{"meta":{"query_hash":"74bb47395206","filters":{"venue":"Nature Computational Science"},"cohort_total":39,"direct_labels_cover":0,"predictions_cover":39,"exported":39,"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/74bb47395206","api":"https://metacan.xera.ac/api/v1/cohort?venue=Nature+Computational+Science"},"results":[{"id":"W3164594474","doi":"10.1038/s43588-021-00075-2","title":"A dynamic metabolic map for diabetes","year":2021,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Pancreatic function and diabetes","field":"Medicine","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":"Queen's University","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Queen's University; Government of Canada; U.S. Department of Health and Human Services","keywords":"Diabetes mellitus; Carbohydrate metabolism; Type 2 diabetes; Computer science; Medicine; Endocrinology","score_opus":0.007742317669071546,"score_gpt":0.29877274639705576,"score_spread":0.29103042872798424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164594474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12514575,0.0018791575,0.820079,0.006090033,0.00068095844,0.00007929602,0.012165195,0.0021635345,0.03171709],"genre_scores_gemma":[0.8600296,0.0012351987,0.12892589,0.0003341907,0.0002189393,0.00017116927,0.0032335613,0.00023940408,0.0056120767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989533,0.00003443183,0.0000040203,0.000034448152,0.00001724333,0.000014491799],"domain_scores_gemma":[0.99962676,0.00022346937,0.000019938037,0.000038228765,0.000050875165,0.000040690975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023534238,0.00045831114,0.00052748295,0.00088572054,0.00051639753,0.0012303793,0.0007754885,0.00090733357,0.009144698],"category_scores_gemma":[0.0029749405,0.00035060084,0.000848459,0.000990431,0.00038559132,0.0014330428,0.0013418602,0.0010050462,0.00090648327],"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":[0.0003333458,0.00006832428,0.0035119196,0.00016380564,0.0001413718,0.00023851088,0.0001262538,0.7890612,0.00078460376,0.11733968,0.013653244,0.07457776],"study_design_scores_gemma":[0.000031736156,0.000017865843,0.0004101451,0.000019324678,0.000025798312,0.00005451595,0.000030590476,0.86904985,0.00014015354,0.12595074,0.004254502,0.000014807463],"about_ca_topic_score_codex":0.009790794,"about_ca_topic_score_gemma":0.0067092916,"teacher_disagreement_score":0.009790794,"about_ca_system_score_codex":0.00065320585,"about_ca_system_score_gemma":0.00070469436,"threshold_uncertainty_score":0.030592024},"labels":[],"label_agreement":null},{"id":"W3181323861","doi":"10.1038/s43588-021-00104-0","title":"Rapid protein model refinement by deep learning","year":2021,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","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":"Pontifical Institute of Mediaeval Studies; University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Graph; Artificial neural network; Refining (metallurgy); Machine learning; Theoretical computer science; Chemistry","score_opus":0.004223012420562871,"score_gpt":0.24831471483131465,"score_spread":0.2440917024107518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181323861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034960337,0.0007268724,0.95313925,0.00067626627,0.00018541495,0.00010313559,0.00050580053,0.006632974,0.0030698844],"genre_scores_gemma":[0.4912498,0.00058352447,0.49725947,0.0007662673,0.00013376452,0.00037038844,0.0021574765,0.0012428077,0.006236578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995377,0.000111388596,0.00002301755,0.00012255726,0.00013866516,0.0000667206],"domain_scores_gemma":[0.9978551,0.0011032583,0.00013183197,0.0005517165,0.00022577732,0.00013231007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011407797,0.0014307875,0.0023437645,0.0010263025,0.0007965724,0.0011552238,0.0036198343,0.0019965104,0.0064789443],"category_scores_gemma":[0.00445158,0.001676651,0.0016156866,0.001083549,0.0010747045,0.002130871,0.0025817868,0.0038693412,0.00206676],"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":[0.00029984638,0.00012854287,0.0010639239,0.00019990386,0.00020383456,0.00014776102,0.000060788017,0.8261827,0.004279217,0.0151571715,0.009811919,0.14246432],"study_design_scores_gemma":[0.00001481514,0.0000074681507,0.000028883165,0.0000034372483,0.000005614293,0.00000793188,0.000003403754,0.99326116,0.00040686718,0.005942095,0.0003151857,0.000003157745],"about_ca_topic_score_codex":0.011478159,"about_ca_topic_score_gemma":0.019460438,"teacher_disagreement_score":0.011478159,"about_ca_system_score_codex":0.0015409045,"about_ca_system_score_gemma":0.0022789303,"threshold_uncertainty_score":0.022822678},"labels":[],"label_agreement":null},{"id":"W3185878616","doi":"10.1038/s43588-021-00103-1","title":"A versatile model for single-cell data analysis","year":2021,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","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":"McGill University Health Centre","funders":"","keywords":"Inference; Computer science; Visualization; Cluster analysis; Gene regulatory network; Data visualization; Data mining; Data science; Artificial intelligence; Gene; Biology; Gene expression","score_opus":0.036703688733312,"score_gpt":0.30072461077465923,"score_spread":0.26402092204134725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185878616","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016250834,0.00004722937,0.99494064,0.00016260112,0.000051862007,0.00002947086,0.00063318515,0.0020681634,0.0004416919],"genre_scores_gemma":[0.14239591,0.00052317645,0.84345585,0.00048266564,0.00013135707,0.0011732155,0.0038711533,0.0029677383,0.004998818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993918,0.00016398892,0.000059303882,0.00014614983,0.0001894556,0.000049238402],"domain_scores_gemma":[0.99725515,0.0014165965,0.00011300777,0.00071886287,0.00032371728,0.00017263686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014861971,0.0008790721,0.0010814484,0.00076117716,0.0006808135,0.001974073,0.0030627148,0.0017114499,0.0068504578],"category_scores_gemma":[0.007222455,0.0007467972,0.0021202678,0.001110663,0.0007005772,0.0019229449,0.0018788805,0.0022769754,0.0030151973],"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":[0.00024643642,0.00009698251,0.0013997951,0.00028931946,0.00023694993,0.0005208069,0.00013088713,0.81918496,0.021117281,0.086518995,0.016107544,0.054149978],"study_design_scores_gemma":[0.000014748424,0.000008135003,0.00005729743,0.000005082246,0.00001271379,0.00005414552,0.0000044412086,0.96741784,0.0019107481,0.026630368,0.003873339,0.000011123738],"about_ca_topic_score_codex":0.0026808085,"about_ca_topic_score_gemma":0.0032020388,"teacher_disagreement_score":0.0068504578,"about_ca_system_score_codex":0.00074817665,"about_ca_system_score_gemma":0.0017166213,"threshold_uncertainty_score":0.022917032},"labels":[],"label_agreement":null},{"id":"W4220825386","doi":"10.1038/s43588-022-00219-y","title":"Bridge over troubled transcripts","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","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 of Toronto","funders":"","keywords":"Computer science; Bridge (graph theory); Transcriptome; Computational biology; RNA; Biology; Genetics; Gene; Gene expression; Anatomy","score_opus":0.010389623417122216,"score_gpt":0.26719349548255034,"score_spread":0.2568038720654281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220825386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1975775,0.0014685432,0.7294993,0.0043449895,0.00190159,0.00032288374,0.00668927,0.0075352644,0.05066067],"genre_scores_gemma":[0.8486248,0.00046402428,0.11796395,0.0014300944,0.00039063435,0.0004445593,0.0071034315,0.0039114975,0.019666977],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965873,0.00076357694,0.0001330636,0.0012882879,0.00067709194,0.0005507754],"domain_scores_gemma":[0.9880816,0.0062808944,0.00054145826,0.0033967828,0.0010622805,0.00063692685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003106999,0.00091958605,0.0012874466,0.0019844312,0.002765708,0.0042880434,0.0025381541,0.0030000925,0.026121924],"category_scores_gemma":[0.02597704,0.0008649358,0.0015871426,0.0024265538,0.0023133967,0.005496628,0.0075740144,0.0043180897,0.0054590493],"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":[0.001309013,0.0001543637,0.016244838,0.0009692736,0.00034037617,0.005492702,0.006327146,0.057574917,0.04108636,0.6360652,0.04263655,0.19179924],"study_design_scores_gemma":[0.00007059325,0.000078973804,0.0030790097,0.00018758397,0.00016846796,0.001106259,0.002105732,0.13409661,0.017061174,0.7733768,0.06858864,0.00008008751],"about_ca_topic_score_codex":0.0012809573,"about_ca_topic_score_gemma":0.0018037767,"teacher_disagreement_score":0.026121924,"about_ca_system_score_codex":0.000983269,"about_ca_system_score_gemma":0.00149193,"threshold_uncertainty_score":0.08738649},"labels":[],"label_agreement":null},{"id":"W4281786804","doi":"10.1038/s43588-022-00249-6","title":"Generative aptamer discovery using RaptGen","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":103,"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":"Core Research for Evolutional Science and Technology; Institute of Genetics; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; Autoencoder; In silico; Computer science; Artificial intelligence; Generative model; Computational biology; Bayesian probability; Hidden Markov model; Embedding; Machine learning; Pattern recognition (psychology); Generative grammar; Biology; Deep learning; Genetics; RNA; Gene","score_opus":0.009713030648766307,"score_gpt":0.31053726335478576,"score_spread":0.30082423270601943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281786804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015241071,0.00008472686,0.98242813,0.00008928318,0.000013673203,0.000027735347,0.00004357444,0.0005655817,0.001506235],"genre_scores_gemma":[0.5381507,0.00021044325,0.45576122,0.00029499823,0.000024812112,0.00023778762,0.0003713597,0.00032916054,0.004619529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956876,0.00015250382,0.00002028685,0.00010224805,0.00011450791,0.000041711155],"domain_scores_gemma":[0.9992366,0.0004782545,0.00006610775,0.000094957235,0.00008952143,0.00003457961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009308086,0.0005547104,0.0006326323,0.00054098864,0.0002997626,0.00061696745,0.00096279354,0.0010369053,0.001588492],"category_scores_gemma":[0.0021014172,0.00072601036,0.00095750834,0.00032811533,0.0008532628,0.00076855836,0.0010226839,0.0009938532,0.00042309068],"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":[0.00003237158,0.000030313686,0.00047562429,0.000050237348,0.000045343088,0.00007860777,0.00005133387,0.94117826,0.0071021123,0.021646595,0.00042045407,0.028888764],"study_design_scores_gemma":[0.0000024999842,0.0000066703014,0.000016557478,0.0000016267338,0.000002473162,0.000013542218,0.0000012567236,0.9957151,0.0012956887,0.0027057112,0.00023589989,0.0000030064884],"about_ca_topic_score_codex":0.0014439828,"about_ca_topic_score_gemma":0.002024651,"teacher_disagreement_score":0.001588492,"about_ca_system_score_codex":0.00071084953,"about_ca_system_score_gemma":0.0007022316,"threshold_uncertainty_score":0.005314052},"labels":[],"label_agreement":null},{"id":"W4281892973","doi":"10.1038/s43588-022-00253-w","title":"AI-powered