628470426,"score_gpt":0.37054773946062236,"score_spread":0.1561777331759181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139610220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23753093,0.0025979497,0.75906485,0.000017435703,0.00027710822,0.00029184914,0.0000074755694,0.000036080615,0.00017633129],"genre_scores_gemma":[0.9983713,0.0001734762,0.0010932126,0.0000062682434,0.0002754457,0.000022774793,0.000004410516,0.00002555172,0.000027563216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768573,0.00026334208,0.0005643175,0.00024903633,0.0005640118,0.0006735827],"domain_scores_gemma":[0.99877584,0.00027521382,0.000051479758,0.00033008718,0.0003418904,0.00022549667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019267305,0.0001690812,0.0003760117,0.00060029095,0.00018197985,0.000101261576,0.00021422016,0.00011782456,0.000090121255],"category_scores_gemma":[0.00011797767,0.00015926313,0.00008559038,0.0014416277,0.0002250024,0.00029175606,0.000052559873,0.00030173134,0.000052555653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020556976,0.000074482414,0.0027394872,0.0000624184,0.0004168587,0.000003513819,0.0016183066,0.94311357,0.019875811,0.020313146,0.00003213195,0.011729723],"study_design_scores_gemma":[0.0000146725715,0.000046525045,0.0005131261,0.000027689213,0.00010840859,0.0000030197436,0.0008481615,0.9555755,0.042198125,0.00046829283,0.000026656448,0.00016976692],"about_ca_topic_score_codex":0.0008563084,"about_ca_topic_score_gemma":0.00020123106,"teacher_disagreement_score":0.76084036,"about_ca_system_score_codex":0.000094578485,"about_ca_system_score_gemma":0.00003696242,"threshold_uncertainty_score":0.6494562},"labels":[],"label_agreement":null},{"id":"W2142106615","doi":"10.5430/air.v4n2p119","title":"A query suggestion method combining TF-IDF and Jaccard Coefficient for interactive web search","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Jaccard index; Information retrieval; Computer science; Ranking (information retrieval); Relevance (law); Web search query; Query expansion; Search engine; Web query classification; Data mining; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.31398555866366706,"score_gpt":0.5142308243938665,"score_spread":0.2002452657301994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142106615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007908675,0.00026358027,0.9879253,0.0019816933,0.00019992242,0.000861701,0.000005513942,0.00020789233,0.00064572593],"genre_scores_gemma":[0.8272508,0.00012136266,0.17205648,0.000083248175,0.00012157315,0.00014117373,0.0000050075855,0.000022005179,0.000198395],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965357,0.00065761496,0.00041510758,0.00073813245,0.00087990134,0.0007735545],"domain_scores_gemma":[0.9950554,0.002307173,0.00006734057,0.00052718277,0.0016981075,0.000344814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071947873,0.00017855763,0.0002631946,0.0005214831,0.00036185098,0.00051476114,0.00083629595,0.00011506807,0.000007111928],"category_scores_gemma":[0.0027007922,0.00016644048,0.00007156971,0.0012841853,0.0002963556,0.0008431449,0.00077107135,0.00063594687,0.000072452705],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025668927,0.0001871976,0.000056445282,0.000031585998,0.00001849511,0.0000227123,0.0036113134,0.00040094127,0.013173036,0.1495556,0.0009586924,0.8317273],"study_design_scores_gemma":[0.00007056513,0.0010024714,0.000013129097,0.00007541338,0.0000036410215,0.000021280408,0.0027499544,0.40499455,0.45894662,0.12783132,0.004080169,0.00021089561],"about_ca_topic_score_codex":0.00017513036,"about_ca_topic_score_gemma":0.000022678469,"teacher_disagreement_score":0.8315164,"about_ca_system_score_codex":0.00022411694,"about_ca_system_score_gemma":0.0004048108,"threshold_uncertainty_score":0.6787246},"labels":[],"label_agreement":null},{"id":"W2148555761","doi":"10.5430/air.v1n2p171","title":"Application of Bayesian Network to stock price prediction","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Toyota Motor Corporation","keywords":"Stock price; Stock (firearms); Econometrics; Computer science; Mean squared prediction error; Bayesian probability; Algorithm; Economics; Artificial intelligence; Series (stratigraphy); Engineering","score_opus":0.1690476692080007,"score_gpt":0.4099954511090571,"score_spread":0.2409477819010564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148555761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00694113,0.000118903576,0.9897229,0.0007976696,0.00024751396,0.0003889357,0.000002106651,0.00009097564,0.0016898443],"genre_scores_gemma":[0.96372175,0.000016651915,0.035633575,0.000065369284,0.0004075055,0.00008968555,0.0000020478672,0.000009343412,0.000054051266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748,0.00023311158,0.00041580584,0.00035552122,0.00072606985,0.0007895172],"domain_scores_gemma":[0.9981877,0.00027081688,0.00006144368,0.00067149114,0.0004854614,0.00032308183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003247408,0.000104356404,0.00013954686,0.00021160745,0.00023339542,0.00009242185,0.0009433824,0.00009362149,0.000022929005],"category_scores_gemma":[0.00021870839,0.00010226406,0.000043316042,0.0019291595,0.00009086292,0.00045458335,0.0003121294,0.00031597036,0.00040923135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017016886,0.0001436031,0.0014349942,0.000013316579,0.0000066899443,3.413852e-7,0.0011885138,0.005480721,0.0059497505,0.4891275,0.0006142424,0.49602333],"study_design_scores_gemma":[0.000012249467,0.00029077902,0.0015934887,0.000048990125,0.0000032697037,0.000004564324,0.00021932616,0.8092802,0.061356165,0.12489883,0.0020962846,0.00019583052],"about_ca_topic_score_codex":0.00016972588,"about_ca_topic_score_gemma":0.000016828651,"teacher_disagreement_score":0.9567806,"about_ca_system_score_codex":0.000075178636,"about_ca_system_score_gemma":0.000085947155,"threshold_uncertainty_score":0.52599776},"labels":[],"label_agreement":null},{"id":"W2148882677","doi":"10.5430/air.v5n1p78","title":"Singular-minutiae points relationship-based approach to fingerprint matching","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Minutiae; Fingerprint (computing); Pattern recognition (psychology); Matching (statistics); Artificial intelligence; Computer science; Orientation (vector space); Fingerprint recognition; Feature (linguistics); Point (geometry); Mathematics; Statistics","score_opus":0.43513860947884847,"score_gpt":0.44013394823791774,"score_spread":0.004995338759069268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148882677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0196256,0.000057590223,0.9680619,0.006650407,0.0003363403,0.0004003086,0.0000018544671,0.00015599972,0.004709952],"genre_scores_gemma":[0.8769808,0.0000012530069,0.122468196,0.0002035835,0.000079803176,0.00004812433,0.0000068290296,0.000011369464,0.000200028],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99605006,0.0005957233,0.0004797808,0.0007191104,0.0015009496,0.00065435644],"domain_scores_gemma":[0.9967883,0.00062526955,0.000060923314,0.0010393441,0.0009374462,0.0005487018],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007569611,0.00013806434,0.00015849117,0.0013179886,0.0004055252,0.00087544305,0.001675631,0.00012124352,0.000024076777],"category_scores_gemma":[0.0029991514,0.0001393723,0.00007066518,0.005558794,0.00016208219,0.00038385417,0.0004992848,0.00056300964,0.0024637606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029124418,0.0005368511,0.0004129885,0.00002132304,0.00000766712,0.000010363298,0.007557908,0.002522651,0.0008238027,0.8728948,0.0015628425,0.113619685],"study_design_scores_gemma":[0.00006766784,0.00017488617,0.0012271041,0.000044439344,0.0000036656836,0.000010940354,0.002930005,0.40323332,0.045668337,0.5373392,0.008796001,0.0005044211],"about_ca_topic_score_codex":0.0004395635,"about_ca_topic_score_gemma":0.00003331501,"teacher_disagreement_score":0.85735524,"about_ca_system_score_codex":0.00027829097,"about_ca_system_score_gemma":0.00049084553,"threshold_uncertainty_score":0.99831295},"labels":[],"label_agreement":null},{"id":"W2151103537","doi":"10.5430/air.v2n2p77","title":"An adaptive methodology to discretize and select features","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad","keywords":"Computer science; Feature (linguistics); Discretization; Artificial intelligence; Machine learning; Data mining; Feature selection; Pattern recognition (psychology); Mathematics","score_opus":0.33541271962095504,"score_gpt":0.4778933484378903,"score_spread":0.14248062881693524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151103537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057406295,0.000089034045,0.9304203,0.010734454,0.00006358267,0.0005920545,0.0000011695727,0.00007802178,0.0006151115],"genre_scores_gemma":[0.907644,0.000031665968,0.09156808,0.0002607186,0.00013554929,0.00019036162,8.566666e-7,0.00000759811,0.00016120044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978885,0.00054329913,0.00018050225,0.00053617463,0.00030961513,0.0005418976],"domain_scores_gemma":[0.99795663,0.00088126067,0.000020613088,0.0005086978,0.00032057418,0.0003122131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012185357,0.000098211465,0.00012641729,0.00016396197,0.00035636328,0.00042814336,0.00090857,0.00006273568,0.000052404903],"category_scores_gemma":[0.00017264273,0.000080119804,0.000023458071,0.0010823705,0.00018988308,0.00038672503,0.00033575745,0.0003508212,0.00045355095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008013263,0.000031804506,0.000017836952,0.0000011107469,0.0000031454267,0.0000017350918,0.0005182942,0.00016410518,0.02321904,0.54340404,0.0008302489,0.43180063],"study_design_scores_gemma":[0.000008966251,0.00073820114,0.0014901498,0.000009851685,0.0000014655038,0.000013228236,0.000783545,0.098186485,0.1059264,0.791776,0.0008687899,0.0001969456],"about_ca_topic_score_codex":0.0010385684,"about_ca_topic_score_gemma":0.00027478108,"teacher_disagreement_score":0.85023767,"about_ca_system_score_codex":0.000022112237,"about_ca_system_score_gemma":0.00004434287,"threshold_uncertainty_score":0.5829631},"labels":[],"label_agreement":null},{"id":"W2154204898","doi":"10.5430/air.v2n3p59","title":"A comparison of organization-centered and agent-centered multi-agent systems","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Java; Multi-agent system; Focus (optics); Middleware (distributed applications); Character (mathematics); Software engineering; Artificial intelligence; Human–computer interaction; Programming language; Distributed computing","score_opus":0.3403236919189198,"score_gpt":0.4377993822394393,"score_spread":0.0974756903205195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154204898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39843273,0.00043143003,0.59878296,0.00041134417,0.00061755104,0.0011482709,0.0000045326647,0.00007599732,0.00009519882],"genre_scores_gemma":[0.99718374,0.00007884403,0.0023637998,0.000013320398,0.00007401435,0.00005371987,0.000009075416,0.000017734703,0.000205781],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967445,0.0004899176,0.00086983194,0.00053975167,0.0008263783,0.0005296256],"domain_scores_gemma":[0.99753684,0.0002574073,0.00021263245,0.0006165937,0.0011503013,0.00022620813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011075583,0.00016732128,0.00032497142,0.0003604029,0.0003068632,0.0005325926,0.00082111417,0.00010911806,0.000117053736],"category_scores_gemma":[0.000380025,0.00015151239,0.000046205318,0.0010944478,0.00015796426,0.00055372005,0.00042231203,0.0002363124,0.00071507326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048704587,0.0041478183,0.19850627,0.0010073471,0.0002492934,0.000023913792,0.035822418,0.002716453,0.36525905,0.17942187,0.0056988755,0.20709798],"study_design_scores_gemma":[0.00012812496,0.00020708998,0.0093835145,0.00017890996,0.0000054843204,0.0000072572384,0.0039386475,0.8763786,0.1084635,0.0005753172,0.00046536952,0.00026815862],"about_ca_topic_score_codex":0.0020015296,"about_ca_topic_score_gemma":0.000086144166,"teacher_disagreement_score":0.8736622,"about_ca_system_score_codex":0.0000983707,"about_ca_system_score_gemma":0.00007907311,"threshold_uncertainty_score":0.9191058},"labels":[],"label_agreement":null},{"id":"W2156786022","doi":"10.5430/air.v3n4p77","title":"A hybrid knowledge discovery system for oil spillage risks pattern classification","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptive neuro fuzzy inference system; Spillage; Computer science; Artificial neural network; Pruning; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Identification (biology); Fuzzy logic; Engineering; Fuzzy control system","score_opus":0.20543472382091832,"score_gpt":0.4084305841573663,"score_spread":0.202995860336448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156786022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6534648,0.000027805105,0.31456974,0.00059065165,0.00059637753,0.0004107504,0.000015577589,0.00012463266,0.030199667],"genre_scores_gemma":[0.9977141,0.000026455713,0.00017559394,0.000027190052,0.00028561495,0.00022582352,0.00001528424,0.00002289135,0.001507031],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99778587,0.00032725345,0.00036271254,0.00050475524,0.0005082565,0.0005111601],"domain_scores_gemma":[0.99890244,0.00040222783,0.0000718166,0.0003814985,0.00009204452,0.00014995085],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026427289,0.0001269928,0.00013412368,0.00012207228,0.0005297184,0.00021523694,0.00032688002,0.000075325,0.00023691448],"category_scores_gemma":[0.00045318203,0.00011769052,0.00008827236,0.00038370036,0.00032337222,0.00032857258,0.00013467141,0.00022927215,0.0044986694],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032661042,0.00008985402,0.00054138695,0.00005494226,0.000004190495,7.813804e-7,0.00023287884,0.00013429615,0.044045556,0.0071320394,0.00048816268,0.9472433],"study_design_scores_gemma":[0.00008935076,0.0004130049,0.0034497953,0.00009954459,0.000012779714,0.000009074726,0.0020402214,0.23350728,0.7191127,0.013803167,0.02706818,0.0003949042],"about_ca_topic_score_codex":0.0007832196,"about_ca_topic_score_gemma":0.0006740292,"teacher_disagreement_score":0.94684833,"about_ca_system_score_codex":0.0004009068,"about_ca_system_score_gemma":0.000024400599,"threshold_uncertainty_score":0.99627644},"labels":[],"label_agreement":null},{"id":"W2158631537","doi":"10.5430/air.v1n1p46","title":"CVD and PVD coating process modelling by using artificial neural networks","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metal and Thin Film Mechanics","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Materials science; Coating; Physical vapor deposition; Chemical vapor deposition; Artificial neural network; Layer (electronics); Deposition (geology); Titanium; Thin film; Process (computing); Multilayer perceptron; Vapour deposition; Composite material; Metallurgy; Computer science; Artificial intelligence; Nanotechnology","score_opus":0.25997472257084836,"score_gpt":0.38843047230387656,"score_spread":0.1284557497330282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158631537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6140923,0.0013551433,0.38310745,0.000029302593,0.00072960235,0.0002203768,0.000004707052,0.00011226888,0.0003488119],"genre_scores_gemma":[0.99822825,0.00011235815,0.0008954139,0.000014465441,0.0006350387,0.000021687429,0.000008328267,0.000054845943,0.000029612032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973966,0.00013640097,0.00046861905,0.00028543742,0.00055937155,0.0011535853],"domain_scores_gemma":[0.99908704,0.0001681065,0.00004591564,0.00021612189,0.00016895014,0.00031388967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199097,0.00020763242,0.00022536589,0.00016083736,0.00042918033,0.00019805893,0.0002422368,0.00016793494,0.00008088369],"category_scores_gemma":[0.00015297827,0.00020634597,0.00004739229,0.0006721307,0.0001389742,0.0004596251,0.00010671206,0.0008232795,0.000056655055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000423019,0.00011329957,0.0001288207,0.00014026284,0.000034447323,0.000006321142,0.0016140824,0.78953475,0.058695838,0.030236239,0.00011461582,0.11933906],"study_design_scores_gemma":[0.000007725014,0.000027545037,4.97074e-7,0.000026134607,0.000007294609,0.000007779354,0.00097623013,0.8563736,0.12975362,0.01255703,0.00006900947,0.00019351629],"about_ca_topic_score_codex":0.00010594663,"about_ca_topic_score_gemma":0.000012594249,"teacher_disagreement_score":0.38413593,"about_ca_system_score_codex":0.000062064246,"about_ca_system_score_gemma":0.000019583038,"threshold_uncertainty_score":0.84145445},"labels":[],"label_agreement":null},{"id":"W2161861544","doi":"10.5430/air.v3n3p35","title":"Predicting reading comprehension scores from eye movements using artificial neural networks and fuzzy output error","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Task (project management); Eye tracking; Mean squared error; Gaze; Machine learning; Reading (process); Fuzzy logic; Word error rate; Eye movement; Feature (linguistics); Pattern recognition (psychology); Speech recognition; Statistics; Mathematics; Engineering","score_opus":0.19902949490260544,"score_gpt":0.3988840134134479,"score_spread":0.1998545185108425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161861544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58553207,0.00007378441,0.4131676,0.00039753848,0.00036681874,0.00016445843,0.0000019313948,0.00017905183,0.00011677518],"genre_scores_gemma":[0.9921013,0.00001275239,0.0073465295,0.00007151809,0.00040835238,0.00001235683,0.000005103517,0.000022815002,0.000019256022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623543,0.00054169336,0.00057482906,0.00093420886,0.0007311207,0.0009827255],"domain_scores_gemma":[0.99776465,0.0008865212,0.00013422208,0.0006805373,0.00033054606,0.00020351686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001928475,0.00023468243,0.00030766986,0.0004128104,0.0009280442,0.0005495932,0.0009945704,0.00020045319,0.000009463484],"category_scores_gemma":[0.0006333257,0.00022674761,0.000063097184,0.0008861461,0.00054108235,0.00041037294,0.0009664128,0.0008835967,0.000044563018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007326279,0.00018472415,0.03268442,0.00001858738,0.00003780615,0.000038732833,0.00072759256,0.022423504,0.034013033,0.11208966,0.00002849656,0.7976802],"study_design_scores_gemma":[0.000027212187,0.00016139205,0.003932844,0.000085203195,0.000004863326,0.0000032275705,0.0003602152,0.8918392,0.01657545,0.086785235,0.000031887383,0.00019326039],"about_ca_topic_score_codex":0.001518408,"about_ca_topic_score_gemma":0.00020275133,"teacher_disagreement_score":0.8694157,"about_ca_system_score_codex":0.00008548344,"about_ca_system_score_gemma":0.000042352818,"threshold_uncertainty_score":0.9246499},"labels":[],"label_agreement":null},{"id":"W2162922797","doi":"10.5430/air.v2n1p1","title":"Cybercrime detection techniques based on support vector machines","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Cybercrime; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Data mining; Kernel (algebra); Statistical classification; The Internet; Mathematics; World Wide