{"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97094154,0.004580541,0.0097621055,0.011020137,0.0010072441,0.0002869557,0.000030350338,0.00009018696,0.0022809145],"genre_scores_gemma":[0.934852,0.000003433182,0.0595496,0.004924048,0.00006274761,0.000016466623,0.00006958572,0.0000049795935,0.00051711575],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990367,0.000009100537,0.000103333405,0.00026051357,0.00039672098,0.00019362045],"domain_scores_gemma":[0.99887675,0.0002931973,0.00003146173,0.00010498923,0.00057807146,0.00011555351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002227158,0.000068087385,0.00013983146,0.00009387111,0.00015102183,0.00003847615,0.000072311515,0.000059821647,0.000083659885],"category_scores_gemma":[0.0010713318,0.000056494406,0.00006193947,0.0005936895,0.00015152092,0.00009486474,0.000027546736,0.00013050773,0.000026957097],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019103948,0.0011454506,0.106069446,0.0006248158,0.00035477936,0.000050734303,0.00045683087,0.012315187,0.46950743,0.33231032,0.02058546,0.056388523],"study_design_scores_gemma":[0.0023890997,0.0001425833,0.53998977,0.000108406784,0.00013574565,0.00003824801,0.000058665104,0.32134148,0.034502313,0.03826692,0.06273473,0.00029201034],"about_ca_topic_score_codex":2.866414e-7,"about_ca_topic_score_gemma":4.4941345e-7,"teacher_disagreement_score":0.4350051,"about_ca_system_score_codex":0.000040753377,"about_ca_system_score_gemma":0.0005426354,"threshold_uncertainty_score":0.2303775},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5942554,0.004674759,0.39563352,0.0012155944,0.00014985153,0.00020913807,0.000018727513,0.000028102395,0.0038149355],"genre_scores_gemma":[0.95210105,0.00001548111,0.046489872,0.0007563916,0.00005350593,0.000008549357,0.00019135902,0.000005465141,0.0003782971],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990192,0.00002078547,0.00009442468,0.00035345284,0.00034181844,0.00017033146],"domain_scores_gemma":[0.9994903,0.0000070170177,0.000044412776,0.00011776922,0.0002766771,0.00006378946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017011864,0.00008107887,0.000056505873,0.000023172694,0.00020613096,0.000044254382,0.00018889626,0.00011387664,0.0000136278395],"category_scores_gemma":[0.00022074649,0.00007611899,0.000030685893,0.00023295275,0.00011809563,0.0000064543133,0.00013589796,0.00021365802,0.0000024503759],"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":[0.000011482309,0.000022032502,0.00006806474,0.000005646853,0.000006769064,0.0000026212463,0.000017507784,0.15249161,0.82790947,0.0049517434,0.00039643687,0.014116605],"study_design_scores_gemma":[0.0005470108,0.00014176275,0.0011711131,0.00001695445,0.0000072004295,0.000040751944,0.000027737124,0.54611266,0.40973315,0.0139461635,0.027863035,0.0003924719],"about_ca_topic_score_codex":9.416692e-7,"about_ca_topic_score_gemma":0.000003420762,"teacher_disagreement_score":0.41817632,"about_ca_system_score_codex":0.000023064558,"about_ca_system_score_gemma":0.00027443637,"threshold_uncertainty_score":0.3104042},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1679282,0.00052896887,0.82999486,0.00030084577,0.00018129266,0.00008722661,0.00026297156,0.000010181401,0.00070548116],"genre_scores_gemma":[0.9123305,0.000004679076,0.085459866,0.00058036944,0.000074287294,0.0000025024367,0.001401744,0.0000054386514,0.00014059794],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989227,0.000011230626,0.00011000236,0.00053217565,0.00025904106,0.00016487265],"domain_scores_gemma":[0.99910045,0.000040197778,0.000040486862,0.0003471351,0.00040617702,0.00006555974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021382181,0.00007764495,0.00008763118,0.0000577848,0.00016152393,0.00006532142,0.00046956955,0.00010292163,0.000006470384],"category_scores_gemma":[0.00022378459,0.000077204,0.000068543515,0.0005760527,0.00011669741,0.000012077069,0.00014587182,0.00008427471,0.0000011929968],"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":[0.000025553234,0.0001369332,0.0006997477,0.000009066181,0.000048675625,0.0000013469811,0.00003988054,0.35436937,0.6426237,0.000519426,0.0006949559,0.0008313535],"study_design_scores_gemma":[0.00029527047,0.00003190525,0.00051182084,0.0000020154143,0.00006952749,0.0000031021873,0.000010442079,0.93526036,0.061498296,0.00079784525,0.0013976207,0.00012178455],"about_ca_topic_score_codex":0.000002523859,"about_ca_topic_score_gemma":0.000036561683,"teacher_disagreement_score":0.74453497,"about_ca_system_score_codex":0.000017846594,"about_ca_system_score_gemma":0.0004296146,"threshold_uncertainty_score":0.31482878},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898842,0.0003783984,0.0074596484,0.00039902914,0.00070572563,0.00010549423,0.000040325478,0.000016258582,0.0010109002],"genre_scores_gemma":[0.99666315,0.0000031256286,0.001222873,0.0017672032,0.00011768757,0.000009804276,0.00009811441,0.000007807023,0.00011025073],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988469,0.000028894026,0.000105240295,0.00034149457,0.00048651398,0.00019099216],"domain_scores_gemma":[0.99967796,0.000014054522,0.000032699,0.00011884591,0.00008854997,0.0000679045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026023015,0.00008298577,0.00006406291,0.000058805548,0.00046216277,0.00003684251,0.0003819706,0.00005452349,0.00006280047],"category_scores_gemma":[0.000033938963,0.000084436404,0.000058422,0.00035596194,0.00015352189,0.0000072281096,0.00007583072,0.00024142278,0.0000021879243],"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":[0.0001365987,0.0001687099,0.005924852,0.000006038536,0.000014755009,0.0000062863483,0.0001331331,0.043948226,0.93911386,0.005300429,0.0026854966,0.0025616204],"study_design_scores_gemma":[0.0038594294,0.0010012501,0.6882473,0.0000101050055,0.000030599687,0.00017530033,0.00006079119,0.032270443,0.07816628,0.0062423544,0.18888159,0.0010545469],"about_ca_topic_score_codex":0.000013676535,"about_ca_topic_score_gemma":0.000007642663,"teacher_disagreement_score":0.86094755,"about_ca_system_score_codex":0.000038561335,"about_ca_system_score_gemma":0.0002470285,"threshold_uncertainty_score":0.3554629},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9186176,0.00031175534,0.08016725,0.0003225572,0.00017228973,0.00008940541,0.00003955496,0.000020913334,0.00025867656],"genre_scores_gemma":[0.9491494,0.0000036882686,0.04953614,0.0010290628,0.00010569171,0.0000034499408,0.00007199417,0.0000050932204,0.00009546425],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989682,0.000039686925,0.00009277636,0.00034892885,0.00040333602,0.00014709074],"domain_scores_gemma":[0.9996312,0.000011819252,0.00006484426,0.00012411246,0.0001315015,0.000036503017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026638922,0.00007874594,0.00006402491,0.0000783124,0.00058125856,0.00004436394,0.00023030111,0.000042697386,0.0000033353783],"category_scores_gemma":[0.00006202153,0.00007014058,0.000050625204,0.00048283554,0.00024534177,0.00001199616,0.00026041357,0.0001685471,3.486603e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019190942,0.000033101558,0.00038018805,7.686433e-7,0.000012377835,0.000002536873,0.000015915028,0.049867965,0.94691694,0.0013190778,0.0003204172,0.0011114925],"study_design_scores_gemma":[0.0003570135,0.0003132215,0.0038605304,0.0000062665767,0.000029095514,0.00014755207,0.0001801461,0.072396845,0.8997704,0.0071749277,0.015167846,0.00059617375],"about_ca_topic_score_codex":0.0000037504049,"about_ca_topic_score_gemma":0.000001672933,"teacher