aptamer generation","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","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 Victoria; University of Alberta","funders":"","keywords":"Aptamer; Identification (biology); Computer science; Task (project management); Artificial intelligence; Computational biology; Engineering; Biology; Systems engineering; Genetics","score_opus":0.007334456239723464,"score_gpt":0.30240960222991603,"score_spread":0.2950751459901926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281892973","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41010997,0.002589179,0.38300666,0.0020429632,0.0018141266,0.00043700062,0.0008764264,0.0046920874,0.19443157],"genre_scores_gemma":[0.93255883,0.00049321365,0.042262968,0.00042614227,0.00007179009,0.0001473436,0.00023875457,0.00016420633,0.023636773],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99985564,0.000010598447,0.0000051084053,0.0000315156,0.00006479736,0.000032207852],"domain_scores_gemma":[0.9998704,0.000043334177,0.000015095177,0.000026620253,0.00002810013,0.000016381722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016633623,0.0003005327,0.0002360273,0.0002447825,0.0003421936,0.0005882981,0.00062851916,0.0006328712,0.008068302],"category_scores_gemma":[0.00042717086,0.0001724699,0.00020278673,0.00028079905,0.00027979186,0.0005771481,0.00072807434,0.00077981554,0.0020589551],"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":[0.00024814138,0.000114895956,0.00046312297,0.00034245732,0.000032120657,0.0002631392,0.00014464425,0.012449402,0.8565396,0.03326431,0.0046512475,0.09148689],"study_design_scores_gemma":[0.00005498586,0.00015792531,0.00042310669,0.000014266403,0.000023530813,0.00030291526,0.00003497194,0.11819831,0.83419615,0.008737157,0.037825033,0.00003162705],"about_ca_topic_score_codex":0.00017933705,"about_ca_topic_score_gemma":0.00028385303,"teacher_disagreement_score":0.008068302,"about_ca_system_score_codex":0.00041526608,"about_ca_system_score_gemma":0.00019838006,"threshold_uncertainty_score":0.026991129},"labels":[],"label_agreement":null},{"id":"W4296126438","doi":"10.1038/s43588-022-00311-3","title":"Challenges and opportunities in quantum machine learning","year":2022,"lang":"en","type":"review","venue":"Nature Computational Science","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":652,"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":"Los Alamos National Laboratory; National Nuclear Security Administration; Office of Science; Verily Life Sciences; Advanced Scientific Computing Research; Laboratory Directed Research and Development; U.S. Department of Energy","keywords":"Quantum machine learning; Quantum; Intersection (aeronautics); Computer science; Focus (optics); Artificial intelligence; Quantum computer; Physics; Quantum mechanics; Engineering; Aerospace engineering","score_opus":0.08765781566270549,"score_gpt":0.33523982952755627,"score_spread":0.2475820138648508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296126438","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008775368,0.99449253,0.0006742748,0.0029417768,0.0004566309,0.0000021333549,0.000010669401,0.000007203144,0.0013270588],"genre_scores_gemma":[0.0014915452,0.9951734,0.00051504903,0.0012077047,0.001051901,0.000007548093,0.000016325805,0.0000042322476,0.0005322617],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995401,0.00015353641,0.00003656958,0.000055233533,0.00016827157,0.00004635578],"domain_scores_gemma":[0.997936,0.0014919917,0.000090159425,0.000051906598,0.0003387829,0.000091130896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019750774,0.00060049776,0.0011183023,0.0018303372,0.00050169695,0.0015827948,0.0010268443,0.0017746463,0.0033930552],"category_scores_gemma":[0.003468753,0.00031550758,0.0003468422,0.0024377622,0.0018399557,0.0042419597,0.0012028081,0.003247008,0.0011338665],"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":[0.00006900494,0.000055291737,0.00023879163,0.010713407,0.00006449308,0.00011499305,0.00012868765,0.0014881449,0.00065352046,0.14172582,0.06037613,0.7843717],"study_design_scores_gemma":[0.000009690067,0.000036683385,0.00022625845,0.0036325299,0.000034140605,0.00024009681,0.00007412304,0.0005201962,0.00022538645,0.052535024,0.94244754,0.000018295457],"about_ca_topic_score_codex":0.001676588,"about_ca_topic_score_gemma":0.0032263752,"teacher_disagreement_score":0.0033930552,"about_ca_system_score_codex":0.0014901827,"about_ca_system_score_gemma":0.0026083195,"threshold_uncertainty_score":0.01135087},"labels":[],"label_agreement":null},{"id":"W4300861583","doi":"10.1038/s43588-022-00315-z","title":"Homeostatic coordination and up-regulation of neural activity by activity-dependent myelination","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":21,"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; Hospital for Sick Children; University of Ottawa; University Health Network","funders":"CIHR Skin Research Training Centre; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Neuroscience; Myelin; Homeostatic plasticity; Oligodendrocyte; Neuroplasticity; Axon; Premovement neuronal activity; Neural activity; Homeostasis; Mechanism (biology); Biology; Chemistry; Central nervous system; Neurotransmission; Physics; Metaplasticity; Cell biology; Receptor","score_opus":0.011651492620085524,"score_gpt":0.26968826925444656,"score_spread":0.25803677663436103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300861583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79514676,0.00058205385,0.18884179,0.00048056783,0.00014320108,0.00002985943,0.0001510501,0.00067621435,0.013948545],"genre_scores_gemma":[0.99471754,0.00009877588,0.004190355,0.000025833444,0.00001815825,0.00001591616,0.00003108566,0.00007267202,0.0008296944],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998609,0.000024617832,0.000008898065,0.000044543136,0.000026270855,0.0000347283],"domain_scores_gemma":[0.9997217,0.000054108972,0.00006584996,0.00006232456,0.00003472545,0.000061315724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002034136,0.00021396017,0.0002583013,0.00022537616,0.00026367113,0.00088261435,0.0007477269,0.0003202876,0.0017002036],"category_scores_gemma":[0.0013322092,0.00023630269,0.00026210633,0.00017739348,0.00042737878,0.00092310936,0.0012315342,0.00039619222,0.000403521],"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":[0.00030508434,0.00013795284,0.006057819,0.00012805739,0.00010778189,0.0002811405,0.00027897817,0.07822373,0.7875279,0.05795102,0.0015797309,0.067420855],"study_design_scores_gemma":[0.00007685447,0.0002920269,0.041265145,0.00003465464,0.00007183081,0.00046576533,0.00020983642,0.7818838,0.081508584,0.08866779,0.00545504,0.00006875512],"about_ca_topic_score_codex":0.0004397465,"about_ca_topic_score_gemma":0.00060789654,"teacher_disagreement_score":0.0017002036,"about_ca_system_score_codex":0.00028510284,"about_ca_system_score_gemma":0.00027794138,"threshold_uncertainty_score":0.0056877136},"labels":[],"label_agreement":null},{"id":"W4300861812","doi":"10.1038/s43588-022-00320-2","title":"The role of ADM in brain function","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Neuroscience and Neuropharmacology Research","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":"McGill University","funders":"","keywords":"Brain function; Neuroscience; Neural activity; Information transmission; Brain activity and meditation; Transmission (telecommunications); Function (biology); Psychology; Physics; Biology; Computer science; Electroencephalography; Telecommunications; Cell biology","score_opus":0.016988920715276863,"score_gpt":0.34456873606098415,"score_spread":0.3275798153457073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300861812","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5090213,0.010552495,0.4003946,0.009410788,0.0010523789,0.000035397945,0.0015315184,0.00064823497,0.06735328],"genre_scores_gemma":[0.9841246,0.0013296492,0.0114650205,0.00026149032,0.00016315092,0.000011751583,0.00015791245,0.000093013085,0.0023933242],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997228,0.00010130264,0.000020000802,0.00007852392,0.000040449995,0.000036955113],"domain_scores_gemma":[0.99915946,0.00038328453,0.00008178764,0.00015440756,0.00010310727,0.00011790152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083692727,0.00036166763,0.00053646124,0.0003904685,0.00038824853,0.0017280795,0.00119959,0.0007492076,0.004255102],"category_scores_gemma":[0.0038731033,0.00024993333,0.00042048984,0.00039314057,0.0011117068,0.0027431143,0.0011640504,0.0011692023,0.0007248803],"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":[0.001053045,0.00031116867,0.031605896,0.0007542663,0.00040695144,0.001407899,0.00043087333,0.06296112,0.070939526,0.6010454,0.0055670417,0.22351679],"study_design_scores_gemma":[0.00010616794,0.00033545477,0.01535791,0.000097366465,0.00015405988,0.0015551635,0.00023453988,0.20886739,0.015688976,0.7412538,0.016292255,0.0000570393],"about_ca_topic_score_codex":0.0006675136,"about_ca_topic_score_gemma":0.00042184163,"teacher_disagreement_score":0.004255102,"about_ca_system_score_codex":0.00040339402,"about_ca_system_score_gemma":0.00046373825,"threshold_uncertainty_score":0.014234781},"labels":[],"label_agreement":null},{"id":"W4313366884","doi":"10.1038/s43588-022-00382-2","title":"A machine learning route between band mapping and band structure","year":2022,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":26,"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":"Horizon 2020 Framework Programme; Max-Planck-Gesellschaft; Deutsche Forschungsgemeinschaft; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Computer science; Artificial intelligence; Pipeline (software); Machine learning; Scalability; Electronic band