Web","score_opus":0.12596580760462492,"score_gpt":0.39552931059270907,"score_spread":0.2695635029880842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162922797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034299925,0.000073706484,0.945487,0.0018782653,0.0015310567,0.00062671583,0.0000024043122,0.00066144584,0.015439462],"genre_scores_gemma":[0.99522066,0.000025780359,0.0035270029,0.00021587359,0.0007808635,0.00008229157,0.0000020471768,0.000015670148,0.00012978517],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99702704,0.0004759532,0.00033539164,0.00041481975,0.00091914553,0.00082763494],"domain_scores_gemma":[0.9983059,0.0004611358,0.000047417572,0.0006552926,0.00029333716,0.00023692568],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0033975642,0.0001566078,0.00014194335,0.0005409545,0.00056650065,0.00025812883,0.0007528048,0.00015203762,0.00034487442],"category_scores_gemma":[0.00036799556,0.00014235597,0.00008901307,0.001471803,0.00015556518,0.00073445693,0.00020778482,0.0006913126,0.0015341102],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006354923,0.00032277152,0.00006262656,0.00000967403,0.000004201768,0.0000048133343,0.00037581325,0.00006513487,0.024061779,0.05841379,0.0004988177,0.916117],"study_design_scores_gemma":[0.0000107713095,0.00068755174,0.00017292821,0.000021744707,0.0000017393721,0.000008149873,0.000038311973,0.09399512,0.87761855,0.01593067,0.011330357,0.00018411905],"about_ca_topic_score_codex":0.0004297411,"about_ca_topic_score_gemma":0.00020493768,"teacher_disagreement_score":0.96092075,"about_ca_system_score_codex":0.00015411671,"about_ca_system_score_gemma":0.0000771867,"threshold_uncertainty_score":0.9992433},"labels":[],"label_agreement":null},{"id":"W2164750193","doi":"10.5430/air.v1n2p1","title":"Combining coordination mechanisms to improve performance in multi-robot teams","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Stigmergy; Negotiation; Robot; Artificial intelligence; Domain (mathematical analysis); Human–computer interaction; Mechanism (biology); Multi-agent system; Distributed computing","score_opus":0.17958176777957213,"score_gpt":0.4121311132034148,"score_spread":0.23254934542384265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164750193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038333308,0.000020893782,0.95857495,0.00077534775,0.00066330744,0.0005589036,2.9721318e-7,0.000093606235,0.000979365],"genre_scores_gemma":[0.9387139,0.000011232774,0.060470413,0.00008088156,0.00007221506,0.0000866691,0.0000017430507,0.00001708785,0.00054585544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99654657,0.0002750537,0.00050580956,0.00043880497,0.0009790409,0.0012547285],"domain_scores_gemma":[0.9983124,0.00037154672,0.000069127695,0.00062579714,0.00033807143,0.00028303714],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005057547,0.00016110267,0.00017872946,0.0006665781,0.00032336186,0.00029791187,0.0013010233,0.00010784157,0.000035969184],"category_scores_gemma":[0.0007491845,0.00016334631,0.000039639643,0.0019357102,0.000092421484,0.0011247571,0.0007490622,0.00068836956,0.0017337456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031066014,0.00033365827,0.0039380877,0.000040350467,0.000010723518,0.0000067162296,0.0074516493,0.24939905,0.06716187,0.42042094,0.000045588502,0.2511603],"study_design_scores_gemma":[0.00003969122,0.00031217275,0.0014551558,0.000047621925,9.4020555e-7,0.0000022890329,0.0007618021,0.81302696,0.18160415,0.0023291847,0.0002147091,0.00020535233],"about_ca_topic_score_codex":0.00017181483,"about_ca_topic_score_gemma":0.00003352056,"teacher_disagreement_score":0.9003806,"about_ca_system_score_codex":0.00032474735,"about_ca_system_score_gemma":0.00011508633,"threshold_uncertainty_score":0.9990435},"labels":[],"label_agreement":null},{"id":"W2168153476","doi":"10.5430/air.v5n1p56","title":"An evolutionary approach for segmentation of noisy speech signals for efficient voice activity detection","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Computer science; Speech recognition; Pattern recognition (psychology); SIGNAL (programming language); Speech segmentation; Entropy (arrow of time); Artificial intelligence; Noise (video); Population; Market segmentation; Speech processing","score_opus":0.2608184382143856,"score_gpt":0.4427501579608874,"score_spread":0.1819317197465018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168153476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15321693,0.000060544564,0.8453623,0.00015705275,0.00013907417,0.0008865251,0.0000078185285,0.000051301457,0.00011844027],"genre_scores_gemma":[0.7629359,0.0000017593886,0.23669355,0.0000091651245,0.00014941723,0.00017297934,0.000006865994,0.00000918931,0.000021185102],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99771684,0.00020889161,0.00030803314,0.00050882413,0.0007670685,0.000490311],"domain_scores_gemma":[0.99737316,0.00046561737,0.00011378014,0.00035970475,0.0015053428,0.00018237869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037010224,0.00011083862,0.00015706773,0.0003071404,0.00034197935,0.00017512072,0.00064247695,0.00008897129,0.0000022893842],"category_scores_gemma":[0.00066620973,0.00010763153,0.00007165482,0.0009447303,0.00014737972,0.0005693016,0.000076770164,0.00016136207,0.000014801875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020276809,0.00040414455,0.000017851276,0.00005686684,0.0000073102265,4.3842624e-7,0.0007184341,0.015752275,0.45406544,0.00081548904,0.00004477022,0.5279142],"study_design_scores_gemma":[0.000034564397,0.00042495524,0.00001230983,0.000007011433,0.0000021136289,0.0000020145553,0.00066758285,0.43464845,0.5550071,0.009099593,0.000028197064,0.00006612541],"about_ca_topic_score_codex":0.000108597604,"about_ca_topic_score_gemma":0.000017782595,"teacher_disagreement_score":0.609719,"about_ca_system_score_codex":0.00018673598,"about_ca_system_score_gemma":0.00033799745,"threshold_uncertainty_score":0.43890864},"labels":[],"label_agreement":null},{"id":"W2169395735","doi":"10.5430/air.v2n3p45","title":"An interpretable classifier for detection of cardiac arrhythmias by using the fuzzy decision tree","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Decision tree; Fuzzy logic; Artificial intelligence; Decision tree learning; Classifier (UML); Computer science; Data mining; Medical knowledge; Cardiac arrhythmia; Machine learning; Pattern recognition (psychology); Medicine; Cardiology","score_opus":0.16418789549810212,"score_gpt":0.44700378269439506,"score_spread":0.2828158871962929,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169395735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9088169,0.00032215784,0.08952951,0.00023312027,0.00026922015,0.00058394176,0.0000058540327,0.000019782106,0.00021956468],"genre_scores_gemma":[0.9975102,0.00008748501,0.0014324526,0.000011103165,0.000541181,0.000086838605,0.0000050104204,0.000020686866,0.00030502625],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981382,0.00019447976,0.00039187763,0.00029924884,0.000566225,0.00041000437],"domain_scores_gemma":[0.99741423,0.00081509334,0.0000621272,0.0005040689,0.0010613243,0.00014317353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017701181,0.00010326899,0.00025325918,0.00022387087,0.00030478672,0.00009189519,0.00018738367,0.00011060132,0.00006729651],"category_scores_gemma":[0.0011297374,0.00007027645,0.00017091722,0.00067710894,0.00023528919,0.00017620882,0.000045665598,0.00033520482,0.0000578287],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010953566,0.00006119416,0.0006821232,0.000014620501,0.000035021123,3.4488716e-7,0.0002027271,0.000079776146,0.49424744,0.000055365297,0.00016211365,0.50434977],"study_design_scores_gemma":[0.000023020473,0.00045582594,0.00021522363,0.00008762893,0.00005001359,0.000001952173,0.0042936946,0.15964915,0.8298722,0.0046968544,0.000576348,0.00007811537],"about_ca_topic_score_codex":0.0020494622,"about_ca_topic_score_gemma":0.000070415495,"teacher_disagreement_score":0.5042716,"about_ca_system_score_codex":0.00010557752,"about_ca_system_score_gemma":0.000082411425,"threshold_uncertainty_score":0.3098187},"labels":[],"label_agreement":null},{"id":"W2170031843","doi":"10.5430/air.v2n1p122","title":"Domain transformation approach to deterministic optimization of examination timetables","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Benchmark (surveying); Mathematical optimization; Transformation (genetics); Constructive; Scheduling (production processes); Domain (mathematical analysis); Optimization problem; Graph; Operations research; Algorithm; Process (computing); Theoretical computer science; Mathematics","score_opus":0.537345404345089,"score_gpt":0.5153357917927699,"score_spread":0.022009612552319147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170031843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1074565,0.00007480537,0.87649673,0.0002510548,0.000182301,0.00036599464,0.000008704814,0.000025428102,0.015138506],"genre_scores_gemma":[0.93029755,0.000008537456,0.0692406,0.000013111152,0.00012946039,0.000046777146,0.00001141217,0.000010336823,0.00024221571],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951508,0.0009537408,0.00087610085,0.0003133277,0.0020811185,0.0006249236],"domain_scores_gemma":[0.99616283,0.0018114413,0.00011498667,0.00046817065,0.0011952751,0.00024731073],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.022879917,0.000105725754,0.0002191609,0.0011654712,0.0003841535,0.00020719082,0.00059488206,0.00009797835,0.00024983598],"category_scores_gemma":[0.0053283153,0.00008764745,0.00008012047,0.0035579766,0.00021910167,0.0006706964,0.00007613059,0.00021021055,0.0008687313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075745374,0.0008528483,0.00041052588,0.00002161531,0.000019802206,3.1916736e-7,0.01553079,0.33622804,0.0055962102,0.3321677,0.00019350291,0.3089029],"study_design_scores_gemma":[0.00006227086,0.00028846334,0.0020319414,0.000045645622,0.000025359057,0.000010596261,0.024536192,0.770238,0.08391998,0.11565549,0.0028116445,0.00037443658],"about_ca_topic_score_codex":0.00006695772,"about_ca_topic_score_gemma":0.000009048499,"teacher_disagreement_score":0.82284105,"about_ca_system_score_codex":0.000065826396,"about_ca_system_score_gemma":0.000098416705,"threshold_uncertainty_score":0.9999092},"labels":[],"label_agreement":null},{"id":"W2177132231","doi":"10.5430/air.v5n1p14","title":"Non-deterministic planning methods for automated web service composition","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; European Commission","keywords":"Computer science; Web service; Task (project management); Implementation; Probabilistic logic; Variety (cybernetics); Service (business); Automated planning and scheduling; Web modeling; World Wide Web; Software engineering; Artificial intelligence; Systems engineering; Engineering","score_opus":0.2911259192209134,"score_gpt":0.5199217177334103,"score_spread":0.22879579851249693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177132231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03487689,0.00014377623,0.95761913,0.0032841642,0.00073732494,0.00078005914,0.000006766505,0.0006467281,0.0019051818],"genre_scores_gemma":[0.76599467,0.000004493794,0.23270367,0.0007530879,0.00029540047,0.00017069285,0.000026105443,0.00002748659,0.00002439556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99664855,0.00050900027,0.0005179285,0.0007173733,0.0006915538,0.0009155899],"domain_scores_gemma":[0.99588645,0.0013778213,0.00010013376,0.00078582077,0.0014407631,0.00040904232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040383176,0.00022231368,0.00027543757,0.00050884817,0.0005070969,0.00054367405,0.001941256,0.00014489944,0.000009809574],"category_scores_gemma":[0.0001291916,0.00020623533,0.00007847249,0.0022229573,0.00009993393,0.00048378657,0.00060122635,0.00042185036,0.00035778238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006813384,0.00078954373,0.00019328568,0.00054196257,0.0001457603,0.00011821392,0.047350198,0.027077423,0.17692122,0.16001415,0.002019084,0.5841478],"study_design_scores_gemma":[0.00006807292,0.00033532907,0.00002708847,0.00008160568,0.0000067591473,0.000017977836,0.0014598623,0.868925,0.0713488,0.054466058,0.0030424607,0.00022102444],"about_ca_topic_score_codex":0.000313412,"about_ca_topic_score_gemma":0.000110253626,"teacher_disagreement_score":0.84184754,"about_ca_system_score_codex":0.00010290851,"about_ca_system_score_gemma":0.00044735102,"threshold_uncertainty_score":0.84100324},"labels":[],"label_agreement":null},{"id":"W2178198718","doi":"10.5430/air.v5n1p36","title":"The improvement of question process method in Q&amp;A system","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National High-tech Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Matching (statistics); Similarity (geometry); Template; Set (abstract data type); Word (group theory); Field (mathematics); Semantic similarity; Blossom algorithm; Information retrieval; Artificial intelligence; Natural language processing; Algorithm; Data mining; Mathematics; Image (mathematics)","score_opus":0.3984937210629831,"score_gpt":0.5197412145710736,"score_spread":0.1212474935080905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2178198718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054660834,0.00012130533,0.94245964,0.0012791728,0.0002327278,0.00034085877,3.140929e-7,0.000037880738,0.00086725125],"genre_scores_gemma":[0.983447,0.000010996435,0.016341733,0.000006503696,0.00006410263,0.0000656094,2.7311165e-7,0.000005052478,0.000058683792],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973595,0.00046344363,0.0004769271,0.00032365497,0.000955198,0.0004212435],"domain_scores_gemma":[0.9979733,0.00048491705,0.0000685308,0.00061256177,0.0007580783,0.000102587896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011905291,0.000071690316,0.000118736614,0.00018484062,0.00012853749,0.0001503063,0.0011840024,0.000052417538,0.0000013439301],"category_scores_gemma":[0.0008551904,0.000051889314,0.000025964264,0.0009698966,0.00009648689,0.00022070389,0.0002820009,0.00029041056,0.000064311695],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018465127,0.00004246251,0.00010785255,0.00003551862,0.000003011561,0.0000028232314,0.0024318933,0.0052646417,0.0021623964,0.47764176,0.00001587295,0.5122733],"study_design_scores_gemma":[0.000018775327,0.00011528416,0.000012071531,0.000084949446,7.542717e-7,0.0000029667426,0.004172416,0.65655446,0.12034815,0.21840104,0.00021347003,0.00007565942],"about_ca_topic_score_codex":0.0013085805,"about_ca_topic_score_gemma":0.0006004372,"teacher_disagreement_score":0.9287862,"about_ca_system_score_codex":0.00020104961,"about_ca_system_score_gemma":0.00032764862,"threshold_uncertainty_score":0.412616},"labels":[],"label_agreement":null},{"id":"W2254811285","doi":"10.5430/air.v5n2p14","title":"A robust BFCC feature extraction for ASR system","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mel-frequency cepstrum; Spectrogram; Speech recognition; Feature extraction; Cepstrum; Computer science; Hidden Markov model; Robustness (evolution); Pattern recognition (psychology); Artificial intelligence; Noise (video); Wavelet","score_opus":0.27237503351879677,"score_gpt":0.4239095653915988,"score_spread":0.15153453187280203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2254811285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054400647,0.00017039328,0.984047,0.007766434,0.00046780816,0.0003823422,0.0000035283792,0.00020240148,0.001520017],"genre_scores_gemma":[0.94756657,0.000034199576,0.050454773,0.000036653444,0.00053928676,0.00012384394,9.5340647e-7,0.000016842496,0.0012268884],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976358,0.00013907284,0.00025702667,0.00056972116,0.0006545062,0.0007438764],"domain_scores_gemma":[0.99778193,0.0008003632,0.00006605445,0.00050696643,0.0006764763,0.00016819622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022020587,0.00012191954,0.00014126208,0.00026863752,0.0005326172,0.0004695096,0.00096764666,0.0001243883,0.00002282322],"category_scores_gemma":[0.0006405122,0.000083103696,0.000080296704,0.00084213866,0.00013195955,0.0008000617,0.00016946557,0.00023982725,0.00053187076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003559281,0.0000424036,0.000027671587,0.000045809702,0.0000069869675,0.000013551552,0.00015667526,0.00003124803,0.087171584,0.079426646,0.0017094375,0.8313324],"study_design_scores_gemma":[0.00003133312,0.00015154414,0.000017623119,0.00019845815,0.000002020557,0.000027414415,0.00045119465,0.0119296,0.9278884,0.053936232,0.0052082674,0.0001579442],"about_ca_topic_score_codex":0.0000275831,"about_ca_topic_score_gemma":0.000041993844,"teacher_disagreement_score":0.9421265,"about_ca_system_score_codex":0.00021143448,"about_ca_system_score_gemma":0.00020720293,"threshold_uncertainty_score":0.68363},"labels":[],"label_agreement":null},{"id":"W2255037847","doi":"10.5430/air.v5n2p1","title":"Identifying student group profiles for diagnostic feedback using snap-drift modal learning neural network","year":2015,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"London Metropolitan University","keywords":"Artificial neural network; Modal; Snap; Computer science; Set (abstract data type); Machine learning; Artificial intelligence; Group (periodic table)","score_opus":0.39879044203692166,"score_gpt":0.5051790844681736,"score_spread":0.10638864243125196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2255037847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32320902,0.0003823136,0.6727572,0.0016662005,0.0009682862,0.0007062019,0.0000010266409,0.00014078626,0.00016893884],"genre_scores_gemma":[0.9539488,0.000024922958,0.045089994,0.00003412418,0.00055985287,0.00021888183,0.0000072121566,0.000016908562,0.00009933623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967914,0.0003789563,0.00042499497,0.00060670066,0.0008785673,0.0009193649],"domain_scores_gemma":[0.9968111,0.0018373029,0.00009510989,0.00041935797,0.00062761095,0.00020949716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035064674,0.00015964457,0.00018626501,0.00024625208,0.00081639056,0.00052012724,0.0013640658,0.00012345715,0.000011858157],"category_scores_gemma":[0.0013454542,0.00015690534,0.00007399244,0.0011043708,0.00025881664,0.0005544918,0.0007225501,0.0007086723,0.00011183709],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003153591,0.00035279256,0.010632921,0.000029827692,0.000036387195,0.000016954751,0.0021153677,0.05728441,0.0012247744,0.8623252,0.0008709397,0.06507888],"study_design_scores_gemma":[0.000067506626,0.00048329754,0.0012117116,0.000103377984,0.000008991257,0.000015595107,0.006747374,0.46274608,0.010432011,0.5171107,0.0007594755,0.00031384212],"about_ca_topic_score_codex":0.00011082394,"about_ca_topic_score_gemma":0.00005498273,"teacher_disagreement_score":0.63073975,"about_ca_system_score_codex":0.00020731376,"about_ca_system_score_gemma":0.00032780465,"threshold_uncertainty_score":0.63984144},"labels":[],"label_agreement":null},{"id":"W2270983878","doi":"10.5430/air.v5n1p135","title":"Cost-sensitive performance metric for comparing multiple ordinal classifiers","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Food and Drug Administration; National Institutes of Health","keywords":"Pairwise comparison; Classifier (UML); Computer science; Metric (unit); Ordinal data; Data mining; Machine learning; Artificial intelligence; Ordinal optimization; Performance