_disagreement_score":0.04714658,"about_ca_system_score_codex":0.000058548336,"about_ca_system_score_gemma":0.00020983975,"threshold_uncertainty_score":0.44706294},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92490786,0.00062668195,0.06802076,0.004262212,0.0006095821,0.00022363625,0.000055709937,0.00008576692,0.0012077735],"genre_scores_gemma":[0.98098695,0.0000042172633,0.01526521,0.0033236784,0.00014138594,0.0000064112073,0.00018285234,0.0000043992736,0.000084896696],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902254,0.000030273719,0.00009009624,0.00032154156,0.00040896813,0.00012656172],"domain_scores_gemma":[0.9996006,0.000006498552,0.0000497457,0.00012823068,0.0001763266,0.000038606355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002886022,0.00006562725,0.000049627204,0.00007460337,0.0004908943,0.000030015264,0.00020832449,0.000046660727,0.000008786408],"category_scores_gemma":[0.000071269154,0.00006108981,0.000039336705,0.00043632442,0.00015614135,0.00000610032,0.00016308881,0.00017815002,0.0000011755272],"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":[0.000013005553,0.00003950082,0.00024452855,6.641826e-7,0.0000072068297,0.0000016701258,0.000010749349,0.010630597,0.97826296,0.0023804123,0.004044299,0.0043643895],"study_design_scores_gemma":[0.00043220812,0.00047081703,0.0054320367,0.0000035022706,0.000019773343,0.00012484724,0.00005931109,0.06948353,0.7859951,0.0066311616,0.13075751,0.00059018994],"about_ca_topic_score_codex":0.0000017486332,"about_ca_topic_score_gemma":0.0000026019839,"teacher_disagreement_score":0.19226786,"about_ca_system_score_codex":0.000039739985,"about_ca_system_score_gemma":0.00014094576,"threshold_uncertainty_score":0.37756118},"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":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009536859,0.9951939,0.0026319802,0.0006804738,0.0005371522,0.00021125219,0.0000065703625,0.00013629252,0.0005928435],"genre_scores_gemma":[0.00056174,0.99074924,0.008369758,0.00015839098,0.00007586143,0.000022379967,0.000024832654,0.00001758566,0.00002021816],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9966866,0.00032227748,0.0004185091,0.00108263,0.0010353308,0.00045465352],"domain_scores_gemma":[0.99787384,0.0013027044,0.00028971545,0.00031102667,0.0000669738,0.00015571772],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0018038723,0.0003636041,0.0007189259,0.0009642062,0.00056914694,0.0002608289,0.0019066853,0.00018826207,0.000009633829],"category_scores_gemma":[0.00026843717,0.00030502706,0.00011783281,0.0011328314,0.00029285174,0.00033460636,0.0015820039,0.002347916,0.00000325727],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.40051e-7,0.00001907564,4.661702e-7,0.00046526518,0.0000051825123,0.00005745691,0.0002698688,0.0049316855,1.6357648e-8,0.090308875,0.0000049468968,0.9039368],"study_design_scores_gemma":[0.000059358423,0.000049717564,0.00004278378,0.00064639637,0.0000066174084,0.00026203526,0.000011116531,0.37053004,1.6417882e-8,0.0059497813,0.62217486,0.00026724447],"about_ca_topic_score_codex":0.000007535495,"about_ca_topic_score_gemma":0.0000044642807,"teacher_disagreement_score":0.9036696,"about_ca_system_score_codex":0.00014736174,"about_ca_system_score_gemma":0.00086535694,"threshold_uncertainty_score":0.9999537},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896638,0.000022177832,0.0072663883,0.0019167857,0.00067331584,0.00025537863,0.00007363266,0.000035168043,0.00009336537],"genre_scores_gemma":[0.99941874,0.0000019173726,0.0001336152,0.00026341408,0.000018518911,0.000015224646,0.000017809725,0.000006927323,0.00012381107],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997811,0.00015895964,0.00014348379,0.0004695059,0.0012465037,0.00017054366],"domain_scores_gemma":[0.9989826,0.00047347625,0.00023458434,0.00009833111,0.00015237054,0.000058652506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005416443,0.00010490636,0.00010842585,0.0002409977,0.00076580193,0.000071461385,0.00021530599,0.000042622145,0.00003237159],"category_scores_gemma":[0.00047806522,0.0001062063,0.000027386362,0.0010668313,0.00026081476,0.00071508036,0.00018534006,0.0003650845,9.832336e-7],"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":[0.00006295307,0.00009832703,0.00032156816,0.000010245948,0.0000011338427,6.772099e-7,0.00008763972,0.044907954,0.9120245,0.007459775,0.00010537606,0.034919824],"study_design_scores_gemma":[0.00039293608,0.00015198569,0.105897106,0.0000030450651,0.0000050259964,0.000029303535,0.000018484876,0.81967306,0.06579711,0.007815205,0.000081429076,0.00013529678],"about_ca_topic_score_codex":0.000016395834,"about_ca_topic_score_gemma":0.0000022694262,"teacher_disagreement_score":0.8462274,"about_ca_system_score_codex":0.00018346786,"about_ca_system_score_gemma":0.00009673469,"threshold_uncertainty_score":0.58900064},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948468,0.000106559775,0.00003133167,0.0031830391,0.0005843001,0.00018422963,0.00000725198,0.000016990061,0.0010394928],"genre_scores_gemma":[0.9973602,0.000004762329,0.00001604469,0.0024893843,0.00001777412,0.000024303448,2.2407417e-7,0.0000034366308,0.00008384429],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99763685,0.00025191563,0.00014846915,0.0003849049,0.0012884666,0.00028937694],"domain_scores_gemma":[0.99803257,0.0016641624,0.00007025193,0.00011697049,0.00006385937,0.00005218247],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011855105,0.00005657135,0.000057947876,0.00022913808,0.0014368895,0.000042296313,0.00094635284,0.000019515315,0.000048085334],"category_scores_gemma":[0.0014207818,0.00004348338,0.000024888765,0.0029380682,0.000900912,0.00021088718,0.0003936834,0.00069153635,0.000006738623],"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":[0.00007064657,0.000073647774,0.0015640284,8.385007e-7,2.0874522e-7,0.000009423263,0.00007049821,0.019033032,0.93674314,0.0358018,0.00016028996,0.00647244],"study_design_scores_gemma":[0.0007774297,0.00063926127,0.10638099,0.000003968411,0.0000024279204,0.00012612494,0.00029914084,0.14255664,0.60767937,0.1092404,0.03206713,0.00022710032],"about_ca_topic_score_codex":0.00000387903,"about_ca_topic_score_gemma":0.000002499522,"teacher_disagreement_score":0.32906374,"about_ca_system_score_codex":0.00006490086,"about_ca_system_score_gemma":0.00038501716,"threshold_uncertainty_score":0.9998631},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9930981,0.00034480673,0.0040385113,0.0009101465,0.00070297165,0.0001802301,0.00012599041,0.00013132274,0.0004679164],"genre_scores_gemma":[0.9825502,0.0000016570588,0.016742054,0.00041178273,0.00014344083,0.00000865135,0.0000434964,0.000013041602,0.000085697066],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9965816,0.00026527568,0.00028754908,0.0008089208,0.0015832387,0.00047342957],"domain_scores_gemma":[0.99883854,0.00042578546,0.00023127496,0.00018229292,0.00014740399,0.00017467518],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002317742,0.00019581664,0.00022845686,0.0002702269,0.0026128322,0.00046224266,0.0009222717,0.000069015805,0.0009964204],"category_scores_gemma":[0.0006636053,0.00017834919,0.000028758399,0.0011445374,0.0006198951,0.00048741337,0.0005901477,0.0009385759,0.000013572947],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021443824,0.000018171173,0.105727516,0.00002790418,0.0000042992588,0.000015276068,0.001304251,0.42136604,0.46652403,0.0031043664,0.00007459227,0.0018121003],"study_design_scores_gemma":[0.0011311015,0.00032919113,0.753101,0.00004537