structure; Physics; Database","score_opus":0.007556286675695394,"score_gpt":0.25754949926567006,"score_spread":0.24999321258997467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313366884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052281613,0.00041735082,0.98956674,0.0012890943,0.000060525723,0.000049746384,0.0003571306,0.0008013789,0.0022298472],"genre_scores_gemma":[0.15364785,0.00091798586,0.8405428,0.00044616728,0.00015030077,0.00020395103,0.0011452847,0.0002763663,0.0026692527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991904,0.0002163549,0.000043634365,0.0003156167,0.0001930097,0.00004102166],"domain_scores_gemma":[0.9975948,0.0013746673,0.00016718743,0.0005339003,0.00027867054,0.000050796938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019255336,0.0009006711,0.000903851,0.0021851417,0.0009575343,0.0041027768,0.0026995826,0.0016415524,0.004546893],"category_scores_gemma":[0.009635864,0.000869459,0.0011799942,0.0019679957,0.0018211285,0.004850687,0.0023770141,0.0035484184,0.0018505738],"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":[0.00009923712,0.0002771398,0.0037667998,0.00047690433,0.00017413154,0.00016112554,0.00034641253,0.19120765,0.0072279186,0.31111857,0.014095656,0.47104856],"study_design_scores_gemma":[0.000009115283,0.00002900574,0.0005090319,0.000049219077,0.000011607116,0.000054545435,0.000056625755,0.5478463,0.0020530182,0.44318435,0.0061749225,0.000022306893],"about_ca_topic_score_codex":0.0025835005,"about_ca_topic_score_gemma":0.002481022,"teacher_disagreement_score":0.004546893,"about_ca_system_score_codex":0.0011901412,"about_ca_system_score_gemma":0.0011284314,"threshold_uncertainty_score":0.015210807},"labels":[],"label_agreement":null},{"id":"W4360999013","doi":"10.1038/s43588-023-00419-0","title":"Multi-view manifold learning of human brain-state trajectories","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":41,"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":"National Institute of General Medical Sciences; Canadian Institute for Advanced Research; Institut de Valorisation des Données; Alfred P. Sloan Foundation; National Institutes of Health; National Science Foundation","keywords":"Manifold (fluid mechanics); Nonlinear dimensionality reduction; Cognitive science; Artificial intelligence; State (computer science); Computer science; Manifold alignment; Neuroscience; Psychology; Engineering; Algorithm","score_opus":0.04350426684592334,"score_gpt":0.3349643886223415,"score_spread":0.29146012177641817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360999013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16084541,0.0006461141,0.8349184,0.00083825673,0.00007925242,0.00006173733,0.0005020476,0.00086220587,0.0012465963],"genre_scores_gemma":[0.93017673,0.00032891968,0.06658608,0.000066287816,0.000047531972,0.00006145219,0.00084240787,0.00014792595,0.0017426355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974746,0.00008442579,0.000009611985,0.00010005589,0.000026854203,0.00003163691],"domain_scores_gemma":[0.99861395,0.00091436645,0.0001116474,0.00015488679,0.00013436381,0.00007065649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009265375,0.00055775064,0.0006324538,0.00075258216,0.00042798155,0.0008857121,0.00066726643,0.0010595472,0.0018060782],"category_scores_gemma":[0.0054453304,0.00050123397,0.0010761439,0.0007109566,0.0006857498,0.0015521566,0.0009631087,0.0019066483,0.0004994855],"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":[0.00034142326,0.0001224682,0.005475885,0.00013054501,0.00020756116,0.00013116123,0.00033881766,0.8248156,0.0069825556,0.02193205,0.0042141136,0.13530774],"study_design_scores_gemma":[0.0000027714623,0.000013403839,0.0007513816,0.0000043670716,0.0000038619773,0.00001711091,0.000010188471,0.99235183,0.00030768412,0.0063664233,0.00016468798,0.0000063099155],"about_ca_topic_score_codex":0.009671979,"about_ca_topic_score_gemma":0.009239471,"teacher_disagreement_score":0.009671979,"about_ca_system_score_codex":0.00077898666,"about_ca_system_score_gemma":0.0010050974,"threshold_uncertainty_score":0.019231379},"labels":[],"label_agreement":null},{"id":"W4367626474","doi":"10.1038/s43588-023-00437-y","title":"Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto; Fleming College","funders":"Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación; National Science Foundation; Vetenskapsrådet; Generalitat de Catalunya; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Adsorption; Artificial neural network; Organic molecules; Molecule; Graph; Computer science; Materials science; Chemistry; Biological system; Chemical engineering; Artificial intelligence; Organic chemistry; Theoretical computer science; Engineering","score_opus":0.0120194387643537,"score_gpt":0.28929276092240425,"score_spread":0.27727332215805056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367626474","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9416424,0.0009927383,0.044915315,0.0008669046,0.0001529238,0.000077047815,0.0023023223,0.0024541107,0.0065963487],"genre_scores_gemma":[0.9772026,0.00012455949,0.019186392,0.00013360799,0.000020807465,0.000034728906,0.0019148493,0.00009308972,0.0012893623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996791,0.00012210674,0.000011235957,0.00005709519,0.000087512184,0.00004302367],"domain_scores_gemma":[0.9988298,0.0007793733,0.00006235851,0.00008232714,0.0001919241,0.00005416019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085826614,0.00094250054,0.0005372628,0.0010973817,0.0004067522,0.00062860636,0.0011383226,0.0014145789,0.0019065193],"category_scores_gemma":[0.0029454217,0.0003047138,0.00058755756,0.00070242275,0.00051755435,0.0008462415,0.00053801126,0.00075821544,0.00032396574],"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":[0.00014707145,0.0000867513,0.0020655943,0.000091312555,0.00006084201,0.000047358735,0.000010576578,0.98231184,0.0022518707,0.0018913952,0.0017671764,0.0092682345],"study_design_scores_gemma":[0.0000048766474,0.000012117208,0.00017027723,0.0000015676176,0.0000015549837,0.000002670226,0.0000028497882,0.9984124,0.00077305094,0.0005373686,0.00007962192,0.0000017016213],"about_ca_topic_score_codex":0.015845427,"about_ca_topic_score_gemma":0.016420482,"teacher_disagreement_score":0.015845427,"about_ca_system_score_codex":0.001535589,"about_ca_system_score_gemma":0.00063335896,"threshold_uncertainty_score":0.03150636},"labels":[],"label_agreement":null},{"id":"W4368374143","doi":"10.1038/s43588-023-00440-3","title":"Score-based generative modeling for de novo protein design","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"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; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Generative grammar; Generative model; Computer science; Protein design; Generative Design; Modular design; Image (mathematics); Protein engineering; Artificial intelligence; Protein structure; Algorithm; Biology; Programming language; Engineering","score_opus":0.02197950252939696,"score_gpt":0.30495942800139725,"score_spread":0.2829799254720003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368374143","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013047876,0.00029527087,0.9829472,0.00028073587,0.000039753828,0.00005109378,0.00021669375,0.0014275188,0.0016939277],"genre_scores_gemma":[0.6673269,0.00048187905,0.32334757,0.0003058848,0.00013593383,0.0003386138,0.0013361908,0.0009836812,0.005743291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916387,0.00039129882,0.000042948213,0.000119722536,0.00021071496,0.0000713844],"domain_scores_gemma":[0.995141,0.0037459624,0.00016061087,0.00040515893,0.00038467158,0.00016249884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022708632,0.00085365673,0.0015183787,0.0012544648,0.0007543302,0.001273261,0.00275988,0.0017644698,0.0047955518],"category_scores_gemma":[0.008116674,0.0011620794,0.0015952338,0.001330251,0.0009879129,0.0014437035,0.0018562124,0.0019395781,0.0012641131],"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":[0.00007203164,0.000035425892,0.00043724346,0.000047184163,0.000051697225,0.000059609523,0.000040411327,0.93769664,0.0008200247,0.024923263,0.0012157154,0.03460074],"study_design_scores_gemma":[0.0000051747948,0.000004427693,0.000019563378,0.000002429542,0.000003692469,0.000005614857,0.0000015542042,0.9888825,0.00015112024,0.010743444,0.00017823509,0.000002227263],"about_ca_topic_score_codex":0.0060572666,"about_ca_topic_score_gemma":0.01141342,"teacher_disagreement_score":0.0060572666,"about_ca_system_score_codex":0.00146834,"about_ca_system_score_gemma":0.0015760709,"threshold_uncertainty_score":0.01604271},"labels":[],"label_agreement":null},{"id":"W4387580187","doi":"10.1038/s43588-023-00526-y","title":"A universal programmable Gaussian boson sampler for drug discovery","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Youth Innovation Promotion Association; Engineering and Physical Sciences Research Council; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Research Councils UK; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Chinese Academy of Sciences; UK Research and Innovation","keywords":"Computer science; Scalability; Clique; Quantum computer; Gaussian; Quantum circuit; Unitary state; Theoretical computer science; Parallel computing; Quantum; Computational science; Computer engineering; Quantum network; Mathematics; Physics","score_opus":0.013246576905900039,"score_gpt":0.28699608852504804,"score_spread":0.273749511619148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387580187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.102159455,0.0005220243,0.88504153,0.00072927505,0.00012520126,0.00009221089,0.00017953779,0.0024531495,0.008697502],"genre_scores_gemma":[0.82477343,0.000250402,0.17133704,0.00032880806,0.000036032223,0.00012223933,0.00014234152,0.00010834804,0.0029014484],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998073,0.000045104993,0.00000760224,0.00004180379,0.000067964545,0.00003025276],"domain_scores_gemma":[0.99976975,0.000100800484,0.000022112667,0.000047814403,0.000031816075,0.000027638594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047891107,0.00017061473,0.00030055016,0.0002698777,0.00025115878,0.00047532024,0.0007299421,0.0003721359,0.0026080294],"category_scores_gemma":[0.0011779206,0.00017486884,0.00021207171,0.0003352472,0.0008603811,0.00071850995,0.0007491837,0.00076386635,0.00043051442],"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":[0.000787008,0.00027526577,0.0022615897,0.00026687348,0.00007902295,0.00020214668,0.00014660008,0.18530303,0.16331649,0.41880462,0.0069878455,0.22156948],"study_design_scores_gemma":[0.000066749024,0.00011159026,0.00019648888,0.000011799286,0.00001631432,0.000051353334,0.000014154661,0.921055,0.032260146,0.040738553,0.0054584695,0.00001937656],"about_ca_topic_score_codex":0.0007606336,"about_ca_topic_score_gemma":0.0012209459,"teacher_disagreement_score":0.0026080294,"about_ca_system_score_codex":0.00057200115,"about_ca_system_score_gemma":0.0010752277,"threshold_uncertainty_score":0.008724749},"labels":[],"label_agreement":null},{"id":"W4388694106","doi":"10.1038/s43588-023-00553-9","title":"Accurately predicting molecular spectra with deep learning","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Computational Drug Discovery Methods","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":"University of Waterloo","funders":"","keywords":"Quantum chemistry; Spectral line; Identification (biology); Computer science; Chemistry; Computational chemistry; Biological system; Molecule; Physics; Organic chemistry; Quantum