metric","score_opus":0.4412564894946245,"score_gpt":0.45241214398568325,"score_spread":0.011155654491058764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2270983878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013379114,0.000020357505,0.9816271,0.0024859186,0.0002325076,0.0009996715,0.000017981441,0.00029202545,0.00094532466],"genre_scores_gemma":[0.9513673,0.00008404741,0.047676705,0.000046053887,0.00012434802,0.0003871778,0.0000067411934,0.000018262515,0.0002893686],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99673253,0.00024366843,0.00049528346,0.00077155826,0.0007948175,0.00096214394],"domain_scores_gemma":[0.995475,0.0020884126,0.00011083078,0.00096825,0.001153195,0.00020432664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003127733,0.00017526593,0.00024052458,0.00076044,0.0005544286,0.00028015906,0.0016740292,0.00011247701,0.000017832042],"category_scores_gemma":[0.002261718,0.00013345822,0.00008127392,0.0019068719,0.00046366928,0.0010548725,0.00047567484,0.0003065764,0.00053525757],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008387272,0.00008421453,0.0012854631,0.000016745118,0.000012362812,0.0000032889777,0.00023100231,0.000021284852,0.0318733,0.15739691,0.0011744008,0.80781716],"study_design_scores_gemma":[0.00006902255,0.00024818353,0.0011107564,0.00006926871,0.0000023230546,0.0000063890325,0.0002931334,0.2217363,0.75431126,0.015497126,0.0064110695,0.00024515513],"about_ca_topic_score_codex":0.000047829537,"about_ca_topic_score_gemma":0.000043728414,"teacher_disagreement_score":0.93798816,"about_ca_system_score_codex":0.00036219857,"about_ca_system_score_gemma":0.00021196468,"threshold_uncertainty_score":0.68798316},"labels":[],"label_agreement":null},{"id":"W2295254979","doi":"10.5430/air.v5n1p160","title":"Effect of parameter values on fingerprint filtering","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Normalization (sociology); Artificial intelligence; Fingerprint (computing); Pattern recognition (psychology); Biometrics; Computer science; Palm print; Gabor filter; Consistency (knowledge bases); Segmentation; Noise (video); Filter (signal processing); Feature extraction; Computer vision; Mathematics; Image (mathematics)","score_opus":0.19135039188560393,"score_gpt":0.44676516596504073,"score_spread":0.2554147740794368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295254979","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4060638,0.000042975076,0.59062386,0.00153584,0.0003659495,0.0002761605,0.0000030633148,0.00007103202,0.0010173516],"genre_scores_gemma":[0.99791795,0.00004207993,0.0017168742,0.0000170551,0.000047746056,0.00002555756,3.2467395e-7,0.000005896919,0.00022650688],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.997424,0.0006191733,0.00033663097,0.00042105076,0.00079693133,0.0004022006],"domain_scores_gemma":[0.99513084,0.0036873647,0.00005745005,0.0007619891,0.00025659736,0.00010575671],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004538995,0.000096599404,0.0001548222,0.0006648921,0.00013913248,0.00013759838,0.0010922949,0.000068193774,0.00013245437],"category_scores_gemma":[0.0026005046,0.000060850893,0.000083230036,0.00147611,0.00026570464,0.00020889475,0.0003243239,0.0001807749,0.0009967361],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037649446,0.000064609834,0.00008159786,0.000018741654,0.0000066389016,0.0000038035198,0.00024391607,0.000004455404,0.046120476,0.084350765,0.00012707216,0.8689403],"study_design_scores_gemma":[0.000019197945,0.00059339456,0.0001607375,0.00005799358,9.4780853e-7,0.0000012302874,0.00001620384,0.0052105333,0.95833206,0.03489535,0.00063076,0.00008157396],"about_ca_topic_score_codex":0.00006272866,"about_ca_topic_score_gemma":0.000003985347,"teacher_disagreement_score":0.9122116,"about_ca_system_score_codex":0.00006883712,"about_ca_system_score_gemma":0.00004520925,"threshold_uncertainty_score":0.99978113},"labels":[],"label_agreement":null},{"id":"W2324343215","doi":"10.5430/air.v5n2p55","title":"System identifications by SIRMs models with linear transformation of input variables","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Control Systems and Identification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Computer science; Transformation (genetics); Sonar; Obstacle; Artificial intelligence; Algorithm; Mathematical optimization; Mathematics","score_opus":0.08706547603729199,"score_gpt":0.3213929739309022,"score_spread":0.23432749789361024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2324343215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075897515,0.00020064805,0.9202482,0.0002422307,0.00013860101,0.0005276193,0.000059549802,0.00014620986,0.0025394317],"genre_scores_gemma":[0.9992068,0.00010815491,0.0002167825,7.881331e-7,0.00006680279,0.0001248336,0.000013963557,0.000024051571,0.0002378167],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99824244,0.000104769286,0.0005769485,0.00020456877,0.00054942654,0.00032184157],"domain_scores_gemma":[0.9987426,0.00020783528,0.000051348467,0.00037789874,0.0005362123,0.00008412898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012947309,0.000106550564,0.00017205747,0.00024732467,0.00013515759,0.00007057355,0.00026498377,0.000084905754,0.000036210047],"category_scores_gemma":[0.00004857549,0.00007449981,0.000040975636,0.0005942107,0.00012664027,0.0005437022,0.000013602047,0.0001230823,0.00019943196],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000513202,0.00007139732,0.000021184871,0.00030380028,0.00005951612,8.6543673e-7,0.0012628795,0.01882391,0.67172486,0.23206633,0.000528145,0.07508581],"study_design_scores_gemma":[0.00006168504,0.00007106902,0.000017755094,0.0003336382,0.000013079787,0.0000036462245,0.0020647403,0.45656502,0.5327319,0.007076899,0.0008888573,0.00017170957],"about_ca_topic_score_codex":0.00032695752,"about_ca_topic_score_gemma":0.0001450256,"teacher_disagreement_score":0.92330927,"about_ca_system_score_codex":0.00013947842,"about_ca_system_score_gemma":0.0000460696,"threshold_uncertainty_score":0.30380142},"labels":[],"label_agreement":null},{"id":"W2479188666","doi":"10.5430/air.v5n2p82","title":"A robust smart device app assisting medical diagnosis","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Touchscreen; Android (operating system); Porting; Embedded system; Computer science; Modular design; Operating system; Linux kernel; Software; Mobile device; Human–computer interaction","score_opus":0.41325748122279404,"score_gpt":0.47721636922099286,"score_spread":0.06395888799819882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2479188666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87915176,0.00079026434,0.053731985,0.054108325,0.00056406745,0.00054370955,0.000008134679,0.0003511778,0.010750545],"genre_scores_gemma":[0.9950754,0.0005449609,0.0011106936,0.00010299333,0.0011246298,0.000115667695,0.0000029066305,0.000028626637,0.0018941137],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99594134,0.000290901,0.0005158792,0.00053175236,0.0019043257,0.00081582944],"domain_scores_gemma":[0.99586326,0.0022761838,0.000050029346,0.0005064867,0.0006896819,0.0006143474],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004128366,0.00014871686,0.00031002786,0.0003910149,0.00036021712,0.000073410396,0.00034328553,0.00019346239,0.0022628517],"category_scores_gemma":[0.010479198,0.0000964775,0.00015823299,0.0012306471,0.0004487147,0.00010493098,0.00018650499,0.0005854191,0.0023967095],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000749146,0.00030899857,0.08533315,0.000047567453,0.000078695535,0.00022189486,0.00013119329,0.0000052874475,0.004431979,0.0014804661,0.0010897845,0.90679604],"study_design_scores_gemma":[0.00048181918,0.001780081,0.014863025,0.0068137757,0.00033447155,0.00020210884,0.0066249208,0.028825285,0.8690146,0.015706666,0.054041605,0.0013115912],"about_ca_topic_score_codex":0.0007729845,"about_ca_topic_score_gemma":0.00031619138,"teacher_disagreement_score":0.9054845,"about_ca_system_score_codex":0.00018326721,"about_ca_system_score_gemma":0.00035509118,"threshold_uncertainty_score":0.99864924},"labels":[],"label_agreement":null},{"id":"W2511891899","doi":"10.5430/air.v6n1p16","title":"Study of hiring decisions by companies using text mining: Factors other than experience","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Salient; Psychology; Personality; Social psychology; Key (lock); Offset (computer science); Applied psychology; Computer science; Artificial intelligence; Computer security","score_opus":0.46583979670412756,"score_gpt":0.45587472822311464,"score_spread":0.009965068481012918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511891899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99471235,0.000025401829,0.0034773042,0.0000965945,0.00015744643,0.0003205853,0.0000025513411,0.000033024222,0.0011747355],"genre_scores_gemma":[0.9995654,0.0000078577095,0.000052713516,0.000019247747,0.00015413687,0.000014828587,0.0000014054763,0.000020880669,0.00016348323],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.998006,0.000055657176,0.00042836965,0.00032759935,0.0007738135,0.00040856827],"domain_scores_gemma":[0.9987195,0.00052538863,0.00011969856,0.0002763398,0.00033302247,0.00002606141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072303705,0.00012863151,0.00017789619,0.0004584221,0.000363072,0.00021175705,0.0003978516,0.000042515596,0.00045456443],"category_scores_gemma":[0.00056716974,0.00008945747,0.000046268233,0.00093727617,0.00023625993,0.0007597748,0.00024052973,0.000101737445,0.0001694494],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027744027,0.002085936,0.39384127,0.000039127295,0.000076003904,0.000014801566,0.026327217,0.00024613095,0.33818465,0.008837462,0.0011166336,0.22895332],"study_design_scores_gemma":[0.00032689536,0.00029232792,0.017034883,0.0005016059,0.000042338426,0.0000016993763,0.7093975,0.009704531,0.25087538,0.0076058954,0.0034084548,0.00080847397],"about_ca_topic_score_codex":0.0030923435,"about_ca_topic_score_gemma":0.00081306725,"teacher_disagreement_score":0.6830703,"about_ca_system_score_codex":0.000057706602,"about_ca_system_score_gemma":0.00002188858,"threshold_uncertainty_score":0.49771616},"labels":[],"label_agreement":null},{"id":"W2528516053","doi":"10.5430/air.v6n1p52","title":"Comparison of three data mining algorithms for potential 4G customers prediction","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Computer science; Data mining; Precision and recall; Predictive modelling; Outlier; Algorithm; Naive Bayes classifier; Recall; Machine learning; Artificial intelligence; Support vector machine","score_opus":0.4806261644470287,"score_gpt":0.4743931880289181,"score_spread":0.006232976418110603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528516053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37020245,0.00017341053,0.61960214,0.0030791138,0.0017233846,0.0015431687,0.00014399069,0.00015195958,0.003380349],"genre_scores_gemma":[0.9974869,0.000021441509,0.0009599178,0.000028786875,0.0011954954,0.0000359005,0.0001143811,0.000020766964,0.00013640449],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979307,0.000025977815,0.00051123224,0.00040901717,0.0006908989,0.00043215466],"domain_scores_gemma":[0.99852604,0.00027827415,0.00015081579,0.00044618562,0.0005793542,0.00001936046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020169488,0.00010766037,0.00018631606,0.0004709962,0.00028908584,0.00015813258,0.00059030746,0.000074643125,0.00023610206],"category_scores_gemma":[0.0004466416,0.000083374805,0.000055547778,0.0006588261,0.0002403433,0.0011101164,0.00034123013,0.00011218544,0.00026799558],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002647296,0.00022042774,0.008880216,0.00010066293,0.000036742582,0.0000016112796,0.00014366489,0.00012665095,0.042124856,0.00936278,0.005876706,0.932861],"study_design_scores_gemma":[0.00037580146,0.00023285771,0.0031251835,0.00029017805,0.000109515895,0.000001662114,0.010118053,0.8587055,0.076232016,0.023366608,0.026964886,0.00047772637],"about_ca_topic_score_codex":0.00043932247,"about_ca_topic_score_gemma":0.00032362022,"teacher_disagreement_score":0.93238324,"about_ca_system_score_codex":0.000047637233,"about_ca_system_score_gemma":0.00004616825,"threshold_uncertainty_score":0.34446305},"labels":[],"label_agreement":null},{"id":"W2556044468","doi":"10.5430/air.v6n1p59","title":"Re-ranking Google search returned web documents using document classification scores","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Information retrieval; Ranking (information retrieval); Computer science; Search engine; World Wide Web; Web page; Learning to rank; Web search engine; Web search query","score_opus":0.3126960264625327,"score_gpt":0.44704871815383274,"score_spread":0.13435269169130004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2556044468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23840979,0.00026310538,0.7323831,0.02298359,0.0005802318,0.001045105,0.000005441401,0.0008650981,0.0034645875],"genre_scores_gemma":[0.98747194,0.00037949483,0.011059512,0.000047272883,0.00013025715,0.00009237804,0.000002420863,0.000024143734,0.00079255964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9945218,0.0005668369,0.0007738472,0.0010821198,0.0018705246,0.0011848722],"domain_scores_gemma":[0.9963775,0.00087139383,0.00015501035,0.0016608507,0.0007222873,0.0002129524],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0039675618,0.0002481642,0.00024404693,0.00095097185,0.0007939266,0.0009866984,0.0027447056,0.00020968207,0.00036421217],"category_scores_gemma":[0.0008158137,0.00018300887,0.0001081161,0.0022504346,0.00069593184,0.0018152691,0.0010313701,0.00047735052,0.0014734515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003252206,0.00009414719,0.0010392498,0.000012540735,0.000017224873,0.000009461096,0.00041319884,0.000019682047,0.17615137,0.43045247,0.0004017985,0.39135635],"study_design_scores_gemma":[0.00010742126,0.00023878965,0.00050523906,0.00020483536,0.0000054809366,0.0000053199183,0.0021961932,0.03983342,0.60463727,0.34877586,0.003050937,0.00043926004],"about_ca_topic_score_codex":0.00018472037,"about_ca_topic_score_gemma":0.00009534473,"teacher_disagreement_score":0.7490622,"about_ca_system_score_codex":0.00063615135,"about_ca_system_score_gemma":0.000402082,"threshold_uncertainty_score":0.999304},"labels":[],"label_agreement":null},{"id":"W2563000706","doi":"10.5430/air.v6n1p80","title":"Can machine learning techniques predict customer dissatisfaction? A feasibility study for the automotive industry","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automotive industry; Competitor analysis; Computer science; Customer satisfaction; Service (business); Support vector machine; Artificial intelligence; Machine learning; Marketing; Business; Engineering","score_opus":0.2094813873278795,"score_gpt":0.42368118883245365,"score_spread":0.21419980150457416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563000706","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95907325,0.00004842755,0.017194433,0.015262862,0.00046520773,0.0052571255,0.000027318069,0.00044164265,0.0022297294],"genre_scores_gemma":[0.997899,0.000014405927,0.00003862259,0.00010573466,0.00074038864,0.00041960028,0.000007986191,0.000025698368,0.0007485424],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979408,0.00012977728,0.00036091285,0.00042182335,0.0006650565,0.0004816342],"domain_scores_gemma":[0.9979712,0.0008160918,0.000107912536,0.000308281,0.0007687511,0.000027783266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035211463,0.00015065915,0.00015030398,0.0003291699,0.0009823751,0.00035593926,0.00036098258,0.00011169556,0.00055022043],"category_scores_gemma":[0.0016915501,0.00008724279,0.00007342309,0.0008792495,0.00027746297,0.0005653509,0.00020806157,0.0006235087,0.00023356956],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000438275,0.0007379484,0.3375542,0.00005673007,0.00008973114,0.0000065251083,0.0010436274,0.000047406556,0.006358735,0.011319414,0.0011486489,0.6411988],"study_design_scores_gemma":[0.001357313,0.0021708277,0.3638181,0.0006189328,0.00048693194,0.000011236105,0.16007875,0.053128403,0.17563117,0.17979714,0.060334492,0.002566702],"about_ca_topic_score_codex":0.0038639752,"about_ca_topic_score_gemma":0.0038126449,"teacher_disagreement_score":0.63863206,"about_ca_system_score_codex":0.0001840604,"about_ca_system_score_gemma":0.000058468246,"threshold_uncertainty_score":0.7555734},"labels":[],"label_agreement":null},{"id":"W2571543094","doi":"10.5430/air.v6n1p91","title":"Factor analysis of teacher professional development and evaluation based on math methods of RaschGSP curve, ISM, GSM and MSM","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics education; Teamwork; Rasch model; Psychology; Pedagogy; Computer science","score_opus":0.6924952795520097,"score_gpt":0.6536971404244697,"score_spread":0.03879813912754004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571543094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95128536,0.00005323625,0.046321426,0.00050952827,0.000090956135,0.0006266076,0.000018273557,0.0000057397524,0.0010888438],"genre_scores_gemma":[0.99219245,0.0000033395434,0.007270598,0.000006453458,0.000017124217,0.000092252354,0.0000040772916,0.0000070795763,0.00040662507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9932241,0.0024787013,0.00091965194,0.0005471895,0.0025810732,0.0002493048],"domain_scores_gemma":[0.991809,0.004826548,0.00046398633,0.0010781554,0.0016936826,0.00012859133],"candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.039732233,0.000112486254,0.00038402667,0.0010637757,0.0006089192,0.00019605631,0.0007857323,0.00010376326,0.000462312],"category_scores_gemma":[0.012790997,0.00008080514,0.00007357453,0.001084216,0.0006858976,0.00018602505,0.0002762406,0.00022213132,0.00004917002],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015277251,0.00033541486,0.0302901,0.000023742386,0.00013479534,5.5717595e-7,0.0035846357,0.00044711717,0.018736182,0.070043765,0.000053723146,0.8761972],"study_design_scores_gemma":[0.00006729922,0.00011265558,0.28056106,0.00007934487,0.00007658891,3.4725352e-7,0.0038879006,0.42706233,0.20763123,0.07956688,0.00078557123,0.00016877438],"about_ca_topic_score_codex":0.00012889152,"about_ca_topic_score_gemma":0.00022080958,"teacher_disagreement_score":0.8760284,"about_ca_system_score_codex":0.00005462341,"about_ca_system_score_gemma":0.00036736482,"threshold_uncertainty_score":0.9955247},"labels":[],"label_agreement":null},{"id":"W2593207864","doi":"10.5430/air.v6n2p1","title":"Predicting rehabilitation treatment helpfulness to stroke patients: A supervised learning approach","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Rehabilitation; Stroke (engine); Helpfulness; Medicine; Physical therapy; Physical medicine and