{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":7,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":7,"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":"bed69831b6d5","filters":{"venue":"Foundations and Trends® in Machine Learning"}},"results":[{"id":"W4231109964","doi":"10.1561/2200000006","title":"Learning Deep Architectures for AI","year":2009,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":6910,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.01003312870247063,"gpt":0.2952870433434787,"spread":0.285253914641008,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001853102,0.0001054973,0.000110902,0.000358336,0.0005068338,0.0001772047,0.0001753758,0.00004309995,0.0000291193],"category_scores_gemma":[0.0000451285,0.0001021335,0.00005054301,0.0005256893,0.00002276472,0.000103595,0.00004359459,0.0003410592,0.000003808709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001945905,"about_ca_system_score_gemma":0.000008151549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004704014,"about_ca_topic_score_gemma":0.00007402358,"domain_scores_codex":[0.999238,0.00004638778,0.0001760076,0.0002822578,0.00007365207,0.0001836633],"domain_scores_gemma":[0.999626,0.00008028842,0.00007119846,0.000144738,0.00003172589,0.00004608299],"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":[0.000003412004,0.00002943102,0.002565856,0.00000236744,0.000003691199,5.996582e-7,0.0003372999,0.00911263,0.0001131645,0.04795246,0.00001943966,0.9398596],"study_design_scores_gemma":[0.0003968998,0.0003315996,0.04333289,0.00001047097,0.000006906874,0.00002137693,0.00002951001,0.8660724,0.0001061329,0.02474139,0.06473073,0.0002197588],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02085891,0.0001094873,0.9721856,0.003780859,0.00002568494,0.0001089535,9.702073e-7,0.0003282179,0.002601388],"genre_scores_gemma":[0.9457645,0.00001241587,0.05282867,0.0002245059,0.000039314,0.00005277373,0.00002525473,0.000006730805,0.001045812],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9396399,"threshold_uncertainty_score":0.4164882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3100366369","doi":"10.1561/2200000071","title":"An Introduction to Deep Reinforcement Learning","year":2018,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1251,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Artificial intelligence; Computer science; Deep learning; Generalization; Field (mathematics); Robotics; Machine learning; Robot; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01379196626407731,"gpt":0.2905965508752665,"spread":0.2768045846111892,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006109281,0.0001825817,0.0001655456,0.0007663849,0.0006742777,0.0004633143,0.0003598064,0.0000559686,0.0003595039],"category_scores_gemma":[0.0001992523,0.0001885887,0.00003091576,0.001161934,0.00006312553,0.0008634281,0.0002209635,0.0004805419,0.0001171342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008844589,"about_ca_system_score_gemma":0.00001712027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001537651,"about_ca_topic_score_gemma":0.00016753,"domain_scores_codex":[0.9983198,0.0001766471,0.0003470065,0.0005224819,0.0002672375,0.0003668279],"domain_scores_gemma":[0.9991729,0.00005394618,0.0001335348,0.000395868,0.0001038068,0.000139907],"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":[0.000008118957,0.0000164881,0.009627651,0.000003602339,0.000009371041,0.00000180447,0.001973486,0.8689428,0.0001832247,0.01633217,0.00004572627,0.1028556],"study_design_scores_gemma":[0.0003111777,0.0006187988,0.01221389,0.000009345769,0.000006118467,0.00001108556,0.00009163598,0.9279959,0.00003972551,0.00009041178,0.05839945,0.0002124618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01342692,0.000017288,0.9794482,0.002559937,0.0003416867,0.00009515802,8.263303e-8,0.0002410844,0.003869673],"genre_scores_gemma":[0.9653999,0.000009713721,0.03051879,0.0001407145,0.0004555797,0.0000172272,0.00007971851,0.00001795861,0.003360393],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.951973,"threshold_uncertainty_score":0.7690423,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2032005951","doi":"10.1561/2200000005","title":"A Survey of Statistical Network Models","year":2010,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":853,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Statistical survey; Computer science; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02131490892849552,"gpt":0.3118738352057813,"spread":0.2905589262772857,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004110871,0.00008728207,0.0001837466,0.0001325422,0.0001143277,0.00003839807,0.00006812611,0.00002218706,0.001225362],"category_scores_gemma":[0.00001531783,0.00008495021,0.00003006454,0.000441542,0.00005509438,0.00007854066,0.00005604184,0.0003726868,0.000001829138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003566504,"about_ca_system_score_gemma":0.00001118466,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006805765,"about_ca_topic_score_gemma":0.004938834,"domain_scores_codex":[0.9992858,0.0001126725,0.0002259464,0.0001553382,0.00007275807,0.0001474427],"domain_scores_gemma":[0.9994798,0.0002288899,0.00008877013,0.0001230693,0.00004283251,0.00003663002],"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":[0.000005879131,0.00005150614,0.7317643,0.000001644846,0.00002870597,2.930499e-7,0.00005486211,0.01369263,0.00001818344,0.1306112,0.0001901109,0.1235807],"study_design_scores_gemma":[0.0001678055,0.00001789891,0.4344636,0.000005966839,0.00001648041,2.876203e-7,0.00000625316,0.5385006,0.000001617488,0.02518119,0.001545269,0.00009296394],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5942988,0.0001551457,0.3813515,0.0000944284,0.00008787137,0.00009678436,0.00009214321,0.00007236697,0.02375093],"genre_scores_gemma":[0.9927197,0.000005125579,0.006282283,0.000003392927,0.00006214254,0.000008691287,0.0006967601,0.000009896418,0.0002120623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.524808,"threshold_uncertainty_score":0.999808,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4317931697","doi":"10.1561/2200000096","title":"Graph Neural Networks for Natural Language Processing: A Survey","year":2023,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":269,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial neural network; Graph; Natural language processing; Artificial intelligence; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02218011106607913,"gpt":0.3132030583237363,"spread":0.2910229472576571,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004010958,0.0001494033,0.0001590386,0.0004905671,0.0003983113,0.0002372915,0.0002618385,0.00004614914,0.000005090649],"category_scores_gemma":[0.0001051326,0.0001373723,0.00005240774,0.00253222,0.00004476559,0.0004846486,0.0001417122,0.0004108706,0.000001856612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001244848,"about_ca_system_score_gemma":0.00000700551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001138449,"about_ca_topic_score_gemma":0.001170855,"domain_scores_codex":[0.998814,0.0001247629,0.000203375,0.0003833499,0.0001094397,0.0003651143],"domain_scores_gemma":[0.9992976,0.0003446491,0.00009363133,0.0001671376,0.00004186119,0.00005516925],"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":[0.00001521786,0.00001634353,0.04142061,0.000009573794,0.000007945108,0.00001203613,0.0004861213,0.1449712,0.00001140312,0.001282503,0.0001126507,0.8116544],"study_design_scores_gemma":[0.0003487748,0.00003447829,0.145578,0.000009764044,0.000002708388,0.000007073322,0.00002116462,0.8530216,5.776513e-7,0.0003851457,0.000455411,0.0001353023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2345864,0.00437303,0.7559283,0.00203922,0.001001239,0.0004018529,0.00001755818,0.001346328,0.0003060319],"genre_scores_gemma":[0.9943288,0.00002672311,0.004321828,0.00007846946,0.00006052289,0.00004168501,0.0004142009,0.00001744899,0.0007103286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8115191,"threshold_uncertainty_score":0.5601879,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2236244207","doi":"10.1561/2200000049","title":"Bayesian Reinforcement Learning: A Survey","year":2015,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":223,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Machine learning; Reinforcement learning; Artificial intelligence; Bayesian inference; Bayesian probability; Variable-order Bayesian network; Prior probability; Inference; Bellman equation; Algorithm; Mathematical optimization; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0465390705497981,"gpt":0.3022379049415592,"spread":0.2556988343917611,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001479584,0.0002161315,0.0002320648,0.0006192418,0.0003316178,0.0004510015,0.0004007877,0.00007362461,0.0001070848],"category_scores_gemma":[0.0006175301,0.000215479,0.00004370805,0.001155608,0.00005793959,0.0006242121,0.0003474737,0.0006992697,0.00006496777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080186,"about_ca_system_score_gemma":0.00006361707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00113169,"about_ca_topic_score_gemma":0.0003716036,"domain_scores_codex":[0.9979447,0.0004275327,0.0004201437,0.0004207795,0.0003861769,0.0004006943],"domain_scores_gemma":[0.9989625,0.0002076822,0.0001983828,0.0003276474,0.0001085285,0.0001952816],"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":[0.000007608463,0.00001615743,0.1566369,0.000004218743,0.00001376265,0.000