mechanics; Biology","score_opus":0.01621159098828589,"score_gpt":0.3282560073648352,"score_spread":0.3120444163765493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388694106","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19851427,0.0011136471,0.7889238,0.0011746181,0.0002018851,0.00007861815,0.0011022164,0.004455691,0.004435242],"genre_scores_gemma":[0.85294694,0.00063632947,0.14144251,0.0003889487,0.00010662211,0.00005628045,0.0011556521,0.00016870993,0.0030979442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997712,0.000053130407,0.000009667253,0.00005716338,0.000077749304,0.00003117784],"domain_scores_gemma":[0.9985734,0.0007826661,0.000154913,0.00019938362,0.00020722751,0.00008242053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064809585,0.00086839666,0.00063922827,0.0012509986,0.00033261473,0.0010294641,0.0009041319,0.0011555519,0.002134884],"category_scores_gemma":[0.0033334233,0.00052379415,0.00060593017,0.0007692739,0.0005641413,0.002328695,0.001156928,0.0021230127,0.0011081954],"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":[0.00059353636,0.0005676495,0.008691409,0.0003178079,0.00017496361,0.00022903048,0.000065338114,0.5840373,0.037537273,0.016251307,0.0102297515,0.3413047],"study_design_scores_gemma":[0.000006932604,0.000010748103,0.00014597867,0.000004125644,0.0000045310558,0.000013563675,0.0000048094807,0.9885244,0.002308691,0.008695572,0.00027627352,0.0000043026103],"about_ca_topic_score_codex":0.0025663285,"about_ca_topic_score_gemma":0.003957452,"teacher_disagreement_score":0.0025663285,"about_ca_system_score_codex":0.00059021765,"about_ca_system_score_gemma":0.00068651576,"threshold_uncertainty_score":0.007141888},"labels":[],"label_agreement":null},{"id":"W4388721914","doi":"10.1038/s43588-023-00544-w","title":"Predictive analyses of regulatory sequences with EUGENe","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","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":"U.S. Department of Health and Human Services; National Institutes of Health; National Institute of General Medical Sciences; Canadian Institute for Advanced Research; National Human Genome Research Institute; U.S. National Library of Medicine; Deutsche Forschungsgemeinschaft","keywords":"Deep learning; Workflow; Computer science; Interoperability; Artificial intelligence; Genomics; Set (abstract data type); Deep sequencing; Software; Data science; Machine learning; World Wide Web; Genome; Programming language; Biology; Database","score_opus":0.009639506463573306,"score_gpt":0.29832994330152096,"score_spread":0.28869043683794765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388721914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036778476,0.00020234191,0.8908987,0.00042268774,0.00009488785,0.000079331,0.0054422724,0.061078984,0.0050022705],"genre_scores_gemma":[0.30902717,0.0003724223,0.6614909,0.00054054294,0.000025273359,0.00040091475,0.012630654,0.006687029,0.0088250395],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935836,0.00011971193,0.00003890152,0.00022504567,0.00021240856,0.00004556084],"domain_scores_gemma":[0.9986129,0.000935002,0.00007835032,0.00022409893,0.00010982938,0.00003975443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017211755,0.00072550477,0.00042192973,0.00096499524,0.00060354656,0.0013323805,0.0016407555,0.0008367339,0.0073085777],"category_scores_gemma":[0.0051549454,0.0005948001,0.0014528123,0.0006298651,0.0007032124,0.0018491651,0.0015376448,0.001842962,0.0019343192],"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":[0.00078994053,0.00027495905,0.013044468,0.00040854927,0.00032537582,0.0005013667,0.00056934956,0.6402628,0.04224123,0.10395069,0.038433414,0.15919791],"study_design_scores_gemma":[0.00002364607,0.000043593096,0.0008478181,0.000024762536,0.00002053274,0.000042353666,0.00002847363,0.93264633,0.017103856,0.033566482,0.01561403,0.000038137263],"about_ca_topic_score_codex":0.0074499664,"about_ca_topic_score_gemma":0.013266589,"teacher_disagreement_score":0.0074499664,"about_ca_system_score_codex":0.0011406244,"about_ca_system_score_gemma":0.0012953941,"threshold_uncertainty_score":0.024449587},"labels":[],"label_agreement":null},{"id":"W4390274368","doi":"10.1038/s43588-023-00580-6","title":"Dendritic excitability controls overdispersion","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Dynamics (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interval (graph theory); Attractor; Neuronal firing; Neuroscience; Dispersion (optics); Computer science; Function (biology); Cable theory; Statistical physics; Dendrite (mathematics); Range (aeronautics); Biological system; Physics; Mathematics; Psychology; Biology; Mathematical analysis; Electrophysiology; Materials science","score_opus":0.017038512297765575,"score_gpt":0.2984889458744503,"score_spread":0.2814504335766847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390274368","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95120925,0.0004399919,0.038230125,0.0005591622,0.00012476298,0.000014710073,0.00020502096,0.0005440958,0.00867297],"genre_scores_gemma":[0.99849796,0.00008330033,0.00058896514,0.000034436678,0.000012168961,0.000004388181,0.00003428116,0.00008094661,0.00066362636],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999838,0.000014672806,0.000012503962,0.000049999304,0.00003317911,0.000051669907],"domain_scores_gemma":[0.99916816,0.00024365573,0.00012744464,0.00015156336,0.00012662241,0.00018253966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027679818,0.00020886977,0.00039431016,0.0003561112,0.00050811825,0.0010627847,0.0007289005,0.0004602754,0.0032458105],"category_scores_gemma":[0.0023436719,0.0001824,0.00027313904,0.00021680127,0.00030607,0.0012344198,0.0011698251,0.00046553003,0.00059859775],"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":[0.0006157303,0.00014540357,0.00726542,0.00016556193,0.00008631513,0.00048763302,0.00027960367,0.033271648,0.86231387,0.04985837,0.0019496998,0.04356069],"study_design_scores_gemma":[0.00014314747,0.00041953698,0.050455518,0.000048381455,0.00016109474,0.0014352051,0.00044800332,0.6608448,0.16321908,0.11490328,0.007791214,0.00013080084],"about_ca_topic_score_codex":0.0005277721,"about_ca_topic_score_gemma":0.000790439,"teacher_disagreement_score":0.0032458105,"about_ca_system_score_codex":0.0006134421,"about_ca_system_score_gemma":0.00042316673,"threshold_uncertainty_score":0.010858297},"labels":[],"label_agreement":null},{"id":"W4391105010","doi":"10.1038/s43588-023-00578-0","title":"Language models for quantum simulation","year":2024,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","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":"Vector Institute; University of Toronto; Perimeter Institute; University of Waterloo","funders":"","keywords":"Computer science; Quantum; Physics; Quantum mechanics","score_opus":0.011950994730520373,"score_gpt":0.31454271207674644,"score_spread":0.3025917173462261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391105010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050209355,0.0034061763,0.83375645,0.015606465,0.0010351965,0.00021073835,0.0017900157,0.001592647,0.09239288],"genre_scores_gemma":[0.88299304,0.0014543098,0.08362345,0.0016001177,0.0008945536,0.00054963987,0.0011767362,0.00080380245,0.026904408],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984219,0.0008388497,0.000078716665,0.00018766633,0.00028070092,0.00019220705],"domain_scores_gemma":[0.9952128,0.003031147,0.00020192658,0.0007634858,0.00051023177,0.0002803938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016363434,0.000861187,0.0022344869,0.0014617781,0.0024899866,0.0035162119,0.002445808,0.0026764139,0.017233633],"category_scores_gemma":[0.008515694,0.0009022987,0.0019281335,0.0013838916,0.003452777,0.006558655,0.0028764012,0.004364468,0.0023175648],"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":[0.000009730392,0.000011551656,0.000034235793,0.000020775875,0.000007372852,0.0000138621845,0.00006621198,0.0069149965,0.00009106119,0.9905201,0.0012059709,0.0011041748],"study_design_scores_gemma":[0.000005930139,0.0000023405967,0.000010809926,0.0000062179865,0.0000031295144,0.0000054176808,0.000017002896,0.04129985,0.000038794693,0.95736825,0.0012370427,0.000005274063],"about_ca_topic_score_codex":0.009019726,"about_ca_topic_score_gemma":0.0069473013,"teacher_disagreement_score":0.017233633,"about_ca_system_score_codex":0.0026890414,"about_ca_system_score_gemma":0.0019957465,"threshold_uncertainty_score":0.057652295},"labels":[],"label_agreement":null},{"id":"W4392058032","doi":"10.1038/s43588-024-00600-z","title":"Accelerating discovery in organic redox flow batteries","year":2024,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Advanced battery technologies research","field":"Engineering","cited_by":9,"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; Structural Genomics Consortium; Muscular Dystrophy Canada; University of Toronto","funders":"Canada First Research Excellence Fund","keywords":"Flow battery; Redox; Battery (electricity); Computer science; Energy storage; Field (mathematics); Flow (mathematics); Biochemical engineering; Data science; Nanotechnology; Environmental science; Process engineering; Engineering; Materials science; Chemistry; Physics; Inorganic chemistry","score_opus":0.01075449243694403,"score_gpt":0.29059302216844496,"score_spread":0.2798385297315009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392058032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57793057,0.014601247,0.30806762,0.014195121,0.0020660101,0.00037884168,0.0015719873,0.0034118148,0.07777676],"genre_scores_gemma":[0.8637416,0.009286344,0.11308892,0.0007341939,0.00037950513,0.00016304312,0.001035518,0.0001625584,0.011408353],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977046,0.00005903732,0.000008338346,0.000034324523,0.000087722576,0.000040108163],"domain_scores_gemma":[0.9993063,0.0003998048,0.000045995068,0.00008078571,0.000120108554,0.0000470146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011180419,0.00040575536,0.0005766873,0.0006617605,0.00044070286,0.0013636001,0.0011475508,0.0010234948,0.0057942304],"category_scores_gemma":[0.0038481986,0.00023930073,0.0004508084,0.00064071105,0.00045765148,0.0028770426,0.0013075722,0.0011123848,0.0009794305],"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":[0.0005620081,0.00047851825,0.004599156,0.0015975408,0.0001275138,0.00030018832,0.00024945513,0.12333419,0.032597728,0.469014,0.019111287,0.3480285],"study_design_scores_gemma":[0.00019014963,0.00022700512,0.00047354584,0.00010756055,0.00008780493,0.00010783858,0.00013013337,0.65208924,0.039396524,0.26617154,0.040986296,0.00003235694],"about_ca_topic_score_codex":0.00082402857,"about_ca_topic_score_gemma":0.0011233231,"teacher_disagreement_score":0.0057942304,"about_ca_system_score_codex":0.00058235606,"about_ca_system_score_gemma":0.0008496552,"threshold_uncertainty_score":0.01938361},"labels":[],"label_agreement":null},{"id":"W4402758992","doi":"10.1038/s43588-024-00676-7","title":"Using labels to limit AI misuse in