{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":5,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":5,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"0a626c358643","filters":{"venue":"Advances in Data Science and Adaptive Analysis"}},"results":[{"id":"W4413527283","doi":"10.1142/s2424922x25500068","title":"Predictive Modeling of Peanut Oil Prices Utilizing a Gaussian Process Regression-Based Machine Learning Framework","year":2025,"lang":"en","type":"article","venue":"Advances in Data Science and Adaptive Analysis","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Machine learning; Multivariate adaptive regression splines; Artificial intelligence; Peanut oil; Computer science; Regression; Gaussian process; Process (computing); Regression analysis; Econometrics; Gaussian; Statistics; Bayesian multivariate linear regression; Mathematics; Chemistry; Raw material","retraction":null,"screen_n_in":null,"score":{"opus":0.1045129636135161,"gpt":0.4450178270199902,"spread":0.3405048634064741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004288719,0.0001547689,0.0004165376,0.00146417,0.0005068155,0.0001534974,0.002059621,0.00005267701,0.00001776039],"category_scores_gemma":[0.006676228,0.0001068599,0.00005924602,0.01296963,0.0007997148,0.002002957,0.0006775873,0.0002890642,7.526297e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000404338,"about_ca_system_score_gemma":0.0002787603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001333743,"about_ca_topic_score_gemma":0.0002754457,"domain_scores_codex":[0.9967532,0.00008202554,0.0006246467,0.001079493,0.001185614,0.0002749762],"domain_scores_gemma":[0.9967721,0.001057018,0.0004168566,0.001048129,0.0006295301,0.00007635025],"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":[0.0001626627,0.000211508,0.1042203,0.00005751733,0.0000921274,0.000004310535,0.001089352,0.6588426,0.000243974,0.02239775,0.00002815666,0.2126497],"study_design_scores_gemma":[0.00007421648,0.00003254341,0.000582855,0.0002897501,0.00008952448,2.165584e-7,0.002837263,0.9709521,0.0002537624,0.02429367,0.0004870487,0.000107036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03848683,0.003434217,0.9507279,0.0005460522,0.00003513981,0.0001322823,0.0001379393,0.00005589097,0.006443701],"genre_scores_gemma":[0.9512193,0.0006032256,0.04801226,0.00005856698,0.000009065922,0.00002208803,0.00002215888,0.000003488411,0.00004983433],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9127325,"threshold_uncertainty_score":0.7992551,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2804563260","doi":"10.1142/s2424922x18400077","title":"Clustering Parkinson’s and Age-Related Voice Impairment Signal Features for Unsupervised Learning","year":2018,"lang":"en","type":"article","venue":"Advances in Data Science and Adaptive Analysis","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hierarchical clustering; Silhouette; Cluster analysis; Computer science; Artificial intelligence; Similarity (geometry); Partition (number theory); Vowel; Pattern recognition (psychology); Unsupervised learning; Speech recognition; Psychology; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02631669709924635,"gpt":0.3299061226931573,"spread":0.303589425593911,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000913023,0.000131959,0.0003072958,0.0004228361,0.0003370752,0.00006858759,0.0002662981,0.00003918575,0.0000207709],"category_scores_gemma":[0.0002280504,0.00010553,0.00003740173,0.001639017,0.0008620003,0.001311664,0.000270392,0.000136604,0.000001898591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003914116,"about_ca_system_score_gemma":0.00007334071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001431956,"about_ca_topic_score_gemma":0.002071579,"domain_scores_codex":[0.9984719,0.00002801647,0.000204043,0.0006583108,0.0003199099,0.0003178847],"domain_scores_gemma":[0.9992357,0.0001045304,0.00007357518,0.000318805,0.0001476487,0.0001197578],"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":[0.001822857,0.0005348686,0.4058657,0.000278736,0.00152994,0.000144358,0.009218171,0.003318312,0.02013342,0.000707444,0.0006114568,0.5558348],"study_design_scores_gemma":[0.004211334,0.002277763,0.2943586,0.0002925331,0.002139591,0.00003404121,0.01753488,0.6285155,0.001220831,0.0007649684,0.04792007,0.0007299324],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9587687,0.01008671,0.02611899,0.001729078,0.000105091,0.0008535567,0.00008621562,0.00006893936,0.002182698],"genre_scores_gemma":[0.9894233,0.002163359,0.007802897,0.0002814496,0.00004345344,0.0000117889,0.00007465062,0.000006453546,0.0001926229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6251972,"threshold_uncertainty_score":0.4303387,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2803504285","doi":"10.1142/s2424922x18400065","title":"Deep Learning of EEG Time–Frequency Representations for Identifying Eye States","year":2018,"lang":"en","type":"article","venue":"Advances in Data Science and Adaptive Analysis","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Spectrogram; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Electroencephalography; Non-negative matrix factorization; Feature (linguistics); Brain–computer interface; Feature extraction; Time–frequency analysis; Speech recognition; Matrix decomposition; Computer