{"meta":{"query_hash":"9c1ace73c2f7","filters":{"venue":"Nature Machine Intelligence"},"cohort_total":76,"direct_labels_cover":1,"predictions_cover":76,"exported":76,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/9c1ace73c2f7","api":"https://metacan.xera.ac/api/v1/cohort?venue=Nature+Machine+Intelligence"},"results":[{"id":"W2903822982","doi":"10.1038/s42256-018-0002-3","title":"Learnability can be undecidable","year":2018,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Simons Institute for the Theory of Computing, University of California Berkeley; Israel Science Foundation; Blavatnik Family Foundation","keywords":"Learnability; Undecidable problem; Axiom; Equivalence (formal languages); Computer science; Simple (philosophy); Theoretical computer science; Mathematical economics; Calculus (dental); Discrete mathematics; Mathematics; Artificial intelligence; Epistemology; Philosophy","score_opus":0.010662934815707872,"score_gpt":0.29627553176362165,"score_spread":0.2856125969479138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903822982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006097136,0.0006241893,0.9527835,0.013720284,0.0018985375,0.00020674526,0.000008994377,0.0006258376,0.024034748],"genre_scores_gemma":[0.95318455,0.000015160138,0.040781174,0.0028768121,0.00046797,0.0000073765887,0.0000051443612,0.0000157424,0.0026460974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791586,0.00014499397,0.00028074638,0.0007128168,0.00048267306,0.00046292317],"domain_scores_gemma":[0.99823195,0.00019441084,0.00010382323,0.0010345922,0.00025411314,0.00018110465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069329026,0.00024193336,0.00021565772,0.00012871997,0.000324618,0.00020689746,0.0018161304,0.0002405202,0.00033103515],"category_scores_gemma":[0.0005930588,0.00019843417,0.00009920249,0.00084578426,0.00019408236,0.00022271073,0.00045415747,0.001404766,0.00019288596],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027513863,0.00023386025,0.00852242,0.00003768569,0.000045881345,0.0000495873,0.0016115156,0.0005632949,0.0004873136,0.24083537,0.006785316,0.74080026],"study_design_scores_gemma":[0.00024722877,0.00083812856,0.0068210177,0.000058966878,0.000016075832,0.00016526041,0.000063852705,0.5426968,0.054253966,0.08817215,0.30557615,0.0010904152],"about_ca_topic_score_codex":0.00063101604,"about_ca_topic_score_gemma":0.0003378981,"teacher_disagreement_score":0.9470874,"about_ca_system_score_codex":0.00007437233,"about_ca_system_score_gemma":0.000109813824,"threshold_uncertainty_score":0.80919105},"labels":[],"label_agreement":null},{"id":"W2943366292","doi":"10.1038/s42256-019-0053-0","title":"Moving beyond reward prediction errors","year":2019,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; Canadian Institute for Advanced Research; University of Toronto; Vector Institute","funders":"","keywords":"Business; Computer science","score_opus":0.006224225736806918,"score_gpt":0.25221000272268734,"score_spread":0.24598577698588042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943366292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043258485,0.0004596941,0.9710661,0.00071471324,0.0026022939,0.0002685733,0.0000025733677,0.00040428917,0.020155875],"genre_scores_gemma":[0.96459013,0.000041616728,0.03104098,0.00089337275,0.000111122696,0.0000053939716,0.0000117228665,0.00001849287,0.003287183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980791,0.000060470305,0.00034203214,0.0005345851,0.0006197447,0.00036402853],"domain_scores_gemma":[0.9985601,0.00015141886,0.00015816769,0.00090363366,0.00012781612,0.00009888591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043158745,0.00021578422,0.00018536393,0.00018719929,0.00010520796,0.000157589,0.0014690965,0.00027887002,0.00015458053],"category_scores_gemma":[0.00022419677,0.0001936553,0.000091740156,0.00060652284,0.000036123438,0.00059954793,0.00042115632,0.0012833481,0.00068513735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015217331,0.000032887765,0.025241053,0.000059876635,0.000040021587,0.000018110819,0.0008365958,0.83449656,0.0009155343,0.09966774,0.0012550046,0.037421398],"study_design_scores_gemma":[0.0000841907,0.00017563933,0.0027424665,0.000054196953,0.0000070620954,0.000033343636,0.000029128441,0.9756142,0.005417157,0.002659484,0.01291193,0.00027118376],"about_ca_topic_score_codex":0.000023359044,"about_ca_topic_score_gemma":0.0000035021417,"teacher_disagreement_score":0.96026427,"about_ca_system_score_codex":0.000093247145,"about_ca_system_score_gemma":0.00005501392,"threshold_uncertainty_score":0.8806283},"labels":[],"label_agreement":null},{"id":"W2956998909","doi":"10.1038/s42256-019-0068-6","title":"Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning","year":2019,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"AI in cancer detection","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; University of Toronto","funders":"Brain Tumour Charity; Princess Margaret Cancer Foundation","keywords":"Interpretability; Artificial intelligence; Deep learning; Transparency (behavior); Transformative learning; Computer science; Salient; Feature (linguistics); Feature engineering; Machine learning; Psychology; Computer security","score_opus":0.006696904013917719,"score_gpt":0.23549843561668682,"score_spread":0.2288015316027691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956998909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05560475,0.013813275,0.92715544,0.0016204511,0.0008505442,0.00024652152,0.0000029743678,0.00028964347,0.0004163939],"genre_scores_gemma":[0.9742304,0.00024380005,0.024761999,0.00027697574,0.00003877066,0.000008523135,0.0000043443424,0.00001494541,0.00042023408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841064,0.00007841276,0.0002916906,0.000578853,0.00032501807,0.0003153661],"domain_scores_gemma":[0.9988348,0.0004511595,0.0001844661,0.0003537975,0.000097500866,0.000078286896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004258596,0.000250055,0.00032013815,0.00017802339,0.00006483917,0.00006424663,0.0008118296,0.0003637634,0.000036788184],"category_scores_gemma":[0.00046677495,0.00021884912,0.000076321376,0.0004905098,0.00006734638,0.00024252583,0.0003953791,0.0016170936,0.000018531759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009888182,0.00013402013,0.017517837,0.00048389393,0.0001271125,0.00007519207,0.0033126522,0.027548946,0.21844265,0.03272341,0.0030445005,0.6964909],"study_design_scores_gemma":[0.0001982489,0.000668143,0.005689246,0.00022105551,0.000012225103,0.00034526817,0.00026532877,0.72405154,0.2225957,0.0014348546,0.04371507,0.0008032911],"about_ca_topic_score_codex":0.000046340163,"about_ca_topic_score_gemma":0.000010834754,"teacher_disagreement_score":0.91862565,"about_ca_system_score_codex":0.0001361176,"about_ca_system_score_gemma":0.000020773141,"threshold_uncertainty_score":0.8924408},"labels":[],"label_agreement":null},{"id":"W2995016195","doi":"10.1038/s42256-019-0133-1","title":"Publisher Correction: Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning","year":2019,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Feature (linguistics); Computer science; Feature engineering; Artificial intelligence; Deep learning; Philosophy; Linguistics","score_opus":0.0057446667449439675,"score_gpt":0.2530969551124466,"score_spread":0.24735228836750264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995016195","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63763136,0.057853863,0.25364047,0.027047193,0.009842518,0.0014450805,0.000006372689,0.00084122096,0.01169195],"genre_scores_gemma":[0.9869564,0.00036813834,0.0049688774,0.0011182611,0.00010977714,0.000007911524,0.000051183368,0.00003742524,0.0063819797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982696,0.00007650647,0.00038704017,0.0005088612,0.00039505961,0.00036291574],"domain_scores_gemma":[0.99870455,0.00054006145,0.00018334278,0.00024304805,0.00014212879,0.00018688203],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00062337064,0.00029411333,0.00056329055,0.00023138792,0.000069196336,0.000055547243,0.00023563321,0.00044146573,0.0002351301],"category_scores_gemma":[0.003142198,0.00023502651,0.00012540804,0.00039343757,0.0001382025,0.00012631225,0.00014686717,0.003517491,0.000014268698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050946156,0.00041012466,0.29750404,0.001130931,0.00038948434,0.00029706224,0.0020744808,0.0077147656,0.08535393,0.0028029282,0.1003305,0.5014823],"study_design_scores_gemma":[0.0011030388,0.0013568276,0.044325463,0.0011659701,0.00014258458,0.0026984627,0.0014183449,0.6036455,0.011918304,0.0003668191,0.33074504,0.0011136704],"about_ca_topic_score_codex":0.00007811144,"about_ca_topic_score_gemma":0.000003830664,"teacher_disagreement_score":0.5959307,"about_ca_system_score_codex":0.0001055056,"about_ca_system_score_gemma":0.00003279245,"threshold_uncertainty_score":0.99878144},"labels":[],"label_agreement":null},{"id":"W3016970897","doi":"10.1038/s42256-020-00257-z","title":"Shortcut learning in deep neural networks","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":1811,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Transferability; Benchmarking; Machine learning; Artificial neural network; Deep neural networks; Robustness (evolution); Perspective (graphical); Management","score_opus":0.010150698655579661,"score_gpt":0.26864828031207677,"score_spread":0.2584975816564971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016970897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018855158,0.001998026,0.98846895,0.0048184474,0.0006761044,0.00018200847,3.588657e-7,0.00049869687,0.001471896],"genre_scores_gemma":[0.9742988,0.00003629863,0.021954022,0.0032681173,0.00036145092,0.000008230485,0.000007294567,0.000029566825,0.000036265606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997452,0.00027378308,0.00044643696,0.00081698067,0.00044772745,0.00056304666],"domain_scores_gemma":[0.99878556,0.00035829796,0.00015150223,0.00041230046,0.000075142685,0.00021722686],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000444301,0.0003210462,0.00033539784,0.00014911495,0.00015662903,0.00017479222,0.002061534,0.00034840405,0.00007787468],"category_scores_gemma":[0.0011504063,0.00030578577,0.000112032605,0.0015136524,0.00006241152,0.00054881035,0.00084556395,0.004810463,0.000041994714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020548146,0.000015078373,0.017371528,0.0000091741995,0.0000069281673,0.0001350446,0.0006708964,0.7979993,0.000019700266,0.011410377,0.000027900702,0.17231348],"study_design_scores_gemma":[0.00010014764,0.00009366644,0.0017061838,0.000014171666,0.000004263122,0.000024240262,0.000043898814,0.99579203,0.00015524148,0.00055602245,0.0011921441,0.00031796517],"about_ca_topic_score_codex":0.000059345624,"about_ca_topic_score_gemma":0.00006518372,"teacher_disagreement_score":0.97241324,"about_ca_system_score_codex":0.00006464163,"about_ca_system_score_gemma":0.00003095293,"threshold_uncertainty_score":0.99993944},"labels":[],"label_agreement":null},{"id":"W3040360568","doi":"10.1038/s42256-020-0201-6","title":"Deep learning decodes the principles of differential gene expression","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute on Aging; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Computational biology; Gene expression; Biology; Gene; Transcriptome; Regulation of gene expression; Genomics; Genome; Expression (computer science); Systems biology; microRNA; Genetics; Computer science","score_opus":0.013541176495804429,"score_gpt":0.2909722330630898,"score_spread":0.27743105656728534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040360568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8338195,0.0044065975,0.16075096,0.0004473611,0.000058046036,0.00012182849,0.000004350622,0.000011728014,0.0003796366],"genre_scores_gemma":[0.99769145,0.0004871043,0.0011110991,0.00018610612,0.0001999919,0.000005404338,0.000035911344,0.000012668407,0.0002702766],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99914485,0.00008413769,0.00016558479,0.00023414199,0.00019837862,0.00017288345],"domain_scores_gemma":[0.99954957,0.000037327674,0.000071555885,0.00018872789,0.00007192116,0.000080929254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011356844,0.00010820677,0.0001055771,0.00001714142,0.000096274736,0.00001844349,0.00039910732,0.00014923688,0.00005667006],"category_scores_gemma":[0.0005832414,0.00006740461,0.00007988384,0.000088695015,0.00007063589,0.0000026066677,0.00025635437,0.00051232893,0.0000048583806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121104975,0.00001518352,0.0038303768,0.000018892128,0.000017786328,0.0000021256637,0.00008428406,0.001946546,0.9821654,0.00016178907,0.0000400083,0.011596533],"study_design_scores_gemma":[0.00005835254,0.00016413015,0.0010220904,0.000011745936,0.000004953119,0.0000040933382,0.000057502337,0.017563513,0.97748953,0.000022848548,0.003522422,0.00007880258],"about_ca_topic_score_codex":0.00001228655,"about_ca_topic_score_gemma":0.000017472366,"teacher_disagreement_score":0.16387194,"about_ca_system_score_codex":0.000003825492,"about_ca_system_score_gemma":0.000020985864,"threshold_uncertainty_score":0.274868},"labels":[],"label_agreement":null},{"id":"W3104435397","doi":"10.1038/s42256-020-00260-4","title":"Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Workflow; Cancer immunotherapy; Identification (biology); Computational biology; Immunotherapy; Human leukocyte antigen; Personalized medicine; Cancer; Medicine; Deep learning; Computer science; Artificial intelligence; Bioinformatics; Antigen; Immunology; Biology; Internal medicine; Database","score_opus":0.017027151284584614,"score_gpt":0.316839999424933,"score_spread":0.2998128481403484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104435397","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78241706,0.040878903,0.17198962,0.0024934853,0.0004595947,0.0011205268,0.0002082503,0.0000467737,0.00038580675],"genre_scores_gemma":[0.9926748,0.0008657579,0.0045892354,0.0011548108,0.00023131812,0.00005200909,0.00020855853,0.000030256104,0.00019320504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989019,0.000027423614,0.0003647104,0.00028229872,0.0001811972,0.00024247551],"domain_scores_gemma":[0.9993185,0.00003168369,0.00016140357,0.0001871334,0.00020858385,0.00009270191],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019627599,0.00020728161,0.0002633628,0.00005304014,0.00009212383,0.0000440125,0.00052810006,0.00021440675,0.000069398804],"category_scores_gemma":[0.0004502848,0.00017762756,0.00017319265,0.00021285258,0.000025926445,0.0000092504115,0.00024238368,0.00031020562,0.000006407585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014505825,0.000112484195,0.009139496,0.00059803925,0.0008131241,0.0000017109086,0.0062392447,0.0074469736,0.7871735,0.0017221658,0.00348534,0.18181731],"study_design_scores_gemma":[0.0007701647,0.0011854026,0.006593565,0.00007534045,0.00013346663,0.000013445563,0.0018238059,0.009503007,0.82777864,0.000115583716,0.15134767,0.00065991114],"about_ca_topic_score_codex":0.000024539835,"about_ca_topic_score_gemma":0.000016200122,"teacher_disagreement_score":0.2102578,"about_ca_system_score_codex":0.0000070798665,"about_ca_system_score_gemma":0.000048738115,"threshold_uncertainty_score":0.7243442},"labels":[],"label_agreement":null},{"id":"W3106073154","doi":"10.1038/s42256-020-0226-x","title":"Finding the ground state of spin Hamiltonians with reinforcement learning","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; National Research Council Canada; Vector Institute; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Reinforcement learning; Computer science; Ground state; Schedule; Simulated annealing; Scaling; Reinforcement; Mathematical optimization; Artificial intelligence; Algorithm; Physics; Mathematics; Materials science; Quantum mechanics","score_opus":0.013641946468402819,"score_gpt":0.26133362586783937,"score_spread":0.24769167939943654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106073154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026241334,0.0009727108,0.9666451,0.0049535036,0.00017831418,0.00019467056,4.7114196e-7,0.00009864335,0.0007152262],"genre_scores_gemma":[0.99273604,0.00006899655,0.0057571684,0.0011900134,0.0000926086,0.0000025940815,0.0000013957012,0.0000107622545,0.00014043979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985981,0.00007456376,0.00028459998,0.00033624933,0.000413998,0.0002925169],"domain_scores_gemma":[0.9991299,0.0001883885,0.0001998123,0.0003086938,0.000086902,0.000086301385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026634085,0.00017052275,0.00017789398,0.000039989674,0.00022261652,0.00011962674,0.0012953295,0.00005632137,0.000009339408],"category_scores_gemma":[0.00007393747,0.0000957261,0.000064568274,0.0007315333,0.000077484496,0.00017267905,0.00047547117,0.0012340825,0.00000802322],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002926366,0.000010684297,0.0007734974,0.00003840604,0.00003006993,0.000037943297,0.0017318525,0.8907985,0.0003647037,0.0079275705,0.00009057992,0.098166905],"study_design_scores_gemma":[0.00006438921,0.00034235703,0.00048163073,0.00007878921,0.000004981721,0.000019732084,0.000035739497,0.9876896,0.007956308,0.0004932038,0.002676172,0.00015706448],"about_ca_topic_score_codex":0.000057284273,"about_ca_topic_score_gemma":0.00001828534,"teacher_disagreement_score":0.9664947,"about_ca_system_score_codex":0.00001874745,"about_ca_system_score_gemma":0.000037749243,"threshold_uncertainty_score":0.5361543},"labels":[],"label_agreement":null},{"id":"W3112662112","doi":"10.1038/s42256-020-00232-8","title":"Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions","year":2020,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada); National Research Council Canada","funders":"Stanford Maternal and Child Health Research Institute; National Institute of Dental and Craniofacial Research; National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences; National Heart, Lung, and Blood Institute; National Institute on Aging; Burroughs Wellcome Fund; U.S. Food and Drug Administration; American Heart Association; National Institutes of Health; U.S. Department of Health and Human Services; March of Dimes Foundation; Hamilton Health Sciences Foundation; Bill and Melinda Gates Foundation; Robertson Foundation; Doris Duke Charitable Foundation","keywords":"Mass cytometry; Computer science; Overfitting; Machine learning; Artificial intelligence; Profiling (computer programming); Immune system; Immunology; Medicine; Artificial neural network; Biology","score_opus":0.013724645410753196,"score_gpt":0.2678726170943851,"score_spread":0.2541479716836319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112662112","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07664562,0.014246445,0.90643644,0.0007392276,0.00047321577,0.00028642113,0.000054859334,0.000085075924,0.0010326668],"genre_scores_gemma":[0.9948078,0.00066909555,0.0034679093,0.00024499878,0.0002467348,0.000010644035,0.00031747718,0.000022882794,0.00021245472],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988927,0.000087334214,0.00034966355,0.00037930754,0.00012186552,0.00016909446],"domain_scores_gemma":[0.9993942,0.000038688835,0.00011083559,0.00018436949,0.00018457278,0.00008737212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014414664,0.00020899177,0.00021684372,0.000048597376,0.00010137225,0.000018032115,0.00031430268,0.00038446274,0.000046351757],"category_scores_gemma":[0.0007947307,0.0001716271,0.00013148237,0.0002253364,0.00010021068,0.00000592719,0.00011357626,0.0008329669,0.000010414041],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002435442,0.00012036695,0.000185853,0.0000431284,0.000034665063,0.0000015763137,0.00030253103,0.000269283,0.9591072,0.0010575781,0.000059805723,0.038574453],"study_design_scores_gemma":[0.00019968246,0.0011307703,0.00012332246,0.000031797994,0.00004245515,0.000010273612,0.000096109114,0.10691654,0.8795039,0.00052116904,0.011202343,0.00022162194],"about_ca_topic_score_codex":0.00010781223,"about_ca_topic_score_gemma":0.00014476378,"teacher_disagreement_score":0.91816217,"about_ca_system_score_codex":0.000015203664,"about_ca_system_score_gemma":0.00004951645,"threshold_uncertainty_score":0.69987506},"labels":[],"label_agreement":null},{"id":"W3118507387","doi":"10.1038/s42256-020-00271-1","title":"Inverse design of nanoporous crystalline reticular materials with deep generative models","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Metal-Organic Frameworks: Synthesis and Applications","field":"Chemistry","cited_by":379,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Ottawa; University of Toronto","funders":"Office of Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Nanoporous; Computer science; Nanotechnology; Materials science; Artificial intelligence; Deep learning","score_opus":0.015385850413319115,"score_gpt":0.24936180132219046,"score_spread":0.23397595090887136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118507387","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033119705,0.0037312387,0.96075284,0.0005040342,0.000062634856,0.00017230482,0.00010047664,0.00006476212,0.001491995],"genre_scores_gemma":[0.8896774,0.0004506524,0.10880215,0.00023268313,0.00009856223,0.000040132043,0.00009114381,0.00004112671,0.0005661747],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984042,0.0000800804,0.0004554865,0.00047849963,0.00035024085,0.0002314636],"domain_scores_gemma":[0.99839646,0.00020576897,0.00024035489,0.00070852996,0.00035704568,0.00009184888],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00022420537,0.0002644913,0.0004012971,0.000037605198,0.00008456348,0.00004521443,0.00032456877,0.00041938067,0.0041654063],"category_scores_gemma":[0.000257345,0.00020679155,0.00007061059,0.00031743498,0.00012376189,0.00008391669,0.00009308572,0.0005399007,0.0000133468975],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067589855,0.00018666721,0.000013503184,0.00010790703,0.00012619476,0.00005586993,0.0002769583,0.023867717,0.95731187,0.015313791,0.00013986787,0.0025320936],"study_design_scores_gemma":[0.00007566747,0.000021588665,0.0000014229188,0.000120950404,0.000073860196,0.000053680218,0.00015446547,0.04157669,0.9480399,0.009149279,0.00050415617,0.00022836101],"about_ca_topic_score_codex":0.000028007735,"about_ca_topic_score_gemma":0.00003018883,"teacher_disagreement_score":0.85655767,"about_ca_system_score_codex":0.000041276693,"about_ca_system_score_gemma":0.00011281289,"threshold_uncertainty_score":0.99674493},"labels":[],"label_agreement":null},{"id":"W3122174941","doi":"10.1038/s42256-021-00401-3","title":"Variational neural annealing","year":2021,"lang":"en","type":"preprint","venue":"Nature Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Perimeter Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada; Compute Canada","keywords":"Parameterized complexity; Simulated annealing; Hamiltonian (control theory); Statistical physics; Computer science; Mathematical optimization; Quantum annealing; Applied mathematics; Annealing (glass); Autoregressive model; Quantum; Mathematics; Algorithm; Physics; Quantum mechanics; Quantum computer","score_opus":0.01440587569263979,"score_gpt":0.29534659283285103,"score_spread":0.28094071714021124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122174941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013746774,0.0077067567,0.98058176,0.006348363,0.00240759,0.00025561234,0.00002791688,0.00027016297,0.0010271416],"genre_scores_gemma":[0.9025636,0.00026379517,0.09390218,0.0021836627,0.00066529005,0.000056486802,0.00017952213,0.000019600408,0.00016590688],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977335,0.00008564263,0.00040469182,0.0009845262,0.00047202923,0.00031965168],"domain_scores_gemma":[0.9979837,0.00021449004,0.00022566493,0.0011668893,0.00028337017,0.00012589662],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00022736657,0.0003262129,0.00028838817,0.00010112257,0.00017707117,0.0006234218,0.0023142667,0.000567006,0.00007460036],"category_scores_gemma":[0.00008821041,0.00030371302,0.00020750183,0.0005311587,0.00003723784,0.0002026717,0.0022122993,0.003170634,0.000021448303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005915172,0.00014415472,0.0004288269,0.00009771836,0.0000821975,0.00011210693,0.00037836182,0.3189817,0.0002171521,0.35001642,0.0023240135,0.3272114],"study_design_scores_gemma":[0.000023553393,0.000011271598,0.000586954,0.00006672416,0.0000112986345,0.000044685963,0.0000054441102,0.96573085,0.0013577124,0.028585155,0.0032204972,0.00035585338],"about_ca_topic_score_codex":0.000074953474,"about_ca_topic_score_gemma":0.000026080395,"teacher_disagreement_score":0.9011889,"about_ca_system_score_codex":0.000053930762,"about_ca_system_score_gemma":0.00015486262,"threshold_uncertainty_score":0.99994147},"labels":[],"label_agreement":null},{"id":"W3136924813","doi":"10.1038/s42256-021-00304-3","title":"Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":99,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mass spectrometry; Resolution (logic); DNA sequencing; Computational biology; High resolution; Computer science; Low resolution; Peptide; Deep sequencing; Algorithm; Biology; Chemistry; Genome; Artificial intelligence; Genetics; Remote sensing; Gene; Chromatography; Geography; Biochemistry","score_opus":0.014365339486563884,"score_gpt":0.29907840961216176,"score_spread":0.2847130701255979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136924813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061736006,0.0010034809,0.93409455,0.0010431511,0.000072429095,0.00022346192,0.00021123776,0.00019871458,0.001416979],"genre_scores_gemma":[0.69817406,0.000066922315,0.3001515,0.00041657066,0.00015552118,0.00014905978,0.00037986878,0.00002274098,0.00048376183],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985903,0.000016725011,0.00035745229,0.0004576665,0.000254841,0.00032305365],"domain_scores_gemma":[0.9988819,0.00019458751,0.0001769353,0.00031653588,0.00033819687,0.00009187978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001684305,0.00020279818,0.00017417624,0.000049947936,0.0002741555,0.00006368662,0.00032732825,0.00031058272,0.00021366314],"category_scores_gemma":[0.00019085122,0.00021552178,0.000102204416,0.00020185886,0.000049335522,0.0001224509,0.00011675423,0.0006897299,0.0000114541435],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013930786,0.00026981212,0.0026912272,0.0004725922,0.00014950927,0.000046615205,0.00029680744,0.077353925,0.38955465,0.47614026,0.0004934022,0.05239191],"study_design_scores_gemma":[0.00020636845,0.000023303372,0.00022079815,0.0001339353,0.00003758911,0.0001454305,0.00013839338,0.06713769,0.8190875,0.1034624,0.009041607,0.00036496134],"about_ca_topic_score_codex":0.000098342105,"about_ca_topic_score_gemma":0.00024888263,"teacher_disagreement_score":0.6364381,"about_ca_system_score_codex":0.00047574274,"about_ca_system_score_gemma":0.00026463883,"threshold_uncertainty_score":0.87887233},"labels":[],"label_agreement":null},{"id":"W3163465248","doi":"10.1038/s42256-022-00509-0","title":"Neural Error Mitigation of Near-Term Quantum Simulations","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; Perimeter Institute; University of Waterloo","funders":"Mitacs; Innovation, Science and Economic Development Canada","keywords":"Quantum computer; Quantum; Computer science; Observable; Artificial neural network; Statistical physics; Quantum error correction; Algorithm; Physics; Quantum mechanics; Artificial intelligence","score_opus":0.010014510924712169,"score_gpt":0.28256420385962877,"score_spread":0.2725496929349166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163465248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5085739,0.0010301593,0.48622036,0.002179588,0.0013756448,0.00022758578,0.000059087786,0.00020766705,0.00012601765],"genre_scores_gemma":[0.9823143,0.0000018319525,0.017099312,0.000434528,0.00006693487,0.0000064248698,0.000024232686,0.000012247563,0.000040214058],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983413,0.00014958942,0.00033988917,0.00040608848,0.0005130252,0.00025009355],"domain_scores_gemma":[0.998854,0.00024776373,0.00019223629,0.0005378277,0.000098820725,0.00006939036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002485693,0.00016779247,0.00018443202,0.00014102845,0.00047356333,0.00007567045,0.0012534996,0.000074902586,0.000087564316],"category_scores_gemma":[0.0000973074,0.00015536539,0.00011469372,0.00087602483,0.000075357675,0.00014009207,0.0006346012,0.0011125227,0.0000044397525],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013133788,0.00012858168,0.001674693,0.000022947806,0.000017943188,0.000022653809,0.0014849982,0.8479448,0.0011171554,0.03947407,0.00013278206,0.107966274],"study_design_scores_gemma":[0.00006172358,0.00015299466,0.0028446286,0.000009942818,0.000004828549,0.000054365788,0.000016684102,0.984172,0.002056233,0.00963166,0.00083045784,0.00016450221],"about_ca_topic_score_codex":0.00003947451,"about_ca_topic_score_gemma":0.000007780124,"teacher_disagreement_score":0.47374037,"about_ca_system_score_codex":0.000035673667,"about_ca_system_score_gemma":0.00007178842,"threshold_uncertainty_score":0.6335616},"labels":[],"label_agreement":null},{"id":"W3164743754","doi":"10.1038/s42256-021-00407-x","title":"A deep generative model enables automated structure elucidation of novel psychoactive substances","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"National Institutes of Health; Genome Alberta; Genome British Columbia; Compute Canada; National Institute of Environmental Health Sciences; Canadian Institutes of Health Research; Genome Canada; Foundation for the National Institutes of Health","keywords":"Generative grammar; Computer science; Pharmacology; Traditional medicine; Medicine; Artificial intelligence","score_opus":0.01884452616783925,"score_gpt":0.3398571225916302,"score_spread":0.32101259642379093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164743754","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019451423,0.0026470576,0.9757805,0.00056360185,0.00048515832,0.00015578022,0.00006865808,0.0001927481,0.00065506273],"genre_scores_gemma":[0.5769356,0.000032428943,0.42265126,0.00026204958,0.00003541557,0.0000048037623,0.000038286165,0.000008653634,0.000031495725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979353,0.00017597932,0.00040744155,0.00065892393,0.00058426143,0.00023810663],"domain_scores_gemma":[0.9979374,0.00041586804,0.00026054026,0.0005267143,0.0007863028,0.00007320649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002543835,0.0002469763,0.00028921003,0.00016258398,0.000103410064,0.00011072418,0.00083330314,0.00021483717,0.000028020242],"category_scores_gemma":[0.00038702923,0.00022207106,0.00010270215,0.0012538806,0.00007542565,0.00065854314,0.00021711808,0.00058935455,0.0000022511335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019079962,0.000112079484,0.000049367143,0.000021115948,0.000049096005,0.0000068960276,0.001048811,0.8173888,0.018356975,0.1417681,0.00007418361,0.02110553],"study_design_scores_gemma":[0.00009041795,0.00001935018,0.0003715388,0.000024715166,0.000007766713,0.000024645991,0.00004042369,0.71677893,0.24320057,0.039243445,0.000036974525,0.00016121549],"about_ca_topic_score_codex":0.000017442779,"about_ca_topic_score_gemma":0.00014452059,"teacher_disagreement_score":0.5574842,"about_ca_system_score_codex":0.00008437163,"about_ca_system_score_gemma":0.00031069224,"threshold_uncertainty_score":0.90557945},"labels":[],"label_agreement":null},{"id":"W3165750456","doi":"10.1038/s42256-021-00337-8","title":"End-to-end privacy preserving deep learning on multi-institutional medical imaging","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":470,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"Deutschen Konsortium für Translationale Krebsforschung; Technische Universität München; Deutsche Forschungsgemeinschaft; Imperial College London; UK Research and Innovation","keywords":"Computer science; USable; Inference; Convolutional neural network; Encryption; Deep learning; Artificial intelligence; Machine learning; Information privacy; End-to-end principle; Computer security; Data mining; World Wide Web","score_opus":0.018574452526781082,"score_gpt":0.3149303193573083,"score_spread":0.2963558668305272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165750456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020170917,0.0030516884,0.9294896,0.061202705,0.0011990403,0.00017989487,0.000011024722,0.0010882074,0.001760765],"genre_scores_gemma":[0.60399204,0.00019192108,0.39302024,0.0024951291,0.00013856098,0.000021216758,0.000039886305,0.00002253521,0.00007844169],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956165,0.00019319633,0.0004947986,0.0013620609,0.001617992,0.00071548583],"domain_scores_gemma":[0.9929295,0.00073159824,0.00013757052,0.0055898386,0.0002971437,0.00031435705],"candidate_categories":["metaresearch","metaepi_narrow","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.0009987683,0.0004028429,0.00033182724,0.0003406635,0.00044976873,0.00035249116,0.027592074,0.00042488595,0.00036538066],"category_scores_gemma":[0.122965075,0.00035637215,0.00013246213,0.0014662981,0.00018839417,0.0007297582,0.07477295,0.0038277756,0.0002582227],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002334378,0.0003734915,0.0046743928,0.000061509214,0.000071575465,0.0022325912,0.0003578792,0.005285458,0.0014739801,0.06271638,0.008054907,0.91467446],"study_design_scores_gemma":[0.0002516472,0.00005298749,0.002009378,0.00035023247,0.000009030034,0.00047829867,0.000047978414,0.85941315,0.059031982,0.042639837,0.035107497,0.0006079609],"about_ca_topic_score_codex":0.00008853612,"about_ca_topic_score_gemma":0.00012283109,"teacher_disagreement_score":0.91406655,"about_ca_system_score_codex":0.00024303503,"about_ca_system_score_gemma":0.00034772055,"threshold_uncertainty_score":0.99988884},"labels":[],"label_agreement":null},{"id":"W3184097711","doi":"10.1038/s42256-021-00370-7","title":"Governing AI safety through independent audits","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":221,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Economic and Social Research Council; Engineering and Physical Sciences Research Council; National Institute of Standards and Technology; National Science Foundation","keywords":"Audit; Business; Risk analysis (engineering); Accounting","score_opus":0.02180139953642077,"score_gpt":0.3918023780780409,"score_spread":0.3700009785416201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184097711","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006028078,0.012292702,0.03731886,0.13468191,0.004569529,0.00036536777,0.000116274095,0.00032200126,0.80430526],"genre_scores_gemma":[0.9774554,0.0027639784,0.0010923261,0.01179761,0.0007642367,0.0000028146658,0.000022714032,0.000019460284,0.006081504],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99751467,0.00030887456,0.00028344916,0.00038105392,0.0010346561,0.0004772804],"domain_scores_gemma":[0.9982415,0.00048580643,0.00012189191,0.00026988174,0.00070424547,0.0001767037],"candidate_categories":["research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011595865,0.00017155966,0.00022786607,0.00002870142,0.0009970887,0.00029754624,0.00049863005,0.00073666975,0.0013506603],"category_scores_gemma":[0.0051295827,0.00016743958,0.00014468007,0.0005694683,0.00025546792,0.0005439994,0.00017237672,0.002526085,0.00017917232],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029428616,0.00011608155,0.0022347774,0.000022727538,0.000064390595,0.00015338273,0.027683662,0.00014198721,0.00019921096,0.93012464,0.00564811,0.033581603],"study_design_scores_gemma":[0.00010637689,0.000037951653,0.0018578059,0.00009120035,0.00002605648,0.000008986883,0.005080191,0.0001067599,0.0048684953,0.15094441,0.8364486,0.00042317813],"about_ca_topic_score_codex":0.0033440606,"about_ca_topic_score_gemma":0.01447806,"teacher_disagreement_score":0.97142726,"about_ca_system_score_codex":0.00024286684,"about_ca_system_score_gemma":0.00074849377,"threshold_uncertainty_score":0.9997751},"labels":[],"label_agreement":null},{"id":"W3190634970","doi":"10.1038/s42256-021-00376-1","title":"Learning function from structure in neuromorphic networks","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Interface Biologics (Canada); McGill University; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canada Research Chairs; Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Neuromorphic engineering; Connectome; Computer science; Reservoir computing; Network dynamics; Artificial intelligence; Artificial neural network; Connectomics; Biological neural network; Encoding (memory); Task (project management); Nervous system network models; Topology (electrical circuits); Neuroscience; Recurrent neural network; Machine learning; Types of artificial neural networks; Functional connectivity; Psychology","score_opus":0.011323235312553987,"score_gpt":0.23294856946216436,"score_spread":0.22162533414961036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3190634970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10712912,0.009011987,0.879242,0.0012765273,0.0027298382,0.000098885444,0.0000021943038,0.00019735868,0.00031209865],"genre_scores_gemma":[0.9932359,0.00016977804,0.0048215482,0.0011801703,0.00043614316,0.0000015794532,0.00003011714,0.000015058552,0.00010969935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810946,0.00023227585,0.00030273965,0.0006837125,0.00029748867,0.00037430553],"domain_scores_gemma":[0.9989103,0.00031774584,0.00010585578,0.00046862217,0.00010865742,0.00008881462],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00015555987,0.00021360503,0.00022177509,0.00008669154,0.00015152326,0.00022451492,0.0007891279,0.00030581062,0.00008407363],"category_scores_gemma":[0.00014884776,0.00018551876,0.00008024965,0.001176981,0.00002501854,0.0002469936,0.00055644894,0.0029202513,0.00000943837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001600292,0.000035023724,0.010274446,0.000007566005,0.000015257789,0.00047103202,0.00011259132,0.7616039,0.0009330177,0.0054651657,0.00016006124,0.22090593],"study_design_scores_gemma":[0.00007767892,0.000056654582,0.010391564,0.00006149849,0.000004267568,0.0000506713,0.000012483388,0.97632504,0.0018905556,0.008444352,0.0024690095,0.00021621623],"about_ca_topic_score_codex":0.00009317044,"about_ca_topic_score_gemma":0.0002948547,"teacher_disagreement_score":0.8861068,"about_ca_system_score_codex":0.000031282714,"about_ca_system_score_gemma":0.000043609936,"threshold_uncertainty_score":0.99938005},"labels":[],"label_agreement":null},{"id":"W3211434666","doi":"10.1038/s42256-021-00413-z","title":"The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Norges Forskningsråd; Illinois Nutrient Research and Education Council; Stiftelsen Kristian Gerhard Jebsen; National Institutes of Health; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Computer science; Benchmarking; Workflow; Interpretability; Interoperability; Machine learning; Data mining; Data science; Artificial intelligence; World Wide Web; Database","score_opus":0.008216227075964772,"score_gpt":0.2522911315569597,"score_spread":0.24407490448099492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211434666","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47364804,0.33075982,0.18619221,0.0016977744,0.0016164639,0.0014973315,0.0009733809,0.00008229316,0.003532693],"genre_scores_gemma":[0.9922255,0.0028035315,0.0027034108,0.000059139886,0.00006936751,0.000036345864,0.00083400356,0.00002314046,0.0012455313],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859476,0.00009960125,0.0005923919,0.00029647,0.00016735251,0.00024942216],"domain_scores_gemma":[0.9984264,0.00016755564,0.0003330595,0.0006059107,0.00042923086,0.000037853464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005582178,0.00020963671,0.00036018944,0.00007775615,0.0002736881,0.000048277016,0.0004410633,0.0002565111,0.000030726434],"category_scores_gemma":[0.0008123936,0.00014431427,0.00045976153,0.0005194352,0.00004384579,0.00000712389,0.00022179048,0.0004067204,0.0000024409792],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023187518,0.00041212008,0.0047566933,0.00037372034,0.012866719,0.000005221843,0.0012644693,0.02827452,0.7526894,0.015118536,0.0013048146,0.180615],"study_design_scores_gemma":[0.00022156903,0.0003741328,0.000584909,0.000040464565,0.00041386357,0.000015504438,0.00092450617,0.11529444,0.78580165,0.000113273025,0.09590756,0.0003081451],"about_ca_topic_score_codex":0.000048899223,"about_ca_topic_score_gemma":0.00026201797,"teacher_disagreement_score":0.51857746,"about_ca_system_score_codex":0.00001877449,"about_ca_system_score_gemma":0.00007295485,"threshold_uncertainty_score":0.5884965},"labels":[],"label_agreement":null},{"id":"W3212461952","doi":"10.1038/s42256-021-00408-w","title":"Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction","year":2021,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Generalization; Computer science; Consistency (knowledge bases); Transfer of learning; Pharmacogenomics; Labeled data; Drug response; Artificial intelligence; Machine learning; Distribution (mathematics); Domain (mathematical analysis); Data