{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":127,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":127,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"fa07bf585f57","filters":{"venue":"Artificial Intelligence Research"}},"results":[{"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Eloisa Vargiu","is_ca":false},{"name":"Mirko Urru","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2115775441977723,"gpt":0.410260957608213,"spread":0.1986834134104407,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004107118,0.0008189813,0.001403587,0.003870361,0.00186177,0.003021067,0.001678249,0.00203931,0.001228508],"category_scores_gemma":[0.008080201,0.0006562243,0.001448448,0.003410746,0.001014588,0.002156233,0.001165856,0.001130376,0.0007827381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009196628,"about_ca_system_score_gemma":0.001066492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01105182,"about_ca_topic_score_gemma":0.01303954,"domain_scores_codex":[0.9963226,0.001187663,0.0003060323,0.0007165942,0.001249246,0.0002178116],"domain_scores_gemma":[0.9927911,0.003688662,0.0005472398,0.001390834,0.001341695,0.0002404893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007397262,0.001446224,0.01036871,0.0004416389,0.0005957544,0.001224169,0.002428713,0.1158025,0.05623011,0.02530379,0.005564742,0.7798539],"study_design_scores_gemma":[0.00006089207,0.0002913091,0.004141542,0.00004074229,0.0002518052,0.00109633,0.0002595475,0.9397591,0.02396388,0.01537472,0.01459874,0.000161483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04540999,0.000579598,0.9452137,0.0003739807,0.00007204533,0.0002872158,0.00006570897,0.002481796,0.005516069],"genre_scores_gemma":[0.4049489,0.0003696035,0.5882555,0.0002444978,0.0001336901,0.0001174673,0.0001821129,0.000137716,0.005610571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01105182,"threshold_uncertainty_score":0.02197492,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Suneel Kumar Kingrani","is_ca":false},{"name":"Mark Levene","is_ca":false},{"name":"Dell Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3684176429372282,"gpt":0.505386192201522,"spread":0.1369685492642938,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006803065,0.0013757,0.002440946,0.00727645,0.002626745,0.003362668,0.002693533,0.002630351,0.0008944196],"category_scores_gemma":[0.03386156,0.0008780399,0.001476564,0.004132612,0.002270846,0.004209595,0.004197785,0.002173058,0.0007740223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001955168,"about_ca_system_score_gemma":0.001779812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004010482,"about_ca_topic_score_gemma":0.005037246,"domain_scores_codex":[0.9933484,0.001798947,0.0004785802,0.002141089,0.001867023,0.0003658032],"domain_scores_gemma":[0.9785373,0.01290202,0.002299236,0.002348391,0.00329504,0.00061804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008462392,0.0001887624,0.04351984,0.0005925383,0.0008096377,0.0004250587,0.001740457,0.4774753,0.0127698,0.04286085,0.01174832,0.4070233],"study_design_scores_gemma":[0.00006073234,0.00008985945,0.00559087,0.0001224777,0.00007779424,0.0003623516,0.0004365485,0.9042374,0.005376069,0.07912745,0.004394055,0.0001243493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04885091,0.001199155,0.946241,0.0004219296,0.00005654639,0.0001103034,0.0003740153,0.0006102608,0.002135908],"genre_scores_gemma":[0.5717149,0.0006481977,0.4236878,0.0002672323,0.0002384164,0.0002735952,0.001559216,0.0002827335,0.001327894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00727645,"threshold_uncertainty_score":0.0359785,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Leana Copeland","is_ca":false},{"name":"Tom Gedeon","is_ca":false},{"name":"Sumudu Mendis","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1990294949026054,"gpt":0.3988840134134479,"spread":0.1998545185108425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009574431,0.0008117073,0.0004264357,0.001059521,0.000175844,0.0005182884,0.0003622884,0.0007271903,0.000976415],"category_scores_gemma":[0.006991494,0.000210578,0.0004152624,0.0006090782,0.0001704765,0.0005877289,0.0002921392,0.0005479368,0.0002575576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003916596,"about_ca_system_score_gemma":0.0002104321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004979937,"about_ca_topic_score_gemma":0.006579438,"domain_scores_codex":[0.9995804,0.00014582,0.00004250384,0.00009253497,0.0001060886,0.00003258273],"domain_scores_gemma":[0.9956792,0.002965145,0.0005318978,0.0001646432,0.0005784907,0.00008065515],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001421184,0.000950038,0.272344,0.0003482116,0.0004664054,0.0003853495,0.0005556939,0.2127654,0.06545166,0.0004053588,0.001126065,0.4437806],"study_design_scores_gemma":[0.00001244923,0.0003023427,0.06619935,0.0000192178,0.00002904723,0.00006983062,0.00006759173,0.9217064,0.01104296,0.0003961116,0.0001317541,0.00002295727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9423149,0.0002033723,0.0561461,0.000108042,0.00002194727,0.00003203906,0.0002275504,0.0003454238,0.0006007333],"genre_scores_gemma":[0.9763221,0.00008146536,0.02258056,0.00001888608,0.00001244801,0.00003358656,0.0003137717,0.00001590009,0.0006213536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004979937,"threshold_uncertainty_score":0.009901881,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Vahid Nourani","is_ca":false},{"name":"Elnaz Sharghi","is_ca":false},{"name":"Mohammad Hossein Aminfar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2342825331444801,"gpt":0.4086283935201503,"spread":0.1743458603756702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002236299,0.0006114522,0.0004759705,0.0004743817,0.0002841788,0.0004270422,0.0006889704,0.0006448912,0.0008159097],"category_scores_gemma":[0.0004384331,0.0002860896,0.000574613,0.0004274525,0.0001901026,0.000446422,0.0002793342,0.0003672944,0.0001552135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005949209,"about_ca_system_score_gemma":0.0007428872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02323028,"about_ca_topic_score_gemma":0.02618207,"domain_scores_codex":[0.9998864,0.00001768894,0.000007576727,0.00003632336,0.00003132802,0.00002058346],"domain_scores_gemma":[0.9998435,0.00004640311,0.00002640931,0.00001003743,0.00006672535,0.000006834936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001910407,0.00002003285,0.003364552,0.0000125088,0.00001630794,0.00005872223,0.00001557876,0.9884561,0.0008623929,0.0001319442,0.0001105862,0.006932156],"study_design_scores_gemma":[0.000001152468,0.00000625226,0.00122377,0.000001054195,0.000004060365,0.000006930826,0.000008388171,0.99835,0.0002452143,0.0001011073,0.00004940418,0.000002698531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8847337,0.0001792094,0.1098882,0.0001176063,0.00003895544,0.00002550619,0.0003466559,0.0005160194,0.004154215],"genre_scores_gemma":[0.9929404,0.00006703402,0.005463469,0.000007274249,0.000005901134,0.00001773916,0.0001795864,0.00001324885,0.001305273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02323028,"threshold_uncertainty_score":0.04619014,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Timothy Kelman","is_ca":false},{"name":"Jinchang Ren","is_ca":false},{"name":"Stephen Marshall","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1107078367755154,"gpt":0.4310887633017481,"spread":0.3203809265262326,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007381549,0.0004994306,0.0004530723,0.0008950944,0.0003229035,0.0006170392,0.0003852781,0.0004798252,0.0005397139],"category_scores_gemma":[0.001124666,0.0001816026,0.0005090223,0.0008243085,0.0004251674,0.00094769,0.000449133,0.0003582551,0.0002310277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002812615,"about_ca_system_score_gemma":0.000237864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001100295,"about_ca_topic_score_gemma":0.001776621,"domain_scores_codex":[0.9996904,0.00006637612,0.0000166507,0.00007918978,0.0001035793,0.00004380496],"domain_scores_gemma":[0.9997038,0.00009982545,0.00004645517,0.00003950315,0.00009718926,0.00001308051],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007905944,0.0001931704,0.008613886,0.0003121393,0.000106686,0.0002746232,0.0003315678,0.03291776,0.4980405,0.002271982,0.0009728197,0.4551742],"study_design_scores_gemma":[0.00002499273,0.0002448561,0.03969226,0.00002457538,0.0001284339,0.0003172546,0.0003823065,0.6630167,0.2898856,0.002780786,0.003436983,0.00006525177],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6062781,0.001064081,0.3889647,0.0003137348,0.00006161368,0.00008095599,0.0001924808,0.0004892026,0.002554988],"genre_scores_gemma":[0.8631778,0.0005242609,0.1341885,0.00007196715,0.0000341906,0.00005404874,0.0002435124,0.00003301577,0.001672759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001100295,"threshold_uncertainty_score":0.003903806,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Eisuke Kita","is_ca":false},{"name":"Masaaki Harada","is_ca":false},{"name":"Takao Mizuno","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1690476692080007,"gpt":0.4099954511090571,"spread":0.2409477819010564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001497945,0.0006201572,0.0008138092,0.001431542,0.0005210412,0.0008932571,0.0009714118,0.0009042067,0.001744142],"category_scores_gemma":[0.00720871,0.0005251259,0.000583813,0.001208999,0.0003493458,0.001551255,0.0005849411,0.0009356416,0.0002938213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236782,"about_ca_system_score_gemma":0.0008842807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01646908,"about_ca_topic_score_gemma":0.01142296,"domain_scores_codex":[0.9991961,0.0003051684,0.00004642533,0.0001307481,0.0002712469,0.00005027609],"domain_scores_gemma":[0.9980502,0.001316671,0.0001282812,0.00006845348,0.0003934826,0.00004298065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008854444,0.00007971314,0.005566387,0.00008619703,0.0001303469,0.0001051772,0.00008502236,0.8014918,0.0008420044,0.02796527,0.002215905,0.1613436],"study_design_scores_gemma":[0.000005200785,0.000006873016,0.0003267574,0.000006238295,0.000008817034,0.0000114889,0.00000326937,0.9920623,0.0002075995,0.006947322,0.0004084048,0.000005751038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02296059,0.0009423678,0.9716099,0.0005662695,0.00009086539,0.00004655494,0.0001233912,0.0003603663,0.003299781],"genre_scores_gemma":[0.75226,0.001786368,0.2402842,0.0001929898,0.0002070814,0.0001576486,0.0004218168,0.00005800266,0.004631879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01646908,"threshold_uncertainty_score":0.03274643,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Chun