159,0.000020034619,0.0003967587,0.0002894174,0.17247912,0.023773776,0.030833686,0.016525239,0.0010752867],"about_ca_topic_score_codex":0.00006033817,"about_ca_topic_score_gemma":0.0000060064162,"teacher_disagreement_score":0.6473735,"about_ca_system_score_codex":0.00016451512,"about_ca_system_score_gemma":0.00026388164,"threshold_uncertainty_score":0.9999168},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99049467,0.000101722566,0.0019837436,0.0050853374,0.00078690384,0.00024234825,0.000022082808,0.00034713178,0.0009360461],"genre_scores_gemma":[0.99712783,0.0000067101646,0.00096926565,0.0012680839,0.000051053183,0.00000986843,0.000004616108,0.000011117386,0.0005514662],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99766034,0.000097913755,0.00021990767,0.0006079124,0.0010904648,0.00032345892],"domain_scores_gemma":[0.9936927,0.0056994073,0.00013579929,0.0001275081,0.00027521243,0.000069400376],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.00085703866,0.00013868048,0.00017172475,0.00033748092,0.00092246244,0.00006514268,0.00044644464,0.000055431738,0.000019061883],"category_scores_gemma":[0.014198918,0.0001284347,0.00006334464,0.00299009,0.0006431398,0.00036004788,0.0002283759,0.00043805456,0.00006797755],"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":[0.000019060963,0.00010625202,0.009374972,0.00007221671,0.000010440703,0.000030958658,0.0014271341,0.10248665,0.78673834,0.095308125,0.0021165966,0.0023092553],"study_design_scores_gemma":[0.00076851173,0.00027906187,0.82432866,0.00008918423,0.000007670733,0.000034699322,0.00019851228,0.067215845,0.07702616,0.024191296,0.0053688865,0.0004915006],"about_ca_topic_score_codex":0.000012185258,"about_ca_topic_score_gemma":0.000014068274,"teacher_disagreement_score":0.8149537,"about_ca_system_score_codex":0.000073643736,"about_ca_system_score_gemma":0.00016243827,"threshold_uncertainty_score":0.9941049},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9573679,0.00005620694,0.040558644,0.00032755622,0.001329233,0.00016475887,0.000009428689,0.000053021475,0.00013324888],"genre_scores_gemma":[0.9983944,0.000001658892,0.0013386332,0.00017307857,0.000055120898,0.000009254547,0.0000099682375,0.000007755914,0.000010082167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960483,0.00039599868,0.0003403211,0.00041825662,0.0025377776,0.00025937954],"domain_scores_gemma":[0.99811274,0.0003267877,0.00040915172,0.00029780646,0.0008017508,0.000051782918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004074776,0.00012622066,0.00016523793,0.00023518846,0.00024803288,0.00006346868,0.0009836829,0.00008606355,0.00016547674],"category_scores_gemma":[0.0010927292,0.00008692064,0.000064171356,0.0028468708,0.00074505253,0.00024028166,0.00022939041,0.00016846057,0.000013118122],"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":[0.0000060698985,0.000012692921,0.00024717645,0.0000036439494,8.8560455e-7,1.1754276e-7,0.000044402477,0.608264,0.38744223,0.0034064432,0.000021416465,0.0005508799],"study_design_scores_gemma":[0.00012470238,0.00004386372,0.08958786,0.000022006892,0.000010393457,0.000004245915,0.00001074746,0.8150682,0.08343063,0.011615165,0.000002636323,0.000079582205],"about_ca_topic_score_codex":0.00002248506,"about_ca_topic_score_gemma":0.000015549771,"teacher_disagreement_score":0.3040116,"about_ca_system_score_codex":0.00006156463,"about_ca_system_score_gemma":0.00020573396,"threshold_uncertainty_score":0.3544521},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28616375,0.000051944957,0.7131115,0.0002673736,0.00005674114,0.0002931702,0.000014753508,0.00001787456,0.00002286146],"genre_scores_gemma":[0.77336305,6.3248586e-7,0.22588322,0.00048132497,0.00010523386,0.00005412052,0.000084982705,0.0000061339247,0.000021336878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915195,0.000020428359,0.00008442735,0.00030001992,0.00021991822,0.00022325935],"domain_scores_gemma":[0.9995111,0.000029777548,0.00003179859,0.00009650982,0.00027194078,0.00005883137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004352997,0.00008091221,0.000054501084,0.000069493,0.00023679086,0.00004187532,0.00022941674,0.00012078188,9.653514e-7],"category_scores_gemma":[0.000331052,0.00007277023,0.000035521523,0.00037410465,0.00010819442,0.0000060694797,0.000045581957,0.00009425452,0.000001778207],"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":[0.000029783103,0.000004469158,0.00003409604,0.0000043467885,0.000002605005,8.1231167e-7,0.00000901547,0.73524654,0.2614865,0.0025600907,0.000050423558,0.00057135697],"study_design_scores_gemma":[0.00022423446,0.00007467063,0.0001996395,0.000007211625,0.0000019920917,0.0000033236338,0.0000033204922,0.87862784,0.09748713,0.023189967,0.00008610288,0.00009455787],"about_ca_topic_score_codex":0.000001307437,"about_ca_topic_score_gemma":0.0000032672556,"teacher_disagreement_score":0.48722833,"about_ca_system_score_codex":0.000031389216,"about_ca_system_score_gemma":0.0006826133,"threshold_uncertainty_score":0.29674837},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08997271,0.00012836087,0.8961344,0.010395183,0.0017643393,0.00055611954,0.000010482136,0.0005969561,0.00044147065],"genre_scores_gemma":[0.9020571,0.0000031018933,0.09694725,0.00038182148,0.00021787459,0.000013622971,0.00001876502,0.0000082958395,0.0003521693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976654,0.000029501596,0.0001776383,0.0006828713,0.00082307885,0.00062150724],"domain_scores_gemma":[0.998692,0.0005285572,0.00009461466,0.00027615362,0.0002565085,0.0001521613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007950498,0.00014511406,0.00013125161,0.00026423257,0.000835878,0.00079199224,0.0015713475,0.00007041052,0.000001657736],"category_scores_gemma":[0.00013646213,0.00011783083,0.0000899788,0.0033643295,0.00020752125,0.0014560965,0.0005910981,0.00029561602,0.000018219598],"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":[0.000015169024,0.000054868848,0.00094474445,0.00002907735,0.000011833476,0.000026666916,0.00033598114,0.4562184,0.00041118398,0.49928313,0.009975564,0.03269338],"study_design_scores_gemma":[0.0002647694,0.000043494358,0.0068865735,0.000029992652,0.000002162473,0.000010832651,0.000021083915,0.93694425,0.00021240546,0.046846457,0.008543038,0.00019494003],"about_ca_topic_score_codex":0.000014822107,"about_ca_topic_score_gemma":0.00000754628,"teacher_disagreement_score":0.8120844,"about_ca_system_score_codex":0.00008651507,"about_ca_system_score_gemma":0.0003635358,"threshold_uncertainty_score":0.7637199},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.265811,0.000095171046,0.7306732,0.000873941,0.00030855808,0.00017177335,0.0000012799761,0.0005647086,0.001500362],"genre_scores_gemma":[0.7829765,0.0000022916272,0.2165881,0.0003188683,0.00005687483,0.000015336838,0.000011222506,0.000010583807,0.000020208308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964259,0.00013129643,0.00023103566,0.0008319381,0.0018518002,0.0005280027],"domain_scores_gemma":[0.99783343,0.0010635087,0.00014225334,0.00027286005,0.00050717004,0.00018076116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014772292,0.00019230806,0.00015296158,0.00052425417,0.00063635036,0.0005514711,0.0015105671,0.000082016995,0.0000075869903],"category_scores_gemma":[0.0006413192,0.00016803335,0.0000540235,0.0064178365,0.00028131058,0.0013595844,0.0004992814,0.0006931011,0.00008856598],"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":[0.0000047550325,0.000013622948,0.00096946786,0.0000052023806,0.0000074428553,0.000062473