{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"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":"748703eaa402","filters":{"venue":"Mathematical Statistics and Learning"}},"results":[{"id":"W2897972648","doi":"10.4171/msl/12","title":"The algorithmic hardness threshold for continuous random energy models","year":2020,"lang":"en","type":"preprint","venue":"Mathematical Statistics and Learning","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Agence Nationale de la Recherche","keywords":"Parameterized complexity; Energy (signal processing); Combinatorics; Function (biology); Mathematics; Random graph; Discrete mathematics; Hypercube; Graph","retraction":null,"screen_n_in":null,"score":{"opus":0.0295859097534933,"gpt":0.2639521725145976,"spread":0.2343662627611043,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006274314,0.0002827067,0.0005214022,0.00002790612,0.0007054716,0.001098987,0.0008326463,0.000146364,0.000007062209],"category_scores_gemma":[0.0004038959,0.0001870842,0.00008586551,0.00005630621,0.00009873241,0.00009907815,0.001712341,0.0006675862,0.000004722136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002033015,"about_ca_system_score_gemma":0.00006785501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002142626,"about_ca_topic_score_gemma":0.00000139384,"domain_scores_codex":[0.9981636,0.0001187001,0.0004476116,0.0005681627,0.0003505622,0.0003513892],"domain_scores_gemma":[0.9973592,0.001645298,0.0002467855,0.0004390495,0.0001355865,0.0001740776],"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":[0.00002110623,0.0000229888,7.380374e-7,0.0001933135,0.00004582643,0.00001216509,0.0004543239,0.001692606,0.000005456814,0.8496285,0.00323973,0.1446833],"study_design_scores_gemma":[0.0002997221,0.00003903474,0.00000195859,0.00007171684,0.00001863178,0.000004142389,0.00002668583,0.5465155,0.000004269337,0.4433173,0.009559129,0.0001419077],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000215259,0.0007315829,0.9972164,0.000635202,0.0003440009,0.0003506186,0.0001317057,0.0001123716,0.0004566184],"genre_scores_gemma":[0.02068201,0.0005394113,0.9765508,0.0002077614,0.0003376772,0.0003055275,0.0001598937,0.00005496046,0.001161925],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5448229,"threshold_uncertainty_score":0.999938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392659422","doi":"10.4171/msl/44","title":"Optimal and instance-dependent guarantees for Markovian linear stochastic approximation","year":2024,"lang":"en","type":"article","venue":"Mathematical Statistics and Learning","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":1,"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":"Office of Naval Research; Multidisciplinary University Research Initiative; National Science Foundation","keywords":"Markov process; Mathematical optimization; Stochastic approximation; Computer science; Mathematics; Applied mathematics; Linear approximation; Nonlinear system; Physics; Statistics; Key (lock)","retraction":null,"screen_n_in":null,"score":{"opus":0.02787074957482795,"gpt":0.3111388852103389,"spread":0.283268135635511,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005350228,0.0002249299,0.0003702464,0.00006619815,0.0002431788,0.000215963,0.00006549981,0.00008634956,0.0001655042],"category_scores_gemma":[0.002539864,0.0001749186,0.00003960214,0.00009908341,0.0001561578,0.00008227058,0.00006580057,0.0003569024,0.00002202014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002432929,"about_ca_system_score_gemma":0.00001885293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004271907,"about_ca_topic_score_gemma":0.000001165063,"domain_scores_codex":[0.9985613,0.00006584215,0.0004139263,0.000365459,0.0002672041,0.0003262204],"domain_scores_gemma":[0.9951037,0.004512452,0.00006190882,0.00009216394,0.00007018026,0.0001595935],"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":[0.00004420586,0.00007219688,0.000004476303,0.001919826,0.00006189118,0.00002313893,0.0009135164,0.0001073901,0.00004393908,0.910212,0.0003439015,0.0862535],"study_design_scores_gemma":[0.0002388885,0.0001814623,0.00001978072,0.0001732981,0.00008253119,0.00003631326,0.0001867105,0.57139,0.000008863533,0.4266719,0.0008352884,0.0001749571],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.010883,0.0002711186,0.9875717,0.0001899431,0.00007757152,0.0003983899,0.0001688851,0.0001515054,0.0002879072],"genre_scores_gemma":[0.1579335,0.00003202965,0.8404831,0.00003195994,0.0001359389,0.00009375896,0.00002948877,0.00005863897,0.00120155],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5712827,"threshold_uncertainty_score":0.7132972,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3008672633","doi":"10.4171/msl/38","title":"Optimal anytime regret with two experts","year":2023,"lang":"en","type":"article","venue":"Mathematical Statistics and Learning","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Regret; Minimax; Time horizon; Constant (computer programming); Mathematical optimization; Computer science; Horizon; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.09688300321347229,"gpt":0.437329077888751,"spread":0.3404460746752787,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001883737,0.0001335481,0.0002816176,0.0001657239,0.000355232,0.0003260845,0.0002267239,0.00003628831,0.001185501],"category_scores_gemma":[0.006583154,0.00008253749,0.00002131778,0.0006355649,0.0002300216,0.00008428015,0.0001868737,0.0003043191,0.0008940107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633088,"about_ca_system_score_gemma":0.00002942725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004506176,"about_ca_topic_score_gemma":0.000002176424,"domain_scores_codex":[0.9973917,0.0001767225,0.0003347673,0.0003809112,0.001312822,0.0004030943],"domain_scores_gemma":[0.9946053,0.0046907,0.00008998544,0.0002321419,0.000180214,0.0002016293],"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":[0.0002773062,0.0002210483,0.003553493,0.0001577559,0.0001217139,0.001945654,0.01368936,0.1174604,0.0003894237,0.1261144,0.03354051,0.702529],"study_design_scores_gemma":[0.0005794237,0.0002846917,0.00112684,0.00004126255,0.000007493186,0.00005544981,0.00266978,0.8703772,0.00003934944,0.1123375,0.01225708,0.0002239238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07777894,0.00002750017,0.9198385,0.0002788625,0.000025544,0.0001347164,0.0000206961,0.0001019466,0.001793223],"genre_scores_gemma":[0.4784819,0.00002409277,0.4882969,0.00006205225,0.0001350292,0.00004490003,0.00002256206,0.00006290474,0.03286966],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7529168,"threshold_uncertainty_score":0.9998839,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}