mining; Drug; Mathematics; Bioinformatics; Medicine; Biology","score_opus":0.012933246470007133,"score_gpt":0.3115565514539093,"score_spread":0.29862330498390216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212461952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23060846,0.0068694972,0.7576273,0.00046404568,0.00020151785,0.0003330189,0.0038488172,0.000025560183,0.000021758951],"genre_scores_gemma":[0.92347777,0.0015755821,0.04055278,0.00023173884,0.0001951342,0.000059488637,0.033595704,0.000020099322,0.0002917126],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99885494,0.00013245926,0.00024947283,0.0005380063,0.000108193264,0.00011694655],"domain_scores_gemma":[0.998738,0.000056112818,0.00011794987,0.00074859866,0.00029389912,0.000045432604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037179314,0.00013100453,0.00013586623,0.000016891046,0.000097695694,0.000013449066,0.0002553852,0.00033076806,0.000013749065],"category_scores_gemma":[0.000598656,0.00012258155,0.00004195522,0.00009261278,0.00005836934,0.000005829967,0.00030567968,0.00014639639,6.3964774e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038073404,0.000059605263,0.00039297776,0.0000104805285,0.000028290497,9.0397776e-7,0.000022397091,0.000036662164,0.9913379,0.0002570351,0.005489493,0.0019835357],"study_design_scores_gemma":[0.00019854272,0.0000668809,0.00025804216,0.000018668115,0.000031575924,0.0000044771537,0.000012269602,0.0034569309,0.92744726,0.00071236887,0.06768441,0.00010856347],"about_ca_topic_score_codex":0.000017861737,"about_ca_topic_score_gemma":0.000044328255,"teacher_disagreement_score":0.7170745,"about_ca_system_score_codex":0.000014935351,"about_ca_system_score_gemma":0.000097406315,"threshold_uncertainty_score":0.49987307},"labels":[],"label_agreement":null},{"id":"W4207067068","doi":"10.1038/s42256-021-00430-y","title":"Neurons learn by predicting future activity","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Defense Advanced Research Projects Agency; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Compute Canada","keywords":"Surprise; Predictive coding; Computer science; Artificial intelligence; Neuroscience; Machine learning; Artificial neural network; Coding (social sciences); Psychology; Mathematics; Communication","score_opus":0.010743879025906233,"score_gpt":0.2650758268683593,"score_spread":0.25433194784245305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207067068","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9562606,0.00090954296,0.0074210465,0.01732826,0.008658282,0.0006835991,0.00058603083,0.0006435479,0.007509041],"genre_scores_gemma":[0.99317163,0.00007244178,0.00003062081,0.0034814982,0.00026694685,0.00002958545,0.000016482749,0.000030499725,0.0029002947],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99801224,0.000269758,0.0001704879,0.00064209854,0.00057232036,0.00033308007],"domain_scores_gemma":[0.9991504,0.00028733886,0.00012804939,0.0003220262,0.000022873746,0.000089290275],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00022466849,0.00020095182,0.00014666704,0.0000908693,0.0007682424,0.00006401125,0.0005565175,0.000111291505,0.00053401466],"category_scores_gemma":[0.00041511343,0.00018495163,0.000096096024,0.0006550495,0.000057627258,0.00017322761,0.00038254965,0.0028360437,0.00002401163],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022161595,0.00033406809,0.0016493807,0.000024870638,0.000007710148,0.00009067186,0.00017848334,0.003534735,0.7616223,0.005522081,0.0140118925,0.21280216],"study_design_scores_gemma":[0.00020005729,0.00073619554,0.0012458581,0.000008926856,0.000021581001,0.00040395706,0.00013933839,0.19059885,0.39724937,0.0025585443,0.40616655,0.00067077717],"about_ca_topic_score_codex":0.00004714086,"about_ca_topic_score_gemma":0.000015594665,"teacher_disagreement_score":0.39215466,"about_ca_system_score_codex":0.00008289143,"about_ca_system_score_gemma":0.000028350807,"threshold_uncertainty_score":0.99946445},"labels":[],"label_agreement":null},{"id":"W4220918847","doi":"10.1038/s42256-022-00452-0","title":"Biological underpinnings for lifelong learning machines","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":225,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Lifelong learning; Computer science; Artificial intelligence; Set (abstract data type); Bridge (graph theory); Biological organism; Perspective (graphical); Cognitive science; Human–computer interaction; Biochemical engineering; Engineering; Psychology; Biology; Biological materials","score_opus":0.01665808754806814,"score_gpt":0.27982117242493176,"score_spread":0.26316308487686363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220918847","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29255846,0.009771495,0.6908637,0.00051149033,0.002282123,0.00054851075,0.000036919402,0.0016742215,0.0017530612],"genre_scores_gemma":[0.99591905,0.00005506483,0.00303233,0.0004501233,0.00021942121,0.00004972818,0.0000387588,0.000035482426,0.00020005701],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990201,0.000047596674,0.00021403965,0.00027000593,0.00014301557,0.00030526205],"domain_scores_gemma":[0.9993679,0.00037767194,0.000043972635,0.00012566226,0.000027412325,0.000057396057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025060333,0.0001904278,0.00018153388,0.00008472853,0.00044872798,0.00002035909,0.00032105538,0.00009995804,0.00012963041],"category_scores_gemma":[0.00022431741,0.00017348028,0.00009771766,0.000288515,0.000028416101,0.000065494954,0.00015661387,0.0017182855,0.0000061011997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005938659,0.000019734236,0.0010928041,0.00004847387,0.00002462629,0.000019318493,0.00021625235,0.8722271,0.009721805,0.003733508,0.0003969866,0.112440035],"study_design_scores_gemma":[0.00021697966,0.00045821883,0.00039392902,0.000027942973,0.000016192069,0.00017216039,0.00023329713,0.81289124,0.054600958,0.012792448,0.11744101,0.0007555996],"about_ca_topic_score_codex":0.0000024159417,"about_ca_topic_score_gemma":0.0000016444139,"teacher_disagreement_score":0.70336056,"about_ca_system_score_codex":0.00006182606,"about_ca_system_score_gemma":0.0000073134124,"threshold_uncertainty_score":0.7465191},"labels":[],"label_agreement":null},{"id":"W4223997639","doi":"10.1038/s42256-022-00472-w","title":"Microscopy analysis neural network to solve detection, enumeration and segmentation from image-level annotations","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ciment Québec (Canada); Université Laval","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Segmentation; Artificial neural network; Pattern recognition (psychology); Annotation; Task (project management); Feature (linguistics); Deep learning; Microscopy; Machine learning","score_opus":0.0055871374031749655,"score_gpt":0.29456917041714104,"score_spread":0.2889820330139661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223997639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28472385,0.0012025766,0.71337587,0.00025075057,0.000069412396,0.0002077474,0.00007703182,0.00003358313,0.00005915839],"genre_scores_gemma":[0.9608104,0.00007013274,0.035961255,0.0015008141,0.00014458346,0.0000798543,0.001290985,0.00001546642,0.0001264685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99889654,0.00008115492,0.00023329865,0.00045848894,0.00017384147,0.0001566957],"domain_scores_gemma":[0.9993795,0.00002776116,0.000101179394,0.00029786036,0.00013377647,0.000059925504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017612214,0.00014728651,0.00014457163,0.00015445886,0.0003208569,0.000075196214,0.00017960228,0.000093655035,0.00011597406],"category_scores_gemma":[0.00008173365,0.00015991992,0.00010641303,0.00080245617,0.000030513123,0.000011854198,0.00020152568,0.00031735553,0.0000030368612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000673488,0.00003410828,0.004402795,0.000001874156,0.00024086646,0.0000024787903,0.00014524905,0.013615438,0.9601771,0.0000110224155,0.0021486057,0.019153113],"study_design_scores_gemma":[0.000057020166,0.00015158411,0.0047169253,0.0000013953774,0.00028172252,0.000005538493,0.000106416905,0.014167345,0.9767475,0.00029603983,0.0032559114,0.00021260734],"about_ca_topic_score_codex":0.000386343,"about_ca_topic_score_gemma":0.0015181467,"teacher_disagreement_score":0.67741466,"about_ca_system_score_codex":0.00003525056,"about_ca_system_score_gemma":0.0000141724295,"threshold_uncertainty_score":0.6521345},"labels":[],"label_agreement":null},{"id":"W4224244413","doi":"10.1038/s42256-022-00479-3","title":"Ethics methods are required as part of reporting guidelines for artificial intelligence in healthcare","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; Institute for Work & Health; Public Health Ontario; Hospital for Sick Children","funders":"","keywords":"Health care; Business; Medical emergency; Medicine; Political science; Law","score_opus":0.46573082295457074,"score_gpt":0.6054017941995015,"score_spread":0.13967097124493072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224244413","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11382482,0.03364081,0.4659977,0.36501324,0.01287506,0.007090915,0.00029347546,0.00042736743,0.00083662476],"genre_scores_gemma":[0.9229012,0.00059031,0.06655103,0.008384479,0.00071372656,0.0004346185,0.00014136676,0.000064289525,0.0002189871],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99221313,0.00064707827,0.004671401,0.0008801949,0.00091982394,0.0006683518],"domain_scores_gemma":[0.99092823,0.002377511,0.0028027904,0.0008759153,0.0027478137,0.00026770722],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.01081946,0.00035013136,0.00090671465,0.0005538904,0.0004732872,0.000025796187,0.00046863302,0.0006155955,0.00029649632],"category_scores_gemma":[0.056846507,0.00034290552,0.0003218549,0.0015996007,0.0001782608,0.00012000829,0.00021039388,0.0036656375,0.000010539529],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015152716,0.00063274807,0.016837258,0.0014544826,0.00006187733,0.00008747778,0.00528837,0.0027365983,0.002064196,0.046190392,0.0012491669,0.92188215],"study_design_scores_gemma":[0.00008469137,0.003019457,0.0010758961,0.0017069778,0.0001816772,0.0006799638,0.0347728,0.06320456,0.45151505,0.40005097,0.0425423,0.0011656624],"about_ca_topic_score_codex":0.0040444923,"about_ca_topic_score_gemma":0.0013333576,"teacher_disagreement_score":0.9207165,"about_ca_system_score_codex":0.00042199335,"about_ca_system_score_gemma":0.0013530981,"threshold_uncertainty_score":0.9999023},"labels":[],"label_agreement":null},{"id":"W4283259549","doi":"10.1038/s42256-022-00498-0","title":"Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec – Nature et technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Recurrent neural network; Curse of dimensionality; Computer science; Gradient descent; Chaotic; Artificial intelligence; Artificial neural network; Generalization; Machine learning; Mathematics","score_opus":0.014088740659743757,"score_gpt":0.27536614395041625,"score_spread":0.2612774032906725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283259549","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9598301,0.0013711525,0.03537069,0.0012008363,0.0015194655,0.000415516,0.00002805361,0.00012316466,0.00014102018],"genre_scores_gemma":[0.99880373,0.00010941201,0.00012666997,0.00070867245,0.000034366105,0.000036110458,0.000072454,0.00001757716,0.00009099718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981848,0.00037979803,0.00026059672,0.0005762343,0.0003349219,0.00026363853],"domain_scores_gemma":[0.9991658,0.0004413862,0.00013119943,0.00016981641,0.000020354268,0.00007146936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002256006,0.00016506661,0.00014864653,0.00016000417,0.00053998525,0.00006164747,0.0002239421,0.00007414321,0.00006576621],"category_scores_gemma":[0.00035717688,0.00015165904,0.000045589066,0.00058469235,0.00006125911,0.00013261427,0.00022540812,0.0018529973,7.886546e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009774118,0.00010984256,0.0146512445,0.000011913099,0.0000010987749,0.000030148225,0.00015303327,0.8927923,0.047417004,0.00039758164,0.00033647637,0.04400165],"study_design_scores_gemma":[0.00014524521,0.00015899717,0.0023640948,0.000026058644,0.0000036735325,0.000023620289,0.00008747516,0.9888196,0.007345145,0.00012905986,0.00073674077,0.00016026251],"about_ca_topic_score_codex":0.00007785956,"about_ca_topic_score_gemma":0.00003571182,"teacher_disagreement_score":0.09602736,"about_ca_system_score_codex":0.00006894498,"about_ca_system_score_gemma":0.0000107957185,"threshold_uncertainty_score":0.8050454},"labels":[],"label_agreement":null},{"id":"W4291288469","doi":"10.1038/s42256-022-00463-x","title":"The transformational role of GPU computing and deep learning in drug discovery","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":260,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Drug discovery; Computer science; Deep learning; Data science; Artificial intelligence; Field (mathematics); Parallelizable manifold; Lead (geology); Machine learning; Bioinformatics; Algorithm","score_opus":0.00461789881164311,"score_gpt":0.27612539862414515,"score_spread":0.27150749981250205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291288469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14514548,0.008383397,0.8434941,0.0012832761,0.00033659162,0.00019371792,0.000007061233,0.000048136008,0.001108215],"genre_scores_gemma":[0.9892173,0.00006140909,0.010505779,0.00013420009,0.000021850548,0.000008212042,0.000006629206,0.0000062246204,0.000038389753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982732,0.00041573195,0.0003483464,0.00026131154,0.00051008194,0.00019133427],"domain_scores_gemma":[0.9979496,0.0016644936,0.00013092438,0.00017657071,0.00004831288,0.000030125673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014696083,0.000115548784,0.00013720966,0.00013642939,0.00038812432,0.00011627001,0.00084994506,0.00003234377,0.0000061739497],"category_scores_gemma":[0.00017222256,0.000094206865,0.000054326356,0.00063783093,0.00006859574,0.00042639984,0.00056918146,0.001120982,8.112655e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018059905,0.000030063073,0.0018442118,0.000009389892,0.0000075455278,0.000002707112,0.0031683762,0.42813337,0.00008757067,0.22895665,0.0000042609436,0.3377378],"study_design_scores_gemma":[0.000067333924,0.000031677897,0.0080631925,0.000011402974,0.000001877353,0.000034460012,0.0005487255,0.94261086,0.0011903609,0.044952504,0.0023752998,0.00011232312],"about_ca_topic_score_codex":0.00008415789,"about_ca_topic_score_gemma":0.00006913773,"teacher_disagreement_score":0.8440718,"about_ca_system_score_codex":0.000061099396,"about_ca_system_score_gemma":0.00007262564,"threshold_uncertainty_score":0.48701712},"labels":[],"label_agreement":null},{"id":"W4306846726","doi":"10.1038/s42256-022-00565-6","title":"Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Compression (physics); Artificial neural network; Artificial intelligence; Materials science; Composite material","score_opus":0.011574333500157998,"score_gpt":0.28090654632412493,"score_spread":0.2693322128239669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306846726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037203215,0.0032891708,0.95368683,0.0032697883,0.0019217797,0.00035863408,0.0000054798493,0.00018782707,0.000077249155],"genre_scores_gemma":[0.99627584,0.000055511693,0.0029102282,0.00033962823,0.000061933504,0.000100018835,0.0000624302,0.000011267318,0.00018315724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832934,0.000241958,0.00029190708,0.00057901826,0.00029150344,0.00026628823],"domain_scores_gemma":[0.99909943,0.0003043468,0.0001451044,0.00031286376,0.00004283703,0.00009542309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027036422,0.00016518262,0.00015522071,0.00013046089,0.00069630553,0.00011387171,0.00050742173,0.00007759774,0.000029128716],"category_scores_gemma":[0.000053007017,0.00015702378,0.000047913927,0.0009564263,0.000041997795,0.00019384672,0.00040384824,0.0017800219,8.449545e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012452945,0.00007530392,0.0067089647,0.0000038992866,0.000002073381,0.0000074705245,0.00019381336,0.86554843,0.0002307096,0.00089190115,0.0039518755,0.12237308],"study_design_scores_gemma":[0.00008793958,0.000089445086,0.002576194,0.000028845996,0.0000032925218,0.000025326548,0.00008239047,0.9927205,0.0004004591,0.00016132426,0.003660385,0.00016393447],"about_ca_topic_score_codex":0.00009502287,"about_ca_topic_score_gemma":0.00004673971,"teacher_disagreement_score":0.9590726,"about_ca_system_score_codex":0.000064518244,"about_ca_system_score_gemma":0.000017160612,"threshold_uncertainty_score":0.7733408},"labels":[],"label_agreement":null},{"id":"W4307715697","doi":"10.1038/s42256-022-00547-8","title":"A fast blind zero-shot denoiser","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"Krembil Foundation; Government of Canada; Government of Ontario; Canadian Institutes of Health Research; Ontario Genomics; Genome Canada","keywords":"Upsampling; Computer science; Artificial intelligence; Noise reduction; Image (mathematics); Flexibility (engineering); Noise (video); Inference; Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.022150820268367375,"score_gpt":0.31770021724274045,"score_spread":0.29554939697437305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307715697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016751714,0.0030817012,0.983405,0.0014136308,0.0015038357,0.0001911274,0.000011518586,0.00021642068,0.008501581],"genre_scores_gemma":[0.8965497,0.000021637352,0.095711455,0.0042840294,0.00012473951,0.000036562422,0.000010040963,0.000023193581,0.0032386356],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975294,0.000351686,0.00030901597,0.00063569134,0.0007546567,0.0004195394],"domain_scores_gemma":[0.9985634,0.00025485037,0.000111879854,0.0008297268,0.00011615312,0.00012396093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010630346,0.00023484505,0.00022572637,0.000252286,0.000508312,0.00021236119,0.0024891812,0.00012243429,0.00043725016],"category_scores_gemma":[0.00018016566,0.00021610264,0.00014123193,0.001212725,0.00005345986,0.00031755754,0.0011717125,0.0018623386,0.00010416412],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029775713,0.00043513434,0.00049570185,0.000037009195,0.00007999236,0.000813525,0.0028159842,0.010473933,0.012222294,0.16077149,0.011930746,0.7996264],"study_design_scores_gemma":[0.0015671175,0.0011657082,0.001003003,0.000057211422,0.000064892185,0.0018224907,0.00020783233,0.28607172,0.19594091,0.20900847,0.30075973,0.0023309153],"about_ca_topic_score_codex":0.000057629055,"about_ca_topic_score_gemma":0.0000067204187,"teacher_disagreement_score":0.8948745,"about_ca_system_score_codex":0.00009077053,"about_ca_system_score_gemma":0.00010447119,"threshold_uncertainty_score":0.88124096},"labels":[],"label_agreement":null},{"id":"W4313430531","doi":"10.1038/s42256-022-00592-3","title":"Interpretability of artificial neural network models in artificial intelligence versus neuroscience","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada); York University","funders":"","keywords":"Interpretability; Artificial neural network; Artificial intelligence; Cognitive science; Computer science; Neuroscience; Neural system; Machine learning; Psychology","score_opus":0.0406434389929488,"score_gpt":0.31454382685757953,"score_spread":0.2739003878646307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313430531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065872766,0.0016067589,0.9205333,0.0018586178,0.0071490086,0.0009992436,0.000046017743,0.00032840922,0.0016058679],"genre_scores_gemma":[0.99020797,0.000041704712,0.008634777,0.0007208188,0.00018609804,0.00013012748,0.000009743503,0.000040536277,0.000028228853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99236083,0.000841411,0.0019308235,0.0018278213,0.0016545432,0.0013845674],"domain_scores_gemma":[0.9957694,0.001119247,0.0005817438,0.0019352627,0.00032684056,0.00026749892],"candidate_categories":["metaepi_narrow","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0030327663,0.00060217147,0.0007424586,0.00065411837,0.00062506064,0.00023784465,0.0058132117,0.00028414972,0.00021862547],"category_scores_gemma":[0.0011078086,0.00064038055,0.0003330763,0.0055335956,0.0005657996,0.0014321321,0.002919094,0.003243351,0.00002969932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003749417,0.00033205101,0.00021191606,0.000016781974,0.0000065053723,0.00006457492,0.0010442203,0.5547665,0.0007696564,0.3279367,0.000035171786,0.114441],"study_design_scores_gemma":[0.000032048036,0.00053521304,0.000094748895,0.000019848534,0.000008263642,0.000027882948,0.00043670918,0.7526555,0.019817816,0.22567698,0.00023719815,0.00045779944],"about_ca_topic_score_codex":0.00065174623,"about_ca_topic_score_gemma":0.00095664675,"teacher_disagreement_score":0.9243352,"about_ca_system_score_codex":0.000417566,"about_ca_system_score_gemma":0.00034642924,"threshold_uncertainty_score":0.99960476},"labels":[],"label_agreement":null},{"id":"W4313449193","doi":"10.1038/s42256-022-00591-4","title":"Language