955,0.00030849598,0.8724751,0.0006007459,0.10760913,0.000016715783,0.017926876],"study_design_scores_gemma":[0.00019799355,0.000088739085,0.05911462,0.000021428079,0.0000031653658,0.00007615114,0.000042904197,0.90659016,0.0009549566,0.03239471,0.0002969122,0.0002182509],"about_ca_topic_score_codex":0.0000043388127,"about_ca_topic_score_gemma":0.0000020353334,"teacher_disagreement_score":0.5171655,"about_ca_system_score_codex":0.00013599895,"about_ca_system_score_gemma":0.00056891795,"threshold_uncertainty_score":0.6852201},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9956296,0.00015108025,0.0036349448,0.00007586448,0.00005524466,0.000050169794,0.000023113033,0.000009425695,0.00037053094],"genre_scores_gemma":[0.994222,0.000010972757,0.0055759153,0.000054958142,0.000035107358,0.0000023579182,0.000061350314,0.000003748837,0.00003359455],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999285,0.000009798424,0.00008428558,0.00021213386,0.00030125922,0.00010755405],"domain_scores_gemma":[0.999527,0.000018106479,0.00006681629,0.00010850321,0.0002421705,0.000037401183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018647667,0.000056309666,0.00005816479,0.00007295829,0.00008713649,0.000012323932,0.00020976357,0.00005822003,0.0000022381134],"category_scores_gemma":[0.000053845506,0.000043783577,0.000022030912,0.0005646257,0.00036042402,0.0000045663,0.00006757011,0.00005683208,0.000001603767],"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":[0.000019743453,0.000013866484,0.0036158033,0.000008114833,0.000023961875,0.00000163501,0.000038859584,0.45793048,0.5362262,0.0016050931,0.0001585498,0.0003576728],"study_design_scores_gemma":[0.00036600343,0.0004543736,0.6032926,0.00002815769,0.000020974365,0.000024470037,0.00014251376,0.13407792,0.25427982,0.0068071824,0.0002611512,0.0002448429],"about_ca_topic_score_codex":0.0000033635247,"about_ca_topic_score_gemma":0.0000037546165,"teacher_disagreement_score":0.5996768,"about_ca_system_score_codex":0.0000123092095,"about_ca_system_score_gemma":0.0002848928,"threshold_uncertainty_score":0.17854424},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99258494,0.000011240458,0.0016231099,0.0025576435,0.0011763442,0.00018159334,0.000018778546,0.00020233732,0.0016439802],"genre_scores_gemma":[0.99774516,0.0000027146932,0.00026411758,0.0017904262,0.00007058923,0.0000058815326,0.0000071043173,0.000005140934,0.00010888241],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813265,0.00004647066,0.00013013445,0.00051637477,0.0008824056,0.0002919612],"domain_scores_gemma":[0.99871004,0.0008862588,0.000044779797,0.00013553933,0.000121902536,0.00010151171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000479292,0.00008683861,0.00008260549,0.00017941918,0.000526636,0.00011939536,0.00033216027,0.00006502684,0.000038982686],"category_scores_gemma":[0.0021667187,0.00007343459,0.0000477336,0.0020125029,0.0003967098,0.00039326734,0.000120890356,0.00026963578,0.00022070686],"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":[0.00003481415,0.000067389155,0.0017455484,0.00001412666,0.0000013226388,0.000033078188,0.00007623987,0.044282485,0.6772885,0.2701491,0.00073771644,0.0055696974],"study_design_scores_gemma":[0.0004314992,0.00007214429,0.30610853,0.000014333541,0.0000032572866,0.000040233623,0.00001978464,0.533471,0.007971962,0.1503049,0.0013401008,0.00022224776],"about_ca_topic_score_codex":0.0000026464857,"about_ca_topic_score_gemma":0.0000015849641,"teacher_disagreement_score":0.66931653,"about_ca_system_score_codex":0.00009075618,"about_ca_system_score_gemma":0.00010989113,"threshold_uncertainty_score":0.40505117},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016239867,0.001031057,0.97892976,0.0017467968,0.0012052864,0.0001881805,0.0000108713575,0.00043940175,0.0002087533],"genre_scores_gemma":[0.8100451,6.8934577e-7,0.18934707,0.00037373236,0.00018990017,0.0000065614113,0.0000071530267,0.000007050579,0.000022696426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824333,0.000018698849,0.00016897268,0.0006175154,0.0006429361,0.00030856425],"domain_scores_gemma":[0.9985507,0.00087363843,0.000036429323,0.00021648918,0.00022625715,0.000096452866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067118945,0.00012716383,0.00009708891,0.0002812068,0.00036623445,0.00065950764,0.00090534316,0.0000827445,0.0000022005138],"category_scores_gemma":[0.00014514464,0.00010436309,0.000076691154,0.0013633693,0.000118846256,0.0008214048,0.00017409661,0.0003052439,0.000012498753],"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":[9.976129e-7,0.000007156927,8.902156e-7,0.000010878618,0.0000023123741,0.000003718885,0.00042343032,0.6308236,0.00012537936,0.33174807,0.00007990621,0.036773633],"study_design_scores_gemma":[0.00006559081,0.00003020274,0.00016687873,0.000030380736,0.0000017823112,0.00001260728,0.000005337041,0.7762087,0.000087808265,0.22250041,0.00077535916,0.00011491597],"about_ca_topic_score_codex":0.0000032859375,"about_ca_topic_score_gemma":5.6560185e-7,"teacher_disagreement_score":0.7938053,"about_ca_system_score_codex":0.000064106884,"about_ca_system_score_gemma":0.00033332355,"threshold_uncertainty_score":0.6359647},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7837353,0.0018437114,0.20844276,0.0016913926,0.0011306992,0.00018823362,0.000018360866,0.0011157542,0.0018337853],"genre_scores_gemma":[0.9767272,0.000013469039,0.023079354,0.00006412447,0.000047335965,0.000008563243,0.0000064867145,0.000012141372,0.000041332933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989209,0.00000628871,0.000121372235,0.0002594587,0.00042553747,0.00026645177],"domain_scores_gemma":[0.99960005,0.00021527095,0.0000060601183,0.00010690881,0.000044505898,0.000027192258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019829159,0.000087858534,0.00007062043,0.00031421808,0.00008455632,0.00032356445,0.0003789736,0.00008127689,0.000059747996],"category_scores_gemma":[0.00023826766,0.000079237194,0.000014669805,0.0018137093,0.00020844737,0.0010897496,0.00013743907,0.000656943,0.000031022635],"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":[0.0000017585056,0.0000064768487,0.00049927714,0.000058699552,0.0000041014027,0.0000538444,0.0001490798,0.9103362,0.03197233,0.0064840047,0.00038395124,0.05005026],"study_design_scores_gemma":[0.000050639606,0.000010951211,0.009578765,0.00007731723,5.952961e-7,0.000019210893,0.000040528426,0.9701621,0.0048400145,0.014227211,0.00085188326,0.00014079387],"about_ca_topic_score_codex":7.32743e-7,"about_ca_topic_score_gemma":0.000008142701,"teacher_disagreement_score":0.1929919,"about_ca_system_score_codex":0.0002516032,"about_ca_system_score_gemma":0.00010391782,"threshold_uncertainty_score":0.3231199},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8836677,0.002021931,0.016684733,0.095344566,0.0015500223,0.00042439156,0.0000039764404,0.00006899952,0.00023368996],"genre_scores_gemma":[0.9692906,0.000009376803,0.018918972,0.011511759,0.000211476,0.0000041209632,0.0000057179573,0.000005387141,0.000042566666],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875975,0.000019552663,0.00023688226,0.0002949045,0.00044777172,0.00024111978],"domain_scores_gemma":[0.99926686,0.00018539389,0.000020542633,0.00008251062,0.00024110179,0.00020358137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075576757,0.00006054525,0.000091853944,0.00038938218,0.0001346874,0.00007020298,0.00009295357,0.00006407081,0.000032116273],"category_scores_gemma":[0.00046147115,0.000053797405,0.000019630852,0.0020273223,0.000081025544