{"meta":{"query_hash":"d0b455a793e5","filters":{"venue":"Frontiers in Bioinformatics"},"cohort_total":32,"direct_labels_cover":0,"predictions_cover":32,"exported":32,"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/d0b455a793e5","api":"https://metacan.xera.ac/api/v1/cohort?venue=Frontiers+in+Bioinformatics"},"results":[{"id":"W3127618774","doi":"10.3389/fbinf.2022.715006","title":"Mimetic Neural Networks: A Unified Framework for Protein Design and Folding","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Mitacs; United States-Israel Binational Science Foundation","keywords":"Folding (DSP implementation); Computer science; Artificial neural network; Protein folding; Artificial intelligence; Chemistry; Engineering","score_opus":0.008332479520323616,"score_gpt":0.22411760810792838,"score_spread":0.21578512858760476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127618774","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016555553,0.0008668853,0.9938659,0.0002782635,0.000057972557,0.00003001863,0.00007036281,0.00035632623,0.0028187174],"genre_scores_gemma":[0.14797378,0.0032491398,0.84049463,0.0004139991,0.00026195697,0.0005650635,0.0003818264,0.0002524466,0.0064071957],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951506,0.00019429633,0.000024919786,0.000081086655,0.00015321605,0.000031488496],"domain_scores_gemma":[0.9996855,0.00014141531,0.000032579133,0.00005988141,0.000055880486,0.000024742672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012824703,0.0011339649,0.0010173585,0.0011117053,0.0005086353,0.0013736162,0.0022971763,0.001338412,0.0023201362],"category_scores_gemma":[0.0018397787,0.00048210516,0.0009693866,0.0009910172,0.0013318767,0.0018417167,0.001700631,0.0018213495,0.0007977001],"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":[0.000064340325,0.000039678707,0.00026025967,0.00020479833,0.00007902538,0.000081174934,0.000044432327,0.5592331,0.0018661227,0.35681126,0.0028355406,0.078480266],"study_design_scores_gemma":[0.000009904252,0.000029210467,0.000036616722,0.000018496345,0.000009616793,0.000023647983,0.0000058713445,0.8447504,0.0005893029,0.14811316,0.0064053964,0.000008362374],"about_ca_topic_score_codex":0.0018731252,"about_ca_topic_score_gemma":0.0028707557,"teacher_disagreement_score":0.0023201362,"about_ca_system_score_codex":0.0010337229,"about_ca_system_score_gemma":0.0011822652,"threshold_uncertainty_score":0.0077616572},"labels":[],"label_agreement":null},{"id":"W3179669991","doi":"10.3389/fbinf.2021.694324","title":"EPIphany—A Platform for Analysis and Visualization of Peptide Immunoarray Data","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computational biology; Visualization; Computer science; Epitope; Biology; Repertoire; Antibody Repertoire; Antigen; Data science; Immunology; Data mining","score_opus":0.05111790531259611,"score_gpt":0.3503095392587165,"score_spread":0.29919163394612036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179669991","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010265623,0.00063803315,0.67853934,0.00043543847,0.00023763503,0.000463867,0.015565451,0.2880043,0.00585025],"genre_scores_gemma":[0.07699888,0.0010801158,0.8622089,0.0009444362,0.00016704679,0.0027287565,0.021586385,0.02481667,0.009468897],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988372,0.00019309537,0.00010541273,0.0002848123,0.00046984444,0.00010972966],"domain_scores_gemma":[0.9975799,0.0013379597,0.0002589411,0.0003102626,0.0003411867,0.00017181193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030692315,0.0016222997,0.0011302582,0.0022402413,0.0005151652,0.0019350797,0.0020987482,0.00074675307,0.020856932],"category_scores_gemma":[0.0048418655,0.00081766816,0.0009436556,0.0010294816,0.000500442,0.0019063778,0.0024108172,0.0016278318,0.0056573297],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0042992597,0.00034773676,0.0071491813,0.0025493826,0.0011651895,0.001920851,0.0014346722,0.008377913,0.24877578,0.020420393,0.29670793,0.40685177],"study_design_scores_gemma":[0.00078117836,0.0004689131,0.014672184,0.00048224244,0.00030037828,0.0017595944,0.00030882904,0.27318987,0.29530728,0.025827087,0.38626045,0.0006419829],"about_ca_topic_score_codex":0.001671335,"about_ca_topic_score_gemma":0.002103428,"teacher_disagreement_score":0.020856932,"about_ca_system_score_codex":0.00064461015,"about_ca_system_score_gemma":0.0015259592,"threshold_uncertainty_score":0.069773436},"labels":[],"label_agreement":null},{"id":"W3197206719","doi":"10.3389/fbinf.2021.690769","title":"Mutation Edgotype Drives Fitness Effect in Human","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"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","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Interactome; Missense mutation; Biology; Genetics; Mutation; Computational