health","year":2024,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","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":"Vector Institute","funders":"","keywords":"Limit (mathematics); Computer science; Artificial intelligence; Psychology; Mathematics","score_opus":0.22009227544467325,"score_gpt":0.5638664346535956,"score_spread":0.3437741592089224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402758992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10789387,0.0033601862,0.7783668,0.038878772,0.0018872039,0.00040518554,0.001414559,0.006111297,0.06168209],"genre_scores_gemma":[0.7463741,0.0008357541,0.23612165,0.005561143,0.00085213175,0.00034551203,0.0010931983,0.0007702724,0.008046277],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9812919,0.009537669,0.0014069142,0.0022000023,0.0047391364,0.0008243246],"domain_scores_gemma":[0.8239106,0.11689035,0.010652822,0.018899554,0.025759151,0.0038874317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015798723,0.00087753474,0.0009758269,0.0028412638,0.0023423878,0.006070406,0.0031037354,0.00385096,0.0058790366],"category_scores_gemma":[0.13928778,0.00067168294,0.00072128983,0.0016080122,0.003979914,0.012357149,0.005783766,0.00501495,0.0023321873],"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":[0.001476629,0.0011891663,0.054544713,0.00094430154,0.00021344972,0.0005119533,0.0053774365,0.023075236,0.010881596,0.14353295,0.03764555,0.720607],"study_design_scores_gemma":[0.00024330872,0.0006035215,0.010360756,0.0014528051,0.0003013141,0.0007840728,0.0031332416,0.2637831,0.026788719,0.5591412,0.13317966,0.00022826328],"about_ca_topic_score_codex":0.0048094443,"about_ca_topic_score_gemma":0.0049504093,"teacher_disagreement_score":0.015798723,"about_ca_system_score_codex":0.0023103126,"about_ca_system_score_gemma":0.004116631,"threshold_uncertainty_score":0.0835526},"labels":[],"label_agreement":null},{"id":"W4405248633","doi":"10.1038/s43588-024-00735-z","title":"A simulated annealing algorithm for randomizing weighted networks","year":2024,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Canada Research Chairs; Government of Canada; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Michael J. Fox Foundation for Parkinson's Research","keywords":"Simulated annealing; Algorithm; Computer science","score_opus":0.005995640519837306,"score_gpt":0.3093278762039367,"score_spread":0.3033322356840994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405248633","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00707573,0.000065523476,0.991248,0.00010476975,0.000030971885,0.000087699336,0.000055292556,0.00040663622,0.0009252407],"genre_scores_gemma":[0.16230129,0.00011971612,0.83304465,0.00015482653,0.00004636865,0.0007657314,0.00037791792,0.0003397661,0.0028496669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991497,0.00040582463,0.00004583536,0.00019497208,0.00014324799,0.000060334944],"domain_scores_gemma":[0.99710375,0.0020561272,0.000177366,0.0002934896,0.00027562777,0.00009364777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017817462,0.0009431469,0.0010089788,0.0010803599,0.0007691027,0.00085531734,0.0015823842,0.0013506647,0.0034394586],"category_scores_gemma":[0.007964104,0.00075580704,0.0010368787,0.0008414031,0.0010392324,0.0010962813,0.0012631429,0.0016804297,0.0008818766],"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":[0.000075885706,0.000043559376,0.00073480274,0.00005347522,0.00007747982,0.00006687406,0.00011007367,0.90891075,0.0029988831,0.034336876,0.0019208204,0.050670497],"study_design_scores_gemma":[0.00001439178,0.000017317783,0.000055007833,0.000004898518,0.000006305515,0.000011112995,0.0000065950403,0.986155,0.00041165127,0.012508298,0.0008042043,0.0000052192663],"about_ca_topic_score_codex":0.0037190611,"about_ca_topic_score_gemma":0.0053438516,"teacher_disagreement_score":0.0037190611,"about_ca_system_score_codex":0.0010916075,"about_ca_system_score_gemma":0.001347841,"threshold_uncertainty_score":0.01150614},"labels":[],"label_agreement":null},{"id":"W4407235352","doi":"10.1038/s43588-024-00764-8","title":"A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"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 Ottawa; McGill University; University of Calgary; Université Laval; University of Saskatchewan; Université de Montréal; Providence Health Care","funders":"National Institute on Minority Health and Health Disparities; National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Center for Advancing Translational Sciences; National Human Genome Research Institute; National Institute of Mental Health; National Heart, Lung, and Blood Institute; National Institute on Aging; National Cancer Institute; Mississippi State Department of Health; National Institute of Neurological Disorders and Stroke; Evans Medical Foundation; National Institute of Diabetes and Digestive and Kidney Diseases; Johns Hopkins University; Jackson State University; U.S. Department of Veterans Affairs; Wake Forest University; American Diabetes Association; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Trait; Whole genome sequencing; Computational biology; Scale (ratio); Biology; Evolutionary biology; Genetics; Genome; Statistics; Computer science; Mathematics; Gene; Geography; Cartography","score_opus":0.022848649728581607,"score_gpt":0.36980171363758896,"score_spread":0.3469530639090074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407235352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058770727,0.00010295946,0.99870193,0.00012631083,0.000019217543,0.000021365588,0.00010010016,0.0002765821,0.00006384234],"genre_scores_gemma":[0.072268836,0.00053538126,0.9236408,0.00035435206,0.0002906798,0.0005958489,0.0009157839,0.00045126272,0.0009470901],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9909444,0.006210074,0.0005817929,0.0009140837,0.0010944244,0.0002551357],"domain_scores_gemma":[0.93914086,0.053386178,0.0014746202,0.0030789024,0.002065033,0.0008543621],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02243458,0.0015048067,0.0032387374,0.0033247531,0.0014993693,0.0036368198,0.005593844,0.0022653253,0.003238979],"category_scores_gemma":[0.06334611,0.0015234157,0.0044552246,0.0039542485,0.0022952515,0.002437805,0.004434529,0.004665406,0.0009796143],"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":[0.00025506606,0.00019155961,0.0053505404,0.00039264865,0.0014132272,0.0006714192,0.0002692722,0.5900213,0.0020766824,0.2854946,0.0059046703,0.10795904],"study_design_scores_gemma":[0.000042390948,0.000026685735,0.00034573104,0.000022323,0.000085439315,0.000078527526,0.000015754438,0.8650923,0.00018372604,0.13262868,0.0014546121,0.00002383525],"about_ca_topic_score_codex":0.010771705,"about_ca_topic_score_gemma":0.012275928,"teacher_disagreement_score":0.9775654,"about_ca_system_score_codex":0.0011104002,"about_ca_system_score_gemma":0.0044933944,"threshold_uncertainty_score":0.1186468},"labels":[],"label_agreement":null},{"id":"W4408673029","doi":"10.1038/s43588-025-00774-0","title":"Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Birth, Development, and Health","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; Mila - Quebec Artificial Intelligence Institute; Montreal Neurological Institute and Hospital","funders":"National Institute on Drug Abuse; National Institute of Mental Health; National Institute on Aging","keywords":"Cohort; Geography; Cognition; Psychology; Statistics; Mathematics","score_opus":0.04359931459905438,"score_gpt":0.38064598308720177,"score_spread":0.3370466684881474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408673029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967334,0.00014871648,0.00062254316,0.00014514627,0.000015829888,0.0000057061284,0.0019456219,0.000017502281,0.00036550232],"genre_scores_gemma":[0.99720746,0.000086165404,0.0007527793,0.00004206902,0.000007839948,0.000017392407,0.0013620124,0.000010808402,0.00051337515],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99942064,0.00014776028,0.000053824268,0.00020637536,0.0000671351,0.00010427174],"domain_scores_gemma":[0.99651575,0.0011305766,0.00055829715,0.0009379596,0.00042912996,0.00042828525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002002141,0.00041223344,0.00041204898,0.0009398176,0.0006212362,0.0013972684,0.0007536411,0.0008275376,0.0024569016],"category_scores_gemma":[0.010462681,0.0003952109,0.0009820112,0.0010984291,0.00047930772,0.00038481777,0.0011973446,0.0010200299,0.00039254644],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017075145,0.000026182586,0.9951415,0.000009159871,0.00020874807,0.000051238676,0.00015058275,0.00041968838,0.00021661511,0.00028242668,0.00039263957,0.002930421],"study_design_scores_gemma":[0.000020072952,0.00009077896,0.9933694,0.000029316006,0.00028540072,0.0002575852,0.00062484056,0.0029507487,0.00027651514,0.00082856714,0.0012515268,0.000015179774],"about_ca_topic_score_codex":0.10620898,"about_ca_topic_score_gemma":0.05426505,"teacher_disagreement_score":0.10620898,"about_ca_system_score_codex":0.0005395318,"about_ca_system_score_gemma":0.0011453322,"threshold_uncertainty_score":0.21118152},"labels":[],"label_agreement":null},{"id":"W4410447861","doi":"10.1038/s43588-025-00806-9","title":"Computational challenges arising in algorithmic fairness and health equity with generative AI","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Neurological Disorders and Stroke; Gordon and Betty Moore Foundation","keywords":"Generative grammar; Equity (law); Health equity; Computer science; Theoretical computer science; Artificial intelligence; Economics; Political science; Health care; Economic growth","score_opus":0.05781304433646098,"score_gpt":0.47423159415029936,"score_spread":0.4164185498138384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410447861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10879203,0.0033731337,0.5874379,0.17763874,0.0008886939,0.00022202561,0.00038542584,0.00016765713,0.121094376],"genre_scores_gemma":[0.9641759,0.00040664102,0.02840334,0.0029408997,0.00065717497,0.00014863725,0.000048388585,0.000060236533,0.0031587887],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97649014,0.01864137,0.0004810398,0.001490071,0.0019900221,0.00090739754],"domain_scores_gemma":[0.83243614,0.15327771,0.0028348353,0.007250409,0.0023542573,0.001846721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033207115,0.0005285471,0.0015939197,0.0009857551,0.0023710309,0.0065688905,0.0032119881,0.0058725188,0.009544927],"category_scores_gemma":[0.14654094,0.0006983205,0.00086588284,0.00092231727,0.016657626,0.007271027,0.0056139133,0.006212713,0.00046407167],"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":[0.000023093877,0.000024257031,0.0008475402,0.000032677406,0.000024381994,0.000040403967,0.00024299831,0.014123233,0.000041105577,0.97789055,0.0009525062,0.0057571894],"study_design_scores_gemma":[0.000008579975,0.0000033111824,0.00010002382,0.000010394311,0.000003859604,0.000013990143,0.000046430698,0.014906129,0.000020860029,0.9842852,0.0005974906,0.000003786729],"about_ca_topic_score_codex":0.0040401397,"about_ca_topic_score_gemma":0.004099384,"teacher_disagreement_score":0.033207115,"about_ca_system_score_codex":0.0037390809,"about_ca_system_score_gemma":0.004731451,"threshold_uncertainty_score":0.17561811},"labels":[],"label_agreement":null},{"id":"W4411100483","doi":"10.1038/s43588-025-00798-6","title":"Advancing