and culture internalization for human-like autotelic AI","year":2022,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Microsoft (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Cognitive science; Cognition; Computer science; Perspective (graphical); Psychology; Artificial intelligence","score_opus":0.009562203511417676,"score_gpt":0.3544471511249803,"score_spread":0.34488494761356264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313449193","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4601173,0.12590633,0.31427467,0.02838032,0.008809839,0.006183125,0.000736003,0.002007676,0.053584725],"genre_scores_gemma":[0.9868179,0.00007199345,0.00025985093,0.0021875235,0.00028165895,0.000049500177,0.00010814983,0.000009233561,0.010214199],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906486,0.000106882486,0.00012966043,0.00022156232,0.00028941114,0.00018759993],"domain_scores_gemma":[0.9996624,0.000032757725,0.00006672824,0.000098408564,0.00008281009,0.000056884564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033472807,0.00009596273,0.00009500997,0.000046124238,0.00095688284,0.00006964721,0.0002590946,0.00011978017,0.00056267966],"category_scores_gemma":[0.0001501381,0.0000793214,0.000056083052,0.00024591442,0.00007088067,0.00014858104,0.00009222875,0.000490629,0.0000045067018],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012431457,0.00018674199,0.0020226731,0.000088710076,0.00007645006,0.00003755767,0.3402818,0.0003809669,0.009881034,0.4499409,0.049892914,0.14708593],"study_design_scores_gemma":[0.00031667465,0.0003184517,0.00046038584,0.000036813533,0.00006132028,0.000040475992,0.036187023,0.0028499542,0.0034337544,0.042042736,0.91367924,0.0005731412],"about_ca_topic_score_codex":0.0018781419,"about_ca_topic_score_gemma":0.002442268,"teacher_disagreement_score":0.86378634,"about_ca_system_score_codex":0.00011156641,"about_ca_system_score_gemma":0.00002939202,"threshold_uncertainty_score":0.73596656},"labels":[],"label_agreement":null},{"id":"W4315646343","doi":"10.1038/s42256-023-00608-6","title":"Publisher Correction: Advancing ethics review practices in AI research","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Engineering ethics; Political science; Medicine; Business; Engineering","score_opus":0.3717985096107391,"score_gpt":0.6182505377202994,"score_spread":0.24645202810956024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315646343","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010476585,0.14824347,0.0018201107,0.7800337,0.02261685,0.0030407214,0.0000036655945,0.0006627256,0.03310213],"genre_scores_gemma":[0.8461032,0.1104485,0.00059302774,0.029059336,0.0012144052,0.00021897009,0.000115710915,0.00006362885,0.012183213],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965827,0.0005306444,0.0006871864,0.0005565155,0.0010072138,0.0006357458],"domain_scores_gemma":[0.99407053,0.003376251,0.00023169653,0.0005856721,0.0015116268,0.0002242166],"candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009138397,0.00017385666,0.00032613092,0.00068434875,0.0002591895,0.000075954704,0.00029477495,0.0006190936,0.00039367663],"category_scores_gemma":[0.054743662,0.00015096088,0.000080095815,0.004743209,0.00012471904,0.00048234296,0.000101408565,0.008683941,0.0007829228],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015893584,0.00023040589,0.030067872,0.0034499087,0.00001972566,0.0001857369,0.002819091,0.00012061894,0.000082502476,0.0014956953,0.5471891,0.4141804],"study_design_scores_gemma":[0.000060869614,0.00069187226,0.008108555,0.017176282,0.000070730355,0.0004796328,0.0069660214,0.012211272,0.006061983,0.028813705,0.9187487,0.00061042386],"about_ca_topic_score_codex":0.0024512073,"about_ca_topic_score_gemma":0.0019510549,"teacher_disagreement_score":0.8356266,"about_ca_system_score_codex":0.0003097931,"about_ca_system_score_gemma":0.001133597,"threshold_uncertainty_score":0.9999951},"labels":[],"label_agreement":null},{"id":"W4360610309","doi":"10.1038/s42256-023-00617-5","title":"Biomonitoring and precision health in deep space supported by artificial intelligence","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Spaceflight effects on biology","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Biological and Physical Sciences Division; National Center for Advancing Translational Sciences; University of California, San Francisco; Science Mission Directorate; Ames Research Center; Office of Science; National Aeronautics and Space Administration; U.S. Department of Energy; National Institutes of Health; National Science Foundation","keywords":"NASA Deep Space Network; Space exploration; Computer science; Space (punctuation); Systems engineering; Spacecraft; Biomonitoring; Artificial intelligence; Data science; Risk analysis (engineering); Engineering; Medicine; Aerospace engineering; Ecology","score_opus":0.017179789738295027,"score_gpt":0.3646613326294831,"score_spread":0.34748154289118804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360610309","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8587149,0.024737602,0.05630424,0.05179638,0.0033698163,0.0029984617,0.0000919318,0.0010151451,0.0009715712],"genre_scores_gemma":[0.9950451,0.00221979,0.0014102755,0.0007216576,0.0001994636,0.000025969573,0.00013067917,0.000041667616,0.00020535076],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975744,0.00016172418,0.0005615988,0.0007234301,0.00033216592,0.00064668176],"domain_scores_gemma":[0.99849373,0.000562819,0.00014576434,0.0004184212,0.00009139281,0.00028787745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011019481,0.00031463732,0.000536435,0.0006058552,0.000096751166,0.000033793043,0.0002255416,0.0004970017,0.000085372856],"category_scores_gemma":[0.0010825854,0.00026326653,0.00006782846,0.0017221888,0.00014944926,0.00008638588,0.00016859354,0.0013575882,0.0001597654],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006554554,0.000248834,0.028054848,0.00019190012,0.000047480124,0.00022658012,0.0009881868,0.000066695306,0.041789524,0.0024491993,0.0022564076,0.9230249],"study_design_scores_gemma":[0.0006396047,0.0032117865,0.04112111,0.0010685998,0.00006185037,0.0005648805,0.0014324756,0.0778892,0.8307025,0.015762735,0.026263062,0.0012822233],"about_ca_topic_score_codex":0.00039871747,"about_ca_topic_score_gemma":0.00034122117,"teacher_disagreement_score":0.9217427,"about_ca_system_score_codex":0.00016517125,"about_ca_system_score_gemma":0.00009599823,"threshold_uncertainty_score":0.99998194},"labels":[],"label_agreement":null},{"id":"W4360612437","doi":"10.1038/s42256-023-00618-4","title":"Biological research and self-driving labs in deep space supported by artificial intelligence","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Lawrence Berkeley National Laboratory; National Center for Advancing Translational Sciences; Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; University of California, San Francisco; Biological and Environmental Research; Science Mission Directorate; Ames Research Center; Office of Science; National Aeronautics and Space Administration; U.S. Department of Energy; Biological and Physical Sciences Division; National Institutes of Health; National Science Foundation","keywords":"Space exploration; Computer science; NASA Deep Space Network; Space (punctuation); Spaceflight; Field (mathematics); Data science; Artificial intelligence; Mars Exploration Program; Exploration of Mars; Spacecraft; Systems engineering; Engineering; Astrobiology; Aerospace engineering; Biology","score_opus":0.022250432140120374,"score_gpt":0.36436065424751685,"score_spread":0.3421102221073965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360612437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96572435,0.004474298,0.026258403,0.0010034927,0.000074091986,0.00049960526,0.000013981392,0.00025902875,0.0016927769],"genre_scores_gemma":[0.9938993,0.0037612508,0.0014979363,0.00013294053,0.00010393577,0.000034841178,0.0002230761,0.000026779253,0.00031997997],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977388,0.00021582114,0.000379634,0.00080403563,0.00031025152,0.00055148936],"domain_scores_gemma":[0.9989608,0.00017097619,0.000067172776,0.00048191327,0.00020018873,0.00011896258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015290885,0.00023157176,0.0002346663,0.00034340264,0.00011713994,0.0000762387,0.0005129016,0.00058447814,0.00006480696],"category_scores_gemma":[0.0009587689,0.00020672134,0.00006851924,0.0013418291,0.00025488684,0.000009740839,0.0005753522,0.0010834745,0.000052249947],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000118184835,0.00042439782,0.023309916,0.000037707476,0.00007609519,0.00014376343,0.00027423157,0.00006411978,0.88944995,0.001971894,0.0065655545,0.07756421],"study_design_scores_gemma":[0.000028153987,0.0003113663,0.00092092314,0.000019612076,0.0000069012754,0.000018538845,0.00023803441,0.005934728,0.97375065,0.0061985743,0.012285643,0.0002868799],"about_ca_topic_score_codex":0.00010042123,"about_ca_topic_score_gemma":0.00059910247,"teacher_disagreement_score":0.08430072,"about_ca_system_score_codex":0.000035266894,"about_ca_system_score_gemma":0.000038730203,"threshold_uncertainty_score":0.84298515},"labels":[],"label_agreement":null},{"id":"W4377820154","doi":"10.1038/s42256-023-00660-2","title":"Geometric deep learning of particle motion by MAGIK","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada)","funders":"","keywords":"Motion (physics); Particle (ecology); Physics; Computer science; Classical mechanics; Geology","score_opus":0.004708177604669399,"score_gpt":0.28104888413572177,"score_spread":0.27634070653105236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377820154","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60591966,0.010789785,0.38084587,0.00018749548,0.00005959914,0.000182376,0.000008655356,0.00016286668,0.001843677],"genre_scores_gemma":[0.99621546,0.0013640565,0.00045518178,0.000089927664,0.000043670276,0.00000762058,0.00022123771,0.000017249182,0.0015856175],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991014,0.000053864016,0.00020691729,0.00027898888,0.0001745978,0.00018425916],"domain_scores_gemma":[0.9994468,0.000028570661,0.00009207735,0.0002735338,0.000115460396,0.00004354716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031639094,0.00011072089,0.00012451534,0.00013408584,0.000041803207,0.000013920654,0.00023272989,0.00019519607,0.00007067467],"category_scores_gemma":[0.00055427593,0.00010447542,0.000095205476,0.0010163842,0.00005115581,0.0000047755757,0.00012995192,0.00031264042,0.00003875345],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020364356,0.000052776428,0.012772252,0.000022505892,0.000041095944,0.0000038982803,0.000025610756,0.0005446185,0.84832656,0.000084755295,0.004378848,0.13372669],"study_design_scores_gemma":[0.000034948876,0.00010238571,0.0011038866,0.0000050980834,0.000016488437,0.0000032231756,0.000029064846,0.009724627,0.9802054,0.00009357813,0.008573802,0.00010745874],"about_ca_topic_score_codex":0.00003523184,"about_ca_topic_score_gemma":0.000013108539,"teacher_disagreement_score":0.39029577,"about_ca_system_score_codex":0.0000093557655,"about_ca_system_score_gemma":0.0000070878727,"threshold_uncertainty_score":0.42603838},"labels":[],"label_agreement":null},{"id":"W4383823251","doi":"10.1038/s42256-023-00688-4","title":"Reusability report: Evaluating reproducibility and reusability of a fine-tuned model to predict drug response in cancer patient samples","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; Vector Institute; Princess Margaret Cancer Centre; Ontario Institute for Cancer Research; University of Toronto; University Health Network","funders":"","keywords":"Reusability; Reproducibility; Context (archaeology); Computer science; Machine learning; Artificial intelligence; Drug; Medicine; Mathematics; Statistics; Pharmacology; Software","score_opus":0.061373902414077974,"score_gpt":0.4167704008217654,"score_spread":0.35539649840768744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383823251","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79618585,0.00038595306,0.19729656,0.004757587,0.00031387265,0.00079936907,0.0000849108,0.00014244214,0.00003345309],"genre_scores_gemma":[0.8310397,0.000011857749,0.16861558,0.00014132154,0.000024607256,0.00010576761,0.000010056058,0.000012862818,0.00003821528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931064,0.0016221572,0.0013267034,0.0025100645,0.0010248424,0.0004098362],"domain_scores_gemma":[0.99137956,0.0041967393,0.00034941928,0.0033432264,0.00056500884,0.00016605797],"candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.021034084,0.0002838568,0.0004813548,0.00034590295,0.00009917371,0.00005678663,0.0010032171,0.0001538697,0.000011408619],"category_scores_gemma":[0.05565791,0.00026041505,0.000108536,0.002474956,0.00015936537,0.00034054028,0.0015689307,0.0007730829,0.0000027858794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010660889,0.00021850428,0.036349572,0.00016667224,0.000016244527,0.000031936062,0.005287869,0.7640158,0.002633145,0.0011762083,0.00016098481,0.18887699],"study_design_scores_gemma":[0.000087620785,0.00016553263,0.1467235,0.00011546016,0.000006323701,0.00001529379,0.000046463207,0.77533394,0.013941206,0.06329107,0.000049115097,0.00022449544],"about_ca_topic_score_codex":0.0009928363,"about_ca_topic_score_gemma":0.0006380071,"teacher_disagreement_score":0.18865249,"about_ca_system_score_codex":0.0003224693,"about_ca_system_score_gemma":0.0005942663,"threshold_uncertainty_score":0.9999848},"labels":[],"label_agreement":null},{"id":"W4384561507","doi":"10.1038/s42256-023-00652-2","title":"Federated benchmarking of medical artificial intelligence with MedPerf","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":149,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"National Institutes of Health; Agence Nationale de la Recherche; National Cancer Institute; National Research Foundation Singapore; National Research Foundation","keywords":"Benchmarking; Software deployment; Computer science; Process (computing); Health care; Cloud computing; Knowledge management; Data science; Big data; Process management; Engineering management; Artificial intelligence; Business; Software engineering; Engineering; Data mining","score_opus":0.08459942258698776,"score_gpt":0.4346746750443853,"score_spread":0.3500752524573975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384561507","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.819351,0.0023308154,0.1346459,0.034243517,0.0030969223,0.0015826132,0.000033498636,0.00076704717,0.0039486806],"genre_scores_gemma":[0.99598604,0.00074464857,0.001216459,0.00094348955,0.00072274316,0.00004031796,0.00013974926,0.000047505455,0.00015903662],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99590045,0.000102498336,0.0010606549,0.00061647524,0.0017198933,0.00060000573],"domain_scores_gemma":[0.9976476,0.0006806991,0.0002386335,0.00040857022,0.0006062712,0.00041822795],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014820164,0.00032430582,0.00053439505,0.0005161698,0.00022863998,0.000049913782,0.00039860685,0.0007567265,0.0015581944],"category_scores_gemma":[0.0014875106,0.00024751062,0.00013552228,0.002550508,0.0003514887,0.00012757904,0.000113181384,0.0017981789,0.00027759766],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066838897,0.00033357352,0.013210627,0.00035255234,0.000101561694,0.00023349712,0.0014837163,0.0003667332,0.0009500398,0.015233074,0.0006924785,0.96637374],"study_design_scores_gemma":[0.0000712257,0.0026131938,0.00526336,0.002336563,0.00020641227,0.0007040483,0.0069584753,0.27400827,0.6821298,0.021163864,0.0035196962,0.0010250905],"about_ca_topic_score_codex":0.0009483836,"about_ca_topic_score_gemma":0.0008580852,"teacher_disagreement_score":0.96534866,"about_ca_system_score_codex":0.0001035342,"about_ca_system_score_gemma":0.00076371554,"threshold_uncertainty_score":0.99999774},"labels":[],"label_agreement":null},{"id":"W4385360935","doi":"10.1038/s42256-023-00689-3","title":"Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"CIHR Skin Research Training Centre; Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Université Laval; National Science Foundation; Government of Canada; Canadian Institute for Advanced Research","keywords":"Task (project management); Resolution (logic); Superresolution; Nanotechnology; Optoelectronics; Image (mathematics); Materials science; Computer science; Computer vision; Artificial intelligence; Systems engineering; Engineering","score_opus":0.005438724644240362,"score_gpt":0.28954572366112785,"score_spread":0.28410699901688746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385360935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016650792,0.00029032349,0.9798447,0.0007700623,0.000029998488,0.00020687132,0.00002159227,0.00066473533,0.0015209089],"genre_scores_gemma":[0.86523855,0.000103783015,0.1341284,0.00014872095,0.000029257522,0.000089816436,0.00009436583,0.000019202887,0.00014790669],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99913234,0.000009928658,0.00019099582,0.0002680272,0.00018531238,0.00021338249],"domain_scores_gemma":[0.99952215,0.00003265825,0.000025107014,0.00024399433,0.00009366057,0.000082410515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016435463,0.00014586296,0.00014857986,0.00028493596,0.00009742132,0.000077642166,0.00014966934,0.00011066047,0.00002902933],"category_scores_gemma":[0.000030414541,0.00012490037,0.000035497294,0.0018284224,0.00003939356,0.00008379278,0.000043581913,0.00027988007,0.000034712637],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012475473,0.00013303277,0.0007428144,0.0002945822,0.00045713523,0.00005397196,0.00069440505,0.014352569,0.77741843,0.0040699746,0.010554162,0.19110416],"study_design_scores_gemma":[0.00006289548,0.00011512762,0.0050824312,0.00009633814,0.00020744973,0.000014112627,0.000040526396,0.28470942,0.7037316,0.0007736795,0.00481118,0.0003552594],"about_ca_topic_score_codex":0.00004067931,"about_ca_topic_score_gemma":0.00006506909,"teacher_disagreement_score":0.84858775,"about_ca_system_score_codex":0.0000703722,"about_ca_system_score_gemma":0.0000108958175,"threshold_uncertainty_score":0.5093289},"labels":[],"label_agreement":null},{"id":"W4386033537","doi":"10.1038/s42256-023-00705-6","title":"The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; Hospital for Sick Children","funders":"","keywords":"Tripod (photography); Transparency (behavior); Guideline; Analytics; Medicine; Health care; Computer science; Data mining; Computer security; Engineering","score_opus":0.2066607610686038,"score_gpt":0.5242173486188747,"score_spread":0.31755658755027094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386033537","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.695406,0.00703986,0.07498011,0.124470904,0.0017169731,0.09569455,0.0001456104,0.0003131262,0.00023283203],"genre_scores_gemma":[0.9949317,0.00022121603,0.00093225425,0.00045536036,0.00022651968,0.0031249975,0.000028901452,0.000021881095,0.000057144953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99599123,0.00016261585,0.0026235061,0.00042927935,0.00041194295,0.00038141274],"domain_scores_gemma":[0.9962428,0.0013178478,0.0010140765,0.00052983576,0.00081968703,0.00007574221],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0054215156,0.00019464886,0.00043095674,0.00012100841,0.00036852888,0.00003248649,0.0002688428,0.00024194963,0.000004006103],"category_scores_gemma":[0.0107417805,0.000109270855,0.00010348086,0.0010992482,0.00016845162,0.00009226478,0.000057644793,0.0016471419,0.0000010931398],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016980483,0.0005528048,0.44891304,0.0012660408,0.000095651085,0.00002578578,0.02362048,0.00042051793,0.00031696365,0.0031976977,0.00072177604,0.5191712],"study_design_scores_gemma":[0.0008912743,0.0132942395,0.19026177,0.0024883198,0.0005998124,0.0001704937,0.17047365,0.40464643,0.07631814,0.12878764,0.01093141,0.0011368195],"about_ca_topic_score_codex":0.005707178,"about_ca_topic_score_gemma":0.0054151556,"teacher_disagreement_score":0.5180344,"about_ca_system_score_codex":0.00010662388,"about_ca_system_score_gemma":0.000464104,"threshold_uncertainty_score":0.99759114},"labels":[],"label_agreement":null},{"id":"W4387673980","doi":"10.1038/s42256-023-00735-0","title":"Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alpha Technologies (Canada); Centre de Santé et de Services Sociaux Cavendish; University of Toronto","funders":"Templeton World Charity Foundation; Fundação para a Ciência