rehabilitation; Psychology","score_opus":0.15318832550406644,"score_gpt":0.42781188130848724,"score_spread":0.27462355580442077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593207864","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9861443,0.000029991437,0.0030377712,0.0023454344,0.00027932174,0.0016388164,0.0000102080185,0.00007325687,0.0064408756],"genre_scores_gemma":[0.99407995,0.00002452843,0.00345815,0.000026498068,0.0003256304,0.0003451334,0.000026839394,0.00003082082,0.0016824795],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9970505,0.00035456885,0.00047316172,0.0005812894,0.0009295083,0.000610976],"domain_scores_gemma":[0.99670655,0.0010677117,0.00008428192,0.00073176983,0.001047868,0.00036180628],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0016735774,0.00017370282,0.0003212372,0.0004883405,0.0012575109,0.00026828167,0.00027127648,0.00012846025,0.00014978732],"category_scores_gemma":[0.010426096,0.00014257609,0.00018467219,0.00030314576,0.00035543385,0.00025321954,0.00013650942,0.00044529134,0.00039395137],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008237213,0.0013396569,0.4994856,0.00012898647,0.000060947346,0.0000053342796,0.006429966,0.0005847804,0.0040376335,0.0017184909,0.00007594863,0.48530892],"study_design_scores_gemma":[0.0016695599,0.032249454,0.67208827,0.000990753,0.00013822244,0.000015090542,0.08383741,0.09508029,0.09714388,0.0044794134,0.011221334,0.0010862927],"about_ca_topic_score_codex":0.0007224238,"about_ca_topic_score_gemma":0.00008409106,"teacher_disagreement_score":0.48422265,"about_ca_system_score_codex":0.0004663899,"about_ca_system_score_gemma":0.00020064232,"threshold_uncertainty_score":0.9979095},"labels":[],"label_agreement":null},{"id":"W2599038115","doi":"10.5430/air.v6n2p10","title":"Bio-inspired multiobjective clustering optimization: A survey and a proposal","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Cluster analysis; Multi-objective optimization; Computer science; Data mining; Quality (philosophy); Mathematical optimization; Machine learning; Mathematics","score_opus":0.2304565960268817,"score_gpt":0.45474251321250253,"score_spread":0.22428591718562083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2599038115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007960163,0.00007211878,0.9885258,0.0016209733,0.0002497733,0.00062044413,0.000006996422,0.00012237411,0.0008213332],"genre_scores_gemma":[0.85204476,0.00011823019,0.14730723,0.000014113367,0.00013536109,0.000088592256,0.000002938811,0.000025006097,0.0002637874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996154,0.0006093868,0.00036398103,0.0009032758,0.001030174,0.0009391578],"domain_scores_gemma":[0.9963029,0.00069800986,0.00010966086,0.001492082,0.0011005596,0.0002967701],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0042840624,0.00018817975,0.00022534284,0.00036996434,0.0023066802,0.0022017115,0.0023540044,0.00011763374,0.000027933935],"category_scores_gemma":[0.0035211395,0.00018174737,0.0000405315,0.0006194078,0.000977917,0.0013860192,0.0029731898,0.00062135124,0.00013331202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001824758,0.00021868071,0.0019352938,0.00005915604,0.000043083484,0.00015060471,0.002453359,0.03549354,0.0020016201,0.019198569,0.000034800116,0.93822885],"study_design_scores_gemma":[0.000054677887,0.00021494,0.0027572461,0.000039743893,8.1134937e-7,0.000018144052,0.00022591524,0.9750751,0.010581627,0.010779132,0.000038435508,0.0002142081],"about_ca_topic_score_codex":0.0017358634,"about_ca_topic_score_gemma":0.0013506506,"teacher_disagreement_score":0.9395816,"about_ca_system_score_codex":0.00016205339,"about_ca_system_score_gemma":0.00033250664,"threshold_uncertainty_score":0.9989922},"labels":[],"label_agreement":null},{"id":"W2604557004","doi":"10.5430/air.v6n2p27","title":"Combining ant colony optimization with 1-opt local search method for solving constrained forest transportation planning problems","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematical optimization; Ant colony optimization algorithms; Computer science; Robustness (evolution); Scale (ratio); Constraint (computer-aided design); Process (computing); Local search (optimization); Operations research; Engineering; Mathematics; Geography","score_opus":0.1574298578786694,"score_gpt":0.4047587758549444,"score_spread":0.24732891797627501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604557004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01489157,0.000040519044,0.98176354,0.00025572476,0.00013635574,0.0010942694,0.000013778339,0.00015117563,0.001653082],"genre_scores_gemma":[0.95874035,0.000033462173,0.04076024,0.000009808427,0.000057127196,0.00019206767,0.00008597759,0.000045109653,0.00007585443],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816674,0.000053322008,0.0003610818,0.00032202242,0.0004900071,0.00060683006],"domain_scores_gemma":[0.9988102,0.0002635628,0.00005683234,0.00033333938,0.00040514278,0.00013096408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001653866,0.00015822587,0.00018531262,0.00026478776,0.0008925056,0.00042082375,0.00037369938,0.00010055837,0.00004124096],"category_scores_gemma":[0.00012975688,0.00015047732,0.000044405264,0.00026755405,0.0003728324,0.00033056983,0.00003188254,0.0002500513,0.000013751717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009275389,0.00003905505,0.0009410338,0.00018786284,0.00004037654,0.000011668676,0.0011719163,0.9311807,0.0011299211,0.035755515,0.00007255849,0.029376615],"study_design_scores_gemma":[0.00012215182,0.00024802244,0.00035677085,0.00016992424,0.0000119133765,0.0000016933674,0.002700463,0.97665787,0.017314153,0.001871853,0.00036310937,0.0001820645],"about_ca_topic_score_codex":0.00020911908,"about_ca_topic_score_gemma":0.0016388475,"teacher_disagreement_score":0.9438488,"about_ca_system_score_codex":0.00010487786,"about_ca_system_score_gemma":0.00007414015,"threshold_uncertainty_score":0.68645215},"labels":[],"label_agreement":null},{"id":"W2604993202","doi":"10.5430/air.v6n2p39","title":"Discrete event based hybrid framework for petroleum products pipeline activities classification","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"DEVS; Adaptive neuro fuzzy inference system; Pipeline (software); Computer science; Event (particle physics); Hybrid system; Sample (material); Data mining; Artificial intelligence; Machine learning; Fuzzy logic; Simulation; Fuzzy control system; Modeling and simulation","score_opus":0.1856368204992833,"score_gpt":0.4312356251678974,"score_spread":0.24559880466861408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604993202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05510523,0.00017556806,0.93210566,0.009160784,0.0009895479,0.00085781893,0.000044865537,0.00052516296,0.0010353861],"genre_scores_gemma":[0.98841774,0.00012999891,0.009650652,0.000011663103,0.00093308825,0.00038266642,0.000018872224,0.000043443062,0.0004118444],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982412,0.00007180792,0.00031772486,0.00040155288,0.00044004538,0.00052769296],"domain_scores_gemma":[0.99826634,0.00026225866,0.00006300619,0.0009748853,0.00034099258,0.000092535985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016342239,0.00015249777,0.00016734998,0.00021184565,0.00076255447,0.00035844077,0.0005496303,0.000088052075,0.00005406443],"category_scores_gemma":[0.0017415516,0.00014700455,0.00007363285,0.00013546331,0.0003242599,0.00034319636,0.000063235464,0.0004902084,0.000085501226],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021865431,0.00018648064,0.0001789212,0.00038841597,0.000029795732,0.0000049288824,0.0002609125,0.005196823,0.052503526,0.04825481,0.0069532692,0.8858235],"study_design_scores_gemma":[0.000011663985,0.00008226066,0.000083318395,0.000058839516,0.0000042068896,8.857104e-7,0.00022273223,0.12344388,0.77617764,0.09436071,0.0054107835,0.00014307504],"about_ca_topic_score_codex":0.000046962236,"about_ca_topic_score_gemma":0.000025061703,"teacher_disagreement_score":0.93331254,"about_ca_system_score_codex":0.00011618859,"about_ca_system_score_gemma":0.00007134624,"threshold_uncertainty_score":0.59946716},"labels":[],"label_agreement":null},{"id":"W2611499273","doi":"10.5430/air.v6n2p51","title":"Active cluster replacement algorithm as a tool to assess bifurcation early-warning signs for von Karman equations","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bifurcation; Cluster analysis; Identification (biology); Algorithm; Normalization (sociology); Cluster (spacecraft); Computer science; Set (abstract data type); Mathematics; Data mining; Nonlinear system; Artificial intelligence; Physics","score_opus":0.3860997493617482,"score_gpt":0.43977245983703767,"score_spread":0.05367271047528949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611499273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14052348,0.000055414384,0.8472589,0.003816825,0.00035387612,0.0014519438,0.00012431366,0.000030471574,0.0063847415],"genre_scores_gemma":[0.9917006,0.000015712976,0.00314847,0.000050325456,0.00036141297,0.00048389164,0.000026625179,0.000027802505,0.0041851434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780875,0.00007946407,0.0007215049,0.00063663046,0.00018493341,0.0005686851],"domain_scores_gemma":[0.997711,0.00048159825,0.00031536102,0.0008775971,0.0004674698,0.00014698706],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003610628,0.00014118232,0.00031546803,0.0005015139,0.0018674976,0.00094202807,0.00059671805,0.000088803834,0.00082586776],"category_scores_gemma":[0.002577837,0.00016157336,0.00015429,0.00037776833,0.00010391817,0.0004786909,0.0002770796,0.00026723288,0.0026882223],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022387099,0.0002233579,0.0012342215,0.00003778315,0.00020126658,0.0000025553595,0.0042885193,0.0012614449,0.0012337336,0.65772504,0.0006035766,0.33296463],"study_design_scores_gemma":[0.00020619824,0.0015733577,0.0030360767,0.000097557924,0.000027676571,0.000001945843,0.0054300283,0.5074915,0.020496875,0.39267182,0.06805639,0.0009105683],"about_ca_topic_score_codex":0.0062329783,"about_ca_topic_score_gemma":0.0008202833,"teacher_disagreement_score":0.85117716,"about_ca_system_score_codex":0.00026442736,"about_ca_system_score_gemma":0.00006704493,"threshold_uncertainty_score":0.9994319},"labels":[],"label_agreement":null},{"id":"W2615489195","doi":"","title":"The Complexity of Integer Bound Propagation","year":2011,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Search tree; Constraint (computer-aided design); Set (abstract data type); Integer (computer science); Fixed point; Tree (set theory); Constraint satisfaction problem; Local consistency; Upper and lower bounds; Time complexity; Polynomial; Algorithm; Discrete mathematics; Computer science; Combinatorics; Search algorithm","score_opus":0.4425624707903778,"score_gpt":0.4107181668670509,"score_spread":0.03184430392332688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615489195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068061063,0.000030305491,0.9810397,0.0014152925,0.00023132583,0.00026423804,7.379492e-7,0.00004511604,0.010167172],"genre_scores_gemma":[0.9923671,0.00004174553,0.0074342196,0.000013446847,0.000025229354,0.00001572301,7.179501e-7,0.0000036245758,0.00009820752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99847347,0.00025859626,0.00031678897,0.00020443983,0.00047532434,0.00027136764],"domain_scores_gemma":[0.9985803,0.0002429274,0.00006723873,0.00041184714,0.0006367532,0.00006095012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019283715,0.00005933626,0.00007131623,0.00013253427,0.00044778775,0.00014240146,0.0006721847,0.000038649065,0.00015852886],"category_scores_gemma":[0.00038275833,0.000042760348,0.000038054106,0.00078114046,0.0012746324,0.00029775233,0.0001754256,0.00022438607,0.0001738262],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012112322,0.00003271643,0.00009709841,0.000002700525,0.0000037180234,7.584666e-7,0.0011647566,0.000019903882,0.0007463243,0.7500231,0.00003926484,0.2478575],"study_design_scores_gemma":[0.000011536451,0.00012355433,0.0014450852,0.000013977416,0.00000119068,0.000004159043,0.0011302829,0.15273745,0.18309464,0.66101086,0.0003428746,0.00008440005],"about_ca_topic_score_codex":0.0002999059,"about_ca_topic_score_gemma":0.00043776765,"teacher_disagreement_score":0.98556095,"about_ca_system_score_codex":0.000041080686,"about_ca_system_score_gemma":0.00014490781,"threshold_uncertainty_score":0.46964362},"labels":[],"label_agreement":null},{"id":"W2620415937","doi":"10.5430/air.v6n2p57","title":"A proposal of privacy preserving reinforcement learning for secure multiparty computation","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Reinforcement learning; Computation; Encryption; Unsupervised learning; Artificial intelligence; Machine learning; Cloud computing; Supervised learning; Theoretical computer science; Algorithm; Computer security; Artificial neural network","score_opus":0.22074976536872212,"score_gpt":0.4530274687240633,"score_spread":0.23227770335534118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620415937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049507942,0.000039926912,0.94785535,0.0009613889,0.00016264072,0.0008075763,0.0000036800325,0.000050908217,0.0006105635],"genre_scores_gemma":[0.9558717,0.000026223584,0.043889783,0.0000061440783,0.00009451554,0.00006991097,0.000012699014,0.000008253286,0.000020750558],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976867,0.00018606018,0.0004321299,0.00045068198,0.00071464205,0.0005297754],"domain_scores_gemma":[0.997398,0.0005267266,0.000220785,0.000918681,0.0008170297,0.00011877731],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0029083146,0.00011484114,0.00017583865,0.00022886178,0.0013560232,0.0006298257,0.0020965454,0.00007999054,0.000020175185],"category_scores_gemma":[0.002007409,0.00010774568,0.00010313353,0.0003200365,0.00033539376,0.0010500961,0.0012091546,0.00038326622,0.000025190926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001320288,0.00019364282,0.00080939004,0.00017999053,0.000029001436,0.0000048127495,0.0054266374,0.009102288,0.008422754,0.7959164,0.000369255,0.17941384],"study_design_scores_gemma":[0.000053859298,0.00033932834,0.00032818972,0.000064479456,0.0000026446203,0.0000010567844,0.0003718655,0.6866179,0.099421695,0.21160702,0.001069183,0.00012275316],"about_ca_topic_score_codex":0.00043724047,"about_ca_topic_score_gemma":0.00012895353,"teacher_disagreement_score":0.9063638,"about_ca_system_score_codex":0.00003286796,"about_ca_system_score_gemma":0.00019197584,"threshold_uncertainty_score":0.9999441},"labels":[],"label_agreement":null},{"id":"W2620830516","doi":"10.5430/air.v6n2p69","title":"The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Preprocessor; Artificial intelligence; Representation (politics); Face (sociological concept); Pattern recognition (psychology); Set (abstract data type); Class (philosophy); Facial recognition system; Image (mathematics); Residual; Computer vision; Algorithm","score_opus":0.26655912910634716,"score_gpt":0.45925236974329414,"score_spread":0.19269324063694698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620830516","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5605629,0.00018693076,0.43460977,0.0032140622,0.00010560243,0.00086834264,0.000017549455,0.000019335148,0.00041549918],"genre_scores_gemma":[0.9906196,0.0004005329,0.008782837,0.000007271451,0.000028304967,0.00011907074,0.000008178211,0.0000051028683,0.000029118855],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99846363,0.0002501594,0.00029797087,0.00039653576,0.00035902805,0.00023264761],"domain_scores_gemma":[0.99721706,0.0013647148,0.00018622066,0.00035496074,0.00082347135,0.00005357627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024014793,0.0000768152,0.0001069356,0.00027793754,0.0010203545,0.0008025815,0.00036587473,0.000061787985,0.0000030026895],"category_scores_gemma":[0.0027022262,0.00006060182,0.000017971472,0.00056428846,0.0004570874,0.0010093539,0.00015238964,0.00016157757,0.000009809088],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016370125,0.00005729462,0.00040372222,0.000033939337,0.0000026877256,0.0000013170863,0.00076690735,0.000015073752,0.09288235,0.004413795,0.00006325019,0.90119594],"study_design_scores_gemma":[0.00011217243,0.0002500941,0.0037132122,0.00015327052,0.0000031284976,0.0000013044158,0.004300239,0.27239797,0.6475523,0.071327716,0.000085266765,0.00010330765],"about_ca_topic_score_codex":0.00015854691,"about_ca_topic_score_gemma":0.000074525386,"teacher_disagreement_score":0.90109265,"about_ca_system_score_codex":0.000030844585,"about_ca_system_score_gemma":0.0001232777,"threshold_uncertainty_score":0.78478444},"labels":[],"label_agreement":null},{"id":"W2625564479","doi":"10.5430/air.v6n2p80","title":"Encoding seasonal patterns using the Kasai algorithm","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Decision Support System Applications","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"U.S. Department of Education","keywords":"Series (stratigraphy); Set (abstract data type); Data set; Computer science; Algorithm; Encoding (memory); Data mining; Artificial intelligence","score_opus":0.41653366520855667,"score_gpt":0.4725117805641511,"score_spread":0.05597811535559444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625564479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39845634,0.00013532197,0.43511277,0.016275201,0.0021906327,0.002394807,0.00003471432,0.00024936532,0.14515084],"genre_scores_gemma":[0.99563223,0.000011971742,0.0007337487,0.00016938135,0.0024463155,0.000078216035,0.000006395693,0.00002961302,0.0008921525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721974,0.00004995365,0.00042793175,0.00044184423,0.0012026246,0.00065792445],"domain_scores_gemma":[0.9973431,0.0003227059,0.00022227134,0.0012707696,0.00080531946,0.000035794656],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.004268435,0.00014396534,0.00016231803,0.0002633556,0.003733533,0.0030053137,0.001814242,0.000075255346,0.0026842433],"category_scores_gemma":[0.000977442,0.00010693978,0.00009925973,0.00045906607,0.00038778022,0.0013508594,0.000968131,0.00040343255,0.008094821],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031650263,0.00023462586,0.017465752,0.00008270778,0.000050175022,0.000077258075,0.00031908246,0.000048191643,0.0037844195,0.19054495,0.0072490154,0.78011215],"study_design_scores_gemma":[0.0000976374,0.00002698964,0.008551265,0.00034348102,0.00005781954,0.00003230858,0.012248215,0.38593477,0.01126672,0.3183829,0.26212773,0.00093017734],"about_ca_topic_score_codex":0.0031052448,"about_ca_topic_score_gemma":0.0003063424,"teacher_disagreement_score":0.779182,"about_ca_system_score_codex":0.000072179115,"about_ca_system_score_gemma":0.00008038082,"threshold_uncertainty_score":0.9982274},"labels":[],"label_agreement":null},{"id":"W2727112484","doi":"10.5430/air.v6n2p93","title":"Analysis of imbalanced data set problem: The case of churn prediction for telecommunication","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Data mining; Random forest; Feature (linguistics); Machine learning; Artificial