real-time infectious disease forecasting using large language models","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"COVID-19 epidemiological studies","field":"Mathematics","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":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Army Research Office; Centers for Disease Control and Prevention; Merck KGaA; National Science Foundation; U.S. Department of Health and Human Services; U.S. Department of Defense","keywords":"Infectious disease (medical specialty); Disease; Computer science; Medicine; Internal medicine","score_opus":0.07656215060837927,"score_gpt":0.4281039677513484,"score_spread":0.3515418171429691,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411100483","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15323338,0.0019049078,0.8298473,0.0071878335,0.0005303816,0.00004889281,0.0011185391,0.0018042822,0.0043245326],"genre_scores_gemma":[0.90335864,0.0007350972,0.09142089,0.00045550233,0.00045674318,0.000059149585,0.0010984765,0.00012926359,0.002286197],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993299,0.00030964037,0.000036234353,0.00015840649,0.00009592513,0.0000699486],"domain_scores_gemma":[0.98770845,0.0104058925,0.00049112766,0.0005227842,0.00061815185,0.00025356808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023749142,0.0007975002,0.0011110445,0.0012542713,0.00056904403,0.0023250056,0.001657441,0.0014452649,0.0031416009],"category_scores_gemma":[0.01702826,0.0006868165,0.00093562296,0.00093788235,0.000603092,0.0036770974,0.0014003936,0.0028704773,0.0010709047],"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":[0.00013854697,0.00013835802,0.005097318,0.00008311711,0.000099444595,0.00009398144,0.00011502345,0.92979956,0.0009766258,0.014844061,0.0036907226,0.044923224],"study_design_scores_gemma":[0.0000036080594,0.0000041647495,0.00006569311,0.0000021670178,0.0000030476203,0.0000030901774,0.0000066320927,0.99385023,0.00006393048,0.0058795456,0.00011600738,0.0000018785929],"about_ca_topic_score_codex":0.013383604,"about_ca_topic_score_gemma":0.01586531,"teacher_disagreement_score":0.013383604,"about_ca_system_score_codex":0.0010400449,"about_ca_system_score_gemma":0.0013842039,"threshold_uncertainty_score":0.026611388},"labels":[],"label_agreement":null},{"id":"W4413401677","doi":"10.1038/s43588-025-00853-2","title":"What’s so hard about RNA-targeting drug discovery?","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","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":"Drug discovery; Computational biology; Drug; Computer science; Biology; Pharmacology; Bioinformatics","score_opus":0.004718065492468867,"score_gpt":0.2872765304396582,"score_spread":0.2825584649471893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413401677","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025618741,0.08658742,0.08180493,0.7717295,0.011193085,0.00008413455,0.00090193417,0.0011849061,0.020895245],"genre_scores_gemma":[0.5762333,0.11807033,0.10945173,0.16603069,0.016619362,0.00040008567,0.0013778119,0.0009850515,0.010831549],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99627626,0.0018216015,0.00017069496,0.0004900271,0.000986447,0.00025499292],"domain_scores_gemma":[0.97468036,0.020769726,0.0008577202,0.0011985039,0.0014011142,0.0010925279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008960529,0.0010303422,0.0024761823,0.0010401173,0.0016068217,0.0050965096,0.0031688425,0.004620362,0.007259956],"category_scores_gemma":[0.036563277,0.0006577195,0.0018196366,0.0012712186,0.004858351,0.016296636,0.002364905,0.0075168754,0.0022435712],"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":[0.000603319,0.00056695304,0.006329534,0.005306034,0.0023565735,0.0007049717,0.00029075565,0.08463002,0.002459793,0.2933582,0.21541025,0.38798347],"study_design_scores_gemma":[0.00010946605,0.00008001684,0.00046006744,0.00045149235,0.0001729998,0.0002973488,0.00030491655,0.028378299,0.00079357123,0.916831,0.05207111,0.000049604565],"about_ca_topic_score_codex":0.0020329626,"about_ca_topic_score_gemma":0.0021334675,"teacher_disagreement_score":0.008960529,"about_ca_system_score_codex":0.0015019409,"about_ca_system_score_gemma":0.0031211623,"threshold_uncertainty_score":0.047388375},"labels":[],"label_agreement":null},{"id":"W4414162661","doi":"10.1038/s43588-025-00864-z","title":"Applying weighted Cox regression to genome-wide association studies of time-to-event phenotypes","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","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 of Waterloo","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Biobank; Minor allele frequency; Proportional hazards model; Genetic association; Type I and type II errors; Regression; Phenotype; Null hypothesis; Allele; Regression analysis","score_opus":0.008313976985895824,"score_gpt":0.31895641225372434,"score_spread":0.31064243526782853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414162661","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032667875,0.000687604,0.9644752,0.0006015613,0.00018397895,0.0000807172,0.00033002996,0.0006959595,0.00027709824],"genre_scores_gemma":[0.5414555,0.00096945395,0.4507553,0.0004206306,0.00038682035,0.0005409625,0.001134302,0.00050895865,0.003828063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.983257,0.013677949,0.0006312983,0.0013423868,0.00074403634,0.0003472447],"domain_scores_gemma":[0.90292805,0.08757537,0.0020262522,0.0053124046,0.0015660278,0.0005919081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03827969,0.0011333736,0.002202256,0.002303767,0.00066926464,0.0021947287,0.0035315962,0.0010956764,0.0033538903],"category_scores_gemma":[0.1097074,0.0012630437,0.0036266085,0.0037193596,0.0007199576,0.0018429514,0.0026179638,0.0022738285,0.0004929822],"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":[0.0019174027,0.00027072092,0.0743764,0.0006347452,0.011236899,0.0009114757,0.00045481866,0.61630297,0.001936895,0.049329594,0.0056414213,0.23698671],"study_design_scores_gemma":[0.00011732512,0.0000810858,0.0024087217,0.000024188374,0.0005158137,0.000106173175,0.000049918126,0.96290964,0.0002517714,0.032204133,0.0013055906,0.000025628811],"about_ca_topic_score_codex":0.018598633,"about_ca_topic_score_gemma":0.023834456,"teacher_disagreement_score":0.03827969,"about_ca_system_score_codex":0.0007112297,"about_ca_system_score_gemma":0.0030267052,"threshold_uncertainty_score":0.20244479},"labels":[],"label_agreement":null},{"id":"W4414249370","doi":"10.1038/s43588-025-00861-2","title":"On the compatibility of generative AI and generative linguistics","year":2025,"lang":"en","type":"review","venue":"Nature Computational Science","topic":"Natural Language Processing Techniques","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":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Generative grammar; Cognitive linguistics; Applied linguistics; Computational linguistics; Language and Communication Technologies; Theoretical linguistics; Quantitative linguistics; Compatibility (geochemistry)","score_opus":0.02464684558482763,"score_gpt":0.3833029036527605,"score_spread":0.3586560580679329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414249370","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000077678764,0.9927879,0.0010821909,0.0016000145,0.00029968328,0.0000025808063,0.000013802374,0.000008641771,0.004127586],"genre_scores_gemma":[0.0024386926,0.99292976,0.0012460555,0.0013388726,0.00077979575,0.000011884643,0.000041425264,0.0000094595,0.0012041951],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994784,0.00018014832,0.00004742862,0.00008933438,0.00017287675,0.00003187616],"domain_scores_gemma":[0.99599886,0.003218364,0.00012740133,0.00011716677,0.0004571041,0.00008115487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019264641,0.0007417582,0.0013344187,0.0030892247,0.00047733827,0.0019573243,0.001447641,0.0018199007,0.0055264547],"category_scores_gemma":[0.0045004687,0.00036822318,0.0003983712,0.0039004304,0.0021528536,0.0045079286,0.0013706478,0.0028451153,0.0027732619],"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":[0.000054571872,0.000045464243,0.00018861143,0.009121332,0.00010909884,0.00015141023,0.00014155392,0.0006055662,0.0004936069,0.17549649,0.050183743,0.7634086],"study_design_scores_gemma":[0.000014853806,0.000030577543,0.00039860694,0.0050762272,0.00008816446,0.0004352864,0.000061668274,0.0002473433,0.00025983178,0.06713356,0.9262328,0.000021064758],"about_ca_topic_score_codex":0.0023083251,"about_ca_topic_score_gemma":0.003288619,"teacher_disagreement_score":0.0055264547,"about_ca_system_score_codex":0.0014930989,"about_ca_system_score_gemma":0.0023399699,"threshold_uncertainty_score":0.018487811},"labels":[],"label_agreement":null},{"id":"W4414777573","doi":"10.1038/s43588-025-00880-z","title":"Proteoform search from protein database with top-down mass spectra","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"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; Bioinformatics Solutions (Canada)","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Deconvolution; Pipeline (software); Identification (biology); Graph; Basis (linear algebra); Search algorithm","score_opus":0.006857039943264111,"score_gpt":0.3020115019358871,"score_spread":0.295154461992623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414777573","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7567235,0.0049790586,0.093069986,0.00077249075,0.00033681723,0.0007312906,0.11156576,0.022665733,0.00915524],"genre_scores_gemma":[0.54132456,0.0031479683,0.20068723,0.00051424274,0.00013753734,0.0004407903,0.24609499,0.0012189021,0.006433772],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997347,0.000013439029,0.0000352181,0.00008639185,0.00007762171,0.000052639014],"domain_scores_gemma":[0.9997508,0.000036236892,0.000049348346,0.000039349332,0.000084611616,0.000039662227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003351766,0.001321989,0.00086181704,0.0045408024,0.00086756673,0.00089733384,0.0005937268,0.0005279175,0.004797263],"category_scores_gemma":[0.00092201855,0.00029662318,0.0014723557,0.0027984974,0.00017055617,0.00070330995,0.0009027485,0.0005211629,0.0026453226],"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":[0.00479656,0.0008170557,0.0313297,0.003036681,0.0006567287,0.0058975914,0.00033597386,0.00182794,0.7458576,0.002130908,0.04477291,0.15854038],"study_design_scores_gemma":[0.0005936685,0.0014512478,0.2214027,0.00045036475,0.0017935102,0.023347694,0.001041264,0.112952255,0.49041647,0.011151374,0.13507707,0.0003223828],"about_ca_topic_score_codex":0.0012326479,"about_ca_topic_score_gemma":0.0016811155,"teacher_disagreement_score":0.004797263,"about_ca_system_score_codex":0.00036241984,"about_ca_system_score_gemma":0.00085158146,"threshold_uncertainty_score":0.016048372},"labels":[],"label_agreement":null},{"id":"W4415045887","doi":"10.1038/s43588-025-00884-9","title":"Transforming