e a Tecnologia; Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa; Ministério da Ciência, Tecnologia e Ensino Superior; Max-Planck-Gesellschaft; National Science Foundation","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Data science; Field (mathematics); Task (project management); Deep learning; Graph; Network science; Complex network; Theoretical computer science","score_opus":0.018700645347449526,"score_gpt":0.28709456819910234,"score_spread":0.2683939228516528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387673980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15426324,0.0022870912,0.83978325,0.00075233274,0.0006506652,0.00085356337,0.00004946741,0.0004996284,0.00086077297],"genre_scores_gemma":[0.99521446,0.000016145668,0.0024104165,0.000037990932,0.0018666516,0.000054853703,0.00030436067,0.000047609166,0.000047505306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764085,0.00029503772,0.0006708618,0.0005136537,0.00035623886,0.00052337954],"domain_scores_gemma":[0.99854225,0.00040138236,0.00031375556,0.00046991164,0.00019483766,0.000077848475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012114082,0.00033289572,0.00038422097,0.00028546844,0.00031479035,0.00007864548,0.0006648429,0.0001743951,0.00019359739],"category_scores_gemma":[0.00003056833,0.00023998815,0.00017107894,0.0025615569,0.00012537024,0.00021522035,0.0001744851,0.0018787081,0.000010189011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015209812,0.00012376164,0.06270662,0.000027132857,0.00006197333,0.000007335281,0.00049667107,0.4901567,0.00017554432,0.012674993,0.00007443055,0.43334273],"study_design_scores_gemma":[0.00004585524,0.00022864013,0.0010737483,0.00017966302,0.000053813837,0.000001647228,0.00035038052,0.9785673,0.005187179,0.013007752,0.0010611283,0.00024287518],"about_ca_topic_score_codex":0.00022277945,"about_ca_topic_score_gemma":0.0015194318,"teacher_disagreement_score":0.8409512,"about_ca_system_score_codex":0.00003983931,"about_ca_system_score_gemma":0.00008256286,"threshold_uncertainty_score":0.9786433},"labels":[],"label_agreement":null},{"id":"W4387773470","doi":"10.1038/s42256-023-00738-x","title":"Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bioinformatics Solutions (Canada); University of Waterloo","funders":"","keywords":"Fragmentation (computing); Tandem mass spectrometry; Deep learning; Peptide; Computer science; Computational biology; Graph; Recurrent neural network; Artificial neural network; Artificial intelligence; Biology; Chemistry; Mass spectrometry; Biochemistry; Theoretical computer science","score_opus":0.01281514757924118,"score_gpt":0.308552899223441,"score_spread":0.2957377516441998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387773470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23399326,0.00013272904,0.76236534,0.00074216153,0.000005098055,0.00026607537,0.000013142332,0.00035940958,0.0021227926],"genre_scores_gemma":[0.7842521,0.000025522593,0.21486598,0.000246063,0.000022844737,0.00022270237,0.00006790801,0.000034387707,0.00026249513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988185,0.000027432592,0.00026837646,0.0003429419,0.00021247634,0.00033025543],"domain_scores_gemma":[0.99924743,0.00019326803,0.00017003607,0.00026897414,0.00006643496,0.000053828553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000345948,0.00018960529,0.00012838778,0.00009731469,0.0002712205,0.000059216283,0.00032276558,0.00014199594,0.000029243292],"category_scores_gemma":[0.00010646331,0.00014623388,0.000050708994,0.0006901698,0.000078729674,0.00008898845,0.000053407162,0.0015223512,0.000003949026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025065461,0.000013141118,0.0030020021,0.000083317456,0.000005936416,0.000014626604,0.0006223129,0.8527177,0.13146715,0.0029101903,0.000002971246,0.0091356],"study_design_scores_gemma":[0.00010508887,0.000010945162,0.0000103981,0.00017497873,0.000006945254,0.000009734427,0.00037880024,0.7076183,0.27400357,0.017462486,0.00006725252,0.00015153512],"about_ca_topic_score_codex":0.00015848222,"about_ca_topic_score_gemma":0.00024393336,"teacher_disagreement_score":0.5502588,"about_ca_system_score_codex":0.00020672496,"about_ca_system_score_gemma":0.000098712204,"threshold_uncertainty_score":0.6613943},"labels":[],"label_agreement":null},{"id":"W4388216531","doi":"10.1038/s42256-023-00752-z","title":"Hierarchical generative modelling for autonomous robots","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Action Observation and Synchronization","field":"Psychology","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Medical Research Council; Medical Research Council Canada; University College London","keywords":"Computer science; Robot; Artificial intelligence; Task (project management); Generative model; Embodied cognition; Hierarchy; Architecture; Generative grammar; Human–computer interaction; Object (grammar); Motor control; Engineering; Systems engineering","score_opus":0.05477753790672926,"score_gpt":0.37359501158865877,"score_spread":0.3188174736819295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388216531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038697226,0.00031227584,0.98608637,0.0028648276,0.0017337233,0.0003702651,0.00003771577,0.00035979884,0.004365302],"genre_scores_gemma":[0.9706966,0.000042041283,0.013507261,0.0017317062,0.0005325704,0.0001334215,0.00036482766,0.000040768442,0.012950787],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883044,0.000071154536,0.0002818448,0.0003829104,0.00016390641,0.00026974003],"domain_scores_gemma":[0.9991795,0.00026603602,0.000083550614,0.00023896422,0.00015830852,0.00007368035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002830027,0.00015090924,0.00015208889,0.00017335262,0.00018743382,0.000035562512,0.00020891563,0.00033418648,0.0007579493],"category_scores_gemma":[0.00013055102,0.00013800022,0.000085894455,0.00056902505,0.000042542077,0.00008509313,0.00003232358,0.00056750875,0.0005148289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013146116,0.000105724495,0.0007938518,0.000021873379,0.00008123851,0.000012257706,0.0025971595,0.45491913,0.00020419854,0.41090935,0.01810151,0.11212224],"study_design_scores_gemma":[0.00014401803,0.000073506795,0.0006572537,0.000007737926,0.000010500774,0.000008107615,0.00012517745,0.9412403,0.0011712264,0.015746012,0.04062525,0.00019087341],"about_ca_topic_score_codex":0.00002654614,"about_ca_topic_score_gemma":0.000017103142,"teacher_disagreement_score":0.9725791,"about_ca_system_score_codex":0.000057555113,"about_ca_system_score_gemma":0.000038659487,"threshold_uncertainty_score":0.8299014},"labels":[],"label_agreement":null},{"id":"W4388775456","doi":"10.1038/s42256-023-00743-0","title":"Differentiable visual computing for inverse problems and machine learning","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Differentiable function; Graphics pipeline; Computer science; Graphics; Computer graphics; Artificial intelligence; Pipeline (software); Robotics; Physics engine; Visualization; Geometric primitive; Human–computer interaction; Computer graphics (images); 3D computer graphics; Robot; Mathematics","score_opus":0.02425735947625895,"score_gpt":0.316345542072652,"score_spread":0.292088182596393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388775456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017653469,0.000643001,0.9795402,0.00030650003,0.0002802477,0.00036776223,0.000005313477,0.0011294471,0.00007405092],"genre_scores_gemma":[0.9808274,0.00030854813,0.01805812,0.00034617653,0.00008092182,0.000021590802,0.000052867344,0.000024395144,0.00027998068],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984711,0.00006718958,0.00030303604,0.0005566598,0.0002512955,0.00035075485],"domain_scores_gemma":[0.9990704,0.00029769886,0.0001289929,0.00024713072,0.00015352538,0.000102233535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057231047,0.00022074093,0.00022170121,0.00038943277,0.00033373115,0.00029052343,0.0006364022,0.00017589967,0.0000059740764],"category_scores_gemma":[0.00013400671,0.00019886163,0.00008143938,0.0011380655,0.00004843888,0.00022263652,0.0006839963,0.0005978963,0.000007863225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015755857,0.00013401077,0.023884589,0.00034807163,0.00006621751,0.000012915676,0.0019322434,0.0019038498,0.0006648872,0.79428595,0.001424321,0.17532717],"study_design_scores_gemma":[0.0000962416,0.00017227768,0.00076074054,0.0000591696,0.000005221573,0.000009713356,0.00000955587,0.9619549,0.0049029016,0.024869513,0.0069293105,0.00023046615],"about_ca_topic_score_codex":0.000049754453,"about_ca_topic_score_gemma":0.000033490523,"teacher_disagreement_score":0.9631739,"about_ca_system_score_codex":0.000017001108,"about_ca_system_score_gemma":0.000019710702,"threshold_uncertainty_score":0.8109342},"labels":[],"label_agreement":null},{"id":"W4389879537","doi":"10.1038/s42256-023-00771-w","title":"Spatially embedded neuromorphic networks","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Neuromorphic engineering; Computer science; Artificial neural network; Artificial intelligence","score_opus":0.01692943218380406,"score_gpt":0.269130583090938,"score_spread":0.25220115090713396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389879537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008001576,0.0012835657,0.9805909,0.0036464399,0.0035201688,0.0002394999,0.000001907,0.0012891914,0.0014267786],"genre_scores_gemma":[0.99212855,0.00021497568,0.00494719,0.0016451683,0.00056150934,0.0000075590133,0.000012360905,0.00002519235,0.00045749545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775636,0.00012301769,0.0003514892,0.0006724515,0.0004604007,0.0006363065],"domain_scores_gemma":[0.9983979,0.00038365924,0.00012329518,0.00081075344,0.000115082854,0.0001693024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046505718,0.00026547845,0.00023933548,0.00019140934,0.0002632969,0.00025836524,0.002190506,0.00026420582,0.000029760122],"category_scores_gemma":[0.00014494745,0.000213788,0.00013810398,0.002233264,0.000053285414,0.00024372929,0.0010253479,0.001513136,0.0002127054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014725863,0.000048586946,0.0008266397,0.00002336284,0.00002762962,0.0006786345,0.00017312373,0.6558001,0.0002457258,0.039952777,0.007997914,0.2942108],"study_design_scores_gemma":[0.000050521165,0.00006874694,0.0010250189,0.00003523145,0.0000032347236,0.000058082784,0.000002911398,0.98910105,0.0008292534,0.0048892987,0.0036862812,0.0002503964],"about_ca_topic_score_codex":0.000027900604,"about_ca_topic_score_gemma":0.000028813205,"teacher_disagreement_score":0.984127,"about_ca_system_score_codex":0.000019168516,"about_ca_system_score_gemma":0.000039448103,"threshold_uncertainty_score":0.87180215},"labels":[],"label_agreement":null},{"id":"W4389888290","doi":"10.1038/s42256-023-00759-6","title":"Multi-modal molecule structure–text model for text-based retrieval and editing","year":2023,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Cheminformatics; Natural language processing; Artificial intelligence; Construct (python library); Generalization; Information retrieval; Chemistry; Programming language","score_opus":0.024998664148025655,"score_gpt":0.3472592376691354,"score_spread":0.32226057352110976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389888290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021370076,0.0004008055,0.9752593,0.001501413,0.00065809244,0.00035711413,0.000118988,0.00029078618,0.000043425436],"genre_scores_gemma":[0.64770144,0.0000043160567,0.3514291,0.0006060246,0.00011286116,0.000008620063,0.000035254674,0.000019469246,0.00008290495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979469,0.00009925129,0.00032190394,0.0007388431,0.00048426221,0.00040883472],"domain_scores_gemma":[0.9980726,0.0010311153,0.00012582318,0.00041922447,0.00022191597,0.00012932059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006800881,0.00026228325,0.00023025593,0.00027428073,0.00021275083,0.00019998002,0.0009165135,0.00022775937,0.0000063644043],"category_scores_gemma":[0.001224072,0.00024373816,0.000106547166,0.00097610475,0.000076906734,0.0003106113,0.00038325318,0.0006717007,0.000009754865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000726303,0.00003733532,0.00011143728,0.000075229385,0.00002055912,0.000014435681,0.0004648142,0.91765016,0.0046446454,0.026676372,0.00038149304,0.04985088],"study_design_scores_gemma":[0.00019146422,0.000043720924,0.0007432131,0.000025016789,0.0000071058043,0.000009722297,0.000010672491,0.94689554,0.021630205,0.030086558,0.0001154199,0.0002413386],"about_ca_topic_score_codex":0.000011067162,"about_ca_topic_score_gemma":0.000019243607,"teacher_disagreement_score":0.6263314,"about_ca_system_score_codex":0.000056545814,"about_ca_system_score_gemma":0.0001618693,"threshold_uncertainty_score":0.99393535},"labels":[],"label_agreement":null},{"id":"W4390830448","doi":"10.1038/s42256-023-00780-9","title":"Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vancouver Coastal Health; Vancouver Coastal Health Research Institute; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Robotics; Robustness (evolution); Human–computer interaction; Robot","score_opus":0.025722801369088645,"score_gpt":0.3078758334009204,"score_spread":0.28215303203183173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390830448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060188256,0.031347573,0.89189667,0.0011670794,0.0047710766,0.00083743886,0.00013992267,0.0009810234,0.008670962],"genre_scores_gemma":[0.9917278,0.0001564087,0.006241018,0.00023634892,0.000181388,0.000013153713,0.000034057564,0.000032113745,0.0013777075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99777573,0.0001560502,0.00044401028,0.00078582997,0.00042980848,0.00040855093],"domain_scores_gemma":[0.9988703,0.00031509934,0.000115712464,0.00038743461,0.00014054499,0.0001708942],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00040861825,0.00034949117,0.0003247361,0.00036634423,0.00036462155,0.0010537232,0.0005471509,0.00018740386,0.00022321226],"category_scores_gemma":[0.00014351646,0.00029927722,0.000112496025,0.00071935763,0.00008901254,0.0006872037,0.00039340823,0.0013880437,0.00010565597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015259336,0.00069285766,0.026080137,0.0019331668,0.0014979655,0.0010606115,0.02985182,0.0057413895,0.11370268,0.027465956,0.0016206328,0.7902002],"study_design_scores_gemma":[0.00016311176,0.00011178627,0.00087546505,0.00056999165,0.000030803956,0.00051556574,0.00020668538,0.89627296,0.021234943,0.0027065338,0.07675702,0.0005551399],"about_ca_topic_score_codex":0.001061051,"about_ca_topic_score_gemma":0.0006063377,"teacher_disagreement_score":0.93153954,"about_ca_system_score_codex":0.0001082227,"about_ca_system_score_gemma":0.00006907217,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W4391166900","doi":"10.1038/s42256-023-00784-5","title":"Anniversary AI reflections","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformative learning; Theme (computing); Generative grammar; Epistemology; Engineering ethics; Psychology; Cognitive science; Computer science; Philosophy; Artificial intelligence; Engineering; Pedagogy; World Wide Web","score_opus":0.01634707780693423,"score_gpt":0.34906016233957354,"score_spread":0.3327130845326393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391166900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020905235,0.008585289,0.94933665,0.010505583,0.0032638055,0.0001994635,0.000009341407,0.0012691886,0.026621634],"genre_scores_gemma":[0.97221303,0.00026229618,0.020958222,0.0029155638,0.0003507128,0.000021211108,0.0000072755684,0.000030848107,0.0032408673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977076,0.00008625126,0.00035580722,0.00086941343,0.00046685233,0.0005140542],"domain_scores_gemma":[0.9984238,0.0002956641,0.000042826807,0.00086266716,0.0002038422,0.00017117352],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044202083,0.0002812526,0.00019133418,0.00047188145,0.00028394067,0.0006491803,0.0016972222,0.00031751025,0.00025289215],"category_scores_gemma":[0.00020710194,0.00025316083,0.00017238226,0.0023604268,0.000111167064,0.0014084844,0.00037745343,0.0018163556,0.0017374504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007278929,0.000056992256,0.000042254764,0.00003802175,0.000036487334,0.00035195533,0.0010291144,0.00103391,0.0016975311,0.81659245,0.009044355,0.17006966],"study_design_scores_gemma":[0.00001839443,0.00016113493,0.00004831904,0.00012800412,0.000017206528,0.00024854002,0.00013784738,0.33673078,0.13453355,0.18427458,0.3431564,0.0005452269],"about_ca_topic_score_codex":0.00019206086,"about_ca_topic_score_gemma":0.00015464152,"teacher_disagreement_score":0.97200394,"about_ca_system_score_codex":0.00016594904,"about_ca_system_score_gemma":0.00018582448,"threshold_uncertainty_score":0.9999921},"labels":[],"label_agreement":null},{"id":"W4391175562","doi":"10.1038/s42256-023-00787-2","title":"Variational autoencoder for design of synthetic viral vector serotypes","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Virus-based gene therapy research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"CIHR Skin Research Training Centre","keywords":"Autoencoder; Computer science; Capsid; Computational biology; Vector (molecular biology); Epitope; Virology; Artificial intelligence; Deep learning; Biology; Virus; Gene; Antibody; Immunology; Genetics","score_opus":0.015272055860928153,"score_gpt":0.3296407372049388,"score_spread":0.31436868134401064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391175562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009952759,0.012667619,0.9753595,0.0006807084,0.0004446568,0.00053798594,0.00014630269,0.00003491488,0.0001755808],"genre_scores_gemma":[0.9853212,0.00015738021,0.01330367,0.00016201146,0.00026723446,0.000064347936,0.00008687591,0.0000338926,0.0006033577],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989788,0.00007678176,0.00018938926,0.00034630974,0.00020749039,0.00020122608],"domain_scores_gemma":[0.9993871,0.00013581789,0.0000332739,0.00025385254,0.00014378178,0.00004620764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042604623,0.00014128261,0.00012065086,0.00008020383,0.00004347691,0.000027221115,0.00029945455,0.00024611127,0.00013274637],"category_scores_gemma":[0.00023925853,0.00011889257,0.000107559215,0.00013944527,0.0000683646,0.0000043194987,0.00005262342,0.00026851115,0.000016863632],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027937097,0.00012201582,0.00007208524,0.00021182078,0.0002901794,0.0000059683134,0.00010714873,0.025126565,0.9210616,0.0089299055,0.0021279063,0.039151106],"study_design_scores_gemma":[0.00008206268,0.0015910066,0.000044250333,0.000037337675,0.000020185416,0.000010965232,0.0000052249375,0.13488553,0.85338455,0.004982847,0.0047925757,0.00016348809],"about_ca_topic_score_codex":0.0000064908772,"about_ca_topic_score_gemma":0.000015306205,"teacher_disagreement_score":0.9753685,"about_ca_system_score_codex":0.000019425812,"about_ca_system_score_gemma":0.0001965177,"threshold_uncertainty_score":0.4848298},"labels":[],"label_agreement":null},{"id":"W4391843481","doi":"10.1038/s42256-024-00797-8","title":"A causal perspective on dataset bias in machine learning for medical imaging","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":68,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children","funders":"Engineering and Physical Sciences Research Council; Royal Academy of Engineering; Alan Turing Institute; Microsoft Research","keywords":"Perspective (graphical); Computer science; Artificial intelligence; Machine learning; Data science; Presentation (obstetrics); Causal inference; Causal structure; Debiasing; Causal model; Risk analysis (engineering); Psychology; Medicine; Cognitive science","score_opus":0.08040236266088432,"score_gpt":0.476153720258939,"score_spread":0.3957513575980547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391843481","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14100167,0.18671355,0.1853152,0.4397344,0.019792568,0.008599316,0.0026155445,0.0020062737,0.014221456],"genre_scores_gemma":[0.99301165,0.00059071847,0.00040136595,0.0034981456,0.00089750136,0.00007752131,0.0011659253,0.00004768854,0.00030950637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766755,0.00010398532,0.0005434866,0.0006720839,0.00057109294,0.0004418162],"domain_scores_gemma":[0.9977488,0.0014552028,0.0000580918,0.0002931585,0.00019839824,0.0002463388],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0012510797,0.00025680705,0.00031160802,0.00049831613,0.00012396286,0.000071852155,0.00022194616,0.0003296605,0.00061796524],"category_scores_gemma":[0.0056353477,0.00020715757,0.000113135866,0.0006685321,0.000106792206,0.00013039159,0.000053980537,0.0028999962,0.00017916458],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001347204,0.0006449654,0.02966413,0.0008232373,0.00011170176,0.0010427705,0.006196672,0.00055413845,0.00024520754,0.06040037,0.019484304,0.8794853],"study_design_scores_gemma":[0.00015790288,0.0010041061,0.001111036,0.0025262597,0.00011546105,0.00065507315,0.004686118,0.76681626,0.01702197,0.021031378,0.18419652,0.0006779322],"about_ca_topic_score_codex":0.0035814838,"about_ca_topic_score_gemma":0.0015447671,"teacher_disagreement_score":0.87880737,"about_ca_system_score_codex":0.00047497338,"about_ca_system_score_gemma":0.00050011906,"threshold_uncertainty_score":0.9994004},"labels":[],"label_agreement":null},{"id":"W4392850630","doi":"10.1038/s42256-024-00807-9","title":"Foundation