Gui","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4113565419835101,"gpt":0.4777785203359856,"spread":0.06642197835247554,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004880753,0.001008921,0.001308149,0.002712412,0.0008874303,0.001043764,0.001203118,0.001427225,0.0007188329],"category_scores_gemma":[0.01062622,0.0002541103,0.001071287,0.002165698,0.0006650546,0.001419252,0.0006052174,0.001292781,0.0001290385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213827,"about_ca_system_score_gemma":0.0007514209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007082934,"about_ca_topic_score_gemma":0.004802805,"domain_scores_codex":[0.9982656,0.0006258702,0.000122801,0.0003668323,0.0004021049,0.0002168281],"domain_scores_gemma":[0.9918282,0.005754322,0.0009233888,0.0005198026,0.0007509647,0.0002233272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007956236,0.0007996667,0.09416442,0.0004855164,0.0003810976,0.00134563,0.0003612588,0.7832174,0.002299813,0.004594165,0.006783982,0.1047713],"study_design_scores_gemma":[0.0000115171,0.00006406829,0.01271539,0.00001612171,0.00002451151,0.0001237891,0.0001660815,0.9819812,0.0008962736,0.003480325,0.00050808,0.00001261294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.839963,0.001355828,0.151788,0.001855898,0.0002556935,0.0001823112,0.002354628,0.000498243,0.001746352],"genre_scores_gemma":[0.9656759,0.0002585066,0.03142334,0.0000961214,0.0001113012,0.0000932147,0.00182038,0.00002439837,0.0004968157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007082934,"threshold_uncertainty_score":0.02581221,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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)","authors":[{"name":"Suthira Plansangket","is_ca":false},{"name":"John Q. Gan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3139855586636671,"gpt":0.5142308243938665,"spread":0.2002452657301994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003163554,0.001603765,0.001688897,0.008409462,0.00106245,0.001076208,0.001580854,0.001339151,0.001891539],"category_scores_gemma":[0.00933863,0.0004242035,0.001286713,0.004183976,0.0005139562,0.002181825,0.0005983197,0.0007317798,0.001356715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008073747,"about_ca_system_score_gemma":0.001926875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007600624,"about_ca_topic_score_gemma":0.005726816,"domain_scores_codex":[0.9949039,0.001389002,0.00038592,0.0006890259,0.002406021,0.0002260521],"domain_scores_gemma":[0.9958315,0.001633742,0.0002454554,0.0002810255,0.00187196,0.0001362563],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007219159,0.0002898102,0.003484324,0.001137747,0.0002865251,0.0002984434,0.0004732955,0.01121731,0.07719967,0.002613991,0.007734373,0.8945426],"study_design_scores_gemma":[0.0004925424,0.001764809,0.01483161,0.0001822882,0.00094508,0.00380787,0.0004812697,0.8165929,0.1096709,0.005166908,0.04528347,0.0007804558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02302726,0.003385011,0.9665023,0.0002172893,0.0002078833,0.0006062969,0.0002486733,0.003858206,0.001947063],"genre_scores_gemma":[0.209022,0.001113444,0.7855524,0.0001683134,0.0004247441,0.0005648034,0.0005285197,0.0002660446,0.002359668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008409462,"threshold_uncertainty_score":0.01673073,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Reona Mogi","is_ca":false},{"name":"Hiroyuki Kasai","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2145145657511374,"gpt":0.4207561876921068,"spread":0.2062416219409694,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000654366,0.001284601,0.001298182,0.003019564,0.0004155209,0.000777529,0.0007403148,0.0007875984,0.001570746],"category_scores_gemma":[0.001292508,0.0002826106,0.001108696,0.001257204,0.0003218112,0.001190564,0.0005819918,0.0006247721,0.001305682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002377994,"about_ca_system_score_gemma":0.0004049806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001500649,"about_ca_topic_score_gemma":0.001603765,"domain_scores_codex":[0.9990919,0.0001030024,0.00005069895,0.0002309651,0.0004422858,0.00008115295],"domain_scores_gemma":[0.9993998,0.0001109253,0.00004583236,0.00005269128,0.0003586042,0.00003212149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000366095,0.0001332303,0.002059219,0.0001850845,0.0001365564,0.0001580516,0.00005516797,0.01078157,0.1340727,0.0006955706,0.00198423,0.8493726],"study_design_scores_gemma":[0.00007166046,0.0004758932,0.01613045,0.00004139961,0.0004475073,0.001011879,0.0001238393,0.7803987,0.1878338,0.001287704,0.01201454,0.0001626462],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03509671,0.0007257914,0.9582143,0.00008956256,0.0001847566,0.0001093946,0.0001328562,0.00305292,0.002393639],"genre_scores_gemma":[0.2934361,0.0008685818,0.6985114,0.0001546936,0.0002517002,0.0002230441,0.0009073818,0.0003504093,0.005296589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003019564,"threshold_uncertainty_score":0.005254626,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Omar Behadada","is_ca":false},{"name":"M. Amine Chikh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1641878954981021,"gpt":0.4470037826943951,"spread":0.2828158871962929,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001019645,0.0004957039,0.0008409751,0.001851709,0.0004095333,0.001021398,0.0006842056,0.001000319,0.001455888],"category_scores_gemma":[0.002964717,0.0001907413,0.0007468783,0.000914033,0.0001692766,0.0008798795,0.0002740387,0.0006001139,0.000845558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002608506,"about_ca_system_score_gemma":0.0005270967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001416899,"about_ca_topic_score_gemma":0.001465852,"domain_scores_codex":[0.9993497,0.0001362849,0.00008861753,0.00009738656,0.0002725149,0.00005544978],"domain_scores_gemma":[0.9989121,0.000615019,0.00006736645,0.00005854853,0.0003135433,0.00003335836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003321619,0.000315494,0.007099283,0.0003521608,0.0001365935,0.0007085373,0.0002009813,0.04253974,0.04893252,0.007065734,0.00568103,0.8866357],"study_design_scores_gemma":[0.00005412872,0.0003880775,0.004635517,0.0001086394,0.0001915897,0.0009712013,0.00008913124,0.9539426,0.02304429,0.008742254,0.0077701,0.00006240994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02640991,0.0005799333,0.9697337,0.0002217787,0.0001145652,0.0001383466,0.0004162894,0.001101401,0.001284254],"genre_scores_gemma":[0.2749435,0.0006361089,0.721269,0.0001657757,0.0001241786,0.0001875343,0.0009698567,0.00004910907,0.001654955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001851709,"threshold_uncertainty_score":0.005392432,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Sheng‐Fuu Lin","is_ca":false},{"name":"Jyun-Wei Chang","is_ca":false},{"name":"Yu-Bi Hong","is_ca":false},{"name":"Yung-Chi Hsu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2209426353081047,"gpt":0.4114545148173586,"spread":0.1905118795092539,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005199594,0.0005808068,0.0004811243,0.0003131692,0.000476455,0.0005262059,0.001104791,0.0007784316,0.001050153],"category_scores_gemma":[0.0008029646,0.0001636306,0.0004468775,0.0002656557,0.0005157981,0.0003944252,0.0005583867,0.0004600771,0.0001964082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005327013,"about_ca_system_score_gemma":0.0007130387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002779598,"about_ca_topic_score_gemma":0.001991985,"domain_scores_codex":[0.9996184,0.00007596214,0.00002497928,0.00007733355,0.000167492,0.00003566009],"domain_scores_gemma":[0.9997807,0.00005731934,0.00003596352,0.0000229549,0.00008232288,0.00002066332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001210486,0.0001006338,0.001403968,0.0001914282,0.0001167475,0.0003551897,0.000368473,0.735255,0.03690353,0.0225753,0.0009820509,0.2016266],"study_design_scores_gemma":[0.00002573935,0.00009216562,0.000155579,0.00000630099,0.00001276557,0.00008207534,0.00001358647,0.994507,0.002361504,0.001498791,0.001232522,0.00001196911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02337211,0.0001742503,0.9720028,0.00007685068,0.00004590041,0.00007274917,0.000007944744,0.0001945742,0.004052699],"genre_scores_gemma":[0.8546883,0.0001579857,0.1423097,0.00007243206,0.00002154958,0.0002408687,0.00002684204,0.00002061555,0.002461691],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002779598,"threshold_uncertainty_score":0.005526841,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Chaitanya Anne","is_ca":false},{"name":"Avdesh Mishra","is_ca":false},{"name":"Md Tamjidul Hoque","is_ca":false},{"name":"Shengru Tu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.7736202196270415,"gpt":0.4283465409963569,"spread":0.3452736786306846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002688402,0.0009479278,0.001268961,0.01063249,0.001282757,0.00314184,0.001626944,0.001604866,0.01263392],"category_scores_gemma":[0.01055181,0.0001754927,0.001350508,0.00818453,0.0005278541,0.00227582,0.0009284728,0.001052098,0.006064916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001499257,"about_ca_system_score_gemma":0.002048324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007187807,"about_ca_topic_score_gemma":0.005193616,"domain_scores_codex":[0.9960724,0.0004120425,0.0005268686,0.0008171496,0.001706147,0.0004653865],"domain_scores_gemma":[0.993431,0.001917231,0.0008484022,0.0009143848,0.002698848,0.0001901297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003406648,0.0003852163,0.01226698,0.0005459996,0.0001319179,0.0002488313,0.00009840311,0.008859498,0.004293501,0.004572207,0.03025791,0.937999],"study_design_scores_gemma":[0.000125096,0.0008931126,0.05457021,0.0004001947,0.0002883496,0.00249836,0.0006411073,0.6990312,0.03246011,0.01649655,0.1923642,0.0002315441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2941181,0.01796066,0.49391,0.003969431,0.003033724,0.005114204,0.06452737,0.0146512,0.1027153],"genre_scores_gemma":[0.7021533,0.003335447,0.2294179,0.0003438088,0.0009665128,0.001241752,0.03314599,0.0002454686,0.02914982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01263392,"threshold_uncertainty_score":0.0422647,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Silvan Schwebke","is_ca":false},{"name":"Carsten Behn","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2143700062847043,"gpt":0.3705477394606224,"spread":0.1561777331759181,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001783529,0.0002326252,0.0002163133,0.0002733357,0.0001222551,0.0002844019,0.0002998458,0.0002846037,0.0006460992],"category_scores_gemma":[0.0005482855,0.0001161772,0.0002429876,0.0001836845,0.0003882024,0.0002469316,0.0002711081,0.0002023408,0.00008010389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002082934,"about_ca_system_score_gemma":0.0001668482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007733277,"about_ca_topic_score_gemma":0.0005072124,"domain_scores_codex":[0.9999456,0.00001173988,0.000003729714,0.00001087952,0.00002226266,0.000005791986],"domain_scores_gemma":[0.9998786,0.00004228401,0.00003270002,0.00001477357,0.00002210211,0.000009579423],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004511486,0.00004053507,0.001592073,0.0001050566,0.00003439064,0.0001384084,0.0001168402,0.868821,0.03078313,0.03263096,0.0004024734,0.06528992],"study_design_scores_gemma":[0.0000029174,0.00002378903,0.0002651471,0.000003437635,0.000002343906,0.00002211911,0.000005605016,0.9960633,0.0006159131,0.002754761,0.0002363763,0.000004297011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0914574,0.0002796994,0.9053836,0.00008008816,0.00002011291,0.00003917501,0.0000191599,0.0002021032,0.002518653],"genre_scores_gemma":[0.9193203,0.0002435574,0.07871252,0.00001687987,0.00001218521,0.00008536429,0.00003393109,0.00002326632,0.001552022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007733277,"threshold_uncertainty_score":0.002161384,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Mehdi