psychiatry with computational and brain-based methods","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Functional Brain Connectivity Studies","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":"Trinity College","funders":"","keywords":"Interpretability; Normative; Mental health; Foundation (evidence); Grounded theory; MEDLINE","score_opus":0.014687276124040832,"score_gpt":0.3461333837988688,"score_spread":0.33144610767482796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415045887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00412565,0.0001494111,0.9891044,0.001182852,0.0002189889,0.0000334854,0.00014654064,0.00041568087,0.0046230964],"genre_scores_gemma":[0.25344354,0.0008476005,0.735019,0.0006068043,0.0005546719,0.00026506014,0.00041248385,0.00081301393,0.008037769],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993837,0.0002968144,0.000025828645,0.000076517754,0.00017544646,0.000041629442],"domain_scores_gemma":[0.99738926,0.0014817783,0.000182,0.000537151,0.00033362964,0.00007618915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015085255,0.00059499376,0.0005224668,0.0016774373,0.00043272384,0.0014070228,0.0009980615,0.00083321007,0.00864795],"category_scores_gemma":[0.011252781,0.00036124172,0.00097531645,0.0009713521,0.0011304794,0.0013484855,0.0022577227,0.0018749026,0.0018341402],"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":[0.00009571104,0.000093722796,0.0013981533,0.00019487437,0.0001397362,0.0001661979,0.00017509489,0.15960474,0.0022479168,0.66170406,0.01035516,0.16382466],"study_design_scores_gemma":[0.00003003468,0.000026102223,0.00045314548,0.000037374703,0.000021883207,0.000094169096,0.00005353725,0.5294195,0.0011539392,0.4579522,0.01074228,0.000015890504],"about_ca_topic_score_codex":0.0038264098,"about_ca_topic_score_gemma":0.0040483563,"teacher_disagreement_score":0.00864795,"about_ca_system_score_codex":0.0006538025,"about_ca_system_score_gemma":0.0015904133,"threshold_uncertainty_score":0.028930247},"labels":[],"label_agreement":null},{"id":"W4415208579","doi":"10.1038/s43588-025-00886-7","title":"ECloudGen: leveraging electron clouds as a latent variable to scale up structure-based molecular design","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Click Chemistry and Applications","field":"Chemistry","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":"McGill University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Latent variable; Benchmark (surveying); Chemical space; Latent variable model; Generative grammar; Variable (mathematics); Scale (ratio); Generative model","score_opus":0.007204081071840983,"score_gpt":0.28553214721250764,"score_spread":0.27832806614066663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415208579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061920565,0.00053721206,0.9265871,0.00053613674,0.00013517526,0.000113350805,0.00073962985,0.00570294,0.0037278472],"genre_scores_gemma":[0.66005474,0.00040974168,0.33409297,0.00034550365,0.000053733907,0.00019715326,0.0010721051,0.00064779964,0.0031262275],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995096,0.00020025707,0.000014745519,0.000085663465,0.00014710399,0.000042568652],"domain_scores_gemma":[0.9988049,0.00071399944,0.000073309064,0.0002725885,0.00008285232,0.000052243737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001164788,0.00048052528,0.00067607604,0.00040789146,0.0003378786,0.0009833346,0.0012146628,0.0008221427,0.0031508969],"category_scores_gemma":[0.003385829,0.00030585297,0.00078132364,0.00047676283,0.0005652442,0.0015512044,0.0013780803,0.0013779129,0.0006557751],"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":[0.0004775277,0.0004402595,0.0037193054,0.00032220737,0.00025507848,0.00014888782,0.000115066956,0.7490629,0.011020075,0.08632953,0.007507212,0.14060192],"study_design_scores_gemma":[0.000041558058,0.000046381392,0.0000951512,0.000006510713,0.000013658191,0.000008474564,0.0000055816367,0.97994107,0.0023581984,0.015952496,0.001523658,0.0000073305932],"about_ca_topic_score_codex":0.001782031,"about_ca_topic_score_gemma":0.003891008,"teacher_disagreement_score":0.0031508969,"about_ca_system_score_codex":0.0005413234,"about_ca_system_score_gemma":0.0008148433,"threshold_uncertainty_score":0.010540843},"labels":[],"label_agreement":null},{"id":"W4416466606","doi":"10.1038/s43588-025-00913-7","title":"Viability of using LLMs as models of human language processing","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","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 of Alberta","funders":"","keywords":"Human language; Language model; Language technology; Universal Networking Language; On Language; Computational linguistics; Work (physics)","score_opus":0.02732870010255197,"score_gpt":0.3824924493057228,"score_spread":0.35516374920317084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416466606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1315018,0.0004970921,0.83468866,0.0029829165,0.00021249364,0.000052379382,0.00023213116,0.0010234613,0.028809102],"genre_scores_gemma":[0.9334785,0.00023348322,0.05930166,0.00025342574,0.00011620029,0.00010105443,0.000087619694,0.00016321361,0.0062649557],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996501,0.00021897629,0.000010765179,0.000040344235,0.00005469523,0.000025126006],"domain_scores_gemma":[0.99768233,0.0016134437,0.000085211446,0.00033273423,0.00016274874,0.00012366603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010255553,0.00047411342,0.00062610075,0.0003713228,0.0003435557,0.0013227295,0.0012637976,0.0010565907,0.0035199379],"category_scores_gemma":[0.005339061,0.00025022682,0.000534528,0.00021313349,0.00086371764,0.0025103744,0.00080677867,0.0011072356,0.0007019282],"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":[0.00020977168,0.00009512157,0.0014960235,0.00012554944,0.00010231024,0.000106135,0.00021684468,0.43394807,0.005786403,0.5320603,0.0016930405,0.024160432],"study_design_scores_gemma":[0.000011223567,0.00002039375,0.00008780479,0.0000045277907,0.000006228593,0.000014259132,0.000014527879,0.83798796,0.00054548203,0.1605478,0.00075474306,0.000005057131],"about_ca_topic_score_codex":0.0016912958,"about_ca_topic_score_gemma":0.001605748,"teacher_disagreement_score":0.0035199379,"about_ca_system_score_codex":0.00072899426,"about_ca_system_score_gemma":0.0007953081,"threshold_uncertainty_score":0.011775434},"labels":[],"label_agreement":null},{"id":"W4416592793","doi":"10.1038/s43588-025-00937-z","title":"Publisher Correction: On the compatibility of generative AI and generative linguistics","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Language and cultural evolution","field":"Social Sciences","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":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Generative grammar; Compatibility (geochemistry); Theoretical linguistics; Generative model","score_opus":0.012990366920664718,"score_gpt":0.3446690089765777,"score_spread":0.33167864205591296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416592793","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033340915,0.0011179028,0.0016574812,0.09655199,0.8786316,0.000023537614,0.0018082042,0.0006990842,0.019176837],"genre_scores_gemma":[0.049577937,0.005441305,0.006199722,0.07614067,0.4096229,0.00020338983,0.003702309,0.0030747596,0.44603685],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931965,0.0015336805,0.000929093,0.0010027282,0.002772273,0.0005657694],"domain_scores_gemma":[0.9181448,0.020168645,0.0022871513,0.008798936,0.048731614,0.001868774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060646576,0.0016934832,0.0020654663,0.005503697,0.005299607,0.008199562,0.0046801586,0.008030593,0.09489094],"category_scores_gemma":[0.092609204,0.0010889351,0.0012863396,0.0044490914,0.0046567074,0.0050660106,0.0032696456,0.01288617,0.045414265],"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":[0.00002468051,0.0000035173532,0.000065665234,0.00007896182,0.000014728766,0.0001222973,0.00006271883,0.000045860957,0.000029245339,0.005595007,0.99014074,0.0038166295],"study_design_scores_gemma":[0.000058422967,0.000016059515,0.0008304291,0.0003710685,0.000064001164,0.00039335105,0.00015565573,0.0004337854,0.0004759162,0.011782967,0.98536474,0.000053585503],"about_ca_topic_score_codex":0.018471565,"about_ca_topic_score_gemma":0.01968969,"teacher_disagreement_score":0.09489094,"about_ca_system_score_codex":0.0043861787,"about_ca_system_score_gemma":0.0060169813,"threshold_uncertainty_score":0.31744182},"labels":[],"label_agreement":null},{"id":"W4416850705","doi":"10.1038/s43588-025-00923-5","title":"Identifying variants of molecules through database search of mass spectra","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Microbial Natural Products and Biosynthesis","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":"Ontario Centre of Excellence for Child and Youth Mental Health","funders":"National Institute of General Medical Sciences; U.S. Department of Health and Human Services; U.S. Department of Energy; National Institutes of Health; National Science Foundation","keywords":"PubChem; Identification (biology); Molecule; Mass spectrometry; Mass spectrum; Streptomyces; Database search engine","score_opus":0.023212658471758726,"score_gpt":0.344988771703539,"score_spread":0.32177611323178024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416850705","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8729698,0.0020368493,0.10921335,0.00033998562,0.000090799935,0.0001320446,0.010215749,0.002419711,0.0025817081],"genre_scores_gemma":[0.90688604,0.000715023,0.080736086,0.00009399611,0.000032025917,0.000047645994,0.010779206,0.00015472322,0.0005553787],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950576,0.00005910387,0.000049199803,0.00020561437,0.00013512145,0.00004525104],"domain_scores_gemma":[0.99882716,0.0005467783,0.00021911676,0.00017345253,0.00016234841,0.0000712355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007205238,0.00070784637,0.00085919286,0.0034752898,0.0004384849,0.0015131962,0.0012192724,0.00068796356,0.0019950827],"category_scores_gemma":[0.0028205654,0.00029960118,0.0012355973,0.0028744654,0.00036202173,0.0012575255,0.0007567544,0.00057055114,0.0005167937],"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":[0.00610278,0.0013530992,0.25668496,0.0017972882,0.0027155546,0.0050405925,0.00040014938,0.085692614,0.20902099,0.018161716,0.008897333,0.40413293],"study_design_scores_gemma":[0.00040051932,0.0010155331,0.057503443,0.00011558312,0.0012330324,0.005437614,0.0005760739,0.8296292,0.06453217,0.02699101,0.012410789,0.00015511562],"about_ca_topic_score_codex":0.0021800522,"about_ca_topic_score_gemma":0.0026363702,"teacher_disagreement_score":0.0034752898,"about_ca_system_score_codex":0.00034669525,"about_ca_system_score_gemma":0.00050397724,"threshold_uncertainty_score":0.00667423},"labels":[],"label_agreement":null},{"id":"W4417112906","doi":"10.1038/s43588-025-00917-3","title":"Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":5,"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":"Lawrence