model for cancer imaging biomarkers","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":171,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Cancer Institute; National Institutes of Health; Deutsche Forschungsgemeinschaft; European Commission","keywords":"Foundation (evidence); Medicine; Cancer; Medical physics; Oncology; Internal medicine; History; Archaeology","score_opus":0.013216732216401123,"score_gpt":0.36509031867862723,"score_spread":0.3518735864622261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392850630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027542573,0.018224467,0.95126194,0.023524525,0.0014665482,0.00044669185,0.00002142237,0.0002918938,0.0020082558],"genre_scores_gemma":[0.97324216,0.0005538741,0.020550499,0.0031480622,0.0005044164,0.0000711152,0.00009424946,0.000057834593,0.0017777899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987597,0.000015202433,0.00025477115,0.00042259827,0.00026574914,0.00028197703],"domain_scores_gemma":[0.99933314,0.00018022786,0.00003966397,0.00019809623,0.000114465096,0.00013441792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043670536,0.00018259294,0.00020297586,0.00021128384,0.00009865106,0.00010506723,0.00014491442,0.0001258717,0.00015932007],"category_scores_gemma":[0.00045844537,0.00014387371,0.00015784372,0.00029624338,0.00008673746,0.00012689069,0.00003652235,0.0009705693,0.000022940198],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010384817,0.000035004134,0.0017679312,0.00044081928,0.00013958738,0.00003952796,0.00028327483,0.0032309187,0.0037673886,0.010791252,0.0065861293,0.9728143],"study_design_scores_gemma":[0.00012118971,0.000022812797,0.0001268386,0.00034756714,0.00011300865,0.00007100685,0.000017377195,0.9449968,0.001534327,0.005383329,0.047117915,0.00014785948],"about_ca_topic_score_codex":0.000092223694,"about_ca_topic_score_gemma":0.000013931515,"teacher_disagreement_score":0.97266644,"about_ca_system_score_codex":0.00013570425,"about_ca_system_score_gemma":0.00018386201,"threshold_uncertainty_score":0.58669996},"labels":[],"label_agreement":null},{"id":"W4393097303","doi":"10.1038/s42256-024-00809-7","title":"Generative AI for designing and validating easily synthesizable and structurally novel antibiotics","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Generative grammar; Antibiotics; Computer architecture; Artificial intelligence; Biology; Microbiology","score_opus":0.019403947999985176,"score_gpt":0.342895393166996,"score_spread":0.32349144516701084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393097303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008823323,0.004815462,0.983829,0.0014916713,0.0005237264,0.00025885183,0.000029720417,0.00012739236,0.00010083037],"genre_scores_gemma":[0.5175186,0.00002542293,0.4819484,0.00038606458,0.000074845,0.0000014855444,0.0000036477718,0.00001086014,0.000030689323],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986343,0.0000766314,0.00023545447,0.00059933995,0.00022394494,0.00023035149],"domain_scores_gemma":[0.99771273,0.0018386656,0.0000531634,0.00018868898,0.00012912044,0.00007761565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063222955,0.00020562416,0.00018330898,0.00014080816,0.00018201169,0.00071661104,0.00037316684,0.0001256944,0.0000029811658],"category_scores_gemma":[0.0005750433,0.00017312655,0.000045346977,0.00035181825,0.00006776478,0.0005838729,0.00026744633,0.00048025578,0.0000012932853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014885654,0.0000193798,0.00026806266,0.00026069995,0.00007383341,0.000018141724,0.0012582109,0.031109868,0.024377258,0.6204291,0.0001209821,0.32204962],"study_design_scores_gemma":[0.00003766552,0.000051800977,0.0002455488,0.00011123964,0.000013085958,0.00009154652,0.000019049658,0.8465303,0.098195545,0.05417159,0.0003414633,0.0001911576],"about_ca_topic_score_codex":0.00001414466,"about_ca_topic_score_gemma":0.00000502579,"teacher_disagreement_score":0.81542045,"about_ca_system_score_codex":0.000033889883,"about_ca_system_score_gemma":0.000101746184,"threshold_uncertainty_score":0.7059896},"labels":[],"label_agreement":null},{"id":"W4393981121","doi":"10.1038/s42256-024-00816-8","title":"Tandem mass spectrum prediction for small molecules using graph transformers","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Metabolomics and Mass Spectrometry Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; Vector Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canadian Institute for Advanced Research; Vector Institute; Canada Research Chairs; University Health Network","keywords":"Computer science; Leverage (statistics); Tandem; Tandem mass spectrometry; Mass spectrum; Transformer; Graph; Spectral line; Collision; Pattern recognition (psychology); Biological system; Artificial intelligence; Fragmentation (computing); Mass spectrometry; Data mining; Algorithm; Theoretical computer science; Chemistry; Materials science; Physics","score_opus":0.012011276400544136,"score_gpt":0.27924272104054815,"score_spread":0.26723144464000403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393981121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034230813,0.037386153,0.92452323,0.00055809977,0.0012843864,0.00039748065,0.00023441364,0.000056840414,0.0013285583],"genre_scores_gemma":[0.98549944,0.0031729112,0.010134828,0.00019236428,0.0004926008,0.000029803728,0.00014723644,0.00004031722,0.00029049988],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988344,0.000021338556,0.00023258486,0.00048570984,0.00012247691,0.00030351864],"domain_scores_gemma":[0.99965024,0.000026921036,0.0000373383,0.00016875395,0.000054767235,0.00006196115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022702084,0.00022542861,0.00017941814,0.00014166054,0.0001175432,0.000056442183,0.00017704316,0.0002874012,0.000019573929],"category_scores_gemma":[0.00007064647,0.00019156071,0.00022343999,0.0002608138,0.00006432541,0.0000052439736,0.000033655953,0.00033346028,0.0000022069335],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015830003,0.00005646649,0.00068625325,0.00020253443,0.00054797926,0.000011798372,0.00007865241,0.0011592598,0.9581457,0.020679541,0.0008431104,0.017430441],"study_design_scores_gemma":[0.00017469906,0.0004811498,0.00016770144,0.000058606325,0.0001658897,0.000047662677,0.00009295611,0.028083663,0.8687059,0.016022861,0.08557398,0.00042492963],"about_ca_topic_score_codex":0.000018890021,"about_ca_topic_score_gemma":0.00007164985,"teacher_disagreement_score":0.9512686,"about_ca_system_score_codex":0.000026202419,"about_ca_system_score_gemma":0.000050965577,"threshold_uncertainty_score":0.7811619},"labels":[],"label_agreement":null},{"id":"W4400090810","doi":"10.1038/s42256-024-00860-4","title":"Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Sequence (biology); Computer science; Function (biology); Biomolecule; Sampling (signal processing); Peptide; Biological system; Artificial intelligence; Chemistry; Nanotechnology; Materials science; Biology","score_opus":0.0058044932883950864,"score_gpt":0.26122234536095823,"score_spread":0.2554178520725631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400090810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10460782,0.048652265,0.8275352,0.00045458405,0.0016529901,0.0001896702,0.00067546614,0.00016501006,0.01606699],"genre_scores_gemma":[0.98987013,0.0010561428,0.003844818,0.0008069319,0.0008962998,0.000014489079,0.0029970626,0.000022677215,0.000491429],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990302,0.000028099688,0.00021541127,0.00035595824,0.00018385443,0.00018651522],"domain_scores_gemma":[0.999553,0.00006598192,0.000047444428,0.00022659409,0.000059037233,0.00004793306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070487564,0.00019518523,0.0001375351,0.00003814358,0.00009758498,0.000079793215,0.00022139674,0.0003518363,0.0001477435],"category_scores_gemma":[0.00006085501,0.00015788963,0.000107494256,0.00012112731,0.000039371378,0.00001508298,0.00011145362,0.0003195952,0.00001771937],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075337244,0.00018440564,0.002474367,0.00022871123,0.0010516576,0.0001009174,0.0010374062,0.017108198,0.513726,0.17548698,0.021701429,0.26614654],"study_design_scores_gemma":[0.0001983996,0.00015373746,0.0007173731,0.00018083367,0.00006760714,0.000077278535,0.000054582957,0.040229883,0.31791377,0.04175279,0.5979462,0.00070752075],"about_ca_topic_score_codex":0.00013074264,"about_ca_topic_score_gemma":0.00020652713,"teacher_disagreement_score":0.8852623,"about_ca_system_score_codex":0.000016323067,"about_ca_system_score_gemma":0.000046303176,"threshold_uncertainty_score":0.6438553},"labels":[],"label_agreement":null},{"id":"W4402059671","doi":"10.1038/s42256-024-00884-w","title":"A step forward in tracing and documenting dataset provenance","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Provenance; Tracing; Computer science; Geology; Paleontology; Programming language","score_opus":0.038030677180901244,"score_gpt":0.4079694461843914,"score_spread":0.36993876900349015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402059671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0891527,0.043743003,0.8282089,0.009661293,0.009043858,0.0014253842,0.0018681823,0.00046176824,0.016434914],"genre_scores_gemma":[0.9944846,0.00004591003,0.003860433,0.00029751766,0.0000757685,0.0000069138164,0.00008550016,0.000008448731,0.0011349054],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99709016,0.00009178909,0.00055918127,0.0010642358,0.0008955204,0.00029913362],"domain_scores_gemma":[0.99805355,0.0010065206,0.00007922121,0.0007384733,0.000048927435,0.000073277515],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0056130923,0.0001539149,0.00018998065,0.00048846955,0.000107273285,0.0013399401,0.0008888415,0.000084696534,0.00012810732],"category_scores_gemma":[0.0022887003,0.00010836031,0.000043426404,0.0015708115,0.00007147353,0.000545101,0.0006417962,0.00066136976,0.00016252253],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011330696,0.000023899294,0.00170559,0.000045124198,0.00000825511,0.00009911135,0.00043901964,0.00040557227,0.000041806477,0.012975343,0.035643425,0.94860154],"study_design_scores_gemma":[0.00007500605,0.000036254947,0.0018065979,0.00032280284,0.000011271638,0.00004069314,0.0004882939,0.46466044,0.0006014792,0.03908136,0.492599,0.00027680537],"about_ca_topic_score_codex":0.00014038943,"about_ca_topic_score_gemma":0.00029257458,"teacher_disagreement_score":0.94832474,"about_ca_system_score_codex":0.00003465673,"about_ca_system_score_gemma":0.00003058549,"threshold_uncertainty_score":0.9996968},"labels":[],"label_agreement":null},{"id":"W4402666674","doi":"10.1038/s42256-024-00887-7","title":"Zero-shot transfer of protein sequence likelihood models to thermostability prediction","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Thermostability; Zero (linguistics); Sequence (biology); Chemistry; Biochemistry","score_opus":0.020881821493231874,"score_gpt":0.28034181348280157,"score_spread":0.2594599919895697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402666674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8525927,0.01530938,0.1290222,0.000366433,0.00032764996,0.00060233835,0.00041345705,0.000017824186,0.0013480322],"genre_scores_gemma":[0.99813026,0.0003199759,0.0011019503,0.00014320329,0.00010122896,0.000051134455,0.000030365385,0.000021806884,0.00010006463],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998873,0.000039260613,0.00026272875,0.00046087953,0.0001603216,0.00020380825],"domain_scores_gemma":[0.99943125,0.000016345228,0.0000166929,0.00033671968,0.00012699225,0.000072025505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024498443,0.00017910857,0.00015084112,0.00004804449,0.00004199384,0.000018901132,0.00024862055,0.00022770934,0.000016900549],"category_scores_gemma":[0.00004183206,0.00014808742,0.0001082195,0.00018433967,0.00006814265,0.000002007399,0.000087118824,0.00026950586,0.00000497303],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007935993,0.000041048832,0.00018355955,0.00008592337,0.00006619659,0.000001841186,0.0003580808,0.0016836274,0.9695583,0.0020739478,0.00007057042,0.025797537],"study_design_scores_gemma":[0.00004442331,0.00042040413,0.00033742705,0.00006842883,0.000023080167,0.000008815147,0.000039054692,0.0035430975,0.9792711,0.010791194,0.0052632424,0.00018971151],"about_ca_topic_score_codex":0.00006602812,"about_ca_topic_score_gemma":0.00006375771,"teacher_disagreement_score":0.14553759,"about_ca_system_score_codex":0.000017738343,"about_ca_system_score_gemma":0.00007813944,"threshold_uncertainty_score":0.60388297},"labels":[],"label_agreement":null},{"id":"W4402875125","doi":"10.1038/s42256-024-00903-w","title":"Development of AI-assisted microscopy frameworks through realistic simulation with pySTED","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Mila - Quebec Artificial Intelligence Institute; Ontario Brain Institute","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canada Research Chairs; National Science Foundation","keywords":"Microscopy; Nanotechnology; Computer science; Materials science; Physics; Optics","score_opus":0.007806803370787348,"score_gpt":0.34437236645541497,"score_spread":0.3365655630846276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402875125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0329671,0.0043063513,0.9613754,0.00009800537,0.00005379956,0.00018268004,0.000008852199,0.00008108157,0.000926733],"genre_scores_gemma":[0.9352383,0.000071631075,0.06378676,0.00031871366,0.000060631763,0.000012307458,0.00027721992,0.000030051408,0.00020436589],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988614,0.00003410315,0.00032975795,0.00041659208,0.00019970292,0.00015849496],"domain_scores_gemma":[0.9992472,0.000043908036,0.00008064619,0.0003866789,0.00020779694,0.00003380526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001575478,0.00019180232,0.00018123888,0.00007838288,0.00004709383,0.00004601661,0.00022949012,0.0005365855,0.000033654764],"category_scores_gemma":[0.00012049161,0.0001499394,0.000076279764,0.0003827869,0.00007930223,0.000008262952,0.000081295075,0.00072872743,0.000005168954],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026109267,0.00015907614,0.0006056527,0.00032486278,0.00043368083,0.000036530266,0.00054264645,0.005100127,0.92473,0.0010787942,0.0016518707,0.06507567],"study_design_scores_gemma":[0.000036380472,0.000093679635,0.00016003728,0.0001983698,0.00005970495,0.000012446979,0.000022970662,0.010301272,0.9593899,0.0003635677,0.029156355,0.0002052953],"about_ca_topic_score_codex":0.000033995748,"about_ca_topic_score_gemma":0.00013860111,"teacher_disagreement_score":0.9022712,"about_ca_system_score_codex":0.000030602743,"about_ca_system_score_gemma":0.000117710304,"threshold_uncertainty_score":0.6114351},"labels":[],"label_agreement":null},{"id":"W4403889525","doi":"10.1038/s42256-024-00912-9","title":"Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto; University Health Network","funders":"Deutsche Forschungsgemeinschaft","keywords":"Segmentation; Medicine; Radiology; Lesion; Nuclear medicine; Computer science; Medical physics; Computer vision; Artificial intelligence; Pathology","score_opus":0.01694126655382513,"score_gpt":0.3551445904687203,"score_spread":0.3382033239148952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403889525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67616975,0.027725363,0.00859011,0.24501969,0.0058517354,0.002150831,0.00021147155,0.003220226,0.031060824],"genre_scores_gemma":[0.99244934,0.0009947222,0.0018790949,0.0036995204,0.00036706484,0.000018502527,0.0002807553,0.000037885977,0.00027308214],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978798,0.00018947996,0.00049583876,0.00061840046,0.0005027932,0.00031369424],"domain_scores_gemma":[0.99820423,0.0011748613,0.000091287366,0.00037701722,0.000049698232,0.00010290709],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0010567226,0.00026039075,0.00028912985,0.00020014615,0.00011007428,0.0001109086,0.0002972156,0.00005290003,0.00011428975],"category_scores_gemma":[0.0014650724,0.00015882331,0.0001081741,0.0004829923,0.00010051105,0.00010038085,0.00008700029,0.0028573263,0.0001324073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008549518,0.0005074449,0.0028705674,0.00016070419,0.00011675404,0.013663781,0.0019734195,0.0038906955,0.0036483407,0.0037030948,0.017680695,0.9509295],"study_design_scores_gemma":[0.0006817279,0.00026729578,0.008136016,0.0013797998,0.00006665488,0.00054246717,0.00029370116,0.96148354,0.0018243958,0.0016779399,0.023398602,0.00024785128],"about_ca_topic_score_codex":0.00071013405,"about_ca_topic_score_gemma":0.000084701256,"teacher_disagreement_score":0.95759284,"about_ca_system_score_codex":0.0002584952,"about_ca_system_score_gemma":0.00011703265,"threshold_uncertainty_score":0.9994431},"labels":[],"label_agreement":null},{"id":"W4405374513","doi":"10.1038/s42256-024-00955-y","title":"Discussions of machine versus living intelligence need more clarity","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"CLARITY; Business; Computer security; Computer science; Biology","score_opus":0.02526274586213429,"score_gpt":0.3360349938133295,"score_spread":0.31077224795119524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405374513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2988637,0.041456368,0.56871116,0.021538446,0.030841842,0.0023418951,0.0012328489,0.0032556392,0.03175807],"genre_scores_gemma":[0.9964471,0.00035714448,0.0015030528,0.0005662545,0.00024283952,0.000013301616,0.000009213488,0.00005200874,0.0008091013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99680555,0.0002253907,0.0006855321,0.0010237581,0.000714568,0.00054518395],"domain_scores_gemma":[0.9959747,0.002876139,0.0001439376,0.00071248395,0.000093361356,0.0001993562],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005072398,0.00046017824,0.00042362648,0.00039081828,0.00020127854,0.00021305574,0.0014723492,0.00040294274,0.00054334913],"category_scores_gemma":[0.0022470711,0.00032574002,0.0002940702,0.0013923119,0.00040816815,0.00040639055,0.000648479,0.0025507433,0.00013240261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061587,0.0009547691,0.0012881499,0.0010621547,0.00018020724,0.0005382677,0.009040918,0.005244759,0.17773515,0.21217981,0.0033349837,0.58782494],"study_design_scores_gemma":[0.00008018332,0.0004102886,0.000488316,0.0010959076,0.0000634626,0.00013267777,0.00045277475,0.21106572,0.77225775,0.0059602098,0.0072790394,0.0007136582],"about_ca_topic_score_codex":0.00020100617,"about_ca_topic_score_gemma":0.00009113437,"teacher_disagreement_score":0.6975834,"about_ca_system_score_codex":0.00007921406,"about_ca_system_score_gemma":0.00008399983,"threshold_uncertainty_score":0.9999195},"labels":[],"label_agreement":null},{"id":"W4405419543","doi":"10.1038/s42256-024-00952-1","title":"Limitations in odour recognition and generalization in a neuromorphic olfactory circuit","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Olfactory and Sensory Function Studies","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Medical Research Council; Canadian Institutes of Health Research; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; UK Research and Innovation; National Science Foundation","keywords":"Neuromorphic engineering; Generalization; Neuroscience; Olfactory system; Computer science; Artificial intelligence; Pattern recognition (psychology); Psychology; Artificial neural