intelligence","score_opus":0.41135654198351007,"score_gpt":0.4777785203359856,"score_spread":0.06642197835247554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2727112484","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9706493,0.00007649105,0.021147972,0.0029557166,0.00016020078,0.0013659813,0.00018860768,0.000026652257,0.0034290603],"genre_scores_gemma":[0.99915546,0.00004808486,0.00021451357,0.000015984806,0.00015970258,0.00004463866,0.00030302381,0.000007325944,0.000051247724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988794,0.00004391124,0.0004042158,0.00021512451,0.00026380524,0.0001935674],"domain_scores_gemma":[0.99718356,0.0002794809,0.00034654533,0.0014626246,0.00071940286,0.000008392408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035659813,0.00006290314,0.00015878881,0.000387809,0.0007372648,0.00025082089,0.0011448893,0.00004113182,0.00007020944],"category_scores_gemma":[0.0008286419,0.000048181853,0.00006551873,0.0007769141,0.0002987378,0.00080242933,0.0004784929,0.0001300581,0.00001516772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005540249,0.00055463554,0.028376536,0.00055812945,0.0009492694,0.000010343641,0.00231942,0.0028581037,0.027789949,0.11671551,0.002457313,0.81685674],"study_design_scores_gemma":[0.00009316573,0.000052089756,0.007754191,0.00005431574,0.00053520635,0.000002544437,0.009900113,0.94142765,0.012463381,0.025479853,0.002102226,0.00013526133],"about_ca_topic_score_codex":0.0069303424,"about_ca_topic_score_gemma":0.0073254122,"teacher_disagreement_score":0.93856955,"about_ca_system_score_codex":0.000019611196,"about_ca_system_score_gemma":0.000030461331,"threshold_uncertainty_score":0.9996826},"labels":[],"label_agreement":null},{"id":"W2743047736","doi":"10.5430/air.v6n2p100","title":"Using simplified geometric models in skill-based manipulation for objects used in daily life","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Japan Society for the Promotion of Science","keywords":"Task (project management); Computer science; Robot; Motion (physics); Geometric modeling; Reliability (semiconductor); Artificial intelligence; Human–computer interaction; Computer vision; Engineering; Mechanical engineering","score_opus":0.41243874875593334,"score_gpt":0.4378936383905531,"score_spread":0.02545488963461978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2743047736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4926306,0.00005218496,0.5062476,0.000055111443,0.00011206826,0.0004902246,0.000003115084,0.00003948065,0.00036965247],"genre_scores_gemma":[0.99671364,0.00003176581,0.003042833,0.000007646834,0.00007256803,0.000072038674,0.000010734153,0.000033005046,0.000015743337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843186,0.00004124446,0.00038280556,0.00027446746,0.00034654842,0.000523086],"domain_scores_gemma":[0.9990861,0.00027020925,0.000047458576,0.00036097874,0.00014767263,0.000087609194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012127082,0.00012192924,0.0001779927,0.0010405788,0.00026282796,0.00029567617,0.00035403072,0.00013189796,0.000024640485],"category_scores_gemma":[0.0008077396,0.00013365706,0.000036556343,0.00058938155,0.000058139096,0.00041703315,0.00004885551,0.00027313546,0.000012890456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042694795,0.000040071365,0.0009165456,0.00011269142,0.0000030007768,0.000002963412,0.00032030654,0.98132044,0.00051772123,0.0013818062,0.0000037283094,0.015338048],"study_design_scores_gemma":[0.00007720238,0.000025218358,0.0014788238,0.00006235735,0.0000016254108,1.00809366e-7,0.00019650008,0.93051034,0.05051124,0.016995676,0.000006581964,0.00013431114],"about_ca_topic_score_codex":0.0008066109,"about_ca_topic_score_gemma":0.0011568746,"teacher_disagreement_score":0.50408304,"about_ca_system_score_codex":0.00018991268,"about_ca_system_score_gemma":0.000094522424,"threshold_uncertainty_score":0.5450376},"labels":[],"label_agreement":null},{"id":"W2773837541","doi":"10.5430/air.v7n1p1","title":"Multiclass patent document classification","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Aeronautics and Space Administration","keywords":"Computer science; Support vector machine; Feature selection; Random forest; Artificial intelligence; C4.5 algorithm; Machine learning; Classifier (UML); Data mining; Document classification; Multiclass classification; Decision tree; Statistical classification; Naive Bayes classifier","score_opus":0.7736202196270415,"score_gpt":0.4283465409963569,"score_spread":0.34527367863068464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773837541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78211206,0.00008940222,0.0060265716,0.0153722325,0.0020904292,0.0014424276,0.000002940319,0.00021390966,0.19264999],"genre_scores_gemma":[0.9969303,0.00004052671,0.000083622304,0.00023698196,0.0011858499,0.00006542117,0.000010032073,0.000023830251,0.0014234432],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976704,0.000051081734,0.00037710957,0.00044167833,0.0007971661,0.0006625776],"domain_scores_gemma":[0.9980808,0.00012013342,0.00015197055,0.00081038836,0.00080297183,0.00003375379],"candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0023490048,0.00015100547,0.00014716976,0.000292763,0.0022176087,0.0021541032,0.001100183,0.00010243277,0.0013554639],"category_scores_gemma":[0.0018242943,0.00012348367,0.00008023897,0.00026444407,0.00045040497,0.0013118893,0.000517774,0.0004365175,0.019630328],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031755422,0.00035151653,0.0035680973,0.00010319184,0.000034986475,0.000035652352,0.0003885881,0.00007965017,0.016008187,0.41411307,0.0039306097,0.5610689],"study_design_scores_gemma":[0.0001799938,0.00015382578,0.011212432,0.00025255256,0.00003653514,0.0000037459247,0.004707242,0.26804638,0.08147896,0.48595166,0.14701746,0.0009591878],"about_ca_topic_score_codex":0.0036967276,"about_ca_topic_score_gemma":0.00046170564,"teacher_disagreement_score":0.5601097,"about_ca_system_score_codex":0.00008248022,"about_ca_system_score_gemma":0.00004064947,"threshold_uncertainty_score":0.99955744},"labels":[],"label_agreement":null},{"id":"W2778728785","doi":"10.5430/air.v7n1p15","title":"Estimating the number of clusters using diversity","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Outlier; Silhouette; Computer science; Pattern recognition (psychology); Entropy (arrow of time); Single-linkage clustering; Cluster (spacecraft); Ground truth; Artificial intelligence; Statistic; Mathematics; Data mining; Fuzzy clustering; Statistics; CURE data clustering algorithm; Physics","score_opus":0.36841764293722823,"score_gpt":0.505386192201522,"score_spread":0.1369685492642938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2778728785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20526934,0.000013462201,0.79200244,0.0010691949,0.00031261248,0.00021349765,0.0000014598191,0.000030208654,0.0010877668],"genre_scores_gemma":[0.8881286,0.0000059581366,0.11161508,0.000011227035,0.00012529484,0.0000064020755,1.6936065e-7,0.000008900783,0.000098386685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685085,0.0003088901,0.00031396202,0.0004373925,0.0013741208,0.0007147758],"domain_scores_gemma":[0.9965597,0.000642062,0.0001308901,0.0018606887,0.0006779163,0.0001287611],"candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0037966713,0.00011244369,0.0001635078,0.00012161362,0.004180655,0.0005990598,0.0052047027,0.00006361121,0.000037838414],"category_scores_gemma":[0.00182559,0.00008785894,0.0000746964,0.000454946,0.0012420313,0.00085703854,0.008622949,0.0005913157,0.0001628954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005034647,0.00014964552,0.0040819217,0.000071079085,0.000045709006,0.000077377816,0.0050855475,0.049978584,0.0045674467,0.07877783,0.00003816965,0.85707635],"study_design_scores_gemma":[0.000016125068,0.000028448723,0.00036993148,0.000043652017,0.0000015995593,0.00001380259,0.00036421625,0.9120841,0.018623516,0.06834649,0.000016205406,0.00009191192],"about_ca_topic_score_codex":0.0025464098,"about_ca_topic_score_gemma":0.00007375716,"teacher_disagreement_score":0.8621055,"about_ca_system_score_codex":0.00012812897,"about_ca_system_score_gemma":0.00015396497,"threshold_uncertainty_score":0.99939513},"labels":[],"label_agreement":null},{"id":"W2782900468","doi":"10.5430/air.v7n1p23","title":"Combining Information Extraction and Text Segmentation methods in Greek Texts","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Segmentation; Computer science; Information extraction; Natural language processing; Artificial intelligence; Information retrieval; Resolution (logic); Text segmentation; Extraction (chemistry); Pattern recognition (psychology)","score_opus":0.15148678799638132,"score_gpt":0.5122084057036119,"score_spread":0.3607216177072306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782900468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0133120045,0.00022286165,0.9845277,0.0007103219,0.00013017755,0.00024285303,3.2346625e-7,0.00013139064,0.0007223345],"genre_scores_gemma":[0.5361818,0.000025511252,0.46366704,0.00004711418,0.000037780097,0.00002124662,0.000001375631,0.000003139721,0.000015012039],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983069,0.00037359883,0.00033717699,0.00024359333,0.0004078825,0.00033085863],"domain_scores_gemma":[0.9987861,0.0004355038,0.0000713889,0.00024125846,0.00040212585,0.000063643034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036922777,0.0000827752,0.00009096698,0.0006064302,0.00025310495,0.00048937014,0.00043611147,0.00008392138,0.000023073106],"category_scores_gemma":[0.00071163505,0.00007842748,0.000013931474,0.0012786187,0.00022146112,0.0024487986,0.0002570314,0.0003853047,0.00011326695],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012881409,0.00001994515,0.00008746402,0.000011226149,0.0000013357604,0.000004733403,0.0030570962,0.0000023736084,0.028009553,0.055247948,0.000042608182,0.9135028],"study_design_scores_gemma":[0.00001768262,0.00016775096,0.0001234642,0.0000471582,6.8544017e-7,0.00002303957,0.00079047953,0.087297365,0.5689512,0.34216872,0.000309212,0.00010326824],"about_ca_topic_score_codex":0.00030990542,"about_ca_topic_score_gemma":0.00013463944,"teacher_disagreement_score":0.9133996,"about_ca_system_score_codex":0.00010272742,"about_ca_system_score_gemma":0.000069685935,"threshold_uncertainty_score":0.47190076},"labels":[],"label_agreement":null},{"id":"W2783668103","doi":"10.5430/air.v7n1p34","title":"Quantitative evaluation of sensitivity in confidential car exterior design","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Color perception and design","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Confidentiality; Computer science; Product design; Sensitivity (control systems); Product (mathematics); Convolutional neural network; Manufacturing engineering; Reliability engineering; Engineering; Artificial intelligence; Computer security; Mathematics; Electronic engineering","score_opus":0.6975095233729774,"score_gpt":0.6009043863013002,"score_spread":0.09660513707167728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783668103","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8712167,0.000050811508,0.12179863,0.00014922334,0.0004204423,0.0007652654,0.000004083934,0.000017279832,0.005577554],"genre_scores_gemma":[0.9988953,0.000007081155,0.00069757714,0.000018406006,0.00014369792,0.00007608843,0.0000021330284,0.0000115328385,0.0001481887],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9939087,0.003910652,0.00044267788,0.00038099996,0.000933227,0.00042375457],"domain_scores_gemma":[0.9968478,0.000992201,0.000065706015,0.000324663,0.0016943497,0.00007526398],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.016018474,0.00009357721,0.00018251615,0.0005000179,0.00012258002,0.00003945112,0.0001536071,0.00012089751,0.008855846],"category_scores_gemma":[0.0011329906,0.00009621262,0.00004846398,0.0009912299,0.0008603117,0.00010236352,0.000053186635,0.00027418666,0.003312052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027077133,0.0007113136,0.00088508823,0.000011443564,0.000041249838,0.00004058079,0.035938468,0.0003878768,0.5091337,0.10375552,0.0009000438,0.34548703],"study_design_scores_gemma":[0.0003800106,0.004477067,0.03946425,0.00013072445,0.000046985355,0.000029085799,0.0705154,0.22143495,0.5357847,0.12700662,0.00020866797,0.00052149565],"about_ca_topic_score_codex":0.0026634515,"about_ca_topic_score_gemma":0.004090215,"teacher_disagreement_score":0.34496555,"about_ca_system_score_codex":0.00012945839,"about_ca_system_score_gemma":0.00024275841,"threshold_uncertainty_score":0.997464},"labels":[],"label_agreement":null},{"id":"W2790151031","doi":"10.5430/air.v7n1p53","title":"Multisequent Gentzen Deduction Systems For B&lt;sub&gt;2&lt;/sub&gt; &lt;sup&gt;2&lt;/sup&gt;-Valued First-Order Logic","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Advanced Algebra and Logic","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Soundness; Sequent; Sequent calculus; Mathematics; Generalization; Order (exchange); Completeness (order theory); Discrete mathematics; Calculus (dental); Computer science; Programming language; Mathematical analysis; Mathematical proof","score_opus":0.16973123076872068,"score_gpt":0.3924163908098235,"score_spread":0.2226851600411028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790151031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.115763284,0.0024892953,0.870807,0.0021870025,0.0030034252,0.0037514148,0.00004609461,0.0006158118,0.0013366884],"genre_scores_gemma":[0.97686833,0.0009829123,0.016706392,0.00019996328,0.0029887029,0.0010136986,0.000050385257,0.0001272089,0.0010623848],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9887945,0.0010212102,0.0017463603,0.0026165654,0.002662332,0.0031589968],"domain_scores_gemma":[0.99174017,0.0013083451,0.00042181238,0.002311853,0.0033596512,0.0008581582],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0050000492,0.0008423353,0.0009164533,0.0010441964,0.0025730554,0.0012320246,0.0035097762,0.00060426537,0.0002311712],"category_scores_gemma":[0.0020168868,0.00077366264,0.00041864841,0.0035015973,0.001522665,0.0015324826,0.0013236296,0.0009838903,0.0038391964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006406254,0.001409907,0.00009946628,0.00043621965,0.0002380888,0.00016729691,0.0053850575,0.02175359,0.1808185,0.58674496,0.010846648,0.19145964],"study_design_scores_gemma":[0.0003080817,0.0018643535,0.00007985395,0.00025636994,0.00004012296,0.00009206998,0.0008137396,0.72283715,0.13052973,0.10552246,0.036394108,0.0012619796],"about_ca_topic_score_codex":0.00020368923,"about_ca_topic_score_gemma":0.00071496353,"teacher_disagreement_score":0.8611051,"about_ca_system_score_codex":0.0008916322,"about_ca_system_score_gemma":0.00063100114,"threshold_uncertainty_score":0.9998048},"labels":[],"label_agreement":null},{"id":"W2793926485","doi":"10.5430/air.v7n1p45","title":"Design of a framework for combating human trafficking and kidnapping using smart objects and Internet-of-things","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Radio-frequency identification; Cloud computing; The Internet; Computer security; Global Positioning System; Workstation; Real-time computing; Telecommunications; Embedded system; World Wide Web; Operating system","score_opus":0.24111992841775448,"score_gpt":0.4130736171858298,"score_spread":0.17195368876807532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793926485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47304705,0.00020217129,0.5262075,0.000009395923,0.000116869836,0.00031655308,5.5951494e-7,0.000027049373,0.00007287231],"genre_scores_gemma":[0.97271764,0.000015915224,0.027027551,0.0000053436256,0.0001816754,0.000014163572,9.2830993e-7,0.00003117264,0.000005583836],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984555,0.00012861955,0.0004901139,0.00020376853,0.00025020828,0.00047178223],"domain_scores_gemma":[0.9983122,0.0010968887,0.00006933967,0.00015289304,0.00027398675,0.00009474068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025118184,0.00012636343,0.00025633964,0.00026647976,0.00020711942,0.000061114784,0.00015065259,0.00013385828,0.0000074675804],"category_scores_gemma":[0.00047092425,0.00013187932,0.00003540647,0.00037908828,0.00040590472,0.00017534675,0.000071568335,0.0002776439,0.0000019492516],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019707785,0.00008774186,0.0016477327,0.0016851758,0.00014716634,0.0000029489192,0.06354769,0.0034955093,0.69802916,0.037369832,0.000028751958,0.1937612],"study_design_scores_gemma":[0.000026799591,0.00024272338,0.000022881593,0.0006798506,0.00000678543,0.000002854786,0.0026259054,0.61014354,0.3782154,0.007912441,0.000010623255,0.00011016536],"about_ca_topic_score_codex":0.00024915306,"about_ca_topic_score_gemma":0.000012620084,"teacher_disagreement_score":0.606648,"about_ca_system_score_codex":0.000044422955,"about_ca_system_score_gemma":0.00003301858,"threshold_uncertainty_score":0.5377883},"labels":[],"label_agreement":null},{"id":"W2885462593","doi":"10.5430/air.v7n2p1","title":"Using Monte Carlo method to estimate the behavior of neural training between balanced and unbalanced data in classification of patterns","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundação Casimiro Montenegro Filho; Instituto Tecnológico de Aeronáutica","keywords":"Monte Carlo method; Computer science; Artificial neural network; Feedforward neural network; Artificial intelligence; Machine learning; Feed forward; Training (meteorology); Analogy; Algorithm; Data mining; Statistics; Mathematics; Engineering","score_opus":0.5927551417592783,"score_gpt":0.5681466352842042,"score_spread":0.024608506475074177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885462593","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5808589,0.0000123212085,0.41816163,0.00057431334,0.000022555718,0.0003334567,0.000014688989,0.000007847101,0.0000143085035],"genre_scores_gemma":[0.96729016,0.000005341977,0.03256654,0.000012116098,0.00006984252,0.000044211138,0.0000025877575,0.000006261018,0.0000029406958],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982185,0.00026572362,0.0004196665,0.00041197753,0.00036008138,0.00032407674],"domain_scores_gemma":[0.9981644,0.000556218,0.000096515854,0.0008709846,0.00023805231,0.00007379834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021506385,0.00007878342,0.00017460664,0.00014257878,0.00015094627,0.00007528745,0.0013532572,0.000040025407,0.000002939345],"category_scores_gemma":[0.0001348725,0.00006182073,0.000017522201,0.0011040829,0.00023059823,0.00022963414,0.0006222524,0.00021101878,0.0000023853142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015015658,0.000049911017,0.02375769,0.000013528744,0.0000057890534,0.0000015304414,0.0020190293,0.001719927,0.12444253,0.013712289,0.0000061928286,0.8342566],"study_design_scores_gemma":[0.00001688861,0.0000929679,0.052690085,0.000046398258,0.000004810699,0.0000018141582,0.00052156125,0.89086974,0.05334731,0.0023157035,0.000020860525,0.00007186757],"about_ca_topic_score_codex":0.0009308708,"about_ca_topic_score_gemma":0.0004712945,"teacher_disagreement_score":0.8891498,"about_ca_system_score_codex":0.00002003729,"about_ca_system_score_gemma":0.000060109196,"threshold_uncertainty_score":0.2520976},"labels":[],"label_agreement":null},{"id":"W2886166822","doi":"10.5430/air.v7n2p26","title":"Proposal of security preserving machine learning of IoT","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Stochastic Gradient Optimization Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cloud computing; Computer science; Server; Computation; Distributed computing; Information leakage; Enhanced Data Rates for GSM Evolution; Terminal (telecommunication); Cloud computing security; Limit (mathematics); Computer security; Artificial intelligence; Computer network; Operating