,0.00017335269,0.000023436112,0.0003832447,0.00006892817],"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":[0.00012900814,0.00046275824,0.042517792,0.001102686,0.000016805929,0.00012504247,0.016478457,0.2883725,0.009206387,0.12872536,0.008726849,0.5041364],"study_design_scores_gemma":[0.00008018541,0.00044091814,0.08153293,0.0023671759,0.000009337958,0.00020064852,0.0006523275,0.8393806,0.005562653,0.04986289,0.019572543,0.00033777335],"about_ca_topic_score_codex":0.0003619523,"about_ca_topic_score_gemma":0.00006601303,"teacher_disagreement_score":0.5510081,"about_ca_system_score_codex":0.00049696345,"about_ca_system_score_gemma":0.0024244795,"threshold_uncertainty_score":0.43009225},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053922865,0.0007075544,0.99250364,0.00017307219,0.00029879005,0.00026916192,0.000016257993,0.00018761297,0.0004516246],"genre_scores_gemma":[0.8820766,8.105874e-7,0.11711535,0.000079999714,0.00057756423,0.000014499845,0.00009735792,0.000010430245,0.000027388302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879634,0.000020772059,0.00020057055,0.0003948984,0.00031526625,0.0002721623],"domain_scores_gemma":[0.9987599,0.000733921,0.00004942925,0.00009775707,0.00028825185,0.00007070911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006238287,0.00012153801,0.00015750785,0.00018752419,0.00035150305,0.0003021412,0.0002671365,0.000053134045,0.000051552637],"category_scores_gemma":[0.000014344955,0.00010485622,0.0001370302,0.0014232574,0.000115105766,0.00022442156,0.00006287083,0.00030436728,0.000003381839],"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":[0.000013829506,0.000020682326,0.00016502799,0.000004714799,0.00006574588,0.0000016676809,0.00005464865,0.5870686,0.00007878616,0.07448095,0.0010742607,0.33697107],"study_design_scores_gemma":[0.00033277992,0.000009897609,0.00010181993,0.000041078554,0.00002317281,8.1599734e-7,0.0000059445942,0.93089086,0.00010955923,0.066297285,0.002066069,0.00012074554],"about_ca_topic_score_codex":0.000012289074,"about_ca_topic_score_gemma":5.037474e-7,"teacher_disagreement_score":0.8766843,"about_ca_system_score_codex":0.00005075916,"about_ca_system_score_gemma":0.00013930126,"threshold_uncertainty_score":0.42759126},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13884912,0.00089753437,0.8587602,0.0008630232,0.0001415714,0.00016472397,0.00028257028,0.00000584738,0.000035403973],"genre_scores_gemma":[0.65114063,0.000009187547,0.34776473,0.00076262443,0.000032857948,0.00002365651,0.00022198269,0.0000024363344,0.000041885745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99877536,0.00006274455,0.0002477237,0.0004655881,0.00015222777,0.0002963535],"domain_scores_gemma":[0.9990579,0.0003276265,0.000077192206,0.00013415942,0.00035500378,0.00004815623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009719924,0.00009997157,0.00021325031,0.00019956716,0.00023218316,0.000024125005,0.00023043939,0.00020550133,0.0000044378517],"category_scores_gemma":[0.0026034657,0.00009137434,0.00007242188,0.0010508915,0.00018150778,0.000004956397,0.000116995245,0.00018536691,0.000001025013],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008415356,0.0002810605,0.16807267,0.00007657288,0.0008617579,0.0000105967265,0.0010807691,0.68958634,0.017237496,0.11998255,0.0007354999,0.0019905572],"study_design_scores_gemma":[0.00048778666,0.00005966857,0.8120876,0.00001847585,0.000087618144,0.0000021971764,0.0005297209,0.14806212,0.00009191098,0.03727976,0.001119162,0.00017397503],"about_ca_topic_score_codex":0.0000069181087,"about_ca_topic_score_gemma":0.00018300129,"teacher_disagreement_score":0.6440149,"about_ca_system_score_codex":0.000113794464,"about_ca_system_score_gemma":0.00037086193,"threshold_uncertainty_score":0.37261376},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9896439,0.00013630485,0.0048969663,0.0032808513,0.00013367186,0.00038503445,0.000012835822,0.00001812359,0.0014923464],"genre_scores_gemma":[0.99498236,0.000049233255,0.002860864,0.0019565085,0.000035051857,0.0000091006805,0.00009953054,0.0000025293982,0.0000047989065],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986532,0.000041576463,0.00021674635,0.00025902,0.0006228319,0.00020667288],"domain_scores_gemma":[0.99879164,0.00076330075,0.000071954106,0.000043548087,0.00026989394,0.000059680635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011191256,0.00008825043,0.00015584787,0.0002055767,0.0006049732,0.0000513129,0.000115284245,0.00010875737,0.0000047705116],"category_scores_gemma":[0.00058431784,0.000060907267,0.00001734048,0.00076959276,0.00019662852,0.00008765973,0.000045418696,0.00046121,0.000002814083],"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":[0.000003745247,0.000026956532,0.9740325,0.000013087891,0.000017623357,0.0000012556433,0.0019857,0.000005987924,0.000010655376,0.020976199,0.000051047475,0.002875244],"study_design_scores_gemma":[0.0003517872,0.000012367205,0.9935836,0.000075101125,0.000017636614,0.000001174264,0.0003339621,0.00020890702,0.0000845964,0.0051960573,0.00007143565,0.00006337131],"about_ca_topic_score_codex":0.000065610286,"about_ca_topic_score_gemma":0.00017010595,"teacher_disagreement_score":0.019551106,"about_ca_system_score_codex":0.00022540143,"about_ca_system_score_gemma":0.0018000534,"threshold_uncertainty_score":0.46530256},"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":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097581446,0.0062890775,0.10630757,0.7526064,0.0008572063,0.0009979482,0.000022121985,0.0001341047,0.035204113],"genre_scores_gemma":[0.97754514,0.00012068193,0.015982712,0.006235586,0.00007575309,0.0000044484095,0.0000049168743,0.0000031425973,0.000027605569],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980261,0.00014093799,0.00016951741,0.00036612348,0.000952688,0.00034466773],"domain_scores_gemma":[0.99857587,0.00044540403,0.00008372874,0.00005482985,0.0006994458,0.00014072201],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003005351,0.00009844958,0.0001580581,0.00023720274,0.0014640518,0.0003457225,0.0002748547,0.00013957954,0.0000036296917],"category_scores_gemma":[0.00040356326,0.00008905353,0.000016278163,0.0011102803,0.0013579678,0.0006426987,0.000114421826,0.0005810286,7.8989746e-7],"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":[0.00000586641,0.000041993433,0.0014148069,0.000013834898,0.000005656353,0.0000024344376,0.010623345,0.025070025,0.0000027409073,0.9337444,0.00009611941,0.028978819],"study_design_scores_gemma":[0.0003988929,0.00005758163,0.22524442,0.0001322532,0.0000025823313,0.0000015757363,0.0026278046,0.025061594,0.000006039988,0.745713,0.00059075194,0.00016347787],"about_ca_topic_score_codex":0.00092751236,"about_ca_topic_score_gemma":0.004555711,"teacher_disagreement_score":0.8799637,"about_ca_system_score_codex":0.00044633352,"about_ca_system_score_gemma":0.004029969,"threshold_uncertainty_score":0.9998359},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6187959,0.000299976,0.3767541,0.0007239351,0.00018427074,0.00024743844,0.000018951634,0.00020192725,0.002773535],"genre_scores_gemma":[0.9343535,0.0000033193992,0.06456737,0.00095665694,0.000055562854,0.0000063718944,0.0000037745174,0.0000057338693,0.000047730715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984807,0.00006397566,0.00024403232,0.00040313535,0.0004282934,0.00037988],"domain_scores_gemma":[0.99579126,0.003579096,0.00011735584,0.00014676225,0.00025884795,0.00010667579],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001362015,0.00013127112,0.00020