Rostamzadeh","is_ca":false},{"name":"Kh. Valipour","is_ca":false},{"name":"Seyed Jalal Seyed Shenava","is_ca":false},{"name":"Mohsen Khalilpour","is_ca":false},{"name":"Navid Razmjooy","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1482055831649221,"gpt":0.3734391937443392,"spread":0.2252336105794171,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009317757,0.0006306698,0.001008476,0.0007554747,0.0004530477,0.0009051567,0.0009912536,0.0009272944,0.001176535],"category_scores_gemma":[0.001933188,0.0003358875,0.0005537978,0.0006380393,0.0006268205,0.0009008255,0.000486571,0.0005656233,0.0001816433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077088,"about_ca_system_score_gemma":0.001028694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446697,"about_ca_topic_score_gemma":0.003582658,"domain_scores_codex":[0.9995571,0.0001411992,0.00001790581,0.00006482529,0.0001581516,0.0000609101],"domain_scores_gemma":[0.99935,0.0003403719,0.00007892377,0.00004034274,0.0001570713,0.00003334568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004598928,0.0000317532,0.0002381672,0.00003277515,0.00001921755,0.00003316381,0.00004216231,0.9654126,0.001401556,0.004732276,0.0004813758,0.02752914],"study_design_scores_gemma":[0.00000599971,0.00001325542,0.00003860013,0.000002121518,0.000002635969,0.000008581288,0.000003692389,0.9987596,0.0003064267,0.000662332,0.0001946146,0.000002212784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06182512,0.0004390899,0.9291754,0.0002138701,0.00003993513,0.00006214175,0.00002500321,0.0001884715,0.008030884],"genre_scores_gemma":[0.8818644,0.0001730573,0.115914,0.00005170338,0.00003165535,0.00008559915,0.00003990522,0.00003351907,0.001806229],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00446697,"threshold_uncertainty_score":0.008881986,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"F. Velasco","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1474488304530515,"gpt":0.3792363958667171,"spread":0.2317875654136656,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003194235,0.0009525152,0.001122832,0.003700229,0.000707982,0.001647004,0.00188938,0.001951925,0.002805374],"category_scores_gemma":[0.0130798,0.000797072,0.0008887982,0.002046321,0.001109382,0.002429693,0.0008782374,0.001532932,0.0004371625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001762874,"about_ca_system_score_gemma":0.001609966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01681345,"about_ca_topic_score_gemma":0.01254116,"domain_scores_codex":[0.9982755,0.0008255612,0.0001009804,0.0003448018,0.0003427315,0.0001103606],"domain_scores_gemma":[0.9931316,0.005659508,0.0004135704,0.0001298402,0.0005326848,0.0001328764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002218411,0.0001374339,0.005678928,0.0002410161,0.0002510967,0.0004189424,0.0002611013,0.8212925,0.001645267,0.05855098,0.002508884,0.108792],"study_design_scores_gemma":[0.00002059873,0.00002650181,0.0008703127,0.00003116803,0.00006844001,0.0001083169,0.00003239949,0.9545799,0.000318125,0.04256809,0.001350279,0.00002580173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01100535,0.0005922682,0.985061,0.0006214143,0.00003012684,0.00006610081,0.000267583,0.0002536993,0.002102458],"genre_scores_gemma":[0.4874358,0.002395726,0.5026639,0.0003105536,0.0003450958,0.0003180466,0.001057813,0.00009568785,0.005377455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01681345,"threshold_uncertainty_score":0.03343117,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Stefan Meinzer","is_ca":false},{"name":"Ulf Jensen","is_ca":false},{"name":"Alexander Thamm","is_ca":false},{"name":"Joachim Hornegger","is_ca":false},{"name":"Bjoern M. Eskofier","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2094813873278795,"gpt":0.4236811888324536,"spread":0.2141998015045742,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002575838,0.0006167611,0.0005257599,0.0009596362,0.000294228,0.0006962111,0.0006639608,0.001150599,0.001615762],"category_scores_gemma":[0.004902544,0.0001942487,0.0006361897,0.0007777902,0.0002117735,0.0009100686,0.0002854265,0.0005868233,0.0003923354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005318535,"about_ca_system_score_gemma":0.0005983559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006324039,"about_ca_topic_score_gemma":0.003350215,"domain_scores_codex":[0.9989098,0.0006164345,0.00005243792,0.0001202993,0.0001925366,0.0001085398],"domain_scores_gemma":[0.9958519,0.002901985,0.0001237972,0.0001398562,0.0008385165,0.0001440115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00411977,0.01046623,0.3878596,0.0004563814,0.0003524943,0.0008991894,0.0004720109,0.1527764,0.02146593,0.001218563,0.003515438,0.4163979],"study_design_scores_gemma":[0.0000762201,0.003010419,0.05317919,0.00002825385,0.00006653236,0.00008212258,0.0005831338,0.9391879,0.002843302,0.0003525779,0.0005649076,0.00002542113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820212,0.0004223404,0.01536014,0.0006027886,0.00003662646,0.0001457343,0.0001419761,0.0001027829,0.001166376],"genre_scores_gemma":[0.990422,0.0001809703,0.00862091,0.00004185064,0.00002205073,0.00005732515,0.0001820725,0.000003643244,0.000469051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006324039,"threshold_uncertainty_score":0.01362252,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Herbert F. Jelinek","is_ca":false},{"name":"Andrew Yatsko","is_ca":false},{"name":"Andrew Stranieri","is_ca":false},{"name":"Sitalakshmi Venkatraman","is_ca":false},{"name":"Adil Bagirov","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5670274782417085,"gpt":0.4842737624100367,"spread":0.08275371583167185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01380194,0.001107303,0.001712789,0.004253807,0.001014609,0.002863159,0.002809779,0.001874711,0.003320988],"category_scores_gemma":[0.05511297,0.0004141898,0.001184886,0.003633612,0.001335438,0.002593926,0.002708159,0.002552916,0.001578656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001225483,"about_ca_system_score_gemma":0.002285237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00158388,"about_ca_topic_score_gemma":0.001665773,"domain_scores_codex":[0.9893315,0.004896371,0.0009804034,0.001685494,0.002679311,0.0004269302],"domain_scores_gemma":[0.9500855,0.03245361,0.004850421,0.007526323,0.004254113,0.0008300542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001406938,0.0005248523,0.07139182,0.002058666,0.0005027633,0.002058183,0.00129049,0.07460218,0.004508927,0.06548593,0.03012573,0.7460436],"study_design_scores_gemma":[0.0001557038,0.000318564,0.01140929,0.0007058074,0.0001797881,0.003019245,0.001475346,0.6807174,0.01404017,0.2540601,0.03379653,0.0001219754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02803367,0.00188537,0.9610497,0.0025708,0.000316115,0.0002255261,0.001663027,0.001321163,0.002934637],"genre_scores_gemma":[0.3843017,0.001649,0.6050982,0.00084451,0.0005516471,0.0003520015,0.004149411,0.0001452947,0.00290825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01380194,"threshold_uncertainty_score":0.07299244,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Amir Mahyar Khorasani","is_ca":false},{"name":"Mohammad Reza Solymany yazdi","is_ca":false},{"name":"Mehdi Faraji","is_ca":false},{"name":"Alex Kootsookos","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2599747225708484,"gpt":0.3884304723038766,"spread":0.1284557497330282,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002486607,0.0005146725,0.0003378105,0.0003398857,0.0002153439,0.0005580955,0.0005481019,0.0007540596,0.0008511097],"category_scores_gemma":[0.0006048947,0.0003796293,0.0005489379,0.0003529373,0.00018515,0.0005044968,0.0002394575,0.0004932699,0.0001893188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004594596,"about_ca_system_score_gemma":0.0004470863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008420997,"about_ca_topic_score_gemma":0.005600412,"domain_scores_codex":[0.9998845,0.0000247092,0.00001043497,0.00002716629,0.00004023568,0.00001283615],"domain_scores_gemma":[0.9998189,0.00009832558,0.00002784303,0.00001013148,0.00004030066,0.000004535901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001531571,0.00001250065,0.0003982101,0.00002689409,0.00001509773,0.00002384935,0.00001349539,0.9886331,0.00210352,0.0003597448,0.0000645573,0.008333671],"study_design_scores_gemma":[5.467004e-7,0.000003849816,0.00008260687,0.000001089273,0.000001630611,0.000002304023,0.000001081371,0.9994167,0.0003316588,0.00008569914,0.00007164256,0.00000121984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430651,0.0006904911,0.8500346,0.0001558133,0.00005728989,0.00006280877,0.0001864716,0.0007099307,0.005037498],"genre_scores_gemma":[0.9416609,0.0005622848,0.05323885,0.0000226971,0.00001797037,0.0001718432,0.0001944321,0.00002696658,0.004104121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008420997,"threshold_uncertainty_score":0.01674396,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Tatiana Vodolazova","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04453799708544237,"gpt":0.3789329910890959,"spread":0.3343949940036536,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004319478,0.001056968,0.001349455,0.004125265,0.0005470625,0.002511965,0.0008260144,0.0005845891,0.002162331],"category_scores_gemma":[0.01416469,0.0003067155,0.0008977648,0.002800107,0.0004772439,0.004238613,0.0005489782,0.0007660569,0.001215156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004364825,"about_ca_system_score_gemma":0.0006376034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001090835,"about_ca_topic_score_gemma":0.001585323,"domain_scores_codex":[0.9976416,0.0009902577,0.0002713917,0.0003464408,0.0006481956,0.0001021544],"domain_scores_gemma":[0.9850487,0.01080839,0.001104668,0.0009017903,0.001986419,0.000150005],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008125384,0.0002632599,0.005132284,0.001126754,0.0002695906,0.0001101551,0.0002098341,0.01167794,0.04098809,0.00198644,0.002207711,0.9352154],"study_design_scores_gemma":[0.0002243112,0.004226542,0.07057118,0.0004858954,0.00202909,0.001131425,0.001627609,0.6681296,0.1955964,0.02672265,0.02880866,0.0004466679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2383966,0.008353319,0.7343176,0.00113409,0.0002589994,0.0005050491,0.002781712,0.008954512,0.005298132],"genre_scores_gemma":[0.6518652,0.001656994,0.3402133,0.0001125443,0.0003458774,0.000274612,0.003738208,0.0004298455,0.001363356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004319478,"threshold_uncertainty_score":0.02284384,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Oluwole