system; Algorithm; Mathematics","score_opus":0.13175287832320842,"score_gpt":0.4080679640283466,"score_spread":0.2763150857051382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886166822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041146412,0.00006847293,0.955317,0.00036732995,0.00010714965,0.00033751878,0.0000021072995,0.000108614986,0.0025453565],"genre_scores_gemma":[0.93468976,0.0000146785615,0.06515364,0.0000044724657,0.00006152846,0.000014438814,0.0000010536031,0.000009161584,0.000051253955],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975617,0.000339487,0.00048957387,0.0003485156,0.000861256,0.00039945697],"domain_scores_gemma":[0.99729025,0.00045190591,0.00013758913,0.0005600575,0.0014729464,0.0000872232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026724588,0.000094754694,0.00018061725,0.0004363471,0.00018774321,0.000058316873,0.0013797324,0.00007219402,0.0001467201],"category_scores_gemma":[0.0018213568,0.0000889469,0.00005072648,0.0016174515,0.0006864862,0.00018948255,0.00080079073,0.00035142875,0.000041109943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046052686,0.00023058854,0.0007901277,0.000068448346,0.00001558313,0.0000029205846,0.0057222475,0.00070746656,0.02784285,0.9188537,0.00008247679,0.045637522],"study_design_scores_gemma":[0.000007143934,0.00040512232,0.00001550169,0.000036293666,8.7409614e-7,0.0000013818055,0.00014228134,0.3990399,0.4671948,0.1330701,0.000037161488,0.0000494583],"about_ca_topic_score_codex":0.0005019651,"about_ca_topic_score_gemma":0.000060838258,"teacher_disagreement_score":0.89354336,"about_ca_system_score_codex":0.00003589708,"about_ca_system_score_gemma":0.00018286773,"threshold_uncertainty_score":0.36271492},"labels":[],"label_agreement":null},{"id":"W2893689752","doi":"10.5430/air.v7n2p34","title":"A study of differences by industry using factor models influencing software development estimates","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Variance (accounting); Software; Sophistication; Estimation; Econometrics; Covariance; Computer science; Factor (programming language); Software development; Industrial engineering; Statistics; Engineering; Mathematics; Business; Systems engineering","score_opus":0.38906768739401826,"score_gpt":0.44864691722007227,"score_spread":0.059579229826054014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893689752","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6134599,0.000035649486,0.38623542,0.000031209915,0.000030252042,0.00012763728,0.0000030264987,0.00006354762,0.000013410641],"genre_scores_gemma":[0.9732486,0.0000041067924,0.026681026,0.000008790031,0.00002173039,0.000016953256,0.0000012212402,0.0000065831505,0.000011016091],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975386,0.00015427277,0.0004940119,0.0005305127,0.0007574369,0.00052511226],"domain_scores_gemma":[0.9981571,0.00041241228,0.000099226105,0.0006762354,0.0005519349,0.00010311539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086159445,0.00014149814,0.00023747387,0.0004949847,0.00055111997,0.00015713884,0.001695686,0.00021610755,0.000049491027],"category_scores_gemma":[0.00056797964,0.0001226057,0.000028219114,0.0017143218,0.00045513187,0.00062224944,0.0009670033,0.0005738687,0.00005285705],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007676551,0.0029287417,0.18243146,0.000072053925,0.00043103698,0.000060815528,0.06315916,0.0016285968,0.043110833,0.026101252,0.00017951518,0.67981976],"study_design_scores_gemma":[0.00004675471,0.0008257995,0.0021031944,0.00010676754,0.000013794383,0.0000051194943,0.013932225,0.18031654,0.7403322,0.061924145,0.000014256274,0.00037923187],"about_ca_topic_score_codex":0.00081426377,"about_ca_topic_score_gemma":0.0005018,"teacher_disagreement_score":0.69722134,"about_ca_system_score_codex":0.00007695992,"about_ca_system_score_gemma":0.00031317148,"threshold_uncertainty_score":0.49997154},"labels":[],"label_agreement":null},{"id":"W2902136541","doi":"10.5430/air.v7n2p43","title":"A Genetic-LVQ neural networks approach for handwritten Arabic character recognition","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Deanship of Scientific Research, King Saud University; King Saud University","keywords":"Learning vector quantization; Computer science; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Feature selection; Classifier (UML); Speech recognition; Intelligent character recognition; Arabic; Character recognition","score_opus":0.18716098341923487,"score_gpt":0.38721426140435183,"score_spread":0.20005327798511696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902136541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019235041,0.00008079263,0.97645223,0.0008119849,0.00032257062,0.001352627,0.0000105349145,0.00038462805,0.0013495982],"genre_scores_gemma":[0.8418024,0.00006898215,0.15509894,0.00033256994,0.0016520612,0.0008256767,0.000034881163,0.000037550803,0.00014693568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959907,0.00045065943,0.00063358527,0.0009956631,0.0007291702,0.0012002259],"domain_scores_gemma":[0.9963798,0.00053686515,0.00010920777,0.0008001209,0.0019039913,0.0002700355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002791906,0.00024556054,0.00027262888,0.0005529329,0.00074277114,0.00079610304,0.0015002699,0.00024268299,0.00014797901],"category_scores_gemma":[0.0004967238,0.00023365684,0.00015865627,0.0014431132,0.0006113778,0.00072639895,0.00039869576,0.00058736565,0.00048776672],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007606958,0.00020730257,0.00004708147,0.000021810394,0.000017657268,0.0000065635486,0.00040854388,0.000028178012,0.0027646185,0.005326482,0.001075347,0.99002033],"study_design_scores_gemma":[0.000046568213,0.0007595291,0.00012837086,0.000032388387,0.0000062307467,0.000029520248,0.00013961774,0.81508875,0.113615744,0.0689316,0.00089246914,0.00032922917],"about_ca_topic_score_codex":0.00008472998,"about_ca_topic_score_gemma":0.000039285173,"teacher_disagreement_score":0.98969114,"about_ca_system_score_codex":0.00009539488,"about_ca_system_score_gemma":0.00009976388,"threshold_uncertainty_score":0.95282495},"labels":[],"label_agreement":null},{"id":"W2905213535","doi":"10.5430/air.v7n2p55","title":"Knowledge representation with T","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Linguistic Studies and Language Acquisition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Vocabulary; Representation (politics); Human language; Set (abstract data type); Semantics (computer science); Knowledge representation and reasoning; Linguistics; Natural language processing; Tacit knowledge; Natural language; Artificial intelligence; Programming language; Knowledge management","score_opus":0.24812727485752342,"score_gpt":0.47541000298627045,"score_spread":0.22728272812874703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905213535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023266386,0.00023255165,0.91836065,0.001246475,0.0004216864,0.00025912587,7.0371914e-7,0.00013019658,0.056082245],"genre_scores_gemma":[0.9900748,0.000017269176,0.00857596,0.000061241946,0.00089472404,0.00002492219,0.0000011437364,0.0000066672683,0.00034326816],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99840575,0.00015288967,0.00017859538,0.00039167816,0.0004565207,0.00041455228],"domain_scores_gemma":[0.9978918,0.00035324742,0.000027822703,0.00052117463,0.001121986,0.000083992614],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010097456,0.00007495459,0.00008657254,0.00017339036,0.0004931691,0.00021747779,0.00059505686,0.00003488429,0.00017022318],"category_scores_gemma":[0.0005065523,0.00005724284,0.00002279051,0.0013815282,0.00038749597,0.00018435379,0.00029646407,0.00015471052,0.0014770121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034577668,0.000100320285,0.000106796135,0.0000059978875,0.000014380531,0.000032899254,0.008167072,0.0000067348424,0.0011075123,0.5877032,0.0012435121,0.40147695],"study_design_scores_gemma":[0.00008754019,0.0024247754,0.0009983592,0.00012862656,0.000009486574,0.000045063003,0.010095592,0.12800159,0.48593503,0.35583243,0.015883576,0.00055794395],"about_ca_topic_score_codex":0.0003210069,"about_ca_topic_score_gemma":0.00026143857,"teacher_disagreement_score":0.96680844,"about_ca_system_score_codex":0.000049976137,"about_ca_system_score_gemma":0.00008403954,"threshold_uncertainty_score":0.9993005},"labels":[],"label_agreement":null},{"id":"W2906805910","doi":"10.5430/air.v7n2p74","title":"Expansion of Particle Multi-Swarm Optimization","year":2018,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Swarm behaviour; Benchmark (surveying); Computer science; Metaheuristic; Mathematical optimization; Key (lock); Swarm intelligence; Algorithm; Artificial intelligence; Mathematics","score_opus":0.31086710282853114,"score_gpt":0.4640725909067391,"score_spread":0.15320548807820794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906805910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062617734,0.000057730118,0.99149495,0.0007443945,0.00024321197,0.000371907,0.000002013646,0.0000857113,0.00073829904],"genre_scores_gemma":[0.6278527,0.00007187097,0.37151718,0.000022605518,0.0001172106,0.000027226755,0.0000020960704,0.000013374027,0.0003757568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996106,0.0006233502,0.0006056476,0.0005543419,0.0014216631,0.0006889408],"domain_scores_gemma":[0.99556315,0.00055156613,0.00008982059,0.0009429914,0.0026161775,0.00023631095],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0038295484,0.000121719444,0.000188031,0.00040816359,0.00037567757,0.00022602026,0.0014075802,0.00009189433,0.0005174757],"category_scores_gemma":[0.0023422984,0.00011381826,0.000055828943,0.0029462557,0.00075725565,0.0005175195,0.0006218624,0.00029532603,0.0008551908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009567796,0.0008011222,0.00017161322,0.000047686724,0.000033363656,0.000022897804,0.0037469002,0.110424794,0.028740596,0.3650181,0.0003960087,0.49050125],"study_design_scores_gemma":[0.000024479428,0.00019193625,0.000016184436,0.000012783687,9.3675095e-7,0.0000017199546,0.00017940534,0.6335368,0.36337742,0.0025281797,0.00005880166,0.00007134098],"about_ca_topic_score_codex":0.0001519411,"about_ca_topic_score_gemma":0.00002213472,"teacher_disagreement_score":0.6215909,"about_ca_system_score_codex":0.000068000736,"about_ca_system_score_gemma":0.00030433747,"threshold_uncertainty_score":0.99992275},"labels":[],"label_agreement":null},{"id":"W2911358689","doi":"10.5430/air.v8n1p1","title":"Hybrid human resources localization and tracking system","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Radio-frequency identification; Software deployment; Python (programming language); Real-time computing; Global Positioning System; Human resources; Software engineering; Telecommunications; Computer security; Operating system","score_opus":0.08193858878035407,"score_gpt":0.34306234083927123,"score_spread":0.26112375205891714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911358689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.976778,0.0003930236,0.015649963,0.000054992877,0.00033140054,0.00043552983,0.0000037633838,0.00027623368,0.00607711],"genre_scores_gemma":[0.9994803,0.00002633004,0.000017056,0.0000047814356,0.00026332805,0.000016616457,0.000006309253,0.000035475452,0.00014978298],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983277,0.00014599698,0.00035504482,0.00026236894,0.00044029416,0.0004686367],"domain_scores_gemma":[0.9992651,0.0001702167,0.000021649772,0.00026723032,0.0001680441,0.00010781113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014072598,0.00012150982,0.00017132923,0.00024913857,0.00026249146,0.00019196072,0.00019715102,0.000077496574,0.00006703174],"category_scores_gemma":[0.000035251793,0.000120933255,0.000035181576,0.0003513691,0.00010396451,0.0001679536,0.000047231955,0.00031561387,0.0007137114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014422674,0.0001271305,0.01276267,0.0037318324,0.00013999431,0.00012300679,0.00984497,0.1284227,0.21225251,0.13216227,0.0014689597,0.4988197],"study_design_scores_gemma":[0.000044855697,0.00015391392,0.00025073506,0.0004057196,0.00000571194,0.000020235946,0.005263905,0.75654954,0.2287475,0.0034411391,0.0047943764,0.00032237693],"about_ca_topic_score_codex":0.00023916127,"about_ca_topic_score_gemma":0.000051875963,"teacher_disagreement_score":0.6281268,"about_ca_system_score_codex":0.00011681652,"about_ca_system_score_gemma":0.0000148810295,"threshold_uncertainty_score":0.91735536},"labels":[],"label_agreement":null},{"id":"W2911526837","doi":"10.5430/air.v7n2p87","title":"Indoor Localization Based on Bluetooth","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Qinglan Project of Jiangsu Province of China; National Social Science Fund of China; Six Talent Peaks Project in Jiangsu Province; Government of Jiangsu Province","keywords":"Bluetooth; Hybrid positioning system; Fingerprint (computing); Global Positioning System; Computer science; Positioning technology; Real-time computing; Process (computing); Outlier; Terminal (telecommunication); Transmission (telecommunications); Indoor positioning system; Positioning system; Wireless; Computer vision; Artificial intelligence; Engineering; Telecommunications","score_opus":0.08013421044768386,"score_gpt":0.3464721814307017,"score_spread":0.2663379709830178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911526837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14374991,0.000118621465,0.78424716,0.00058310514,0.0010470141,0.0011518657,0.0000121872135,0.0017578083,0.06733232],"genre_scores_gemma":[0.9993887,0.00003317161,0.00019399133,0.00007794075,0.00006579405,0.000033235116,0.000012667352,0.00003550026,0.00015901323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983263,0.000074570875,0.0002669285,0.00025157726,0.0005920736,0.0004885569],"domain_scores_gemma":[0.999035,0.00024978994,0.000014621289,0.0004279605,0.0002080246,0.000064595],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00068301277,0.00012856779,0.00013239816,0.00050820387,0.00012915788,0.00010502472,0.00034631373,0.00016577897,0.00085050723],"category_scores_gemma":[0.00025885354,0.00012183675,0.000045193086,0.0011903401,0.00013421335,0.00011020983,0.000040132963,0.00042169692,0.004575252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005100036,0.000080340076,0.0014559267,0.000081772974,0.0000104835335,0.000007831003,0.00021972455,0.7813691,0.0046888078,0.099043354,0.0012564107,0.111735255],"study_design_scores_gemma":[0.000019648814,0.00010906095,0.000035257908,0.000031245403,9.963496e-7,3.005878e-7,0.00035067313,0.6922686,0.29414427,0.010357887,0.0025646605,0.0001174023],"about_ca_topic_score_codex":0.000025847417,"about_ca_topic_score_gemma":0.000021861557,"teacher_disagreement_score":0.8556388,"about_ca_system_score_codex":0.0001333186,"about_ca_system_score_gemma":0.000046516172,"threshold_uncertainty_score":0.9961998},"labels":[],"label_agreement":null},{"id":"W2921850771","doi":"10.5430/air.v8n1p25","title":"Body sensor networks for monitoring performances in sports: A brief overview and some new thoughts","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wearable computer; Computer science; Focus (optics); Wireless sensor network; Point (geometry); Human–computer interaction; Multimedia; Embedded system; Computer network","score_opus":0.20290455410229685,"score_gpt":0.4145204979329958,"score_spread":0.21161594383069893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921850771","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74058694,0.0034338993,0.25004268,0.0014714414,0.0016835006,0.0021479563,0.0000032354585,0.00014115647,0.0004892176],"genre_scores_gemma":[0.9956806,0.00093711773,0.001968801,0.000034218767,0.000806652,0.00007413343,0.0000012589395,0.000016310254,0.00048090896],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972593,0.0001911383,0.00047681935,0.00066043,0.0006743842,0.0007379305],"domain_scores_gemma":[0.99799293,0.0009637062,0.00008463527,0.0005068216,0.00026098985,0.00019092289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026703821,0.00016259612,0.00029953232,0.00036074044,0.00018579332,0.00047479643,0.0006197775,0.000115715244,0.00002999026],"category_scores_gemma":[0.0001765893,0.0001565965,0.000067507026,0.0009369108,0.00007908164,0.0013955575,0.00031705736,0.00042250482,0.00014129635],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000074218224,0.000091191025,0.0122680245,0.000116641844,0.000012845979,0.000018103388,0.0014916661,0.00028347797,0.0012565142,0.012230715,0.00011233277,0.9720443],"study_design_scores_gemma":[0.0003516048,0.0008468629,0.01163289,0.0015809956,0.00000882034,0.00006671504,0.00334912,0.80695915,0.068007015,0.08837107,0.0177773,0.0010484606],"about_ca_topic_score_codex":0.0004755687,"about_ca_topic_score_gemma":0.00012345109,"teacher_disagreement_score":0.9709958,"about_ca_system_score_codex":0.000098029755,"about_ca_system_score_gemma":0.00019621028,"threshold_uncertainty_score":0.63858205},"labels":[],"label_agreement":null},{"id":"W2922359089","doi":"10.5430/air.v8n1p14","title":"Design of a hybrid intelligent system for the management of flood disaster risks","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Flood myth; Computer science; Data mining; Risk management; Hazard; Fuzzy logic; Data science; Risk analysis (engineering); Cluster analysis; Emergency management; Artificial intelligence; Geography; Business","score_opus":0.21048801436202988,"score_gpt":0.4069277261261775,"score_spread":0.19643971176414762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922359089","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19001448,0.00018683144,0.79859555,0.0002792157,0.0004491599,0.004778935,0.000010739446,0.000028255234,0.0056568016],"genre_scores_gemma":[0.9939952,0.00025968647,0.004773828,0.0000070161495,0.000027545724,0.00023994897,0.000002487644,0.000018917988,0.0006754048],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99736744,0.00019818994,0.0005594193,0.00039259854,0.0009732928,0.0005090522],"domain_scores_gemma":[0.9984865,0.0005777666,0.00012902539,0.00066873274,0.00007305069,0.0000649048],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003408813,0.00014179594,0.00021749934,0.00008172083,0.00015411137,0.000042958363,0.0008759573,0.000031098974,0.00076915754],"category_scores_gemma":[0.000019938721,0.00009781162,0.00011093148,0.0005039145,0.00035979573,0.00011671401,0.00062642875,0.00016081054,0.00082583743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010768245,0.0011235717,0.0024654001,0.001478875,0.0006586519,0.000018947825,0.0023723296,0.27127272,0.025411086,0.11606787,0.0017072001,0.5763465],"study_design_scores_gemma":[0.00014897643,0.0010773576,0.00057469384,0.0003134365,0.00013266692,0.0000025807321,0.02854264,0.28225496,0.6712599,0.0126495985,0.0027174822,0.00032574497],"about_ca_topic_score_codex":0.0005381562,"about_ca_topic_score_gemma":0.000028212899,"teacher_disagreement_score":0.8039807,"about_ca_system_score_codex":0.00014782685,"about_ca_system_score_gemma":0.000016347616,"threshold_uncertainty_score":0.99995214},"labels":[],"label_agreement":null},{"id":"W2927683843","doi":"10.5430/air.v8n1p41","title":"A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Autoencoder; Pattern recognition (psychology); Deep learning; Artificial neural network; Feature (linguistics); Stage (stratigraphy); Algorithm; Multilayer perceptron; Backpropagation; Perceptron; Feature extraction; Encoder; Convolutional