network; Mathematics","score_opus":0.5551404066950919,"score_gpt":0.3029025578432834,"score_spread":0.2522378488518085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405419543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98580956,0.0035240818,0.0036626698,0.0011986229,0.0015000026,0.00035175806,0.00006336205,0.00019048789,0.0036994675],"genre_scores_gemma":[0.9975747,0.0011405163,0.000024483534,0.0007967303,0.000064301785,0.00001933004,0.000012929526,0.000015986594,0.0003510485],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987385,0.00018529405,0.0002620822,0.0004638162,0.00017514576,0.00017512888],"domain_scores_gemma":[0.99921346,0.0005749686,0.00003392933,0.00010730084,0.000029926307,0.00004044356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001798738,0.00014813196,0.00013351146,0.0004388411,0.00007602085,0.00007986203,0.00008739194,0.00014586959,0.00007484908],"category_scores_gemma":[0.0011479014,0.00013713061,0.00003239821,0.00084325194,0.00007483724,0.0002893487,0.0000342627,0.0008032854,0.00008671329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024510286,0.00056624773,0.037745852,0.00066426035,0.000029705157,0.0013018247,0.015869848,0.004399886,0.4125701,0.048420627,0.00087514793,0.47731143],"study_design_scores_gemma":[0.0011047728,0.0007640478,0.20386331,0.0013128548,0.00008612786,0.0008145019,0.0016560259,0.14884391,0.3726097,0.20449144,0.061755795,0.0026975016],"about_ca_topic_score_codex":0.000027417656,"about_ca_topic_score_gemma":0.00041393502,"teacher_disagreement_score":0.4746139,"about_ca_system_score_codex":0.000048204285,"about_ca_system_score_gemma":0.000024929883,"threshold_uncertainty_score":0.55920243},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"grok","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W4405515214","doi":"10.1038/s42256-024-00935-2","title":"Leveraging ancestral sequence reconstruction for protein representation learning","year":2024,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"University of British Columbia; Australian National University","keywords":"Sequence (biology); Embedding; Computer science; Representation (politics); Protein sequencing; Artificial intelligence; Machine learning; Sequence learning; Sequence space; Peptide sequence; Biology; Mathematics; Genetics","score_opus":0.023926692299639327,"score_gpt":0.32338062380146526,"score_spread":0.2994539315018259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405515214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.893343,0.013436587,0.090656176,0.0004263757,0.00086747383,0.00045044243,0.000023069266,0.000027265256,0.0007696227],"genre_scores_gemma":[0.9924271,0.00032986279,0.0060259015,0.000054496406,0.0003810144,0.00006043492,0.000048249556,0.000016904869,0.0006560138],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992023,0.000027277792,0.00015323926,0.0003830325,0.000073407755,0.00016073372],"domain_scores_gemma":[0.99971133,0.000019760068,0.00003932414,0.00012515408,0.00007561158,0.000028826504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001578117,0.00011920784,0.00008475856,0.00004072509,0.00010729874,0.0000540484,0.00011210231,0.00014106471,0.000007783301],"category_scores_gemma":[0.00015338248,0.000110075736,0.00007707319,0.000105508385,0.00004945567,0.0000023208972,0.00004257015,0.0002715105,0.0000036397955],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039542316,0.00000590087,0.0014839112,0.000057350644,0.00004724263,0.0000024236977,0.00011382415,0.0015971208,0.8025932,0.000884457,0.00008938068,0.19308566],"study_design_scores_gemma":[0.00007294684,0.00023745018,0.0005400952,0.000090946894,0.000020503026,0.00008683788,0.00017980089,0.013404247,0.949761,0.0056953477,0.029642932,0.00026789086],"about_ca_topic_score_codex":0.00002374043,"about_ca_topic_score_gemma":0.00001436039,"teacher_disagreement_score":0.19281776,"about_ca_system_score_codex":0.000016081403,"about_ca_system_score_gemma":0.00004306571,"threshold_uncertainty_score":0.44887584},"labels":[],"label_agreement":null},{"id":"W4406104136","doi":"10.1038/s42256-024-00960-1","title":"Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Mass Spectrometry Techniques and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"End-to-end principle; Tandem mass spectrometry; Tandem; Mass spectrometry; Computer science; Database; Chemistry; Chromatography; Computer security; Materials science","score_opus":0.010354761731488998,"score_gpt":0.31659866869427405,"score_spread":0.30624390696278503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406104136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035891687,0.0007176853,0.9410894,0.0037113419,0.00011436077,0.000516344,0.00047142376,0.0004951128,0.04929517],"genre_scores_gemma":[0.92369866,0.000078651836,0.07190173,0.00083951186,0.00019212713,0.00017352079,0.00052644714,0.000045223893,0.0025441044],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976269,0.000044698067,0.0004031271,0.00085885334,0.00047063656,0.00059581286],"domain_scores_gemma":[0.9979956,0.00061335554,0.00010449285,0.00076889276,0.000321657,0.00019600602],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050753093,0.00037512524,0.00038489513,0.00047574114,0.00029372342,0.000103618295,0.00073264644,0.00043549106,0.0022157282],"category_scores_gemma":[0.00051088526,0.00036091582,0.00020970382,0.0015092217,0.000099926976,0.00006642457,0.00019568703,0.0020319615,0.000033696528],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051788957,0.00059643557,0.0043175053,0.0008247968,0.0003239619,0.000077929464,0.00015854213,0.0022356943,0.53632194,0.33122155,0.0030618098,0.12034198],"study_design_scores_gemma":[0.00016362114,0.00009275772,0.00015869948,0.00009865177,0.000054100346,0.0000064229735,0.00009775943,0.024014963,0.93718916,0.0037849965,0.03397765,0.00036119667],"about_ca_topic_score_codex":0.00015324658,"about_ca_topic_score_gemma":0.00005942164,"teacher_disagreement_score":0.9201095,"about_ca_system_score_codex":0.00034348443,"about_ca_system_score_gemma":0.00016965948,"threshold_uncertainty_score":0.9998843},"labels":[],"label_agreement":null},{"id":"W4406334158","doi":"10.1038/s42256-024-00969-6","title":"Investigating machine moral judgement through the Delphi experiment","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Psychology of Moral and Emotional Judgment","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Naval Information Warfare Center Pacific; Defense Advanced Research Projects Agency; Allen Institute for Artificial Intelligence","keywords":"Morality; Judgement; Delphi; Delphi method; Computer science; Artificial intelligence; Engineering ethics; Psychology; Epistemology; Engineering; Philosophy","score_opus":0.0786320923324565,"score_gpt":0.3533610626654572,"score_spread":0.2747289703330007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406334158","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31227165,0.02720925,0.09048296,0.31606296,0.014527478,0.003186426,0.0001647593,0.0009992968,0.23509523],"genre_scores_gemma":[0.9556034,0.00015390743,0.0017325184,0.040693842,0.00012081091,0.00005971606,0.000009021076,0.000013266809,0.0016134831],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99788827,0.0001795611,0.000405507,0.0006417567,0.0004856974,0.00039919716],"domain_scores_gemma":[0.99895495,0.00030396023,0.00011855024,0.00050090376,0.000050825623,0.00007078114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002990898,0.0002835946,0.0002024069,0.000079404395,0.00048109205,0.000070563816,0.0009923936,0.00017576058,0.00027326358],"category_scores_gemma":[0.0005065446,0.00018150538,0.000118227756,0.0005690287,0.00039395466,0.00013965477,0.000354913,0.0011669194,0.00013239378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012266544,0.00068332424,0.0024703285,0.00007122229,0.00007184094,0.000053815318,0.0020959128,0.0030314117,0.08251484,0.818477,0.03482206,0.055585578],"study_design_scores_gemma":[0.00032306847,0.00016247164,0.0018638584,0.00014520917,0.000034405162,0.000052347677,0.00028220224,0.008631137,0.7656737,0.15455107,0.06783717,0.00044335448],"about_ca_topic_score_codex":0.00013657616,"about_ca_topic_score_gemma":0.00002578389,"teacher_disagreement_score":0.6831589,"about_ca_system_score_codex":0.00008850158,"about_ca_system_score_gemma":0.000044162312,"threshold_uncertainty_score":0.7401575},"labels":[],"label_agreement":null},{"id":"W4406405186","doi":"10.1038/s42256-024-00956-x","title":"The design space of E(3)-equivariant atom-centred interatomic potentials","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":154,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"FAS Division of Science, Harvard University; Division of Materials Research; Materials Research Science and Engineering Center, Harvard University; AstraZeneca; Engineering and Physical Sciences Research Council; Leverhulme Trust; Office of Science; Advanced Scientific Computing Research; U.S. Department of Energy; Science and Technology Facilities Council; Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Dell EMC; Harvard University; National Science Foundation","keywords":"Extrapolation; Computer science; Tensor (intrinsic definition); Equivariant map; Benchmark (surveying); Set (abstract data type); Theoretical computer science; Atom (system on chip); Cluster (spacecraft); Algorithm; Topology (electrical circuits); Artificial intelligence; Mathematics; Parallel computing; Geometry","score_opus":0.009464871633739156,"score_gpt":0.2865035647673488,"score_spread":0.2770386931336096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406405186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051006112,0.0064114714,0.9277981,0.0044093383,0.005427556,0.00094830187,0.00004502054,0.00026805451,0.0036860518],"genre_scores_gemma":[0.9738448,0.00013135755,0.024062723,0.00038195384,0.00008700594,0.000024893234,0.0000040316213,0.000023006456,0.0014402252],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9964681,0.00076806406,0.00084203744,0.0006777152,0.0006232374,0.0006208112],"domain_scores_gemma":[0.99680746,0.0013304511,0.00044959126,0.0010091573,0.00030579587,0.0000975302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024685597,0.00035058215,0.00044990727,0.00018576223,0.00046067446,0.0003528799,0.002292592,0.0002718388,0.00075137627],"category_scores_gemma":[0.0019940997,0.00022905308,0.00013951858,0.0007720246,0.0005599002,0.000200277,0.00059307506,0.0007483106,0.00020798757],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030905596,0.00008224198,0.0002654322,0.000077193785,0.000027235103,0.0000150319775,0.00031164227,0.0077589178,0.86611694,0.117655925,0.002390561,0.0049898294],"study_design_scores_gemma":[0.00013817183,0.00007913683,0.000363564,0.00021502582,0.000030845156,0.000021711676,0.0001071688,0.020440266,0.9545127,0.016187921,0.0076504643,0.0002530111],"about_ca_topic_score_codex":0.00022543632,"about_ca_topic_score_gemma":0.00003696943,"teacher_disagreement_score":0.9228387,"about_ca_system_score_codex":0.00010005538,"about_ca_system_score_gemma":0.00026055562,"threshold_uncertainty_score":0.93405133},"labels":[],"label_agreement":null},{"id":"W4406833231","doi":"10.1038/s42256-024-00942-3","title":"Moving towards genome-wide data integration for patient stratification with Integrate Any Omics","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hospital for Sick Children; Princess Margaret Cancer Centre; Canadian Institute for Advanced Research; Vector Institute; University of Toronto; University Health Network","funders":"","keywords":"Omics; Risk stratification; Genome; Computational biology; Stratification (seeds); Biology; Medicine; Bioinformatics; Genetics; Internal medicine; Gene","score_opus":0.010137433439886677,"score_gpt":0.2859616925736326,"score_spread":0.2758242591337459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406833231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06671008,0.0045425138,0.9244837,0.00096685096,0.00047063627,0.0007064166,0.00067575835,0.00002179515,0.0014222175],"genre_scores_gemma":[0.9801357,0.00076225953,0.013751868,0.001209362,0.00012869433,0.000055885514,0.0037609227,0.0000211773,0.00017417507],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99883497,0.000023331764,0.00028259592,0.00055685925,0.00010628774,0.00019597917],"domain_scores_gemma":[0.998672,0.00006701239,0.00012352092,0.00080525107,0.00028149819,0.000050695122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020568588,0.00019969164,0.00014766157,0.000074053176,0.000101032245,0.000088742076,0.0005410827,0.00023669319,0.000005628791],"category_scores_gemma":[0.00066026626,0.00016353973,0.000046980636,0.00017632866,0.00005782485,0.000010798448,0.0002005201,0.0002927312,0.0000015434965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014423458,0.0003298953,0.003724262,0.00017264196,0.00031618454,0.00000486354,0.00034457137,0.005515616,0.1903796,0.018423885,0.0064707072,0.7728754],"study_design_scores_gemma":[0.0003661998,0.00079689367,0.0023759755,0.00010834874,0.000116707466,0.000008365726,0.00045325884,0.028797036,0.8312163,0.003977772,0.13120823,0.00057496864],"about_ca_topic_score_codex":0.00009835492,"about_ca_topic_score_gemma":0.0011036169,"teacher_disagreement_score":0.91342556,"about_ca_system_score_codex":0.000057130692,"about_ca_system_score_gemma":0.0003006763,"threshold_uncertainty_score":0.6668957},"labels":[],"label_agreement":null},{"id":"W4408387458","doi":"10.1038/s42256-025-01007-9","title":"Transformers and genome language models","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Vector Institute; Public Health Ontario; University of Toronto; University Health Network","funders":"","keywords":"Computer science; Transformer; Computational biology; Genome; Biology; Gene; Genetics; Engineering; Electrical engineering","score_opus":0.004233397503920612,"score_gpt":0.258521215793566,"score_spread":0.2542878182896454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408387458","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.798831,0.10937577,0.0679981,0.0007578896,0.00028771904,0.00027678788,0.0000729081,0.000010220056,0.022389635],"genre_scores_gemma":[0.9948572,0.0026197238,0.0008006007,0.0008183472,0.00005129892,0.0000087248045,0.00002109399,0.000008484628,0.00081454706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994041,0.00001498564,0.00011681439,0.00026068467,0.000053396874,0.00015004638],"domain_scores_gemma":[0.99974644,0.0000110565725,0.000019198453,0.00015275161,0.00003545832,0.000035068555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000089838395,0.00012522483,0.00010516703,0.000042956737,0.000066779125,0.000014557473,0.0001393537,0.00016398015,0.000006411311],"category_scores_gemma":[0.000021829976,0.000108795226,0.00004710326,0.000081434315,0.00006482795,6.745222e-7,0.000081657185,0.00017481647,0.0000013981528],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010892622,0.000045987657,0.0011403055,0.00006565311,0.00019945636,0.0000048635748,0.0005136354,0.0010063674,0.9066639,0.008930247,0.0002149683,0.08110571],"study_design_scores_gemma":[0.0006857697,0.00048189546,0.012894816,0.00005586389,0.00014164993,0.000048105583,0.0012874099,0.0051947273,0.84115833,0.021775633,0.11518728,0.0010885],"about_ca_topic_score_codex":0.00003177043,"about_ca_topic_score_gemma":0.00007469717,"teacher_disagreement_score":0.1960262,"about_ca_system_score_codex":0.0000057318794,"about_ca_system_score_gemma":0.000023831282,"threshold_uncertainty_score":0.44365406},"labels":[],"label_agreement":null},{"id":"W4408627751","doi":"10.1038/s42256-025-01012-y","title":"Active exploration and reconstruction of vascular networks using microrobot swarms","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Micro and Nano Robotics","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Aerospace engineering; Business; Engineering","score_opus":0.008523555639919494,"score_gpt":0.2725900443200011,"score_spread":0.26406648868008165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408627751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06574911,0.0016564007,0.9312412,0.000060324895,0.0004074197,0.00014748072,0.000009220938,0.000010992041,0.00071783515],"genre_scores_gemma":[0.9947157,0.000108595224,0.004978161,0.000032583153,0.000088977846,0.0000022463087,0.000017378892,0.000006591286,0.00004980291],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99941975,0.000033047396,0.00019541511,0.00018335221,0.00005806094,0.000110345856],"domain_scores_gemma":[0.9995964,0.00004573137,0.00010166772,0.00013698751,0.000095939366,0.000023273715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087698216,0.00011333813,0.00015865521,0.00008383446,0.00007249413,0.00001819338,0.00008454724,0.000111515255,0.000044479053],"category_scores_gemma":[0.000008924009,0.00010332809,0.000060142174,0.00025080668,0.00005821516,0.00013342773,0.000047130274,0.0003716797,7.985078e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006931483,0.000091792135,0.01575584,0.000051727533,0.00023000318,5.621477e-7,0.00032436068,0.057480123,0.021154067,0.030496538,0.000071773924,0.8742739],"study_design_scores_gemma":[0.0003295586,0.000053752865,0.0013002456,0.00034976422,0.00020006174,0.000005301223,0.0007600824,0.24206237,0.71287066,0.0410078,0.00067097147,0.00038944025],"about_ca_topic_score_codex":0.0001508527,"about_ca_topic_score_gemma":0.00000816689,"teacher_disagreement_score":0.9289665,"about_ca_system_score_codex":0.00001856019,"about_ca_system_score_gemma":0.00003259022,"threshold_uncertainty_score":0.42135975},"labels":[],"label_agreement":null},{"id":"W4408883834","doi":"10.1038/s42256-025-01011-z","title":"A text-guided protein design framework","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Mila - Quebec Artificial Intelligence Institute; Université de Montréal; HEC Montréal; University of Toronto","funders":"","keywords":"Computer science","score_opus":0.019761338966663072,"score_gpt":0.3944418112685903,"score_spread":0.37468047230192725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408883834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026553555,0.002376252,0.81589776,0.034045525,0.0022446709,0.0011285988,0.000003811946,0.00025568283,0.14139235],"genre_scores_gemma":[0.95598483,0.00007499312,0.02658088,0.0025096,0.0002866951,0.00008806682,0.0000030754818,0.0000071253976,0.014464748],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986797,0.00030101484,0.0002119279,0.00025379405,0.00033205937,0.00022147797],"domain_scores_gemma":[0.9989908,0.00030196406,0.00007084623,0.0002475822,0.00032091627,0.00006790027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086681615,0.00010997858,0.00010885969,0.00013735749,0.00045230688,0.00015012674,0.0004643406,0.00040073035,0.00064299884],"category_scores_gemma":[0.0038441762,0.0001031458,0.000041260035,0.0015740517,0.00014538961,0.00013216023,0.000033174492,0.00070000795,0.000105726904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017578526,0.00006787466,0.0003133194,0.000008648817,0.00001050012,0.0000011121392,0.0031220107,0.00011537975,0.0001298617,0.93736476,0.011776798,0.047072142],"study_design_scores_gemma":[0.00005172727,0.000031682725,0.00042441426,0.00018855203,0.000015520074,5.551059e-7,0.0028305214,0.0008988326,0.019571884,0.5051966,0.47052163,0.00026813883],"about_ca_topic_score_codex":0.00033551513,"about_ca_topic_score_gemma":0.00034250223,"teacher_disagreement_score":0.95332944,"about_ca_system_score_codex":0.00015236874,"about_ca_system_score_gemma":0.00069054746,"threshold_uncertainty_score":0.7040386},"labels":[],"label_agreement":null},{"id":"W4410007609","doi":"10.1038/s42256-025-01033-7","title":"Lossless data compression by large models","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lossless compression; Compression (physics); Computer science; Data compression; Materials science; Algorithm; Composite material","score_opus":0.01614885772911826,"score_gpt":0.32290467102190135,"score_spread":0.30675581329278306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410007609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009632098,0.012155408,0.979853,0.0013547849,0.0013283213,0.00017559632,0.00040162372,0.00023091793,0.0044040494],"genre_scores_gemma":[0.9177517,0.0006180032,0.07554677,0.0034393077,0.00011114988,0.0000124254675,0.00085363525,0.00001882105,0.0016481503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782133,0.00008662524,0.0003251686,0.0009436883,0.00043962136,0.00038353584],"domain_scores_gemma":[0.9967923,0.0001717466,0.000098555254,0.0027183504,0.000115067836,0.00010396818],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00043484574,0.00024500125,0.00023975263,0.00013033359,0.00026948497,0.00024478952,0.0059543774,0.0002826069,0.000037605918],"category_scores_gemma":[0.000082078,0.00019065996,0.00004571112,0.00064820366,0.00004495026,0.001316762,0.0047754147,0.0010100004,0.00003620384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025452155,0.0003579322,0.00014004955,0.000056362427,0.000034880668,0.000037274614,0.00010768336,0.0008731496,0.000577431,0.3338642,0.28961393,0.37431163],"study_design_scores_gemma":[0.000101296944,0.000013090373,0.0000446931,0.00011263961,0.000005857604,0.0000050736403,0.000007683107,0.8540062,0.0049222847,0.027067643,0.11352141,0.00019214657],"about_ca_topic_score_codex":0.00009403581,"about_ca_topic_score_gemma":0.000017651102,"teacher_disagreement_score":0.9176554,"about_ca_system_score_codex":0.00003175387,"about_ca_system_score_gemma":0.00007234986,"threshold_uncertainty_score":0.99942386},"labels":[],"label_agreement":null},{"id":"W4411428681","doi":"10.1038/s42256-025-01044-4","title":"Generalized