561393,0.00015724116,0.0006629093,0.00004715971,0.0002540132,0.00007172046,0.000015994805],"category_scores_gemma":[0.01304615,0.00010726916,0.000064273,0.0010480508,0.00016978105,0.00026586337,0.00034078548,0.000271681,0.0000036544275],"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":[0.000021156806,0.00012390461,0.00985194,0.00012867,0.000021879596,0.000027546976,0.00040859974,0.59946495,0.0011058247,0.3862698,0.0007338289,0.001841889],"study_design_scores_gemma":[0.00010082471,0.000004590262,0.0045836973,0.00006084132,0.000010980697,0.0000013095728,0.000018997129,0.583393,0.000011349403,0.41171187,0.000027235337,0.0000753347],"about_ca_topic_score_codex":0.000032673626,"about_ca_topic_score_gemma":0.000015242933,"teacher_disagreement_score":0.31555763,"about_ca_system_score_codex":0.00035038084,"about_ca_system_score_gemma":0.00030488614,"threshold_uncertainty_score":0.9952674},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93676174,0.011095002,0.042377,0.0046441127,0.00229457,0.00018067611,0.000016971024,0.000021444714,0.002608462],"genre_scores_gemma":[0.9917767,0.00009539445,0.0035814142,0.0017634241,0.00013393734,0.0000107099195,0.000060191716,0.0000037910365,0.0025744545],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990662,0.000016565024,0.000121241064,0.00037541756,0.00025402033,0.00016656026],"domain_scores_gemma":[0.99947906,0.000023019453,0.000049932536,0.00015997888,0.00024731012,0.00004072515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002916221,0.000078824254,0.000058031605,0.00006340233,0.0003846086,0.00038992547,0.00035344154,0.00007243468,0.0000072160624],"category_scores_gemma":[0.00014291238,0.00007031072,0.00004288128,0.0004008853,0.00028352288,0.00004632478,0.00010657371,0.00014012081,0.0000054552984],"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":[0.000060201095,0.00014542033,0.004636909,0.00003802502,0.0000515636,0.0000022888032,0.00023329169,0.07359699,0.7265547,0.09186852,0.043775246,0.059036832],"study_design_scores_gemma":[0.0012617635,0.00010249538,0.28552082,0.00025143987,0.00003563927,0.000010147237,0.000639913,0.020585068,0.2360567,0.023081562,0.43150124,0.00095320976],"about_ca_topic_score_codex":0.000009155023,"about_ca_topic_score_gemma":0.000008253428,"teacher_disagreement_score":0.490498,"about_ca_system_score_codex":0.000036121215,"about_ca_system_score_gemma":0.00052961864,"threshold_uncertainty_score":0.376006},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9580221,0.0011303088,0.03165629,0.007361347,0.00031195767,0.0004802582,0.000019872394,0.00001266017,0.0010051914],"genre_scores_gemma":[0.97525775,0.000019036368,0.019683663,0.0037322314,0.00005609345,0.00004030206,0.00003990493,0.0000037075365,0.0011673358],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989311,0.00006222259,0.00022378234,0.00031537082,0.0002771365,0.0001904054],"domain_scores_gemma":[0.99860156,0.00037064817,0.00014157404,0.000120014854,0.0007082722,0.000057916746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008788222,0.00008708281,0.00015804383,0.00013807486,0.00019795058,0.000011480864,0.00023060343,0.00013239153,0.000007929372],"category_scores_gemma":[0.0043733995,0.00007446586,0.0000411658,0.0006365658,0.000060794275,0.000003879021,0.00019854092,0.00009201004,0.000022635868],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013905505,0.00019627034,0.20713112,0.00005574317,0.0003322528,9.565732e-7,0.00054786477,0.28871447,0.3806386,0.001695666,0.101821356,0.018726671],"study_design_scores_gemma":[0.00047349953,0.0002433204,0.90722597,0.00010114086,0.00003826259,8.483416e-7,0.00014914044,0.004892762,0.01660131,0.008211116,0.061743844,0.00031880607],"about_ca_topic_score_codex":0.0000032749551,"about_ca_topic_score_gemma":0.000004588838,"teacher_disagreement_score":0.7000948,"about_ca_system_score_codex":0.00014076386,"about_ca_system_score_gemma":0.00021131853,"threshold_uncertainty_score":0.5235684},"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":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000011595394,0.7331946,0.26518875,0.0005171592,0.000349211,0.00040909328,0.0000318194,0.00006617049,0.00024203099],"genre_scores_gemma":[0.0033828835,0.28353497,0.71026975,0.0025017667,0.00017749822,0.00004569492,0.000029456953,0.000011789939,0.000046196004],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99752885,0.00023564178,0.00037481336,0.00078420737,0.0008621825,0.00021430283],"domain_scores_gemma":[0.994753,0.0029284824,0.0004024339,0.000513214,0.0013375795,0.00006530996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001046426,0.00030253053,0.0006329192,0.00032023055,0.00044130094,0.00027814542,0.0021185381,0.0002531092,0.0000022919987],"category_scores_gemma":[0.005667737,0.00017607943,0.000111141264,0.002045117,0.0008260997,0.000130786,0.0007307693,0.0011620156,0.0000014687399],"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":[6.920901e-7,0.000020112207,6.2252445e-7,0.00063098874,0.00001032146,0.000001858347,0.000066256354,0.00009763833,7.574965e-7,0.7794797,0.0006576032,0.21903345],"study_design_scores_gemma":[0.000114193375,0.00015401404,0.000016146107,0.00892576,0.00009220816,0.000016325095,0.0000037857071,0.06478085,0.00034682683,0.86459565,0.060273983,0.0006802815],"about_ca_topic_score_codex":0.000003912954,"about_ca_topic_score_gemma":0.0000022299228,"teacher_disagreement_score":0.44965965,"about_ca_system_score_codex":0.0001532731,"about_ca_system_score_gemma":0.0017525174,"threshold_uncertainty_score":0.71803105},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26253864,0.00008354418,0.72182924,0.0031333668,0.000019467769,0.000616262,0.00016891204,0.00018436507,0.011426233],"genre_scores_gemma":[0.5673742,0.0000012149171,0.43188533,0.00013149434,0.000036377172,0.0001292892,0.00007782203,0.0000047690423,0.00035947628],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986226,0.000004657185,0.0001351602,0.00048333677,0.000506593,0.00024763984],"domain_scores_gemma":[0.9992392,0.00007453133,0.000053773245,0.00031665387,0.00024147323,0.00007434063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017152357,0.000115857816,0.00009065864,0.00008259397,0.00035848058,0.00009455022,0.00056548876,0.00009066236,0.00013307437],"category_scores_gemma":[0.00005981289,0.00009508074,0.000021338372,0.0008141014,0.00022153938,0.00023522592,0.00012561888,0.00058595906,0.000009196852],"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":[0.00006554802,0.000090981775,0.0016022641,0.000049768485,0.000012519631,0.0000072245034,0.000042969223,0.004195482,0.8026556,0.18517223,0.00015200232,0.005953408],"study_design_scores_gemma":[0.00027024053,0.000014131439,0.00061235356,0.0001371891,0.0000048528623,0.000002894887,0.000024467716,0.01357355,0.8681682,0.11489117,0.0021235016,0.00017747015],"about_ca_topic_score_codex":0.00005578245,"about_ca_topic_score_gemma":0.000007825148,"teacher_disagreement_score":0.3048356,"about_ca_system_score_codex":0.00026648742,"about_ca_system_score_gemma":0.0004974719,"threshold_uncertainty_score":0.38772798},"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":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09189714,0.00022973724,0.81339705,0.08945587,0.00076728285,0.00038243405,0.00001751944,0.00014706828,0.0037059241],"genre_scores_gemma":[0.7924801,5.266862e-7,0.18187203,0.02555776,0.000029010378,0.000014819646,0.0000023252737,0.000005793849,0.000037614118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796116,0.000108120614,0.00017095005,0.00076288613,0.00073540