neural network; Machine learning","score_opus":0.1028277012464106,"score_gpt":0.3815450569620924,"score_spread":0.2787173557156818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927683843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019864703,0.000013619827,0.97658557,0.0007976294,0.00013146346,0.0019640382,0.0000047331237,0.00011349503,0.0005247471],"genre_scores_gemma":[0.9491713,0.0000062743898,0.049586628,0.00011482957,0.00021172917,0.00066967687,0.000014669542,0.000026657292,0.00019823253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974127,0.00023684753,0.00025549135,0.0007346879,0.0007128109,0.0006474525],"domain_scores_gemma":[0.9972715,0.0014930198,0.00007375805,0.00055787317,0.00046597052,0.00013792547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001338988,0.00018021408,0.00018312577,0.00024592705,0.00051460497,0.00031645596,0.0006482842,0.00009646809,0.00013771695],"category_scores_gemma":[0.00009150099,0.00014444077,0.00007831751,0.0008912906,0.0001458471,0.00035430116,0.00011884622,0.0006024828,0.00054809474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001060749,0.00019763758,0.0002902218,0.000017243825,0.000011578562,0.0000061422634,0.0005770019,0.07057109,0.0026425943,0.031615622,0.000061464474,0.8939033],"study_design_scores_gemma":[0.000087237844,0.0009590725,0.00025567124,0.000044135137,0.0000031598051,0.0000010107855,0.00019870422,0.97377557,0.01779055,0.005056052,0.0016412283,0.00018763127],"about_ca_topic_score_codex":0.00016014552,"about_ca_topic_score_gemma":0.00019665534,"teacher_disagreement_score":0.9293066,"about_ca_system_score_codex":0.00012278052,"about_ca_system_score_gemma":0.00011237923,"threshold_uncertainty_score":0.70448315},"labels":[],"label_agreement":null},{"id":"W2936965162","doi":"10.5430/air.v8n1p51","title":"Adaptations of Relief for continuous domains of bioinformatics","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Categorical variable; Feature (linguistics); Feature selection; Set (abstract data type); Margin (machine learning); Metric space; Euclidean geometry; Feature vector; Computer science; Euclidean distance; Metric (unit); Pattern recognition (psychology); Artificial intelligence; Mathematics; Machine learning; Discrete mathematics; Engineering; Geometry","score_opus":0.13959635976547657,"score_gpt":0.4132818983240236,"score_spread":0.27368553855854705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936965162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040366624,0.000050452923,0.9543994,0.00079015654,0.00012276243,0.00047253811,0.000016846521,0.00002870353,0.0037525357],"genre_scores_gemma":[0.92829406,0.000046488647,0.07135972,0.000009730573,0.0000200493,0.000020411779,0.0000143658835,0.0000049703776,0.00023018404],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998511,0.00010665603,0.00048852095,0.00018522904,0.00045823987,0.00025031515],"domain_scores_gemma":[0.99743295,0.0010347015,0.00015424701,0.00062441063,0.0007021611,0.000051511364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020094889,0.000059999024,0.0001540562,0.00027325042,0.000089311274,0.000052587628,0.0007719121,0.000052093248,0.000028694301],"category_scores_gemma":[0.0009084272,0.00005447156,0.000056704463,0.0007293422,0.00013956107,0.0002702872,0.0001381415,0.00016386593,0.00015716143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019638142,0.00008454008,0.00026950045,0.00005659198,0.000006961915,1.223349e-7,0.0012379269,0.00050595345,0.009357954,0.7427903,0.00014612211,0.2455244],"study_design_scores_gemma":[0.0000513885,0.0006647504,0.00060831103,0.00006018065,0.0000033063648,0.000001370754,0.0021936202,0.82978207,0.09208356,0.069080144,0.0053587556,0.00011252686],"about_ca_topic_score_codex":0.0001453095,"about_ca_topic_score_gemma":0.00002621105,"teacher_disagreement_score":0.8879275,"about_ca_system_score_codex":0.000019516365,"about_ca_system_score_gemma":0.00015107504,"threshold_uncertainty_score":0.22212857},"labels":[],"label_agreement":null},{"id":"W2948187326","doi":"10.5430/air.v8n1p61","title":"Implementation of an intelligent clustering methodology for classification of terrorist acts","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Religion and Sociopolitical Dynamics in Nigeria","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Adaptive neuro fuzzy inference system; Mean squared error; Cluster analysis; Rank (graph theory); Computer science; Artificial intelligence; Terrorism; Data mining; Fuzzy set; Statistics; Fuzzy logic; Mathematics; Pattern recognition (psychology); Machine learning; Fuzzy control system; Geography","score_opus":0.5349637969748505,"score_gpt":0.6127611264949728,"score_spread":0.0777973295201223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948187326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8606338,0.00002815981,0.1306339,0.0009789048,0.0005293625,0.001232289,0.000017661478,0.000022464754,0.0059234807],"genre_scores_gemma":[0.99350524,0.00013660645,0.0059888964,0.000015963713,0.000112707945,0.00004739273,0.000014646389,0.000011791239,0.00016674705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969269,0.00110543,0.00061135204,0.00027917663,0.0005976104,0.00047954934],"domain_scores_gemma":[0.99690646,0.0016842838,0.00014548661,0.0002832366,0.00084473274,0.00013579146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075277644,0.00007590124,0.00022338367,0.00023454738,0.00022467124,0.000041666244,0.000432577,0.00015118427,0.00037378675],"category_scores_gemma":[0.0010519695,0.00007777251,0.000090884714,0.00043639095,0.00068868446,0.00016153618,0.00006604935,0.0001788756,0.000039757993],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070541515,0.00007623776,0.000578509,0.000046315625,0.000011257782,1.5259427e-7,0.012938822,0.000044985038,0.036261316,0.8662913,0.000010989332,0.08366961],"study_design_scores_gemma":[0.00005094682,0.0006345068,0.0008245487,0.000033526485,0.000008906716,2.819227e-7,0.2852321,0.017687857,0.1842683,0.50905395,0.0020513902,0.00015372528],"about_ca_topic_score_codex":0.0053543346,"about_ca_topic_score_gemma":0.0068885707,"teacher_disagreement_score":0.35723734,"about_ca_system_score_codex":0.00016746092,"about_ca_system_score_gemma":0.0003142761,"threshold_uncertainty_score":0.8094188},"labels":[],"label_agreement":null},{"id":"W2978558391","doi":"10.5430/air.v8n2p1","title":"Use of a text mining method for classifying citizen report data and analyzing the occurrence trend of local problems","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Safety Warnings and Signage","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prioritization; Data collection; Transport engineering; Task (project management); Business; Computer science; Environmental planning; Geography; Engineering; Process management; Statistics","score_opus":0.560830461341556,"score_gpt":0.5316667656691771,"score_spread":0.029163695672378864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978558391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40653566,0.00076093245,0.58895856,0.0006629546,0.00029831796,0.0012197219,0.00018954651,0.000021629197,0.0013526764],"genre_scores_gemma":[0.99392396,0.000042858064,0.005494743,0.00001057545,0.000050635663,0.000040731284,0.000042729283,0.000015435611,0.00037830943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99719155,0.00051287306,0.0007655073,0.0006209638,0.00041426212,0.0004948498],"domain_scores_gemma":[0.99276,0.0055164876,0.00027866452,0.0010742059,0.00029786327,0.000072774266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007655547,0.000120033925,0.00031830271,0.00025157223,0.00016064254,0.00006620779,0.00068467535,0.00010741184,0.00029594655],"category_scores_gemma":[0.0012749012,0.00008948347,0.000065995875,0.0007432257,0.00053591887,0.00017623091,0.00042762308,0.00036438406,0.000017029173],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041693344,0.00014892287,0.012024578,0.00018410503,0.0001591705,0.000020585421,0.010018561,0.00047458766,0.029108582,0.018617352,0.0011475852,0.92767906],"study_design_scores_gemma":[0.00043969988,0.0025079665,0.00815416,0.0012511925,0.00023382607,0.00026066188,0.15101358,0.6843159,0.08209523,0.027580373,0.041042987,0.0011044097],"about_ca_topic_score_codex":0.0007954243,"about_ca_topic_score_gemma":0.00017452416,"teacher_disagreement_score":0.92657465,"about_ca_system_score_codex":0.000018613471,"about_ca_system_score_gemma":0.00009614062,"threshold_uncertainty_score":0.364903},"labels":[],"label_agreement":null},{"id":"W3003613697","doi":"10.5430/air.v8n2p15","title":"A simple classification framework for predicting Alzheimer’s disease from region-based grey matter volume and APOE genotype status","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Classifier (UML); Neuroimaging; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature selection; Machine learning; Magnetic resonance imaging; Disease; Alzheimer's Disease Neuroimaging Initiative; Alzheimer's disease; Medicine; Psychology; Pathology; Neuroscience; Radiology","score_opus":0.15691622517919734,"score_gpt":0.3779157151439693,"score_spread":0.22099948996477198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003613697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.255835,0.0014824847,0.73562884,0.005699785,0.000114413,0.0009133765,0.00018035338,0.000023950844,0.000121812925],"genre_scores_gemma":[0.99356073,0.00011677711,0.0042724465,0.0008769033,0.00065392384,0.000087205735,0.00038681875,0.000027999482,0.000017213973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984534,0.00008816268,0.00032673118,0.00043128637,0.0002110646,0.0004893637],"domain_scores_gemma":[0.9987968,0.00017215464,0.00007826982,0.00033199406,0.0002465664,0.0003742103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033590916,0.00013216434,0.00012394425,0.00004142307,0.00026303454,0.0001474619,0.00022130169,0.00016831976,0.00007453041],"category_scores_gemma":[0.00046839553,0.0001318748,0.00006405048,0.00016354992,0.00020234779,0.000010066297,0.00012729478,0.00025203082,0.000090522786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.009123549,0.0005229844,0.22839177,0.0004476618,0.000597775,0.000024013072,0.0043846047,0.006453619,0.073114485,0.028018892,0.048211854,0.6007088],"study_design_scores_gemma":[0.00014797326,0.000628453,0.008994404,0.000056936864,0.00007180992,6.372881e-7,0.0017534634,0.845953,0.025432488,0.08998301,0.02647613,0.0005016881],"about_ca_topic_score_codex":0.00010400027,"about_ca_topic_score_gemma":0.000031293024,"teacher_disagreement_score":0.8394994,"about_ca_system_score_codex":0.000016287913,"about_ca_system_score_gemma":0.00017573126,"threshold_uncertainty_score":0.5377698},"labels":[],"label_agreement":null},{"id":"W3037569383","doi":"10.5430/air.v9n1p1","title":"A study of the possibilities of text mining and machine learning for score evaluation and review content","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Purchasing; Computer science; Product (mathematics); Variety (cybernetics); The Internet; Feature (linguistics); Word of mouth; Information retrieval; Test (biology); Word (group theory); Advertising; World Wide Web; Artificial intelligence; Marketing; Mathematics; Business","score_opus":0.5759902700237656,"score_gpt":0.4667204456302909,"score_spread":0.10926982439347466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037569383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9655554,0.006346816,0.023401378,0.0037489303,0.000014645486,0.0009011277,0.0000037320883,0.000012519202,0.00001541383],"genre_scores_gemma":[0.99871534,0.0005105555,0.00068503123,0.00003452605,0.000006548933,0.000035936344,0.000001040328,0.0000022072018,0.000008825861],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985925,0.00035454845,0.00029792273,0.00024984073,0.00037855236,0.0001266514],"domain_scores_gemma":[0.99868345,0.0005010615,0.00008645222,0.00026251966,0.00043458547,0.00003192539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028501009,0.000050089264,0.00016751152,0.000076679105,0.0001627978,0.00002765117,0.00041635463,0.000026768379,0.000007870036],"category_scores_gemma":[0.0035003906,0.00003425512,0.000027885131,0.00064755545,0.00024602728,0.000115404684,0.00042752826,0.00014064119,8.2501026e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055793058,0.00016300331,0.025378248,0.00050031743,0.00006344711,8.749689e-7,0.013799202,0.00007559043,0.005859534,0.022411633,0.00005442531,0.93163794],"study_design_scores_gemma":[0.0002374011,0.0039922926,0.0058236597,0.0011200189,0.00016069865,0.0000055939413,0.055634134,0.80987984,0.09620315,0.026523145,0.00016493312,0.00025514053],"about_ca_topic_score_codex":0.00016827522,"about_ca_topic_score_gemma":0.00018776953,"teacher_disagreement_score":0.9313828,"about_ca_system_score_codex":0.000008731783,"about_ca_system_score_gemma":0.00005678546,"threshold_uncertainty_score":0.41905475},"labels":[],"label_agreement":null},{"id":"W3043271413","doi":"10.5430/air.v9n1p12","title":"An algorithm for glare detection via photometric, colorimetric, and global positioning features","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; GLARE; Ground truth; Global Positioning System","score_opus":0.11478013586893686,"score_gpt":0.4263289298579716,"score_spread":0.31154879398903473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043271413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052188253,0.00027209127,0.992298,0.00082028285,0.0001339362,0.00076992164,0.000011132509,0.00031940886,0.00015639767],"genre_scores_gemma":[0.81366986,0.000040448784,0.18578176,0.00016517293,0.0002035907,0.00011556784,0.000005332443,0.000010216784,0.000008054145],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975887,0.00020217814,0.0002748475,0.00067137316,0.00069550413,0.0005674041],"domain_scores_gemma":[0.9983614,0.00032620775,0.000055507997,0.00031850112,0.0006800771,0.00025832347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013100464,0.00014201007,0.0001604481,0.00041133672,0.0005518889,0.0007298418,0.0008425178,0.0001105929,0.000011766109],"category_scores_gemma":[0.0005503919,0.00014738496,0.00004889436,0.004266905,0.00014696221,0.00087610754,0.00028531265,0.00031039905,0.000039679664],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027698683,0.00006188061,0.000024882538,0.000012571781,0.000008499492,0.000009348957,0.0002611729,0.0000069019393,0.050627604,0.007188454,0.000071488044,0.9416995],"study_design_scores_gemma":[0.000023107526,0.001093565,0.00012326239,0.000007198719,0.0000027065018,0.000008388354,0.00015468891,0.29620925,0.6767885,0.025243156,0.00021373166,0.00013245708],"about_ca_topic_score_codex":0.00026580982,"about_ca_topic_score_gemma":0.000035567038,"teacher_disagreement_score":0.94156706,"about_ca_system_score_codex":0.00016944783,"about_ca_system_score_gemma":0.00007116855,"threshold_uncertainty_score":0.70378816},"labels":[],"label_agreement":null},{"id":"W3045837878","doi":"10.5430/air.v9n1p27","title":"Weighting features by the value displacement rebound","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Weighting; Computer science; Metric (unit); Similarity (geometry); Value (mathematics); Space (punctuation); Metric space; Displacement (psychology); Theoretical computer science; Scheme (mathematics); Artificial intelligence; Data mining; Machine learning; Mathematics; Discrete mathematics; Image (mathematics); Engineering","score_opus":0.16784386997036702,"score_gpt":0.3802964394506846,"score_spread":0.21245256948031757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045837878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02401412,0.0014941702,0.87947637,0.08137125,0.0002722851,0.0005691504,0.0000070261526,0.00018205495,0.012613604],"genre_scores_gemma":[0.99604994,0.000056538098,0.0024835214,0.0005326848,0.00030732097,0.000020983907,0.0000032377664,0.0000118066755,0.00053397065],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971117,0.00034306172,0.0003601131,0.0005302623,0.0009922639,0.00066260603],"domain_scores_gemma":[0.99845725,0.00054827385,0.00006812684,0.0005070197,0.00021887524,0.00020042577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021179304,0.0001303357,0.00015074572,0.000065172324,0.0010522155,0.0009775794,0.0017511521,0.000052293864,0.00013788018],"category_scores_gemma":[0.00057070766,0.0000866861,0.00009863438,0.0015523188,0.0002590422,0.00031206632,0.0007884129,0.00058432407,0.00043790997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028316397,0.000054859847,0.000051786545,0.000013475627,0.000040131537,0.000014340572,0.0047731544,0.0009472591,0.012536474,0.5661274,0.013960894,0.40145186],"study_design_scores_gemma":[0.00002377147,0.00036949583,0.00003745792,0.000031139392,0.000010021007,0.000008660118,0.004614652,0.73738724,0.1556299,0.046514485,0.05505724,0.00031590852],"about_ca_topic_score_codex":0.00040075261,"about_ca_topic_score_gemma":0.000052445797,"teacher_disagreement_score":0.9720358,"about_ca_system_score_codex":0.000051461437,"about_ca_system_score_gemma":0.00007869043,"threshold_uncertainty_score":0.9426821},"labels":[],"label_agreement":null},{"id":"W3081610157","doi":"10.5430/air.v9n1p36","title":"Properly initialized Bayesian Network for decision making leveraging random forest","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian network; Computer science; Random forest; Decision tree; Node (physics); Data mining; Enhanced Data Rates for GSM Evolution; Conditional probability; Inference; Probabilistic logic; Influence diagram; Bayesian probability; Bayesian inference; Product (mathematics); Artificial intelligence; Machine learning; Mathematics; Statistics; Engineering","score_opus":0.22726395622954948,"score_gpt":0.4239395341372076,"score_spread":0.1966755779076581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081610157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037349407,0.00017714995,0.9884066,0.006443116,0.00015508366,0.0006016743,0.000004943133,0.00022013501,0.00025634878],"genre_scores_gemma":[0.87907356,0.000029596611,0.120089896,0.00036435862,0.0003106268,0.00010099483,0.0000059746367,0.000014088474,0.000010897327],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704164,0.0002593481,0.0005087147,0.00074346503,0.0006064175,0.0008404064],"domain_scores_gemma":[0.996849,0.0018719503,0.000072383584,0.00065333175,0.00038814585,0.00016518435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002706858,0.00015212952,0.00029060635,0.00025279677,0.0008089732,0.00048882037,0.001994609,0.00014608818,0.000069908645],"category_scores_gemma":[0.002947621,0.00013311968,0.00014109672,0.00223859,0.00022564593,0.00054081396,0.0006674156,0.00049734884,0.00024202476],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048799397,0.000043581105,0.0004348517,0.000018815263,0.00003537208,0.000041614014,0.00087776536,0.0041219597,0.00038662198,0.23459736,0.0020063142,0.75694776],"study_design_scores_gemma":[0.00007810584,0.00020360303,0.00002746195,0.000068844645,0.000007802302,0.0000036279368,0.0002658856,0.6634124,0.0034111035,0.3292309,0.0031286872,0.00016156392],"about_ca_topic_score_codex":0.00005080179,"about_ca_topic_score_gemma":0.0001682726,"teacher_disagreement_score":0.8753386,"about_ca_system_score_codex":0.000044660435,"about_ca_system_score_gemma":0.00016272653,"threshold_uncertainty_score":0.62220496},"labels":[],"label_agreement":null},{"id":"W3087778363","doi":"10.5430/air.v9n1p45","title":"Hybrid