biological foundation model with unified nucleic acid and protein language","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"Limiting; Computer science; Foundation (evidence); Nucleic acid; Computational biology; Biological data; RNA; Artificial intelligence; Biology; Bioinformatics; Engineering; Genetics; Gene","score_opus":0.008705454329113174,"score_gpt":0.2724784050386553,"score_spread":0.2637729507095421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411428681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7556028,0.0032278053,0.2386031,0.00047535385,0.00005053153,0.0004107404,0.000009646233,0.000030735326,0.0015892865],"genre_scores_gemma":[0.9818397,0.00015055372,0.015717484,0.000581577,0.00004307888,0.00004762894,0.00006645217,0.000011548697,0.001541967],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920213,0.00006508963,0.00013402778,0.00035552768,0.00008746785,0.00015576316],"domain_scores_gemma":[0.9995879,0.000009369561,0.000050027345,0.00025660283,0.000054716693,0.000041406605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001665233,0.00016037597,0.00013073976,0.000048118392,0.00008241283,0.00003461505,0.0001878713,0.00031225634,0.00002772198],"category_scores_gemma":[0.00011191129,0.00011246534,0.00003474741,0.00011340891,0.000069651294,0.0000043071555,0.000089068475,0.00020813345,0.000004015822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025855174,0.000024144347,0.00009775564,0.000016657728,0.000027827535,0.0000029711696,0.000028880668,0.00009918658,0.9564614,0.015297981,0.00003570082,0.027648916],"study_design_scores_gemma":[0.00014758155,0.00015999562,0.000060220304,0.000031829284,0.000010488822,0.0000073472897,0.0000311996,0.0033227152,0.9903886,0.0039988477,0.0016818813,0.000159296],"about_ca_topic_score_codex":0.000018189485,"about_ca_topic_score_gemma":0.000035148918,"teacher_disagreement_score":0.22623691,"about_ca_system_score_codex":0.000008686879,"about_ca_system_score_gemma":0.00004322986,"threshold_uncertainty_score":0.45862034},"labels":[],"label_agreement":null},{"id":"W4413038007","doi":"10.1038/s42256-025-01072-0","title":"High-level visual representations in the human brain are aligned with large language models","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Computer science; Artificial intelligence; Brain activity and meditation; Natural language processing; Cognitive psychology; Psychology; Neuroscience; Electroencephalography","score_opus":0.01296838644040375,"score_gpt":0.3531533419880927,"score_spread":0.3401849555476889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413038007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061625924,0.0003331723,0.9154471,0.01879992,0.000086523214,0.00051357737,0.000027592087,0.00019774867,0.0029684498],"genre_scores_gemma":[0.97558147,0.000003092829,0.01985988,0.003905787,0.000046249428,0.00013504957,0.000038536793,0.0000129627,0.00041698918],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803793,0.00027458253,0.00031605794,0.00062401546,0.00041168593,0.00033574476],"domain_scores_gemma":[0.99802905,0.0005978076,0.00014200048,0.0010740418,0.00010966042,0.00004744312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000672017,0.00022414082,0.00020590553,0.00029482544,0.00033372283,0.00019666567,0.0019805818,0.00016557687,0.000018158005],"category_scores_gemma":[0.00024799866,0.00015515625,0.000058539674,0.0017108566,0.00006455926,0.00028062428,0.0003028882,0.0010004303,0.000020735391],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008891647,0.0002611256,0.00843823,0.000018825143,0.000021511969,0.00003232559,0.0028578478,0.012771342,0.00048935413,0.9685365,0.0007021826,0.0058618635],"study_design_scores_gemma":[0.0006001126,0.00009879271,0.21212922,0.00016002175,0.000023126178,0.00002981832,0.0011747243,0.6984182,0.0027325144,0.083466165,0.0006348056,0.00053250813],"about_ca_topic_score_codex":0.0025968044,"about_ca_topic_score_gemma":0.0025282842,"teacher_disagreement_score":0.9139555,"about_ca_system_score_codex":0.0000543869,"about_ca_system_score_gemma":0.000061770996,"threshold_uncertainty_score":0.6327088},"labels":[],"label_agreement":null},{"id":"W4413290922","doi":"10.1038/s42256-025-01088-6","title":"Boosting the predictive power of protein representations with a corpus of text annotations","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"Vector Institute; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Predictive power; Computer science; Natural language processing; Artificial intelligence; Physics","score_opus":0.005744969379399131,"score_gpt":0.29586281015831767,"score_spread":0.29011784077891856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413290922","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60587984,0.013962375,0.35938123,0.003585322,0.00026139457,0.0009811833,0.00018324949,0.00004253655,0.015722841],"genre_scores_gemma":[0.99575824,0.000028680373,0.0034604375,0.000121688194,0.000014985965,0.000026253265,0.000020811043,0.000004461765,0.00056447077],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993718,0.000048619644,0.0001900483,0.00017723005,0.000120128214,0.00009216147],"domain_scores_gemma":[0.9992922,0.00009746958,0.000118351876,0.00027019368,0.00020460523,0.00001721344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013678998,0.00008033296,0.00010381696,0.000044188775,0.00005884055,0.000005177458,0.00022979631,0.00014605274,0.000008733614],"category_scores_gemma":[0.0009395414,0.00004808392,0.000042738426,0.00029197018,0.0003517322,0.0000017638139,0.00007621475,0.00024815914,4.0484235e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022377898,0.0008529848,0.08150557,0.0003797465,0.0012206427,0.000014860941,0.003647037,0.002707012,0.56210744,0.03323651,0.005070467,0.30701995],"study_design_scores_gemma":[0.00022297636,0.00081166864,0.019378822,0.00024821586,0.00005497137,0.000012705593,0.0014679765,0.00068972254,0.9674702,0.0020510072,0.00745109,0.00014058984],"about_ca_topic_score_codex":0.000055003584,"about_ca_topic_score_gemma":0.000055435998,"teacher_disagreement_score":0.4053628,"about_ca_system_score_codex":0.000004243492,"about_ca_system_score_gemma":0.00009014233,"threshold_uncertainty_score":0.19608054},"labels":[],"label_agreement":null},{"id":"W4413337851","doi":"10.1038/s42256-025-01080-0","title":"The importance of negative training data for robust antibody binding prediction","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Training (meteorology); Antibody; Training set; Computer science; Medicine; Immunology; Artificial intelligence; Geography; Meteorology","score_opus":0.06454479274959338,"score_gpt":0.4149155629044256,"score_spread":0.3503707701548322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413337851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39058372,0.057819724,0.4202317,0.059232727,0.005006782,0.007131556,0.0073020956,0.0003419975,0.0523497],"genre_scores_gemma":[0.9848515,0.0012696788,0.0076253,0.00046618926,0.0002653995,0.00002508326,0.00074833946,0.000016688706,0.0047318568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864686,0.000037130303,0.0003756113,0.0003442404,0.00032120757,0.00027497404],"domain_scores_gemma":[0.9978834,0.0011404041,0.000112095906,0.00055561314,0.00024909407,0.00005942916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007794794,0.00013869707,0.00025143847,0.00011412673,0.00026051805,0.000023377499,0.00054870703,0.00015189381,0.000030454174],"category_scores_gemma":[0.0018395191,0.00008459544,0.00008729748,0.0004624639,0.00020919394,0.000085861,0.00021501197,0.0007612684,0.00000243889],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.006669248,0.0004310793,0.27446264,0.0017799679,0.0014692413,0.000054220178,0.0013333836,0.0004290497,0.028819788,0.10769781,0.023248201,0.5536054],"study_design_scores_gemma":[0.0019270127,0.0015248501,0.080213904,0.0026577131,0.00054801017,0.00016565029,0.0034042306,0.52968323,0.119482085,0.021166688,0.23854068,0.00068592204],"about_ca_topic_score_codex":0.00005233368,"about_ca_topic_score_gemma":0.00010229079,"teacher_disagreement_score":0.5942677,"about_ca_system_score_codex":0.000033318163,"about_ca_system_score_gemma":0.00019710818,"threshold_uncertainty_score":0.3449702},"labels":[],"label_agreement":null},{"id":"W4413337918","doi":"10.1038/s42256-025-01095-7","title":"Electron-density-informed effective and reliable de novo molecular design and optimization with ED2Mol","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Electron; Medicine; Materials science; Physics; Quantum mechanics","score_opus":0.002351546693073317,"score_gpt":0.2585464554660313,"score_spread":0.256194908772958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413337918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23575152,0.00074701646,0.76167613,0.00039900083,0.00015728381,0.00057291146,0.0000024508445,0.0001143848,0.0005792907],"genre_scores_gemma":[0.77117556,0.000088733716,0.22779325,0.00071806734,0.00002034992,0.00004576523,0.000003773394,0.000016529753,0.00013795315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982595,0.00021762801,0.00023565326,0.00057085167,0.00028157383,0.00043481926],"domain_scores_gemma":[0.99888414,0.0004335835,0.0001239088,0.00030047592,0.00015550265,0.00010239419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011200205,0.00026489247,0.0002745811,0.00017094673,0.00025585538,0.00026861322,0.0003373605,0.00021782285,0.000067752575],"category_scores_gemma":[0.0008685986,0.00020747156,0.000019635423,0.0004595149,0.0002623971,0.00025480954,0.00017560336,0.00053773104,0.000009249052],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057278195,0.000045076926,0.0024779537,0.00020292419,0.000021771368,0.00005181111,0.00034831217,0.5699512,0.41363704,0.010175682,0.00010082536,0.002414643],"study_design_scores_gemma":[0.00021489138,0.0002983525,0.00095006096,0.00015522989,0.000038569593,0.00014490227,0.000019619285,0.18589921,0.80865794,0.0031805264,0.00016120917,0.00027950926],"about_ca_topic_score_codex":0.00013719885,"about_ca_topic_score_gemma":0.000021406793,"teacher_disagreement_score":0.53542405,"about_ca_system_score_codex":0.000106792686,"about_ca_system_score_gemma":0.000180028,"threshold_uncertainty_score":0.8460445},"labels":[],"label_agreement":null},{"id":"W4414183195","doi":"10.1038/s42256-025-01109-4","title":"Aligning generalization between humans and machines","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Generalization; Abstraction; Cognition; Generative grammar; Key (lock); Human intelligence","score_opus":0.00935696220116273,"score_gpt":0.29798816362097796,"score_spread":0.28863120141981524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414183195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016605493,0.0015040396,0.98896384,0.0014607017,0.00047412614,0.00014559322,0.0000015074437,0.00021757865,0.005572068],"genre_scores_gemma":[0.95478946,0.00011067038,0.042203326,0.0011198617,0.00009465216,0.0000054695997,0.00001736428,0.000010013579,0.0016491819],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870926,0.00007142426,0.00030241773,0.00041910465,0.00025842534,0.00023935767],"domain_scores_gemma":[0.99910444,0.00018254078,0.00009998452,0.00045483463,0.00010119161,0.000056999706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003155791,0.00019143237,0.00018543004,0.00024643386,0.00022176176,0.00024131735,0.000862533,0.00019859667,0.000013182604],"category_scores_gemma":[0.00021514011,0.00017142584,0.000042835916,0.00065181556,0.000056817156,0.00029570083,0.0004366908,0.00059099565,0.00001278656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042295555,0.00001573165,0.12722863,0.00008746506,0.00007251012,0.000008876946,0.00056936604,0.13036968,0.00035765738,0.5805565,0.0010578332,0.15967152],"study_design_scores_gemma":[0.00011578773,0.00006360663,0.016993683,0.00011355143,0.000028680995,0.0000058078303,0.000011155237,0.9450496,0.009386623,0.016634157,0.011239593,0.00035775255],"about_ca_topic_score_codex":0.000048648533,"about_ca_topic_score_gemma":0.000010938527,"teacher_disagreement_score":0.95312893,"about_ca_system_score_codex":0.000036546975,"about_ca_system_score_gemma":0.000039758736,"threshold_uncertainty_score":0.6990543},"labels":[],"label_agreement":null},{"id":"W4414428208","doi":"10.1038/s42256-025-01086-8","title":"Error-controlled non-additive interaction discovery in machine learning models","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Robustness (evolution); Trustworthiness; Feature (linguistics); Deep learning; Class (philosophy); Feature engineering","score_opus":0.010909791565430808,"score_gpt":0.27465114743579716,"score_spread":0.26374135587036635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414428208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004833373,0.0019539937,0.9728516,0.0009026515,0.00067675475,0.00029448612,0.000009035978,0.000111847585,0.018366257],"genre_scores_gemma":[0.9925036,0.00015807844,0.0039938055,0.00043464344,0.000059916416,0.000030963743,0.000029894807,0.000012108983,0.0027770037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979356,0.0001497384,0.00058443425,0.0006555738,0.00029694164,0.00037769563],"domain_scores_gemma":[0.998722,0.0004372336,0.00023275682,0.0004283663,0.0001237169,0.000055923425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060412736,0.00029464837,0.00052804814,0.0005517554,0.00018655015,0.00033650838,0.0009236916,0.00024019372,0.00004472082],"category_scores_gemma":[0.00037050794,0.00024003691,0.00022061603,0.0013613175,0.000044923134,0.0015048906,0.00043471056,0.0020037869,0.000019342287],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070459314,0.00030173192,0.002723241,0.000053613938,0.000270263,0.00009582981,0.0017954384,0.43427157,0.0005446416,0.18393344,0.00027185888,0.37503377],"study_design_scores_gemma":[0.00041497155,0.000060638038,0.00027392647,0.00012746431,0.00002014142,0.0000077477725,0.00013952516,0.98724747,0.0016156189,0.008641596,0.0012289663,0.0002219092],"about_ca_topic_score_codex":0.0006695649,"about_ca_topic_score_gemma":0.00074941426,"teacher_disagreement_score":0.9876702,"about_ca_system_score_codex":0.00013855915,"about_ca_system_score_gemma":0.00006886902,"threshold_uncertainty_score":0.97884214},"labels":[],"label_agreement":null},{"id":"W4416388619","doi":"10.1038/s42256-025-01142-3","title":"Convolutional architectures are cortex-aligned de novo","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Convolutional neural network; Representation (politics); Feature (linguistics); Architecture; Key (lock); Visual cortex; Deep learning; Network architecture","score_opus":0.016142582528014742,"score_gpt":0.32353839404964563,"score_spread":0.30739581152163087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416388619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7459374,0.0017286084,0.17635778,0.010937165,0.0028374223,0.00083137845,0.0004046457,0.00072714523,0.060238443],"genre_scores_gemma":[0.98655397,0.00010386665,0.0006067528,0.01047151,0.00008679937,0.000017856113,0.000014119347,0.00001086102,0.002134238],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99864477,0.00014537356,0.00021823378,0.00042867067,0.00026932533,0.00029360433],"domain_scores_gemma":[0.99919456,0.0003554939,0.00007442277,0.00021632995,0.000074388925,0.000084818545],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016444255,0.00018129054,0.00015383071,0.00021550605,0.00020024013,0.00005622705,0.00035109025,0.00026356362,0.0011696238],"category_scores_gemma":[0.0011610761,0.0001601378,0.00010041349,0.0005108679,0.00016958134,0.000046816614,0.000070448805,0.0006855729,0.00025587788],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048635635,0.00045774286,0.011899591,0.00018583733,0.000031584674,0.00012679279,0.0006014,0.0017836019,0.7206487,0.11255438,0.0056836777,0.14554031],"study_design_scores_gemma":[0.00039683658,0.00009851217,0.060008597,0.00026047655,0.00003578175,0.00033309436,0.00014600212,0.016184403,0.81492513,0.07249729,0.034507785,0.00060608034],"about_ca_topic_score_codex":0.00003002999,"about_ca_topic_score_gemma":0.0001195141,"teacher_disagreement_score":0.24061659,"about_ca_system_score_codex":0.000091653455,"about_ca_system_score_gemma":0.000095700954,"threshold_uncertainty_score":0.99974346},"labels":[],"label_agreement":null},{"id":"W4416998002","doi":"10.1038/s42256-025-01155-y","title":"Structure as an inductive bias for brain–model alignment","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Artificial neural network; Inductive bias; Network structure; Neural system","score_opus":0.051609091894570525,"score_gpt":0.3813532778599522,"score_spread":0.3297441859653817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416998002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.688006,0.00023084358,0.27991587,0.012227934,0.0023910485,0.0019797317,0.0007877414,0.0003798918,0.014080924],"genre_scores_gemma":[0.97907937,0.000043595235,0.002139626,0.01619807,0.00008928492,0.00003689426,0.000049523598,0.000017549268,0.002346065],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985144,0.00011601764,0.00023254598,0.0006085288,0.0002641628,0.00026431787],"domain_scores_gemma":[0.9990965,0.00032735753,0.00007754773,0.0002964374,0.00010904935,0.00009307556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018256468,0.00021757036,0.00016978614,0.00018954236,0.0002095301,0.00008316095,0.00038069556,0.000361056,0.00037996258],"category_scores_gemma":[0.0013586773,0.00018422904,0.000091304675,0.00038489196,0.00009410896,0.00022879154,0.00006324095,0.00071814144,0.000045738794],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037309714,0.00025508116,0.0001643273,0.000088886234,0.000019499234,0.0000057169605,0.0016147706,0.0031752079,0.5265527,0.19450055,0.004747469,0.2685027],"study_design_scores_gemma":[0.00016668827,0.00015050107,0.00010895237,0.000039938554,0.0000153379,0.000012990064,0.00015354472,0.060447723,0.7624941,0.16603154,0.010138167,0.00024056382],"about_ca_topic_score_codex":0.0000329148,"about_ca_topic_score_gemma":0.00009960212,"teacher_disagreement_score":0.29107338,"about_ca_system_score_codex":0.00008581361,"about_ca_system_score_gemma":0.000096024574,"threshold_uncertainty_score":0.7512642},"labels":[],"label_agreement":null},{"id":"W4417269368","doi":"10.1038/s42256-025-01154-z","title":"Deciphering RNA–ligand binding specificity with GerNA-Bind","year":2025,"lang":"en","type":"article","venue":"Nature Machine Intelligence","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Virtual screening; MALAT1; Benchmark (surveying); RNA; Small molecule; Drug discovery; Deep learning; Binding site","score_opus":0.006392768097142391,"score_gpt":0.2615052933426644,"score_spread":0.255112525245522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417269368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68168277,0.0048956876,0.29698434,0.00034537693,0.00053647475,0.00032134476,0.000019162953,0.000041008523,0.015173853],"genre_scores_gemma":[0.9912291,0.00028261312,0.0050527253,0.00032367106,0.0001657963,0.00001659151,0.000020754273,0.000022582437,0.0028861286],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988402,0.000041463743,0.00020177488,0.00046637555,0.0001777237,0.00027244817],"domain_scores_gemma":[0.9993215,0.00003558854,0.00007780851,0.000420391,0.00007884339,0.00006586259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023816548,0.00022565547,0.0001757561,0.00007579773,0.00013851808,0.000059062237,0.00040713395,0.0003419011,0.00005709378],"category_scores_gemma":[0.00013354707,0.00017860418,0.000075024356,0.00023750364,0.00005962399,0.0000053239482,0.00013260067,0.0003875881,0.000019098616],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002817684,0.000038644397,0.0015482584,0.000028304321,0.000076911514,0.000017194927,0.000023478333,0.00012539075,0.9509269,0.0023275472,0.00027839124,0.044327218],"study_design_scores_gemma":[0.00009226938,0.00013270587,0.00021503464,0.000085797255,0.000016847845,0.000016566877,0.000037075984,0.00007452475,0.9757999,0.00074988126,0.022566458,0.00021294701],"about_ca_topic_score_codex":0.000024795248,"about_ca_topic_score_gemma":0.00007202173,"teacher_disagreement_score":0.30954638,"about_ca_system_score_codex":0.000022185906,"about_ca_system_score_gemma":0.000059276997,"threshold_uncertainty_score":0.72832674},"labels":[],"label_agreement":null}]}