vision; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.06256078229606446,"gpt":0.3901468920768915,"spread":0.327586109780827,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000859716,0.00009765122,0.0002152105,0.0005041439,0.0003742961,0.0001097778,0.0009974493,0.00001638739,0.00002917939],"category_scores_gemma":[0.001473726,0.0000833388,0.00003659709,0.00287934,0.00136663,0.00363573,0.0004310329,0.0000761304,0.000005920482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001993649,"about_ca_system_score_gemma":0.00004044363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007170599,"about_ca_topic_score_gemma":0.0002406741,"domain_scores_codex":[0.9982485,0.00006800282,0.000284879,0.0007766146,0.0003572076,0.0002648614],"domain_scores_gemma":[0.9985568,0.000518717,0.0001921126,0.0004660652,0.000213255,0.00005305419],"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":[0.000104518,0.0002175258,0.05263917,0.00006831974,0.0001438196,0.00001247698,0.00617183,0.02110071,0.81615,0.01067198,0.0001220805,0.09259757],"study_design_scores_gemma":[0.0002156609,0.0001956458,0.004984941,0.00004481516,0.0001282061,0.0000014229,0.00195742,0.8866592,0.09927849,0.00514713,0.001179361,0.0002077268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5715334,0.001376799,0.423665,0.0002889787,0.0001828656,0.0003530543,0.0002074284,0.00005003175,0.002342378],"genre_scores_gemma":[0.9867153,0.0005138629,0.0125115,0.00007717409,0.00002543396,0.000007769026,0.00002208905,0.000004210347,0.0001226572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8655584,"threshold_uncertainty_score":0.5035406,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413527255","doi":"10.1142/s2424922x2550007x","title":"Predictions of Residential Property Prices for Ningbo City of Zhejiang Province in China Using Machine Learning","year":2025,"lang":"en","type":"article","venue":"Advances in Data Science and Adaptive Analysis","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"China; Property (philosophy); Business; Residential property; Agricultural economics; Geography; Economics; Economic geography; Archaeology","retraction":null,"screen_n_in":null,"score":{"opus":0.04413277427496717,"gpt":0.2910167598939952,"spread":0.2468839856190281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001837228,0.00008094648,0.0003973479,0.001069491,0.0001344833,0.00003472837,0.0004605751,0.0000295138,0.00000951856],"category_scores_gemma":[0.0005735579,0.0000752498,0.00004077369,0.002110395,0.0003542942,0.001880092,0.0002867182,0.00008975536,1.269715e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008190775,"about_ca_system_score_gemma":0.0001187672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003060834,"about_ca_topic_score_gemma":0.008069717,"domain_scores_codex":[0.9987394,0.00001246695,0.0005402941,0.0004804192,0.00004539522,0.0001819994],"domain_scores_gemma":[0.9991634,0.00007655549,0.000379908,0.0003068549,0.00005058636,0.00002272792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006692189,0.00007238331,0.95677,0.0000602955,0.00006651735,4.212329e-7,0.0002578539,0.03282259,0.000126412,0.005300563,0.000003105974,0.004452932],"study_design_scores_gemma":[0.0002763772,0.00003823095,0.06123807,0.00005418183,0.00005699156,1.590755e-7,0.0002736215,0.9335549,0.0001826222,0.003118861,0.001098866,0.0001071018],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.809541,0.004114309,0.1751828,0.00013708,0.0001481486,0.0004378374,0.0005202169,0.00001117234,0.009907394],"genre_scores_gemma":[0.9905601,0.001646723,0.007665568,0.000007168182,0.000009065902,0.000005174556,0.00002131502,0.000003047951,0.00008181173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9007323,"threshold_uncertainty_score":0.4627085,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3210359963","doi":"10.1142/s2424922x21420043","title":"Research on Intelligent Management System of Meteorological Archives Based on Big Data Framework","year":2021,"lang":"en","type":"article","venue":"Advances in Data Science and Adaptive Analysis","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Vanier College","funders":"","keywords":"Big data; Computer science; Geospatial analysis; Data management; Data science; Analytics; Visualization; Data analysis; Data processing; Data mining; Business intelligence; Data visualization; Database; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.5419266624503839,"gpt":0.5022757446171617,"spread":0.03965091783322217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.008175008,0.0001286718,0.0003889624,0.001722673,0.0003461988,0.0002043672,0.007487353,0.00005448854,0.00002789965],"category_scores_gemma":[0.004445101,0.00008512623,0.00004219526,0.01548912,0.002002809,0.0008308454,0.005677063,0.0003419182,0.00002022094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004151517,"about_ca_system_score_gemma":0.0001142675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002367135,"about_ca_topic_score_gemma":0.000219329,"domain_scores_codex":[0.9944342,0.0002983252,0.0005458815,0.001940197,0.002411191,0.0003701576],"domain_scores_gemma":[0.9887081,0.004081742,0.0001865008,0.006639292,0.0002889543,0.00009535183],"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":[0.0000631635,0.000244102,0.00207874,0.00001104724,0.0000555856,0.00003336467,0.0000264671,0.005220299,0.00006675965,0.2253039,0.0002831336,0.7666134],"study_design_scores_gemma":[0.0002723617,0.0003763986,0.01907811,0.0003399735,0.0002282688,0.000002102149,0.04175087,0.7680562,0.002453297,0.06571905,0.1013148,0.0004086426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01241103,0.00267982,0.9629402,0.003741923,0.000201915,0.0004762494,0.003068001,0.00005434224,0.01442652],"genre_scores_gemma":[0.9567162,0.002162469,0.04076429,0.0001300059,0.00001987037,0.00001931497,0.0001506451,0.000002850918,0.00003430184],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9443052,"threshold_uncertainty_score":0.9978826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}