approaches to feature subset selection for data classification in high-dimensional feature space","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature selection; Interpretability; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Support vector machine; Computer science; Feature (linguistics); Intersection (aeronautics); Feature vector; Linear classifier; k-nearest neighbors algorithm; Filter (signal processing); Machine learning; Data mining; High dimensional; Engineering","score_opus":0.584270800689593,"score_gpt":0.4137573621001507,"score_spread":0.17051343858944235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087778363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055353038,0.00007953278,0.7658712,0.17686228,0.00023660512,0.0012914934,0.000070441274,0.00012213165,0.000113244874],"genre_scores_gemma":[0.9321395,0.000014934799,0.066303864,0.00054803165,0.00031754366,0.00015621848,0.00034344327,0.000017183589,0.00015926783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972297,0.00026297304,0.00024107036,0.0010128301,0.00070163695,0.00055176724],"domain_scores_gemma":[0.9984399,0.00037114177,0.00005056224,0.000600049,0.00028549504,0.00025285064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015236974,0.00014661564,0.0001725999,0.00028542106,0.00029183482,0.0003227743,0.0013768486,0.00012830691,0.000022559792],"category_scores_gemma":[0.0008441086,0.00013665014,0.000037501224,0.0015357763,0.00006081036,0.00070840673,0.000562458,0.0006044233,0.0003725761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068324467,0.0004186413,0.00045239576,0.00011290194,0.000024927147,0.000015963838,0.0017787163,0.004368041,0.09007811,0.12368432,0.24396437,0.53441834],"study_design_scores_gemma":[0.000044470173,0.00020951712,0.00021140151,0.000041805048,0.000002268198,0.0000036987556,0.0002702747,0.82135254,0.15387161,0.017127931,0.006687067,0.00017739802],"about_ca_topic_score_codex":0.00012692863,"about_ca_topic_score_gemma":0.00020678947,"teacher_disagreement_score":0.8767865,"about_ca_system_score_codex":0.00008274644,"about_ca_system_score_gemma":0.00019056434,"threshold_uncertainty_score":0.5572431},"labels":[],"label_agreement":null},{"id":"W3096938982","doi":"10.5430/air.v9n1p54","title":"Investigation of differential evolution and particle swarm optimization in search performance","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Differential evolution; Benchmark (surveying); Computer science; Task (project management); Key (lock); Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.2163849946774048,"score_gpt":0.3695313440947222,"score_spread":0.15314634941731742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096938982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34365407,0.000043509077,0.6539631,0.0019290935,0.000039624956,0.00027276267,0.000001039677,0.000026579257,0.00007023003],"genre_scores_gemma":[0.9724603,0.00015116301,0.027262544,0.000021429709,0.000054116765,0.00002312666,0.0000034490577,0.000008750825,0.000015111751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968711,0.0006011487,0.0005148182,0.00046914964,0.0010722796,0.00047146913],"domain_scores_gemma":[0.9984326,0.00035950582,0.000053889,0.00029302321,0.00060662685,0.0002543808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018815551,0.000100897785,0.00017317527,0.00032313316,0.00015691291,0.00017036048,0.0006084042,0.000074267075,0.00006142701],"category_scores_gemma":[0.0008566746,0.00010175067,0.00002351112,0.0026991395,0.00031748385,0.00061366346,0.00044693027,0.0004020307,0.000055201344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019526645,0.0001702196,0.017814664,0.00028088994,0.00001938232,0.000013990607,0.009347593,0.6288658,0.034096885,0.18747428,0.00004529726,0.12167578],"study_design_scores_gemma":[0.00004619126,0.00019811171,0.0015039344,0.000021252343,9.801038e-7,8.0607964e-7,0.00026906896,0.8619414,0.13390416,0.0020338513,0.0000023612515,0.00007786907],"about_ca_topic_score_codex":0.00012731717,"about_ca_topic_score_gemma":0.000010313333,"teacher_disagreement_score":0.62880623,"about_ca_system_score_codex":0.00008163668,"about_ca_system_score_gemma":0.00022953887,"threshold_uncertainty_score":0.41492718},"labels":[],"label_agreement":null},{"id":"W3102966675","doi":"10.5430/air.v10n1p57","title":"Estimation of the number of clusters on d-dimensional sphere","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Estimation; Mathematics; Spherical model; SPHERES; Computer science; Statistical physics; Algorithm; Physics; Engineering","score_opus":0.1450089567830776,"score_gpt":0.4388428390960517,"score_spread":0.29383388231297414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102966675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06987434,0.000036816014,0.9238615,0.0017462533,0.00021967219,0.00011244836,0.0000017470121,0.000008700047,0.004138502],"genre_scores_gemma":[0.86204374,0.000006282245,0.13758498,0.000063003776,0.000024977813,0.000004358501,5.044231e-7,0.000004503068,0.0002676695],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787104,0.0005530912,0.00030486868,0.00026029552,0.00077808026,0.00023260654],"domain_scores_gemma":[0.9981857,0.00060989335,0.000065959925,0.0006309397,0.00045491566,0.00005260203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014917953,0.00006672255,0.0001272136,0.000047045458,0.00011681486,0.000038408834,0.00063093106,0.000060700193,0.00012538307],"category_scores_gemma":[0.0005262045,0.000047646132,0.00008420538,0.0009654912,0.00021314652,0.00011101681,0.00034809695,0.00026402844,0.00006609001],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011110643,0.00009221092,0.000027824459,0.000015024875,0.000005998211,0.0000036293227,0.00031670617,0.0025963408,0.0054317815,0.5605113,0.00014231082,0.43084577],"study_design_scores_gemma":[0.0000085895645,0.000025108095,0.00006408856,0.000055174714,0.0000011863006,0.000005182931,0.00005084204,0.1966922,0.48865741,0.31436962,0.00003357726,0.000037015587],"about_ca_topic_score_codex":0.00006367061,"about_ca_topic_score_gemma":0.000020934476,"teacher_disagreement_score":0.7921694,"about_ca_system_score_codex":0.000027746728,"about_ca_system_score_gemma":0.00028503896,"threshold_uncertainty_score":0.19429529},"labels":[],"label_agreement":null},{"id":"W3142950774","doi":"10.5430/air.v10n1p1","title":"Comparison of Centralized and Distributed Intelligent Particle Multi-Swarm Optimization on Search Performance","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Multi-swarm optimization; Computer science; Metaheuristic; Task (project management); Swarm behaviour; Mathematical optimization; Artificial intelligence; Algorithm; Mathematics; Engineering","score_opus":0.33676114848408845,"score_gpt":0.47563296381230696,"score_spread":0.13887181532821852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142950774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09385429,0.00026049418,0.9040722,0.0010551483,0.00013841325,0.00039701807,0.000012020541,0.000058316567,0.00015204989],"genre_scores_gemma":[0.9054267,0.0007386955,0.09353783,0.000020696356,0.000034743047,0.000029761439,0.000027526952,0.000016036425,0.00016801829],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99493206,0.0010046833,0.00082007574,0.0007371844,0.0016460968,0.0008599126],"domain_scores_gemma":[0.99580526,0.0010190204,0.00009202683,0.00084078667,0.0018825848,0.00036029678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027134428,0.00018142031,0.00036104195,0.00029761545,0.00040989532,0.0003906941,0.00087491266,0.000111525784,0.00021137869],"category_scores_gemma":[0.0018282852,0.00017623436,0.00006320789,0.0027121445,0.00046749134,0.00034859142,0.00070621615,0.0006211465,0.00014949919],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009713188,0.0013954783,0.0032191793,0.00008907144,0.00004079503,0.000030114668,0.0028718961,0.7563245,0.003760848,0.054888416,0.000084267944,0.17719829],"study_design_scores_gemma":[0.000053843738,0.00017521209,0.000221564,0.000034700213,0.0000021877977,0.0000032137273,0.0006599654,0.60165524,0.39677513,0.0002580543,0.000062727624,0.000098143115],"about_ca_topic_score_codex":0.000056538054,"about_ca_topic_score_gemma":0.000015502781,"teacher_disagreement_score":0.8115724,"about_ca_system_score_codex":0.00012970192,"about_ca_system_score_gemma":0.00036565817,"threshold_uncertainty_score":0.71866286},"labels":[],"label_agreement":null},{"id":"W3145788058","doi":"10.5430/air.v10n1p12","title":"Decision branch joint venture ex-fold T-z re-validation","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Decision tree; Computer science; Artificial intelligence; Classifier (UML); Machine learning; Naive Bayes classifier; Bayesian probability; Cross-validation; Support vector machine","score_opus":0.14151438015703058,"score_gpt":0.3811960004164651,"score_spread":0.23968162025943454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145788058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74670607,0.00296685,0.21371813,0.0012675994,0.004552013,0.0007709203,0.000015273026,0.0006517745,0.029351376],"genre_scores_gemma":[0.9983504,0.0002244129,0.00015554213,0.00002142851,0.00039751435,0.000038306654,0.000009293172,0.000029699482,0.00077339966],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99786323,0.0001885684,0.00043616045,0.00030450747,0.00074379036,0.00046375193],"domain_scores_gemma":[0.99880743,0.00023933222,0.000019696316,0.00041619482,0.0003753015,0.000142054],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012195334,0.00012307774,0.00017875675,0.00021432144,0.0001902254,0.00023757956,0.00018179501,0.00015058221,0.000907202],"category_scores_gemma":[0.00054377195,0.00012429355,0.00009599428,0.0009839243,0.00005321256,0.00016005801,0.00005631384,0.0005084294,0.0022531068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004878522,0.00007372266,0.000034593148,0.0000661272,0.00003627745,0.0000622893,0.0006364152,0.022208665,0.36043796,0.0066164965,0.004460107,0.60531855],"study_design_scores_gemma":[0.00003830827,0.000051228384,0.000066276705,0.00010258094,0.0000039383513,0.000015852745,0.0014115557,0.111293234,0.83577234,0.021456033,0.029608928,0.00017972568],"about_ca_topic_score_codex":0.00008367015,"about_ca_topic_score_gemma":0.00034556375,"teacher_disagreement_score":0.60513884,"about_ca_system_score_codex":0.00014893115,"about_ca_system_score_gemma":0.0000619002,"threshold_uncertainty_score":0.9985238},"labels":[],"label_agreement":null},{"id":"W3153043709","doi":"10.5430/air.v10n1p34","title":"A study of quality prediction for large-scale open source software projects","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Software Engineering Research","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Deliverable; Quality (philosophy); Resolution (logic); Software; Scale (ratio); Computer science; Open source software; Open source; Product (mathematics); Data science; Data mining; Process management; Business; Artificial intelligence; Engineering; Systems engineering; Mathematics","score_opus":0.2997543026205094,"score_gpt":0.472607513630679,"score_spread":0.1728532110101696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153043709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3049713,0.000055928638,0.6926954,0.00029584402,0.00018168842,0.0015884154,0.000018524315,0.00014140244,0.000051531148],"genre_scores_gemma":[0.97002274,0.000008589816,0.02869573,0.000016171783,0.000112869115,0.000596888,0.000009432607,0.00002758377,0.0005099783],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9953545,0.0007703503,0.00064225023,0.00089348864,0.0014764952,0.0008629529],"domain_scores_gemma":[0.9924296,0.0038080842,0.000074097916,0.0013570846,0.0021467127,0.0001843819],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007923038,0.0001439737,0.00031315105,0.0003260554,0.00052640424,0.0007343715,0.00245619,0.000104289735,0.000036507],"category_scores_gemma":[0.0104042385,0.00014602189,0.00008157829,0.00252586,0.000109409404,0.0005512995,0.0024164014,0.00049473054,0.000051734678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066982897,0.017397577,0.092508346,0.00096006924,0.00035880183,0.00016926377,0.12008777,0.008921482,0.01869857,0.09339513,0.0034471694,0.643386],"study_design_scores_gemma":[0.0009708716,0.0060473355,0.022551974,0.00034750326,0.000024384943,0.000038329974,0.0858576,0.1692729,0.6450554,0.062648736,0.0060997964,0.0010851444],"about_ca_topic_score_codex":0.0005097191,"about_ca_topic_score_gemma":0.00075785234,"teacher_disagreement_score":0.66505146,"about_ca_system_score_codex":0.00013585309,"about_ca_system_score_gemma":0.0008742989,"threshold_uncertainty_score":0.99793154},"labels":[],"label_agreement":null},{"id":"W3155043239","doi":"10.5430/air.v10n1p43","title":"Development process of multiagent system for glycemic control of intensive care unit patients","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Glycemic; Intensive care unit; Process (computing); Medicine; Health care; Intensive care medicine; Control (management); Inference; Computer science; Risk analysis (engineering); Process management; Engineering; Artificial intelligence; Diabetes mellitus","score_opus":0.15561443800742505,"score_gpt":0.39986883737901197,"score_spread":0.2442543993715869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155043239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4812424,0.0005594186,0.5157827,0.00007910444,0.00045722254,0.0009794137,0.000019034711,0.000042882384,0.0008377968],"genre_scores_gemma":[0.99641794,0.000009589997,0.0033498374,0.00001670468,0.000038963914,0.00010478588,0.000011998402,0.000010268743,0.00003991445],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975706,0.00018319998,0.00061679597,0.00044679682,0.0006953721,0.00048728622],"domain_scores_gemma":[0.98146397,0.00051684753,0.00015390401,0.00042441767,0.017319385,0.00012147564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069599797,0.00013050858,0.0003103792,0.0001960229,0.0001935125,0.00005780165,0.00085615803,0.00008811883,0.000012915561],"category_scores_gemma":[0.0019220992,0.00011527152,0.00009112954,0.0008373521,0.0001691072,0.00013362478,0.0002737945,0.000180299,0.000041431267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011291421,0.0010850491,0.01020744,0.004094194,0.00025820083,0.00005208551,0.12302484,0.0010039189,0.022292627,0.24668875,0.00013939808,0.59002435],"study_design_scores_gemma":[0.00021485581,0.00035809932,0.00022788235,0.00029002473,0.00000776413,0.000002069296,0.04636435,0.019756572,0.9307863,0.0014561556,0.0003772023,0.00015871265],"about_ca_topic_score_codex":0.000046225567,"about_ca_topic_score_gemma":0.000082891165,"teacher_disagreement_score":0.9084937,"about_ca_system_score_codex":0.00013767235,"about_ca_system_score_gemma":0.000895889,"threshold_uncertainty_score":0.47006363},"labels":[],"label_agreement":null},{"id":"W3166799205","doi":"10.5430/air.v10n1p64","title":"Workplace and human resource safety monitoring using internet of things","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"IoT-based Smart Home Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Radio-frequency identification; Computer security; Internet of Things; Global Positioning System; Microcontroller; Arduino; Wireless; Computer science; Wearable computer; Resource (disambiguation); The Internet; Identification (biology); Telecommunications; Embedded system; World Wide Web; Computer network","score_opus":0.1426526126243922,"score_gpt":0.38278918050557237,"score_spread":0.24013656788118018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166799205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97287583,0.00096311455,0.019484086,0.00005065027,0.00030335455,0.00018249663,0.0000020681246,0.00010009008,0.0060383216],"genre_scores_gemma":[0.99815077,0.00003793556,0.0011483007,0.0000026107712,0.00023714294,0.000006165854,0.0000030597994,0.000041523046,0.0003724839],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978495,0.000252134,0.00052055344,0.0003004384,0.00055372104,0.0005236422],"domain_scores_gemma":[0.998797,0.00040132302,0.000034870292,0.0003634343,0.00027432176,0.00012902403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017914413,0.00014131963,0.00024745116,0.0002554774,0.00017955042,0.00011825843,0.00024850716,0.00012539366,0.000079126075],"category_scores_gemma":[0.0002289365,0.00015637926,0.000052441996,0.00080308167,0.00021670022,0.00017759007,0.00019795784,0.00058499543,0.000039442344],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086829554,0.00009753008,0.007888103,0.00081236026,0.00009900754,0.0001405938,0.011392498,0.016802594,0.89375687,0.018024249,0.00027838658,0.050620973],"study_design_scores_gemma":[0.000026171649,0.000045076733,0.00015477836,0.00046393898,0.0000064090978,0.00001661121,0.006835579,0.044908967,0.94464797,0.0014387136,0.00126731,0.0001884921],"about_ca_topic_score_codex":0.0005236003,"about_ca_topic_score_gemma":0.00003470915,"teacher_disagreement_score":0.05089108,"about_ca_system_score_codex":0.0001751579,"about_ca_system_score_gemma":0.000055011507,"threshold_uncertainty_score":0.63769615},"labels":[],"label_agreement":null},{"id":"W3199709140","doi":"10.5430/air.v11n1p1","title":"Classification of Echocardiogram View using A Convolutional Neural Network","year":2021,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Parasternal line; Convolutional neural network; Computer science; Artificial intelligence; Papillary muscle; Artificial neural network; Short axis; Deep learning; Pattern recognition (psychology); Computer vision; Medicine; Long axis; Cardiology; Mathematics","score_opus":0.3198568844847613,"score_gpt":0.5234243938226205,"score_spread":0.20356750933785922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199709140","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9875934,0.0046024416,0.004282375,0.0005224021,0.00026564088,0.0004728647,0.000022143482,0.000027718563,0.0022110012],"genre_scores_gemma":[0.9984785,0.00032340712,0.0006661302,0.00003310143,0.00033315475,0.000022939712,0.00006803555,0.000014236265,0.000060533646],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9979924,0.00025146749,0.00034696673,0.00030145523,0.0007061695,0.00040156627],"domain_scores_gemma":[0.9981227,0.0002084642,0.00004678035,0.00035563178,0.0010895215,0.00017689784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006240465,0.00009245616,0.00026401354,0.00010805229,0.00016125219,0.000034387576,0.00006963467,0.00007053291,0.00023272399],"category_scores_gemma":[0.00031059695,0.00008752817,0.0005519527,0.0011238344,0.00024504302,0.000058496196,0.00007617553,0.00022072312,0.00009069865],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009859056,0.0020557428,0.42492172,0.00035913038,0.0015996225,0.00028817705,0.00023908081,0.009282699,0.03503267,0.07116527,0.00080064696,0.45326933],"study_design_scores_gemma":[0.00040062235,0.0007503561,0.66993666,0.00076424377,0.00095925207,0.00016533693,0.0037334585,0.23448308,0.041737437,0.04444162,0.00214767,0.000480261],"about_ca_topic_score_codex":0.00012101582,"about_ca_topic_score_gemma":0.000010446901,"teacher_disagreement_score":0.45278907,"about_ca_system_score_codex":0.00012945062,"about_ca_system_score_gemma":0.00047554885,"threshold_uncertainty_score":0.3569295},"labels":[],"label_agreement":null}]}