286,0.00026149262],"domain_scores_gemma":[0.98863894,0.010849827,0.00006494998,0.00011466975,0.00025351596,0.00007808273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079324085,0.00016427343,0.00015198313,0.0003830641,0.0010066992,0.00013315096,0.00028858555,0.00007481503,0.000009225231],"category_scores_gemma":[0.0041945647,0.00013595526,0.000035931957,0.0020209774,0.0011092402,0.00033468963,0.00006390761,0.00039011345,0.0000028484053],"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":[0.00011651136,0.00011004337,0.0044170986,0.000049502247,0.0000129375085,0.000004106837,0.00012351135,0.25724792,0.007289752,0.7155004,0.0012526552,0.0138755115],"study_design_scores_gemma":[0.0021139616,0.0002608045,0.12981929,0.00017406922,0.000027804906,0.00008444719,0.00010004575,0.46843418,0.014664117,0.37803435,0.005712549,0.0005743927],"about_ca_topic_score_codex":0.0000035658013,"about_ca_topic_score_gemma":0.0000105202635,"teacher_disagreement_score":0.700583,"about_ca_system_score_codex":0.00009165387,"about_ca_system_score_gemma":0.0010544462,"threshold_uncertainty_score":0.7742818},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66245687,0.00014665697,0.32825384,0.002728118,0.0001537833,0.00017878467,0.000026572625,0.000119056596,0.005936322],"genre_scores_gemma":[0.9571576,3.7375622e-7,0.039988827,0.0023047728,0.000049051378,0.000027143158,0.00004642849,0.000007674856,0.0004181324],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983828,0.000011705033,0.00018142833,0.0005613693,0.00052653236,0.0003361851],"domain_scores_gemma":[0.9990357,0.00019934194,0.00005687689,0.00027360235,0.00029118088,0.00014328623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020928332,0.00015518136,0.00011875149,0.00007738407,0.00041939336,0.00012832128,0.0005920161,0.00014921729,0.00019432098],"category_scores_gemma":[0.00020223239,0.00015926262,0.00004299639,0.0014053541,0.00012296012,0.00006544917,0.00009675051,0.0004244029,0.000014432067],"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":[0.000019485771,0.000029463605,0.0000958906,0.000015434747,0.000007794601,0.0000011355036,0.000025953013,0.28670767,0.7013027,0.011000459,0.000401913,0.0003921163],"study_design_scores_gemma":[0.00025735665,0.000008268351,0.00030400153,0.000044303146,0.000015935337,0.0000067606247,0.0000073885194,0.036059443,0.92364573,0.036802936,0.0026721866,0.00017568585],"about_ca_topic_score_codex":0.000011074248,"about_ca_topic_score_gemma":6.937265e-7,"teacher_disagreement_score":0.29470074,"about_ca_system_score_codex":0.0002588246,"about_ca_system_score_gemma":0.00097065157,"threshold_uncertainty_score":0.6494541},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9953163,0.00016492879,0.0020706395,0.0001001456,0.000116685034,0.00012143642,0.000008448987,0.000022994367,0.0020783867],"genre_scores_gemma":[0.99665207,3.581777e-7,0.0027073454,0.00059719186,0.000014719615,8.0482283e-7,0.0000013933155,0.0000026694968,0.000023470371],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988076,0.00005632134,0.00023252475,0.0003684283,0.00038080808,0.00015430084],"domain_scores_gemma":[0.999192,0.0002618323,0.00014965021,0.00014504313,0.00022148759,0.000030011333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004129779,0.000079197096,0.00014230324,0.00019448162,0.0002098038,0.0000184754,0.00045112055,0.00007939043,0.0000099302115],"category_scores_gemma":[0.0007864116,0.00006646609,0.0000410667,0.0010683595,0.0010121057,0.00023350398,0.00012737536,0.00021281619,5.088988e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008229247,0.00006229111,0.00029406408,0.00004227023,7.7712184e-7,0.000009893079,0.0006341716,0.013165999,0.9722014,0.012181448,0.0000024685373,0.0013969897],"study_design_scores_gemma":[0.00020238834,0.000042413347,0.0014274387,0.00007731348,0.000007727959,0.000036185746,0.00012948495,0.07236552,0.87013733,0.055472024,0.0000039353304,0.00009823638],"about_ca_topic_score_codex":0.000033126104,"about_ca_topic_score_gemma":0.0000024777294,"teacher_disagreement_score":0.102064066,"about_ca_system_score_codex":0.000030105371,"about_ca_system_score_gemma":0.0003931704,"threshold_uncertainty_score":0.37291458},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5207521,0.0016354304,0.03213957,0.10584067,0.02095364,0.0015032728,0.00002394339,0.00015306244,0.31699836],"genre_scores_gemma":[0.9952253,0.0000035830356,0.0010711374,0.0028094712,0.00016856166,0.0000036290528,0.0000041113667,8.901353e-7,0.0007133026],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99900144,0.00012293768,0.000095817224,0.00019599604,0.0004744736,0.000109354725],"domain_scores_gemma":[0.9982344,0.00042136107,0.00005918163,0.00007018049,0.0011784472,0.00003644539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071789353,0.000055241897,0.00006917075,0.000049209568,0.0010021974,0.000132533,0.00019063352,0.00006860154,0.000024046238],"category_scores_gemma":[0.0042005107,0.000034229764,0.00002018817,0.0008861607,0.0010658852,0.00013137951,0.0000405321,0.00023784685,0.0000012914738],"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":[0.000009231508,0.00003437551,0.0022828334,0.000002438096,0.0000069473763,3.0248793e-7,0.004333121,0.0039955955,0.00008212347,0.94613934,0.040012404,0.0031012895],"study_design_scores_gemma":[0.00061382755,0.00019367448,0.3151535,0.00014333524,0.000042073818,0.0000014714589,0.0093623465,0.114520706,0.0022018924,0.5012341,0.056063112,0.00046992837],"about_ca_topic_score_codex":0.00019560111,"about_ca_topic_score_gemma":0.00044664665,"teacher_disagreement_score":0.47447324,"about_ca_system_score_codex":0.00011082355,"about_ca_system_score_gemma":0.00045592702,"threshold_uncertainty_score":0.7708193},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92191297,0.0023797192,0.06204928,0.006576145,0.0006295285,0.0004952651,0.00011502826,0.000031170493,0.005810903],"genre_scores_gemma":[0.90151685,0.000017425571,0.098074295,0.00027694483,0.000029821234,1.1258531e-7,0.000014219571,0.0000021515127,0.000068158944],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988536,0.000019106648,0.00019050484,0.00026705302,0.0005337694,0.00013594113],"domain_scores_gemma":[0.99911064,0.00012851029,0.00006326691,0.00015882001,0.0005072383,0.000031542007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044659743,0.00006508791,0.00015131375,0.00017224794,0.0000808902,0.000014615327,0.0002250437,0.0000596052,0.000046809302],"category_scores_gemma":[0.00052047824,0.000049170827,0.000039586143,0.0012875604,0.00037199768,0.00015285866,0.00008502474,0.00024135556,0.0000021227415],"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":[0.000047522088,0.00006257523,0.00043868643,0.00016458462,0.000017680239,0.000005616945,0.00006290162,0.00031450205,0.952229,0.045175493,0.00028894175,0.001192489],"study_design_scores_gemma":[0.00032501988,0.00003782282,0.06525146,0.00035374376,0.00003095271,0.000012182286,0.00004646378,0.0028419134,0.9226426,0.008268802,0.00011995978,0.00006909664],"about_ca_topic_score_codex":0.000031235122,"about_ca_topic_score_gemma":0.0000012969833,"teacher_disagreement_score":0.06481277,"about_ca_system_score_codex":0.00003759586,"about_ca_system_score_gemma":0.0004221339,"threshold_uncertainty_score":0.20051281},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93032837,0.00009186111,0.0679867,0.00029451688,0.0006416702,0.0002209205,0.000018759703,0.00012315095,0.0002940532],"genre_scores_gemma":[0.96149355,0.0000011405798,0.03762876,0.0007914329,0.000013342071,0.000010236758,0.000031295192,0.00