Charles Akinyokun","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2054347238209183,"gpt":0.4084305841573663,"spread":0.202995860336448,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030417,0.0005379884,0.0008923388,0.001737862,0.000613013,0.001212062,0.001206168,0.0009621865,0.003231265],"category_scores_gemma":[0.001900537,0.0003737404,0.0006454078,0.001123138,0.0002807688,0.001369489,0.0009361776,0.0005942777,0.0009287188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006309415,"about_ca_system_score_gemma":0.001196423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006390803,"about_ca_topic_score_gemma":0.006944391,"domain_scores_codex":[0.9993727,0.00007144822,0.00008716665,0.0002185038,0.0001975364,0.00005270136],"domain_scores_gemma":[0.9993939,0.000221839,0.00006086323,0.00007777276,0.000218715,0.00002685198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005602585,0.000528282,0.004736606,0.0003041225,0.0002346718,0.0004872464,0.000380509,0.06967286,0.02661624,0.0033654,0.004017832,0.8890961],"study_design_scores_gemma":[0.00005966171,0.0002563084,0.002917806,0.00004325774,0.0001337732,0.0003296708,0.0001167017,0.9701043,0.01625617,0.003600236,0.006132015,0.00005001331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0545813,0.0003577918,0.9320171,0.0002387523,0.00004779432,0.0002961647,0.0007459311,0.008690122,0.003025041],"genre_scores_gemma":[0.4884226,0.0003227729,0.5026376,0.0002467699,0.0000386111,0.0005993178,0.001838351,0.0000825657,0.005811431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006390803,"threshold_uncertainty_score":0.01270723,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Nysia I. George","is_ca":false},{"name":"Tzu‐Pin Lu","is_ca":false},{"name":"Ching‐Wei Chang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4412564894946245,"gpt":0.4524121439856832,"spread":0.01115565449105876,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02760312,0.00234684,0.002773519,0.01074922,0.001172582,0.004283334,0.002605608,0.002432778,0.00185627],"category_scores_gemma":[0.1024035,0.0004260801,0.002019366,0.007259983,0.00187368,0.004963192,0.002083699,0.002394096,0.0007093009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002610377,"about_ca_system_score_gemma":0.002605515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002282899,"about_ca_topic_score_gemma":0.002463671,"domain_scores_codex":[0.9658115,0.01424115,0.004200508,0.002994026,0.01197114,0.0007816207],"domain_scores_gemma":[0.9165828,0.0542912,0.008217691,0.006556434,0.01341986,0.0009319957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001325691,0.0006198834,0.04579625,0.001997854,0.001947645,0.000320593,0.0005134159,0.3522324,0.01275563,0.04722109,0.01186426,0.5234052],"study_design_scores_gemma":[0.00009160125,0.002160382,0.02644376,0.0003196096,0.0005573699,0.0009986589,0.000545345,0.891349,0.01590463,0.04919123,0.01207289,0.0003654725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05220794,0.004559099,0.932609,0.0008366368,0.0006351211,0.0008274466,0.001704967,0.001039525,0.005580353],"genre_scores_gemma":[0.5240356,0.001337991,0.4678696,0.0003569271,0.0003614776,0.001216388,0.002553972,0.0002514224,0.002016781],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02760312,"threshold_uncertainty_score":0.145981,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Hanif Mohaddes Deylami","is_ca":false},{"name":"Yashwant Singh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1259658076046249,"gpt":0.3955293105927091,"spread":0.2695635029880842,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001421599,0.0009741362,0.001126431,0.003916055,0.0004397975,0.001058663,0.0009243142,0.0009150957,0.001432736],"category_scores_gemma":[0.005245496,0.0002487463,0.0006764309,0.001710036,0.0003653329,0.001861976,0.000529918,0.001009225,0.0009591166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003401448,"about_ca_system_score_gemma":0.0003466584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048767,"about_ca_topic_score_gemma":0.000897427,"domain_scores_codex":[0.9981149,0.0004533996,0.0001693684,0.0002738853,0.000858815,0.0001296542],"domain_scores_gemma":[0.9969549,0.001369153,0.0005302267,0.0002523091,0.0008206711,0.0000727657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003016544,0.0005289039,0.01895807,0.0003522808,0.0002365807,0.0002358092,0.0001709579,0.04693364,0.01386174,0.003755471,0.00629049,0.9083744],"study_design_scores_gemma":[0.00003789168,0.0003691197,0.00976048,0.00008221371,0.00007768345,0.0006471273,0.0001681581,0.9550347,0.02240772,0.0054194,0.005928231,0.00006714318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1275083,0.002011665,0.858668,0.0007829209,0.0003127891,0.0002606332,0.0004744708,0.004992799,0.004988544],"genre_scores_gemma":[0.8287072,0.0006485935,0.1673586,0.000119793,0.0001611473,0.0001442566,0.0006034209,0.00005821891,0.002198841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003916055,"threshold_uncertainty_score":0.007518232,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Musibau A. Ibrahim","is_ca":false},{"name":"Ramakrishnan Mukundan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2590340276027194,"gpt":0.4149809644527375,"spread":0.1559469368500181,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031349,0.0008279825,0.0008458404,0.002701959,0.0003675169,0.0005827403,0.0007129436,0.0006238683,0.00118406],"category_scores_gemma":[0.001503412,0.0003441056,0.001059485,0.001353008,0.0002986105,0.0008104846,0.0006794197,0.0006797914,0.0005701735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477144,"about_ca_system_score_gemma":0.0003903316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002084561,"about_ca_topic_score_gemma":0.003494705,"domain_scores_codex":[0.9993396,0.00007715053,0.0000510127,0.0001420134,0.0003185488,0.00007160214],"domain_scores_gemma":[0.9993458,0.0002193623,0.00008958059,0.0001016487,0.0002048724,0.00003861657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002950668,0.0001802438,0.003477992,0.0002182186,0.0001112177,0.0002722122,0.000223845,0.03244113,0.1378443,0.001351448,0.001621222,0.8219631],"study_design_scores_gemma":[0.00002455025,0.0004547331,0.01635232,0.00005123744,0.0001960947,0.0006888558,0.00009146518,0.9139274,0.06129579,0.001950857,0.004902376,0.00006425359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1786669,0.004591936,0.8128857,0.0002143156,0.0002127853,0.0001738454,0.0001672546,0.001289915,0.001797299],"genre_scores_gemma":[0.6103286,0.002177165,0.3844405,0.00007578548,0.0002017834,0.00006410727,0.0002463955,0.00005862371,0.002407133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002701959,"threshold_uncertainty_score":0.005454361,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2790151031","doi":"10.5430/air.v7n1p53","title":"Multisequent Gentzen Deduction Systems For B<sub>2</sub> <sup>2</sup>-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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Wei Li","is_ca":false},{"name":"Yuefei Sui","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1697312307687207,"gpt":0.3924163908098235,"spread":0.2226851600411028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001976556,0.0005454903,0.0008244841,0.001335653,0.001310373,0.002106274,0.001354413,0.001127679,0.003077142],"category_scores_gemma":[0.003892201,0.000572078,0.001991534,0.0008726918,0.002017696,0.002452926,0.002652874,0.002845313,0.0008163463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002392737,"about_ca_system_score_gemma":0.001625975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002154779,"about_ca_topic_score_gemma":0.003932145,"domain_scores_codex":[0.9981602,0.0004128142,0.0001337593,0.0005462241,0.0005835365,0.0001635005],"domain_scores_gemma":[0.998273,0.000892738,0.0001289788,0.0002343715,0.0003791035,0.00009169617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000101972,0.00007126521,0.0006626293,0.0001739358,0.0001068049,0.0003433048,0.0007492054,0.02672318,0.008017685,0.9067129,0.001793755,0.05454329],"study_design_scores_gemma":[0.00004367925,0.00004266869,0.0003235974,0.00003705809,0.00006099058,0.0001357905,0.00006805924,0.1162803,0.006411065,0.8713251,0.005227578,0.00004415946],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04836874,0.0004158053,0.9419683,0.0005489745,0.0001157998,0.0001550193,0.0002954254,0.001436424,0.006695527],"genre_scores_gemma":[0.4334149,0.0002808996,0.5579534,0.000557301,0.00008599328,0.0001794722,0.000599376,0.0001603518,0.006768274],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003077142,"threshold_uncertainty_score":0.01736063,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Rehabilitation; Stroke (engine); Helpfulness; Medicine; Physical therapy; Physical medicine and rehabilitation; Psychology","authors":[{"name":"Chia‐Lun Lo","is_ca":false},{"name":"Hsiao‐Ting Tseng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1531883255040664,"gpt":0.4278118813084872,"spread":0.2746235558044208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002207112,0.0009345961,0.001289551,0.001771968,0.0004990851,0.0008356256,0.0009265901,0.001123708,0.001020778],"category_scores_gemma":[0.005454564,0.0002604746,0.000928706,0.0009413317,0.0002657459,0.0005856663,0.0004841141,0.001354876,0.0002738512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006830671,"about_ca_system_score_gemma":0.001356986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007030722,"about_ca_topic_score_gemma":0.00766421,"domain_scores_codex":[0.9990108,0.000400468,0.0001228852,0.000235669,0.0001146667,0.0001154361],"domain_scores_gemma":[0.9957327,0.003130217,0.0002924171,0.0001483292,0.0005180704,0.0001782332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001505889,0.005583389,0.2001936,0.0004309402,0.0009395715,0.0004339857,0.0003838866,0.274374,0.001914228,0.0005631268,0.01106903,0.5026084],"study_design_scores_gemma":[0.00005793767,0.0003085508,0.01249625,0.00003284035,0.000122946,0.00007259815,0.0001084467,0.9846879,0.0006734421,0.0009867214,0.0004308301,0.00002164987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8605174,0.001776237,0.1277709,0.002508059,0.0002371134,0.000576088,0.002693386,0.001315431,0.00260541],"genre_scores_gemma":[0.9730831,0.0002414261,0.02322488,0.0002019608,0.0001467053,0.0001534066,0.002173384,0.00001718147,0.000757888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007030722,"threshold_uncertainty_score":0.01397961,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Masatoshi Sakawa","is_ca":false},{"name":"Takeshi