Berkeley National Laboratory","keywords":"Leverage (statistics); Deep learning; Artificial neural network; Deep neural networks; Inverse problem; Graph; Nanosensor; Nonlinear system","score_opus":0.005558610148396685,"score_gpt":0.2685541761931333,"score_spread":0.2629955660447366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417112906","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49745774,0.000854133,0.47501644,0.0011959975,0.00015495397,0.000072829935,0.00027105887,0.0009864189,0.023990331],"genre_scores_gemma":[0.9594463,0.00011591775,0.0360944,0.00014252865,0.00001834155,0.000041891915,0.00014985485,0.00015498258,0.0038357994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999156,0.000022649901,0.000002430813,0.000016208318,0.000022116907,0.000020948815],"domain_scores_gemma":[0.99967897,0.000192973,0.000027665486,0.000023638126,0.000041225936,0.000035599394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003244037,0.0005749067,0.00070609327,0.00031504108,0.00028528285,0.00074890704,0.0009155536,0.0010402456,0.0019574473],"category_scores_gemma":[0.0008733727,0.00042151462,0.0003446256,0.00030608752,0.000681139,0.0007941369,0.0006981309,0.00077334547,0.00024971896],"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":[0.000038006656,0.000034212793,0.00018167272,0.000030024936,0.000012952786,0.00003403894,0.000013068578,0.98510736,0.003134965,0.007136368,0.00052690506,0.0037504197],"study_design_scores_gemma":[0.0000019036411,0.0000028478253,0.000014425918,7.2971966e-7,4.891366e-7,9.982821e-7,0.0000014986448,0.9988624,0.00020592222,0.00087384635,0.000034134217,8.314338e-7],"about_ca_topic_score_codex":0.0058988845,"about_ca_topic_score_gemma":0.0081935115,"teacher_disagreement_score":0.0058988845,"about_ca_system_score_codex":0.0010945762,"about_ca_system_score_gemma":0.00066607544,"threshold_uncertainty_score":0.011729121},"labels":[],"label_agreement":null},{"id":"W4417152112","doi":"10.1038/s43588-025-00906-6","title":"SciSciGPT: advancing human–AI collaboration in the science of science","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"Northwestern University; National Science Foundation","keywords":"Transparency (behavior); Testbed; Domain (mathematical analysis); Design science; Design science research; Maturity (psychological); Scope (computer science)","score_opus":0.031999207090962,"score_gpt":0.4601782248313903,"score_spread":0.4281790177404283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417152112","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":"methods","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":"methods","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016911954,0.0020835395,0.79484046,0.025018806,0.0021461016,0.0015261879,0.0032307026,0.084471725,0.06977062],"genre_scores_gemma":[0.07690349,0.0016184372,0.8866814,0.004283811,0.0006451318,0.0017439155,0.007844009,0.010089231,0.010190564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98981255,0.0054790583,0.00049008307,0.0011804112,0.0023417375,0.00069622375],"domain_scores_gemma":[0.9610798,0.019182732,0.001174225,0.009356415,0.0033041998,0.005902643],"candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.019557243,0.0014113185,0.00083268544,0.0034738204,0.00320089,0.009577199,0.005492959,0.0050687054,0.019852774],"category_scores_gemma":[0.0593967,0.0008379787,0.001950655,0.0033388596,0.0047854106,0.012585518,0.025223715,0.006141147,0.010190742],"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":[0.0009094019,0.0008919596,0.0061726617,0.0023599903,0.00024536916,0.001517328,0.008880591,0.01663619,0.016907705,0.24489047,0.3075469,0.39304137],"study_design_scores_gemma":[0.00035082732,0.00033606432,0.0013388838,0.00054612366,0.00006175398,0.00081431144,0.0011520417,0.064096875,0.0074374448,0.29251534,0.6311525,0.0001977464],"about_ca_topic_score_codex":0.0038626846,"about_ca_topic_score_gemma":0.0046423837,"teacher_disagreement_score":0.9965262,"about_ca_system_score_codex":0.0020806051,"about_ca_system_score_gemma":0.01264998,"threshold_uncertainty_score":0.103429794},"labels":[],"label_agreement":null},{"id":"W4417153685","doi":"10.1038/s43588-025-00909-3","title":"Decoding omics via representation learning","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","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":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Autoencoder; Profiling (computer programming); Decoding methods; Representation (politics); Feature learning; Encoding (memory)","score_opus":0.004835519410217555,"score_gpt":0.28748608036591233,"score_spread":0.2826505609556948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417153685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011223996,0.001012664,0.97430676,0.0018421093,0.00030219552,0.00009091163,0.002503624,0.0059778187,0.0027397957],"genre_scores_gemma":[0.34529236,0.0021462194,0.6303105,0.0014916586,0.0006424878,0.0003659235,0.012882757,0.0008589053,0.0060091806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989573,0.00037713116,0.00007215612,0.00028531387,0.00021256624,0.00009555124],"domain_scores_gemma":[0.9960586,0.0024780452,0.00016267643,0.00081205805,0.0004095157,0.00007910822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011851048,0.0014435381,0.001296139,0.002361089,0.0005739568,0.0024491455,0.0013329383,0.0014448762,0.0048321057],"category_scores_gemma":[0.008988996,0.0005568439,0.0020291014,0.002746022,0.0007772379,0.0030379246,0.002514307,0.0028946442,0.0030246798],"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":[0.00044759424,0.00023936547,0.0024261714,0.00060876465,0.00035139913,0.00040918108,0.00013842329,0.09704522,0.012549104,0.059049062,0.031088814,0.7956469],"study_design_scores_gemma":[0.00004924582,0.00005552604,0.00048375767,0.000102771395,0.00010618013,0.00014618355,0.00005640244,0.74572253,0.0064432197,0.23790978,0.008897208,0.000027157355],"about_ca_topic_score_codex":0.0030560596,"about_ca_topic_score_gemma":0.0033697162,"teacher_disagreement_score":0.0048321057,"about_ca_system_score_codex":0.0009432237,"about_ca_system_score_gemma":0.001720019,"threshold_uncertainty_score":0.016165018},"labels":[],"label_agreement":null},{"id":"W4417454615","doi":"10.1038/s43588-025-00928-0","title":"AI-guided molecular design with recipes included","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Machine Learning in Materials Science","field":"Materials 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":"McMaster University","funders":"","keywords":"Generative Design; Generative grammar; Set (abstract data type)","score_opus":0.0073824733063226535,"score_gpt":0.3193867711825728,"score_spread":0.31200429787625017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417454615","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006708504,0.00044382134,0.91458374,0.0004967184,0.0008295783,0.00035840104,0.0014885511,0.00795806,0.06713254],"genre_scores_gemma":[0.10148998,0.0003852979,0.8685346,0.00047735922,0.00009784865,0.00095039303,0.0012910296,0.0034729782,0.023300482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997112,0.00007085302,0.000014765438,0.000055264914,0.000119393044,0.000028447259],"domain_scores_gemma":[0.99938107,0.0001833115,0.000028915934,0.00018115633,0.00018158201,0.000043906068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005205331,0.00094635313,0.000974722,0.00050744764,0.0009215373,0.0009548832,0.0016921076,0.0013740924,0.053132758],"category_scores_gemma":[0.0024318288,0.00059150567,0.00078433927,0.00064450357,0.00049362617,0.0008936925,0.0008675473,0.0024540282,0.013442671],"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":[0.00028489923,0.00028720137,0.00047710372,0.0013009654,0.00012625242,0.00025248955,0.00014674128,0.42393455,0.024402928,0.26723036,0.049430627,0.23212592],"study_design_scores_gemma":[0.00013115031,0.00008428546,0.00014726543,0.00006995897,0.00003751504,0.000075380696,0.000026543581,0.8319205,0.013145681,0.059796628,0.094505735,0.000059352635],"about_ca_topic_score_codex":0.0021624742,"about_ca_topic_score_gemma":0.0037546412,"teacher_disagreement_score":0.053132758,"about_ca_system_score_codex":0.00066670484,"about_ca_system_score_gemma":0.0014772093,"threshold_uncertainty_score":0.17774677},"labels":[],"label_agreement":null},{"id":"W7117725920","doi":"10.1038/s43588-025-00924-4","title":"MATTERIX: toward a digital twin for robotics-assisted chemistry laboratory automation","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Machine Learning in Materials Science","field":"Materials Science","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":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto; Public Health Ontario","funders":"","keywords":"Workflow; Modular design; Virtual Laboratory; Rendering (computer graphics); Graphics; Scalability; Automation","score_opus":0.006261251077759137,"score_gpt":0.294315736134926,"score_spread":0.28805448505716685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117725920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035071168,0.0027187283,0.8190663,0.0049004056,0.00396962,0.00038330344,0.001223027,0.03621181,0.096455686],"genre_scores_gemma":[0.21383072,0.0016761249,0.71333957,0.0029722108,0.0010970057,0.00033712434,0.0026388855,0.0029694133,0.06113895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717796,0.00039213593,0.00010308842,0.0006193487,0.0013120794,0.00039539507],"domain_scores_gemma":[0.99812084,0.0001622853,0.00010921431,0.00061204494,0.00053682475,0.00045869913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029352128,0.001098472,0.0010309048,0.0015173677,0.0011807763,0.0035853193,0.0033093023,0.0019435984,0.022902526],"category_scores_gemma":[0.002945524,0.00070357305,0.0006679727,0.0013432859,0.0018491079,0.007198111,0.00917961,0.0028399036,0.009187875],"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":[0.0018212775,0.00046498724,0.0029996794,0.00063189794,0.0001348558,0.00023011702,0.00037424953,0.0068670283,0.08178212,0.34101322,0.115399376,0.44828114],"study_design_scores_gemma":[0.0002808754,0.0011310382,0.001989574,0.00019803483,0.00013406225,0.00082463544,0.00022873629,0.10344545,0.13887373,0.1100175,0.6427271,0.00014936198],"about_ca_topic_score_codex":0.00079433137,"about_ca_topic_score_gemma":0.0013595615,"teacher_disagreement_score":0.022902526,"about_ca_system_score_codex":0.0010397042,"about_ca_system_score_gemma":0.0028303096,"threshold_uncertainty_score":0.076616585},"labels":[],"label_agreement":null}]}