007336901,0.001010488,0.8160558,0.000002830401,0.005321153,0.00009279529,0.02083096],"study_design_scores_gemma":[0.000790716,0.000207591,0.04959874,0.00001674413,0.000005486397,0.00001307129,0.00006400618,0.9257292,0.000002412519,0.0001554297,0.02317936,0.0002372948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00440627,0.0001102843,0.9754209,0.0005987748,0.0002232921,0.00009258708,4.754638e-7,0.0002337435,0.0189137],"genre_scores_gemma":[0.9800444,0.00002885283,0.008560605,0.00006780464,0.00003635856,0.00001436065,0.0002063629,0.00001953804,0.01102169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9756382,"threshold_uncertainty_score":0.8786981,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4412591781","doi":"10.1561/2200000115","title":"Artificial intelligence for science in quantum, atomistic, and continuum systems","year":2025,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Quantum; Statistical physics; Cognitive science; Theoretical physics; Physics; Psychology; Quantum mechanics","retraction":null,"screen_n_in":null,"score":{"opus":0.0234980485780742,"gpt":0.332690736448169,"spread":0.3091926878700949,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002974054,0.0001741391,0.000294549,0.0009244195,0.0006056424,0.0007585893,0.0002950106,0.00005667042,0.000119796],"category_scores_gemma":[0.001338263,0.0001652748,0.00002033939,0.001161495,0.0005618587,0.0003655841,0.0001930212,0.0002414441,0.000009825771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007850186,"about_ca_system_score_gemma":0.00008247361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001268743,"about_ca_topic_score_gemma":0.0009787841,"domain_scores_codex":[0.998021,0.0001783386,0.0005535329,0.0006215913,0.0001996443,0.0004259057],"domain_scores_gemma":[0.9990327,0.000479042,0.00015288,0.0001868569,0.00008151817,0.00006703247],"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":[0.000219497,0.000208339,0.1435556,0.0003403582,0.000007302575,0.00001428173,0.002109568,0.06175705,0.1875068,0.5035124,0.00004056068,0.1007283],"study_design_scores_gemma":[0.0002834351,0.00009094741,0.05212278,0.0001765797,0.00001275933,0.00001156178,0.0003579216,0.9332697,0.001283513,0.01007968,0.002031859,0.0002792467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569163,0.0002756344,0.03962999,0.0007099485,0.000829023,0.0003026633,0.00001516506,0.00007727707,0.001244054],"genre_scores_gemma":[0.9953125,0.00001860581,0.003956165,0.00002949855,0.00003831239,0.00008509169,0.00001530875,0.0000101329,0.0005344489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8715127,"threshold_uncertainty_score":0.7315094,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406780284","doi":"10.1561/2200000112","title":"Generalization Bounds: Perspectives from Information Theory and PAC-Bayes","year":2025,"lang":"en","type":"article","venue":"Foundations and Trends® in Machine Learning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Generalization; Bayes' theorem; Mathematics; Mathematical economics; Computer science; Bayesian probability; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.005312522459407974,"gpt":0.2607480985491077,"spread":0.2554355760896997,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001738819,0.00008080086,0.0000811087,0.0002644278,0.0003543795,0.0004021902,0.000101383,0.00003093886,0.00003237485],"category_scores_gemma":[0.00004094563,0.00007510267,0.0000157983,0.0005628354,0.00004223692,0.0007701578,0.00009438782,0.0001400688,0.000003667352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000019204,"about_ca_system_score_gemma":0.00001114607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003309482,"about_ca_topic_score_gemma":0.0001764895,"domain_scores_codex":[0.9994185,0.00009550887,0.000152967,0.0001782971,0.00005823799,0.00009650904],"domain_scores_gemma":[0.9996195,0.0001403091,0.00005967112,0.0001243854,0.00003174414,0.00002432167],"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":[0.000004069294,0.00001745707,0.008329594,0.000003331218,0.0000112391,3.044528e-7,0.00207582,0.001610414,0.00004143084,0.5810428,0.00007294025,0.4067906],"study_design_scores_gemma":[0.0007329553,0.00003620674,0.216441,0.00004622803,0.00002073222,0.000004483326,0.0007840021,0.6058293,0.00002010984,0.1258312,0.0499941,0.0002596982],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2031018,0.001430284,0.787194,0.003546655,0.0001005694,0.0001046693,0.000010656,0.0001438657,0.0043675],"genre_scores_gemma":[0.9928691,0.0002187441,0.006141742,0.0001552563,0.00002622704,0.00002233508,0.0001363783,0.000002804994,0.0004274274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7897673,"threshold_uncertainty_score":0.3878329,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}