000924636,0.00002097543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724936,0.00021276248,0.00039635086,0.00066744816,0.0010437,0.0004303866],"domain_scores_gemma":[0.9985589,0.00040220632,0.00028078922,0.00025355833,0.00039705553,0.000107502165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009933561,0.00019998197,0.00023734194,0.00032235548,0.00069739076,0.0002383707,0.0008599039,0.000105366635,0.0003084846],"category_scores_gemma":[0.0008827276,0.00017525596,0.00005780972,0.0013917391,0.0008952111,0.00023803175,0.00013501605,0.00027024522,0.000013815204],"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":[0.00003250003,0.000035229717,0.0006587983,0.000013807362,0.0000013433645,0.0000010321127,0.000037107722,0.58830947,0.4089349,0.0017488905,0.00004252826,0.00018442766],"study_design_scores_gemma":[0.00035156132,0.00013744645,0.0047241068,0.000035900135,0.0000048931183,0.000004444721,0.000009379166,0.69656664,0.2958072,0.0021157628,0.000092105656,0.00015054618],"about_ca_topic_score_codex":0.000024858968,"about_ca_topic_score_gemma":0.0000033805927,"teacher_disagreement_score":0.11312768,"about_ca_system_score_codex":0.000108339955,"about_ca_system_score_gemma":0.00017181798,"threshold_uncertainty_score":0.7146731},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93669796,0.00015042977,0.029988216,0.009416089,0.0031487204,0.00055803766,0.000014349942,0.00004148418,0.01998473],"genre_scores_gemma":[0.99089533,5.9641525e-7,0.007810589,0.0011303843,0.00003181058,0.000006027484,0.0000024255776,0.000002035377,0.00012081962],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.988559,0.00012309021,0.00079012656,0.0014899516,0.008431337,0.0006064663],"domain_scores_gemma":[0.9928051,0.0016772696,0.00033572767,0.0012399404,0.0038271123,0.00011479927],"candidate_categories":["metaresearch","bibliometrics","sts","scholarly_communication","open_science"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.049687903,0.000142611,0.00020425986,0.0037872205,0.0029199538,0.002226219,0.007754151,0.00004955969,0.000021165599],"category_scores_gemma":[0.020261498,0.00009450134,0.000044724162,0.057121947,0.010662032,0.002723872,0.0013416952,0.00040196837,0.00001931168],"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":[0.000011652985,0.00016662962,0.006032695,0.000008737332,0.0000016800668,0.000003894073,0.0018385106,0.16556,0.023223355,0.7807196,0.005238998,0.01719424],"study_design_scores_gemma":[0.00044927865,0.00006590744,0.3216568,0.000111003756,0.000006735648,0.0000064625733,0.0037881054,0.27977076,0.006181876,0.3839359,0.0037750683,0.00025210177],"about_ca_topic_score_codex":0.000035173915,"about_ca_topic_score_gemma":0.00007166208,"teacher_disagreement_score":0.3967837,"about_ca_system_score_codex":0.000360898,"about_ca_system_score_gemma":0.0041471235,"threshold_uncertainty_score":0.9988096},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27099967,0.00037919777,0.7205679,0.0005627701,0.0005140338,0.00011171515,0.0000017833743,0.000016437967,0.0068465327],"genre_scores_gemma":[0.98339754,0.00001128539,0.015475928,0.0007994395,0.000073428244,0.000002208313,0.000055790002,0.0000024338487,0.00018195668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941045,0.000011396105,0.00011548299,0.00018956316,0.00014467942,0.00012844233],"domain_scores_gemma":[0.99965477,0.000029062812,0.00005056972,0.00008838685,0.00014477351,0.000032466934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027212378,0.00005561887,0.00004635126,0.00006555615,0.0002493526,0.00005651757,0.00018741428,0.000097598575,0.0000030139388],"category_scores_gemma":[0.00014540523,0.000054042597,0.000026927373,0.0003426208,0.00011167661,0.000008251893,0.00011353252,0.00017323993,0.000003920834],"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":[0.00004902383,0.00003403428,0.012329876,0.000018263772,0.000033034692,0.0000011241958,0.00013208242,0.7011152,0.11866959,0.035455126,0.0027600671,0.12940256],"study_design_scores_gemma":[0.0005310328,0.00006866978,0.035032175,0.000024906476,0.000011232358,0.000016749604,0.000083338484,0.8889964,0.025833985,0.03514414,0.013983833,0.00027351914],"about_ca_topic_score_codex":0.000002326807,"about_ca_topic_score_gemma":0.0000026866435,"teacher_disagreement_score":0.7123979,"about_ca_system_score_codex":0.000022889366,"about_ca_system_score_gemma":0.00013757749,"threshold_uncertainty_score":0.22037931},"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":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32286882,0.00010342214,0.6678249,0.00546909,0.0007880347,0.0003267599,0.0000051245243,0.00020234044,0.0024115057],"genre_scores_gemma":[0.7494409,5.270415e-7,0.24661097,0.0038093396,0.000027378606,0.000016344193,0.0000029406774,0.0000067555934,0.00008483908],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99690163,0.00019772824,0.0002898644,0.00079648895,0.001347883,0.00046638673],"domain_scores_gemma":[0.9982926,0.0004144399,0.00014395139,0.0003449453,0.0006832322,0.000120809855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025640603,0.00019991602,0.00020474633,0.0003246121,0.00070979365,0.0005135989,0.0013776716,0.000113531285,0.00019570698],"category_scores_gemma":[0.0012264736,0.00015186633,0.000028906214,0.0021234038,0.0010719552,0.0005538739,0.00031863272,0.0003636226,0.00009204953],"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":[0.000034438042,0.00002772593,0.0003362162,0.000011370721,0.0000021914084,0.00001268759,0.00007948309,0.47878298,0.4833167,0.036246073,0.0008547395,0.00029542166],"study_design_scores_gemma":[0.0008654311,0.00019979509,0.02690101,0.00020789025,0.000018718907,0.00008532105,0.000030420935,0.2263152,0.6436004,0.09914122,0.0019819466,0.0006526575],"about_ca_topic_score_codex":0.000028185532,"about_ca_topic_score_gemma":0.0000031028364,"teacher_disagreement_score":0.42657208,"about_ca_system_score_codex":0.00016178079,"about_ca_system_score_gemma":0.0011143101,"threshold_uncertainty_score":0.619293},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8581731,0.00006406029,0.13441835,0.0038808188,0.0014922163,0.0003911537,0.00017038138,0.00034504596,0.0010648778],"genre_scores_gemma":[0.93130183,2.7863462e-7,0.06772451,0.0005914071,0.00008826299,0.000032770437,0.00003615441,0.000008704698,0.00021605655],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780375,0.000036689464,0.00033232148,0.0006571483,0.0007698965,0.00040019667],"domain_scores_gemma":[0.998478,0.00031081305,0.0001845943,0.0002556381,0.00066738797,0.00010357705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000948252,0.0001776615,0.0001863953,0.0001447917,0.0005693397,0.0008923265,0.00095617113,0.00014205999,0.00015938081],"category_scores_gemma":[0.0014512248,0.00016434483,0.000048172344,0.0011818308,0.0006141889,0.00079210906,0.00022808353,0.0002085432,0.00006899575],"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":[0.000033942095,0.000055616598,0.00065337506,0.00014626842,0.0000028392828,0.0000020437315,0.00010689017,0.10003855,0.89373475,0.004098617,0.0006738402,0.00045325546],"study_design_scores_gemma":[0.0016888307,0.0000851112,0.24400914,0.00030238638,0.000025428153,0.00004860487,0.00014047795,0.40673736,0.31609395,0.026768662,0.0030950368,0.001005006],"about_ca_topic_score_codex":0.0000034506904,"about_ca_topic_score_gemma":4.921519e-7,"teacher_disagreement_score":0.57764083,"about_ca_system_score_codex":0.00023640133,"about_ca_system_score_gemma":0.0007571057,"threshold_uncertainty_score":0.86047256},"labels":[],"label_agreement":null}]}