Matsui","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2473736253788599,"gpt":0.4067232692812456,"spread":0.1593496439023857,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003033098,0.001458441,0.0009616301,0.0008330223,0.0007903056,0.001663851,0.001487924,0.001146317,0.003175039],"category_scores_gemma":[0.004899367,0.0006267076,0.001479012,0.001169406,0.001481777,0.00180643,0.002343307,0.001985048,0.0003589634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001545343,"about_ca_system_score_gemma":0.001613979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002479329,"about_ca_topic_score_gemma":0.002450761,"domain_scores_codex":[0.9982814,0.0008245098,0.00006494799,0.0002202424,0.0004711142,0.0001377552],"domain_scores_gemma":[0.9980076,0.001582502,0.0001504547,0.00006180527,0.0001355757,0.00006202173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000513148,0.00004503832,0.0003247212,0.000177974,0.00005680355,0.0001300333,0.0002200004,0.8064765,0.001860401,0.1548656,0.0005467498,0.03524474],"study_design_scores_gemma":[0.0000071103,0.00002319641,0.00004754803,0.00001170027,0.000007809524,0.00001951909,0.0000129003,0.9715873,0.0004389367,0.02718433,0.0006518086,0.000007910582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002032132,0.0000814814,0.9968365,0.00004718017,0.000005171358,0.00001802138,0.000006176251,0.0000227576,0.0009505042],"genre_scores_gemma":[0.3697078,0.0006647733,0.6254979,0.00009442186,0.00007938585,0.0004699054,0.000091558,0.0000851068,0.003309197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003175039,"threshold_uncertainty_score":0.01604074,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Jonathan Leet","is_ca":false},{"name":"John Gibbons","is_ca":false},{"name":"Charles C. Tappert","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3227113673669672,"gpt":0.4872202259271605,"spread":0.1645088585601934,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007461685,0.001531508,0.0009936198,0.001774351,0.0009133492,0.001514428,0.0006681233,0.001343928,0.003113593],"category_scores_gemma":[0.02400014,0.0002422571,0.0007410633,0.0008553964,0.0007174265,0.001431186,0.001315234,0.0006212483,0.001788155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004913704,"about_ca_system_score_gemma":0.0005218969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001318518,"about_ca_topic_score_gemma":0.001673406,"domain_scores_codex":[0.9901322,0.003059897,0.001220235,0.001289357,0.003895173,0.0004030749],"domain_scores_gemma":[0.9786739,0.01084765,0.002000156,0.002345268,0.005631623,0.0005014621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008293963,0.001785341,0.05400603,0.001919727,0.001076102,0.0007368829,0.002558165,0.01264791,0.4154055,0.001903456,0.006078314,0.4935886],"study_design_scores_gemma":[0.000503068,0.02433706,0.2638136,0.0001629633,0.0006590703,0.002999665,0.002144519,0.1577677,0.5331576,0.001748834,0.01213254,0.0005734258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9211433,0.0005122186,0.06978961,0.00007711779,0.0001934672,0.001041665,0.001409784,0.001790463,0.004042408],"genre_scores_gemma":[0.9214254,0.0002041065,0.07085074,0.00008214763,0.00006347659,0.0009551624,0.002559089,0.0001970965,0.003662765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007461685,"threshold_uncertainty_score":0.03946161,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Oluwole Charles Akinyokun","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2117852498213675,"gpt":0.4454563822907867,"spread":0.2336711324694192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001678563,0.0003290382,0.0002951394,0.0004263828,0.0003437149,0.0005996281,0.0005983522,0.0005116306,0.001479937],"category_scores_gemma":[0.003780918,0.0001279902,0.000215679,0.0003515557,0.0004619105,0.0005774967,0.0004351754,0.0005711336,0.0001493591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005749837,"about_ca_system_score_gemma":0.0007757787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003099362,"about_ca_topic_score_gemma":0.002222306,"domain_scores_codex":[0.9993551,0.0002762681,0.00003466617,0.0000875288,0.0001983319,0.00004799134],"domain_scores_gemma":[0.9984384,0.0009564494,0.0001202171,0.0001511139,0.0002575847,0.00007623585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00377409,0.009611969,0.01273003,0.0006785168,0.0001810985,0.0005689016,0.001290751,0.6834993,0.105345,0.01612697,0.0008770952,0.1653163],"study_design_scores_gemma":[0.0001840802,0.005137337,0.004494362,0.0000264971,0.00004949163,0.00008948369,0.000466741,0.9363228,0.04850603,0.00319028,0.001492196,0.00004053428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9609447,0.0001110273,0.03556172,0.0001234201,0.00002516613,0.0002117408,0.00007103408,0.0001225618,0.002828539],"genre_scores_gemma":[0.9811359,0.00006701901,0.01808293,0.00001612128,0.000003430456,0.0000837824,0.00004133523,0.000004539482,0.0005649493],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003099362,"threshold_uncertainty_score":0.008877158,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"A. Ruziyeva","is_ca":false},{"name":"Stephan Dempe","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3967703050662592,"gpt":0.4747694166049607,"spread":0.07799911153870154,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00398364,0.001135552,0.001612773,0.001657145,0.001175846,0.002989132,0.001140091,0.00151896,0.003109787],"category_scores_gemma":[0.007296482,0.0004237771,0.0008054766,0.002126573,0.001833998,0.003128242,0.001468426,0.001977632,0.0009017272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001478101,"about_ca_system_score_gemma":0.001424457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001262264,"about_ca_topic_score_gemma":0.001438133,"domain_scores_codex":[0.9977811,0.001109305,0.0001040561,0.0002071952,0.0006662688,0.0001320794],"domain_scores_gemma":[0.9983537,0.0009418312,0.0001845199,0.0001438846,0.0002915151,0.00008464533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001402781,0.00007050652,0.0006734446,0.0002987536,0.0001026598,0.00009690219,0.0001532946,0.4239228,0.002535309,0.4499183,0.002034094,0.1200535],"study_design_scores_gemma":[0.00001537206,0.0002021944,0.0002450616,0.00005938216,0.0000248037,0.00006949301,0.00005778913,0.7913709,0.00162187,0.2012389,0.005042622,0.000051525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01290309,0.001684391,0.9767958,0.000331665,0.00009729726,0.00004114783,0.0000389066,0.0000969795,0.00801077],"genre_scores_gemma":[0.4905092,0.002581769,0.4938909,0.0001901699,0.0001486461,0.0002861166,0.0001395244,0.0001496095,0.01210403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00398364,"threshold_uncertainty_score":0.0210678,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Lin Han","is_ca":false},{"name":"Andrew Lowe","is_ca":false},{"name":"Ahmed M. Al‐Jumaily","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1327944310658652,"gpt":0.3400204300141485,"spread":0.2072259989482833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012165,0.0007272311,0.0006960297,0.0006535508,0.0004276687,0.0009867634,0.001292949,0.0009316124,0.002181275],"category_scores_gemma":[0.002528728,0.0003171591,0.0004386858,0.0005875254,0.0002945766,0.0007198118,0.0006548844,0.0009442007,0.0009078351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000670975,"about_ca_system_score_gemma":0.0008084556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005412876,"about_ca_topic_score_gemma":0.004018161,"domain_scores_codex":[0.9993846,0.0001107125,0.00005885778,0.0001874187,0.0001916705,0.00006661761],"domain_scores_gemma":[0.9991816,0.0003210127,0.00007823324,0.00004385328,0.0003539007,0.00002136708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002062813,0.0001486475,0.001740599,0.0000912436,0.00006545141,0.00007335148,0.00005109183,0.1797585,0.01017128,0.002085569,0.001839031,0.803769],"study_design_scores_gemma":[0.00001048716,0.00004779136,0.0006464469,0.00001284147,0.00001227431,0.00003358222,0.000007182215,0.9944232,0.00326634,0.0008813023,0.0006509626,0.000007596501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01442177,0.0003541394,0.9824827,0.000132193,0.00005827148,0.0001009089,0.00004299416,0.001168351,0.001238697],"genre_scores_gemma":[0.3548748,0.0005455157,0.6354973,0.0002557382,0.0001040806,0.0005681019,0.0003264356,0.0001005057,0.007727476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005412876,"threshold_uncertainty_score":0.01076275,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"B. Premanode","is_ca":false},{"name":"Jumlong Vonprasert","is_ca":false},{"name":"C. Toumazou","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5693082736622433,"gpt":0.5277332758791052,"spread":0.04157499778313811,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001619879,0.0005201445,0.0007493233,0.000483471,0.0001759649,0.0005722272,0.0007327152,0.0006712887,0.000413763],"category_scores_gemma":[0.003253142,0.0002438447,0.0007471222,0.0004959927,0.0002454186,0.001330962,0.0004675945,0.0008307149,0.0001022587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003419353,"about_ca_system_score_gemma":0.0003907491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003044534,"about_ca_topic_score_gemma":0.001761461,"domain_scores_codex":[0.9995387,0.0001727606,0.00003043093,0.0001083,0.0001114352,0.0000385165],"domain_scores_gemma":[0.9989693,0.0005581479,0.0001621654,0.000102947,0.0001719917,0.00003548017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001128019,0.00006880521,0.003680646,0.00004024357,0.00009119818,0.00005260371,0.000042713,0.8795221,0.005780957,0.004734919,0.0004086914,0.1054643],"study_design_scores_gemma":[0.000001027542,0.000008112148,0.0001657964,5.206671e-7,0.000001735377,0.000003157484,6.913156e-7,0.9992071,0.0002954294,0.0002910861,0.00002341477,0.000001964368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1300309,0.0002523473,0.8687819,0.0001160479,0.00003329017,0.00001710506,0.00004768901,0.0002461916,0.0004746136],"genre_scores_gemma":[0.8840871,0.0002343396,0.1144727,0.00003655191,0.00004326859,0.00004682767,0.0001438064,0.00003188257,0.0009034455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003044534,"threshold_uncertainty_score":0.008566797,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Jianchen Yang","is_ca":false},{"name":"Fei Guo","is_ca":false},{"name":"Huogen Wang","is_ca":false},{"name":"Zhiyong Ding","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2075417322219517,"gpt":0.4848427964391487,"spread":0.277301064217197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004083089,0.000780588,0.0006248072,0.000927701,0.0005368326,0.0007152297,0.001124656,0.0007205479,0.004493673],"category_scores_gemma":[0.0008382613,0.0004706496,0.0008203037,0.0008167712,0.0003540659,0.001585441,0.0008367944,0.001251403,0.001908887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004173003,"about_ca_system_score_gemma":0.0006000575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001382087,"about_ca_topic_score_gemma":0.001676645,"domain_scores_codex":[0.9994231,0.00004607289,0.00003047492,0.0001884504,0.0002735392,0.0000382941],"domain_scores_gemma":[0.999632,0.00004025338,0.00005083174,0.00008798078,0.0001682423,0.00002057703],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001255644,0.00005175677,0.0004544849,0.0001631403,0.00005999729,0.000127482,0.0001809679,0.02030725,0.1199799,0.01096236,0.003750589,0.8438365],"study_design_scores_gemma":[0.00006317251,0.000343512,0.001795076,0.00004292808,0.0000876617,0.001671796,0.000144646,0.7710311,0.1788843,0.006446651,0.03937092,0.0001181759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002344484,0.0000962511,0.9965616,0.00002847123,0.00002791573,0.00002846756,0.00001492874,0.0003487249,0.0005491024],"genre_scores_gemma":[0.03407945,0.0001892754,0.9628279,0.0000323779,0.00003402403,0.00006031959,0.0001283493,0.0000928388,0.002555436],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004493673,"threshold_uncertainty_score":0.01503289,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Celso Luiz de Souza","is_ca":false},{"name":"Flávio Luis Cardeal Pádua","is_ca":false},{"name":"Cristiano F. G. Nunes","is_ca":false},{"name":"Guilherme Tavares de Assis","is_ca":false},{"name":"Giani D. Silva","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1868483344823545,"gpt":0.352247924350347,"spread":0.1653995898679925,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001458522,0.001177022,0.001919926,0.01043739,0.001010996,0.00348396,0.002240831,0.001469141,0.002408233],"category_scores_gemma":[0.003056801,0.0006166306,0.001476193,0.006976687,0.0009324031,0.004419429,0.003019907,0.001332469,0.003164398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001355628,"about_ca_system_score_gemma":0.002279809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005953609,"about_ca_topic_score_gemma":0.006127605,"domain_scores_codex":[0.9966535,0.0004355163,0.0004134584,0.0006319497,0.001631059,0.0002346116],"domain_scores_gemma":[0.9986933,0.0001408404,0.0001097375,0.0004155311,0.0005604151,0.00008020088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001814262,0.0003605077,0.001186626,0.0007584456,0.0001671504,0.000295508,0.0004840572,0.00886807,0.08369938,0.01920448,0.01523578,0.8695586],"study_design_scores_gemma":[0.0001524179,0.0008058412,0.008873951,0.0004493149,0.0006157722,0.002770878,0.001480421,0.6597675,0.1400048,0.03422662,0.1504745,0.000377977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004870862,0.002295815,0.9872542,0.0001729663,0.00008456277,0.0003582177,0.0005927209,0.00215432,0.002216349],"genre_scores_gemma":[0.05286822,0.002563436,0.9347383,0.0001849238,0.000249622,0.0006294542,0.003929413,0.000164517,0.004672049],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01043739,"threshold_uncertainty_score":0.0118379,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Hadi Karimi","is_ca":false},{"name":"Hossein Navid","is_ca":false},{"name":"Asghar Mahmoudi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1606214297457101,"gpt":0.4137018668726264,"spread":0.2530804371269163,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000599038,0.0004901371,0.0004803652,0.0005194232,0.0001565723,0.0003056603,0.0004145741,0.0006301906,0.0005190737],"category_scores_gemma":[0.001267094,0.000223026,0.0002918928,0.0002460911,0.0002818395,0.000521385,0.0003279834,0.0002485216,0.0001930883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002635386,"about_ca_system_score_gemma":0.000199284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007932645,"about_ca_topic_score_gemma":0.001763353,"domain_scores_codex":[0.99946,0.0000870123,0.00002723515,0.0001426817,0.0002510728,0.00003197696],"domain_scores_gemma":[0.9992496,0.0003046773,0.0001456794,0.00006740967,0.0001931401,0.00003946434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001023649,0.0002520139,0.0381307,0.0004332192,0.00007343849,0.0004763202,0.0002595833,0.0139374,0.8400829,0.0001272473,0.0002099949,0.1049935],"study_design_scores_gemma":[0.00005473681,0.00310639,0.1649453,0.00005752387,0.000195101,0.001267305,0.0004619623,0.3722498,0.4558084,0.0004157585,0.001329761,0.0001079751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9001873,0.0006446198,0.09783889,0.00005657972,0.00004160429,0.00007418309,0.00007522636,0.0003619079,0.0007196276],"genre_scores_gemma":[0.9582456,0.0002718867,0.04033929,0.00003867086,0.00001152355,0.00004804659,0.00007794946,0.00001513933,0.0009518439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007932645,"threshold_uncertainty_score":0.003168046,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Suthira Plansangket","is_ca":false},{"name":"John Q. Gan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3126960264625327,"gpt":0.4470487181538327,"spread":0.1343526916913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002177247,0.002084431,0.002613525,0.01415017,0.001043716,0.003020129,0.0008963738,0.0007415737,0.001683629],"category_scores_gemma":[0.007157785,0.0002833493,0.001144953,0.008151489,0.0003761273,0.002208801,0.000709089,0.0007997677,0.002079989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009796917,"about_ca_system_score_gemma":0.001708074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122478,"about_ca_topic_score_gemma":0.02367995,"domain_scores_codex":[0.9948345,0.000663437,0.000393048,0.0004038304,0.003317606,0.0003874772],"domain_scores_gemma":[0.993677,0.001266174,0.000573552,0.0007647133,0.00344287,0.0002756436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001288416,0.0009127766,0.03733686,0.0009945253,0.0004879487,0.0003492533,0.0002511133,0.007676493,0.04282876,0.001189049,0.020925,0.8857598],"study_design_scores_gemma":[0.0004424992,0.003428978,0.1916569,0.00028503,0.001732534,0.002577116,0.001912434,0.5387188,0.2103077,0.006907361,0.04135054,0.0006800836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7951041,0.01889098,0.1467133,0.00103758,0.001170933,0.001414224,0.006379598,0.01201948,0.01726967],"genre_scores_gemma":[0.8725692,0.002609556,0.1074867,0.000132398,0.0003702704,0.0002162496,0.006562289,0.0004797572,0.009573569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01415017,"threshold_uncertainty_score":0.02435303,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Georges Katsirelos","is_ca":false},{"name":"Lucas Bordeaux","is_ca":false},{"name":"Nina Naroditska","is_ca":false},{"name":"Moshe Y. Vardi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4425624707903778,"gpt":0.4107181668670509,"spread":0.03184430392332688,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004279196,0.001025562,0.001619294,0.001107469,0.001863673,0.006354688,0.00302206,0.001849221,0.01321607],"category_scores_gemma":[0.03373686,0.0009920738,0.00216738,0.002904812,0.002606675,0.01179588,0.003904441,0.00455524,0.002309848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002898431,"about_ca_system_score_gemma":0.002923114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004541015,"about_ca_topic_score_gemma":0.003873173,"domain_scores_codex":[0.9928093,0.002025795,0.0004044035,0.001164761,0.002374333,0.001221335],"domain_scores_gemma":[0.9565377,0.03657946,0.001340461,0.003643427,0.00146959,0.000429338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00145685,0.0003849737,0.004168014,0.001613129,0.0002353359,0.0005175493,0.001232195,0.2788041,0.008166364,0.3804395,0.03536938,0.2876126],"study_design_scores_gemma":[0.0001471024,0.00005190842,0.0007025928,0.0001015477,0.0001001134,0.0002436606,0.0002622524,0.4958062,0.004218779,0.4906403,0.007677089,0.00004856055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1415798,0.002104793,0.7792831,0.01350884,0.0003484047,0.0004825496,0.002787396,0.003665515,0.0562396],"genre_scores_gemma":[0.635612,0.001806942,0.3430706,0.00156559,0.0003589993,0.0004599679,0.00314212,0.001306466,0.01267737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01321607,"threshold_uncertainty_score":0.04421216,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Ta-Wen Kuan","is_ca":false},{"name":"An‐Chao Tsai","is_ca":false},{"name":"Po-Hsun Sung","is_ca":false},{"name":"Jhing-Fa Wang","is_ca":false},{"name":"Hsien-Shun Kuo","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2723750335187968,"gpt":0.4239095653915988,"spread":0.151534531872802,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007036891,0.001228992,0.0008578362,0.001328839,0.0005207644,0.0007006767,0.0007707587,0.0009452146,0.003936534],"category_scores_gemma":[0.001424987,0.0002934509,0.0004689248,0.0008368873,0.0002405312,0.000898511,0.0003597199,0.0006319399,0.003711607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004270386,"about_ca_system_score_gemma":0.0006809455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002914303,"about_ca_topic_score_gemma":0.002735649,"domain_scores_codex":[0.9989648,0.0001371606,0.00008461979,0.0002110895,0.000530815,0.00007148496],"domain_scores_gemma":[0.9994195,0.00007355893,0.00003914753,0.00006351114,0.0003865042,0.00001781004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004157535,0.00008698113,0.0005076701,0.0002417486,0.00004278396,0.0001854927,0.00005085307,0.01116529,0.2488667,0.002419266,0.01040406,0.7256134],"study_design_scores_gemma":[0.0001034555,0.0007224079,0.007359276,0.00009077795,0.0001434596,0.001545099,0.00006138156,0.7026435,0.2323554,0.002040462,0.05275351,0.0001812987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01038978,0.0009621407,0.981883,0.0001568697,0.0002249077,0.0001861404,0.0003840533,0.003627751,0.002185249],"genre_scores_gemma":[0.2855131,0.001060904,0.7013098,0.0002902737,0.0002608204,0.0005775213,0.002776664,0.0002874593,0.007923349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003936534,"threshold_uncertainty_score":0.01316899,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Ehsan M. Nasroullahi","is_ca":false},{"name":"Kagan Tumer","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1795817677795721,"gpt":0.4121311132034148,"spread":0.2325493454238426,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003860863,0.001076662,0.0009211822,0.000736848,0.0005424395,0.001303266,0.001740238,0.0009829904,0.001280261],"category_scores_gemma":[0.008826022,0.0004801079,0.000366803,0.0004209161,0.0009705973,0.002361435,0.003543648,0.001239971,0.0003283143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005993796,"about_ca_system_score_gemma":0.0006650508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006634235,"about_ca_topic_score_gemma":0.000843778,"domain_scores_codex":[0.997961,0.0008065891,0.0001790132,0.000291366,0.0005273245,0.0002345491],"domain_scores_gemma":[0.993989,0.00312345,0.0008773401,0.00117293,0.0005045317,0.0003327803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005312117,0.0006498559,0.005752517,0.0002268404,0.0001820044,0.0001521236,0.0003231876,0.7366875,0.02716482,0.0146935,0.0008907464,0.2127456],"study_design_scores_gemma":[0.000145719,0.0005438424,0.001196737,0.00002130692,0.00005219585,0.00006415022,0.00005948227,0.9789217,0.009986833,0.007886531,0.001088273,0.00003320986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3612828,0.000672919,0.6293088,0.0004714079,0.0001001007,0.0001618807,0.00002151351,0.001499554,0.006481078],"genre_scores_gemma":[0.9412794,0.00008758304,0.057737,0.0000458164,0.00002556124,0.00007382804,0.00001820984,0.00004037957,0.0006920525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003860863,"threshold_uncertainty_score":0.02041841,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Danilo Cunha","is_ca":false},{"name":"Dávila Patrícia Ferreira Cruz","is_ca":false},{"name":"Alexandre Alberto Politi","is_ca":false},{"name":"Leandro Nunes de Castro","is_ca":false},{"name":"Renato Dourado Maia","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2304565960268817,"gpt":0.4547425132125025,"spread":0.2242859171856208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000912979,0.001206473,0.001263759,0.002296953,0.0004841658,0.001803893,0.001255468,0.001473351,0.001483679],"category_scores_gemma":[0.001097871,0.0005418533,0.001122339,0.004891748,0.000637814,0.001458035,0.0008970145,0.00125618,0.001053283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006640851,"about_ca_system_score_gemma":0.0008525151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001582173,"about_ca_topic_score_gemma":0.001051695,"domain_scores_codex":[0.9995549,0.00009332099,0.00004497588,0.0001079853,0.0001711614,0.00002774509],"domain_scores_gemma":[0.9996397,0.0001538643,0.00002888229,0.00002794894,0.0001252416,0.00002433084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009349817,0.0002196745,0.001663169,0.005161556,0.0002267868,0.0002477076,0.0002438186,0.0851929,0.005253172,0.0569214,0.01396784,0.8308085],"study_design_scores_gemma":[0.00004324075,0.0004449406,0.004022249,0.00256686,0.0003068968,0.00159558,0.0005107921,0.4110692,0.007938749,0.09596424,0.4752775,0.0002599136],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.005748317,0.4810268,0.4939792,0.001732162,0.0007685473,0.0001320262,0.0001541293,0.0002927761,0.01616609],"genre_scores_gemma":[0.08768076,0.6005853,0.2980887,0.001190971,0.002026231,0.0003950262,0.0006635329,0.0002156826,0.009153802],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002296953,"threshold_uncertainty_score":0.004963398,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Edwin Raja Dhas","is_ca":false},{"name":"S. Kumanan","is_ca":false},{"name":"C. P. Jesuthanam","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5156509725328996,"gpt":0.4893299351033655,"spread":0.02632103742953407,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009759515,0.0007492422,0.0006044258,0.0004805422,0.000264526,0.0008802463,0.0006307617,0.0006586246,0.0009581837],"category_scores_gemma":[0.002052178,0.0003549275,0.0005894267,0.0003809669,0.0003291194,0.0008800196,0.0003428702,0.0005074288,0.0001696853],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005225572,"about_ca_system_score_gemma":0.0005411539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002985182,"about_ca_topic_score_gemma":0.002295083,"domain_scores_codex":[0.9995389,0.000152071,0.00003467555,0.00008274369,0.0001552741,0.00003639516],"domain_scores_gemma":[0.9992597,0.0004495572,0.0001091085,0.00004858163,0.0001154444,0.00001764494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001023132,0.00009379219,0.002249942,0.00009498272,0.00005760675,0.00006951264,0.00004955353,0.9204471,0.005918286,0.001887308,0.0002580042,0.06877147],"study_design_scores_gemma":[0.000004826673,0.00003357831,0.0003121837,0.000005650157,0.000007150476,0.000006573521,0.000005307755,0.9969509,0.001762522,0.0007776231,0.0001280592,0.000005693152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0821503,0.0003105102,0.9150143,0.0001358206,0.00003787674,0.00006895779,0.00005050519,0.0003913437,0.001840342],"genre_scores_gemma":[0.8991655,0.0003143809,0.0991931,0.00003277286,0.00001647473,0.00009501554,0.00007349216,0.00001871859,0.001090507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002985182,"threshold_uncertainty_score":0.005935609,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Siti Khatijah Nor Abdul Rahim","is_ca":false},{"name":"Andrzej Bargieła","is_ca":false},{"name":"Rong Qu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.537345404345089,"gpt":0.5153357917927699,"spread":0.02200961255231915,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001162712,0.0007678508,0.0006052658,0.0007430448,0.0003178616,0.0007747894,0.001187666,0.0005280073,0.004777766],"category_scores_gemma":[0.002911695,0.0004640038,0.0009309998,0.001137862,0.0006125829,0.0009991084,0.000830341,0.001683833,0.0007853568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026009,"about_ca_system_score_gemma":0.001824297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002680532,"about_ca_topic_score_gemma":0.00312105,"domain_scores_codex":[0.9991559,0.0003136001,0.00003571941,0.0001662896,0.0002596939,0.0000688024],"domain_scores_gemma":[0.9989129,0.0007014198,0.00008968313,0.0001358232,0.0001290824,0.00003104229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004461832,0.00008033642,0.0002972306,0.0001359255,0.00002649149,0.00003352493,0.00005728974,0.8449269,0.003134152,0.03990901,0.001810348,0.1095442],"study_design_scores_gemma":[0.00001225846,0.00003687113,0.0001094262,0.00001079244,0.000007501605,0.00002436454,0.00002076734,0.9754327,0.002033867,0.01730723,0.004995898,0.000008270871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002341652,0.00007195361,0.9954832,0.00005194723,0.00002421987,0.00003472492,0.00004828651,0.0001451133,0.001798932],"genre_scores_gemma":[0.1317288,0.0003083785,0.8630325,0.00008531217,0.00005013364,0.0002830087,0.0003142553,0.0002602796,0.003937195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004777766,"threshold_uncertainty_score":0.01598322,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Varun Ojha","is_ca":false},{"name":"Paramartha Dutta","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1066371448699131,"gpt":0.3417188493709027,"spread":0.2350817045009896,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001790409,0.0007880717,0.0008254188,0.0006097223,0.0004856217,0.0008865281,0.0004344926,0.0009426128,0.0009676965],"category_scores_gemma":[0.004433816,0.0001837019,0.0003777591,0.0004904694,0.0004405853,0.0004139731,0.0003747706,0.0005530121,0.0001828393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008060667,"about_ca_system_score_gemma":0.001077384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01113345,"about_ca_topic_score_gemma":0.003829947,"domain_scores_codex":[0.9993172,0.0002686609,0.0000438835,0.00009170563,0.0001699785,0.0001086521],"domain_scores_gemma":[0.9973409,0.001717361,0.0001635744,0.00008057769,0.0006353655,0.00006226627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003997692,0.00007290851,0.003537943,0.0001031766,0.00007249563,0.00005373066,0.00006014829,0.96231,0.002873466,0.001203674,0.0004718719,0.02884079],"study_design_scores_gemma":[0.000003830952,0.00005930991,0.0006503202,0.000004344288,0.000006442854,0.000009971277,0.00001450351,0.9978295,0.001246599,0.0001065175,0.00006514329,0.000003543382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6288657,0.00182597,0.3522078,0.0006758647,0.0001601961,0.0001128994,0.0001065759,0.0008538766,0.01519112],"genre_scores_gemma":[0.9709508,0.0002925188,0.02710823,0.00004601912,0.00001129959,0.00004548463,0.000095326,0.00004137365,0.001408959],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01113345,"threshold_uncertainty_score":0.02213728,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Andreas Schmidt Jensen","is_ca":false},{"name":"Jørgen Villadsen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3403236919189198,"gpt":0.4377993822394393,"spread":0.0974756903205195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005072713,0.00040335,0.0005197285,0.000928545,0.0007912001,0.003926113,0.001270931,0.001380636,0.002132215],"category_scores_gemma":[0.007895689,0.0002797369,0.0004598972,0.0005929829,0.001204565,0.002720478,0.001864236,0.0006812811,0.0004298552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001465171,"about_ca_system_score_gemma":0.001396309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001379031,"about_ca_topic_score_gemma":0.001037408,"domain_scores_codex":[0.9952819,0.002072793,0.0002013045,0.0003063062,0.001806441,0.0003312238],"domain_scores_gemma":[0.9936,0.002574053,0.0005862041,0.001007264,0.001492948,0.0007395777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009214818,0.0004331336,0.01214639,0.0009953654,0.0003594708,0.000537937,0.005685306,0.1975165,0.01219668,0.5703584,0.003045429,0.1958039],"study_design_scores_gemma":[0.0004102391,0.002016688,0.01652172,0.0004602424,0.0003522443,0.0007213501,0.004279052,0.6853,0.00932584,0.199593,0.08082798,0.0001916917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4238033,0.01014067,0.4479209,0.00262959,0.0003684832,0.0005995664,0.0001078479,0.001125751,0.1133038],"genre_scores_gemma":[0.944159,0.001017735,0.05178932,0.0001448213,0.00005323037,0.0001105162,0.00007026744,0.00004754996,0.002607584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005072713,"threshold_uncertainty_score":0.02682739,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Niloofar Mozafari","is_ca":false},{"name":"Sattar Hashemi","is_ca":false},{"name":"Ali Hamzeh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4264192062234075,"gpt":0.4872538406245796,"spread":0.06083463440117209,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004345395,0.001357806,0.001481493,0.00549507,0.001226809,0.001868776,0.002822417,0.001546105,0.001654768],"category_scores_gemma":[0.009961274,0.0008538174,0.002415372,0.006247174,0.001901635,0.003003834,0.002044769,0.002760816,0.001166366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001869964,"about_ca_system_score_gemma":0.001927332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005562381,"about_ca_topic_score_gemma":0.004533329,"domain_scores_codex":[0.9962407,0.001035963,0.0003200429,0.0009177011,0.001325123,0.0001603718],"domain_scores_gemma":[0.9953822,0.001941367,0.0004469235,0.0007184242,0.001343208,0.0001678129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001602056,0.0001461581,0.00507481,0.0006506529,0.0004800966,0.0002772887,0.0005899241,0.4210598,0.006334155,0.1962122,0.01009728,0.3589175],"study_design_scores_gemma":[0.00001103922,0.00006045524,0.0008607609,0.00003205296,0.00002961295,0.0001358436,0.00007551063,0.9143606,0.001468384,0.07485354,0.008066509,0.00004576197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008486232,0.0003114855,0.9981477,0.0001092733,0.00004285882,0.00004593951,0.00008030121,0.0002183329,0.0001954282],"genre_scores_gemma":[0.06980352,0.001606264,0.9244331,0.0002205798,0.0004522267,0.0004622279,0.0009381341,0.0002314957,0.001852514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005562381,"threshold_uncertainty_score":0.02298093,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"K.O. Cheng","is_ca":false},{"name":"Ngai-Fong Law","is_ca":false},{"name":"Wan-Chi Siu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4163733186004739,"gpt":0.4713816371110752,"spread":0.05500831851060128,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01823623,0.001583201,0.002947572,0.003354724,0.001472481,0.00170328,0.002387377,0.001516409,0.001260574],"category_scores_gemma":[0.05117457,0.001229591,0.002697465,0.004373875,0.001192257,0.001618315,0.002605533,0.003153645,0.001088312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006008541,"about_ca_system_score_gemma":0.002202218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002004846,"about_ca_topic_score_gemma":0.002299261,"domain_scores_codex":[0.9829016,0.01212298,0.001176703,0.001683527,0.00176748,0.0003476713],"domain_scores_gemma":[0.9708391,0.01936688,0.001711993,0.004091317,0.003573725,0.000416952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001105433,0.0004309675,0.01686766,0.001177781,0.002257346,0.0006602361,0.001614955,0.3666746,0.01803828,0.02589365,0.008233322,0.5570456],"study_design_scores_gemma":[0.00007897225,0.0001494498,0.002403023,0.00009009905,0.0001324539,0.0002663081,0.0001696679,0.9565675,0.008420446,0.02815875,0.003457994,0.0001053349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003127466,0.0001508277,0.995761,0.0000861603,0.00002767048,0.00006120106,0.00008423699,0.0005865883,0.0001147972],"genre_scores_gemma":[0.06913035,0.0002252516,0.928829,0.0001213933,0.00003324309,0.0004214768,0.0007488149,0.0002168152,0.000273712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01823623,"threshold_uncertainty_score":0.09644359,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Reza Akbari","is_ca":false},{"name":"Vahid Zeighami","is_ca":false},{"name":"Ismail Akbari","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4928005156978104,"gpt":0.5737288236780013,"spread":0.0809283079801909,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006858975,0.0006278525,0.0006286527,0.0009078654,0.000522039,0.0006502294,0.0008323757,0.0009926084,0.002813211],"category_scores_gemma":[0.001415012,0.0002141447,0.0005588362,0.00135758,0.0003102128,0.0005049461,0.0004226606,0.0007622979,0.0003870117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007507195,"about_ca_system_score_gemma":0.001662897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011903,"about_ca_topic_score_gemma":0.008426418,"domain_scores_codex":[0.9995383,0.0001784228,0.00002128058,0.00004546985,0.000175917,0.00004060628],"domain_scores_gemma":[0.9996978,0.000131551,0.00002685264,0.00001588581,0.0001109934,0.00001699825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006050907,0.0000990737,0.001050972,0.0002123784,0.0000712897,0.0001247844,0.00007988036,0.7881261,0.003176855,0.02355828,0.003468637,0.1799713],"study_design_scores_gemma":[0.00002465907,0.00004828374,0.0001787258,0.00001802765,0.00001552442,0.00007624833,0.00001709765,0.9923097,0.0006503552,0.002784275,0.003870225,0.000006949547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02049335,0.0009865918,0.9650252,0.0003650346,0.0001522338,0.0001808735,0.00008324035,0.0002735576,0.01243996],"genre_scores_gemma":[0.2809024,0.001158961,0.7093476,0.0002002122,0.00006166522,0.0004567807,0.0002540654,0.00005819722,0.007560072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01011903,"threshold_uncertainty_score":0.02012026,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Fatih Nayebi","is_ca":false},{"name":"Alain Abran","is_ca":false},{"name":"Jean‐Marc Desharnais","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1494272806277806,"gpt":0.4149915128714236,"spread":0.265564232243643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00791575,0.001190933,0.001780596,0.004014917,0.0006856021,0.002438829,0.001531576,0.001150835,0.0005811257],"category_scores_gemma":[0.03160903,0.0007481138,0.001271844,0.001426905,0.0005882254,0.002483883,0.001534431,0.001457468,0.0002905038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001386994,"about_ca_system_score_gemma":0.002936547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005487093,"about_ca_topic_score_gemma":0.00517097,"domain_scores_codex":[0.9946243,0.002396006,0.0004240798,0.0007439228,0.001507768,0.0003038451],"domain_scores_gemma":[0.9800285,0.01280904,0.002048321,0.001743518,0.003142535,0.0002280327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002602966,0.000377301,0.02286613,0.000192007,0.0002412353,0.0001244814,0.000275102,0.7824255,0.007416943,0.006141523,0.001416248,0.1782631],"study_design_scores_gemma":[0.00001346018,0.00005591521,0.002266414,0.00002323959,0.00002862173,0.00002990421,0.00003205259,0.9921665,0.002669473,0.002393634,0.0003006797,0.00002014286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.094997,0.000176753,0.9025099,0.0002539895,0.00001288875,0.0001216899,0.0001480201,0.0009368266,0.0008429481],"genre_scores_gemma":[0.7587066,0.0001656827,0.2394801,0.0000772947,0.00002773823,0.0003282659,0.0007194151,0.0001122091,0.0003826015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00791575,"threshold_uncertainty_score":0.04186296,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Xinran Yu","is_ca":false},{"name":"Turgay Korkmaz","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.4411128027856867,"gpt":0.4560858783839468,"spread":0.01497307559826017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001300982,0.001102619,0.000932186,0.00481149,0.001118607,0.000711609,0.00157208,0.001173491,0.001280323],"category_scores_gemma":[0.004527939,0.00024421,0.001006657,0.005637782,0.0003899593,0.001850741,0.0008217064,0.001219822,0.0005743454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007076943,"about_ca_system_score_gemma":0.001513822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008879299,"about_ca_topic_score_gemma":0.01571134,"domain_scores_codex":[0.998641,0.0002630386,0.0001636137,0.0003819078,0.000384101,0.0001664821],"domain_scores_gemma":[0.9967304,0.001577898,0.000359602,0.0005719171,0.000566344,0.0001939195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002184637,0.003498115,0.1129566,0.002051828,0.0005898944,0.003532971,0.0006422431,0.1441467,0.01619135,0.007091983,0.08899473,0.6181189],"study_design_scores_gemma":[0.0001877513,0.0003564931,0.02265696,0.00004224623,0.00007551083,0.001232356,0.0004601175,0.9450179,0.008482644,0.007853728,0.01358036,0.0000539595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8466941,0.001408969,0.07935067,0.0009045206,0.0001818401,0.0006917487,0.06090647,0.007470719,0.002390941],"genre_scores_gemma":[0.6318888,0.0006259986,0.2292785,0.0001762647,0.00007457051,0.0007506351,0.1347245,0.000159935,0.002320759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008879299,"threshold_uncertainty_score":0.01765525,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Keyword spotting; Computer science; Spotting; Speech recognition; Natural language processing; Keyword search; Artificial intelligence; Information retrieval","authors":[{"name":"Ella Tetariy","is_ca":false},{"name":"Yossi Bar-Yosef","is_ca":false},{"name":"Vered Silber‐Varod","is_ca":false},{"name":"Michal Gishri","is_ca":false},{"name":"Ruthi Alon-Lavi","is_ca":false},{"name":"Vered Aharonson","is_ca":false},{"name":"Ami Moyal","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2760964527524231,"gpt":0.4496519770236475,"spread":0.1735555242712243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009527513,0.000606878,0.0005192811,0.001319337,0.000383235,0.0009678353,0.0007494378,0.0005237667,0.002933738],"category_scores_gemma":[0.004375607,0.0002411705,0.0004068041,0.0006872801,0.0004129742,0.001668569,0.001158033,0.000486975,0.00136899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003812191,"about_ca_system_score_gemma":0.0007113056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001670062,"about_ca_topic_score_gemma":0.003183682,"domain_scores_codex":[0.9991965,0.0002496565,0.00006962461,0.0002045468,0.0002228474,0.00005684281],"domain_scores_gemma":[0.9982625,0.0009013849,0.000155806,0.0002922835,0.0003115058,0.00007659842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009040579,0.00023178,0.007450477,0.0003189803,0.00009660026,0.0005128381,0.0009064148,0.02232388,0.1752536,0.004373271,0.001107741,0.7865203],"study_design_scores_gemma":[0.0000578755,0.0006501123,0.01964701,0.00005076242,0.0001260346,0.001751644,0.001293153,0.6391284,0.3177316,0.008097637,0.01131662,0.0001491308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2133556,0.0003995416,0.7782382,0.0001142924,0.00006012633,0.0001809599,0.0003571816,0.003254731,0.004039241],"genre_scores_gemma":[0.6940569,0.0001844263,0.3027487,0.00004621088,0.00001681063,0.0001320698,0.0005554628,0.0002196572,0.002039619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002933738,"threshold_uncertainty_score":0.009814322,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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","authors":[{"name":"Hirofumi Miyajima","is_ca":false},{"name":"Noritaka Shigei","is_ca":false},{"name":"Syunki Makino","is_ca":false},{"name":"Hiromi Miyajima","is_ca":false},{"name":"Yohtaro Miyanishi","is_ca":false},{"name":"Shinji Kitagami","is_ca":false},{"name":"Norio Shiratori","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2207497653687221,"gpt":0.4530274687240633,"spread":0.2322777033553412,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001457428,0.0005730439,0.000843893,0.0004166357,0.0006925819,0.001116013,0.001729203,0.001299818,0.00443903],"category_scores_gemma":[0.002464907,0.0002828699,0.0009293907,0.0005416571,0.00131575,0.001474527,0.001487026,0.001779833,0.0005914301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001002726,"about_ca_system_score_gemma":0.001351361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001380963,"about_ca_topic_score_gemma":0.000749399,"domain_scores_codex":[0.9989278,0.0004211337,0.00004212609,0.0002420216,0.000250278,0.0001165506],"domain_scores_gemma":[0.9992406,0.0003565599,0.0000711809,0.00009340907,0.0001436752,0.00009447815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001448078,0.0001026326,0.000664697,0.0001792456,0.0000905095,0.000305249,0.0002225881,0.3331524,0.004002867,0.5799546,0.004380364,0.07680009],"study_design_scores_gemma":[0.00003669141,0.00006153945,0.00006440219,0.0000100045,0.00001142574,0.00006961253,0.0000109763,0.9357793,0.0004697083,0.06010805,0.003365988,0.00001240879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003825748,0.0003063596,0.9897198,0.0003851011,0.00008077417,0.00004153114,0.00001926374,0.0001307265,0.00549065],"genre_scores_gemma":[0.714248,0.0009592607,0.2687062,0.00043729,0.0002577237,0.0003280932,0.00007233539,0.00009209934,0.01489895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00443903,"threshold_uncertainty_score":0.01485002,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}