biology; Gene","score_opus":0.004234037600124879,"score_gpt":0.2263624174473527,"score_spread":0.2221283798472278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197206719","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99460477,0.00014121157,0.0039923443,0.000023483159,0.00000357637,0.0000064854257,0.0003516283,0.0000924453,0.0007840604],"genre_scores_gemma":[0.99739033,0.00006192995,0.0018459542,0.000020946647,0.0000013056443,0.000004472162,0.00050121255,0.000015994823,0.00015784052],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99983,0.00003041084,0.000010513535,0.000067909394,0.000046491394,0.000014715373],"domain_scores_gemma":[0.9997254,0.00011092111,0.00007413749,0.000031714935,0.00003293972,0.000024931947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018713107,0.00035999276,0.00023526815,0.00045572518,0.00016577507,0.00029126203,0.00019268389,0.00030927104,0.0010621328],"category_scores_gemma":[0.00095222396,0.000121331024,0.00033169513,0.00038344928,0.0002756066,0.00016028977,0.00028927118,0.00018190587,0.00021784894],"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":[0.0010078226,0.00015336978,0.3595724,0.00027481813,0.00046519365,0.002216918,0.00015699859,0.11837531,0.4763588,0.004226144,0.0009622633,0.03622989],"study_design_scores_gemma":[0.00005456703,0.0006713591,0.5390463,0.00003609723,0.00022785689,0.007949822,0.00022575706,0.2824004,0.16029929,0.004994745,0.0040224693,0.00007141379],"about_ca_topic_score_codex":0.0013287321,"about_ca_topic_score_gemma":0.0014488209,"teacher_disagreement_score":0.0013287321,"about_ca_system_score_codex":0.0002540338,"about_ca_system_score_gemma":0.00012521603,"threshold_uncertainty_score":0.003553152},"labels":[],"label_agreement":null},{"id":"W3215944587","doi":"10.3389/fbinf.2021.693836","title":"GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Google","keywords":"Computer science; Cloud computing; Data mining; Cluster analysis; Software; Database normalization; Profiling (computer programming); Visualization; Web application; Analytics; Computational biology; Biology; Machine learning; Operating system","score_opus":0.025441506026609988,"score_gpt":0.274368793850721,"score_spread":0.248927287824111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215944587","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006973367,0.0011835836,0.37655264,0.0012089062,0.00062990584,0.0010035818,0.09959114,0.50512165,0.007735306],"genre_scores_gemma":[0.105602115,0.0025090731,0.4271197,0.0027202817,0.0004244522,0.0047320104,0.36602595,0.08286872,0.007997697],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977996,0.00026426208,0.00025326537,0.0005128725,0.00087547145,0.00029456706],"domain_scores_gemma":[0.99715424,0.00064315135,0.00027746416,0.00069438195,0.00070574845,0.0005250197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003670655,0.002795049,0.0024882997,0.002907647,0.0015795857,0.0044325762,0.0047670915,0.0013339618,0.014262841],"category_scores_gemma":[0.0055268006,0.0013841355,0.0023734933,0.0043697986,0.0010281359,0.0039049922,0.0055807875,0.0038908916,0.014340427],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004933998,0.00047158953,0.006923078,0.0028915594,0.0008806693,0.0015390839,0.0012564679,0.017402122,0.08493065,0.028855816,0.7044694,0.14544551],"study_design_scores_gemma":[0.0017132973,0.0003597131,0.012312415,0.00057097,0.0003257973,0.00084524247,0.00062467734,0.28432623,0.090143956,0.074813366,0.5330937,0.0008705573],"about_ca_topic_score_codex":0.0058327257,"about_ca_topic_score_gemma":0.004092844,"teacher_disagreement_score":0.014262841,"about_ca_system_score_codex":0.0017191522,"about_ca_system_score_gemma":0.0035935156,"threshold_uncertainty_score":0.047713995},"labels":[],"label_agreement":null},{"id":"W4200109798","doi":"10.3389/fbinf.2021.803176","title":"HSDFinder: A BLAST-Based Strategy for Identifying Highly Similar Duplicated Genes in Eukaryotic Genomes","year":2021,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genome; Gene; Biology; Computational biology; Evolutionary biology; Genetics","score_opus":0.02338666037640625,"score_gpt":0.2551842949264738,"score_spread":0.23179763455006758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200109798","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052577972,0.006487287,0.44340646,0.0010259001,0.0011555454,0.0018368205,0.20769463,0.2735115,0.012303963],"genre_scores_gemma":[0.053632133,0.0022777072,0.74191386,0.00044952906,0.00013855896,0.0019698825,0.1808644,0.015234823,0.0035190876],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978975,0.00042876336,0.00029964925,0.00062262145,0.0005787541,0.00017265353],"domain_scores_gemma":[0.99846506,0.0007348712,0.00025326447,0.00018755207,0.00019968848,0.0001594882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035939082,0.0038871032,0.003286939,0.010353724,0.0028021939,0.0024775937,0.003667653,0.0019650683,0.021464117],"category_scores_gemma":[0.0066248756,0.0020806459,0.00256752,0.007477417,0.0008217965,0.0042096972,0.004151792,0.003022919,0.01600196],"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":[0.006233235,0.00073418254,0.017201537,0.018343553,0.0017371761,0.004724166,0.0027184854,0.0053839516,0.22112066,0.012563579,0.39155194,0.3176875],"study_design_scores_gemma":[0.0020176352,0.0012032344,0.027662346,0.0019914408,0.0014108784,0.013288211,0.0015240539,0.0737978,0.17657922,0.0296145,0.67002064,0.000890123],"about_ca_topic_score_codex":0.0013947763,"about_ca_topic_score_gemma":0.0022070769,"teacher_disagreement_score":0.021464117,"about_ca_system_score_codex":0.0009777979,"about_ca_system_score_gemma":0.0020055643,"threshold_uncertainty_score":0.07180458},"labels":[],"label_agreement":null},{"id":"W4205677568","doi":"10.3389/fbinf.2021.763540","title":"Integrative In Silico Investigation Reveals the Host-Virus Interactions in Repurposed Drugs Against SARS-CoV-2","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Scleroderma Society of Ontario","keywords":"KEGG; Computational biology; Drug repositioning; Interactome; In silico; Biology; Virtual screening; Drug; Docking (animal); Druggability; Interaction network; TLR9; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Drug discovery; Gene; Coronavirus disease 2019 (COVID-19); Bioinformatics; Pharmacology; Genetics; Gene ontology; Medicine; Gene expression; Infectious disease (medical specialty); Disease","score_opus":0.027112269352801328,"score_gpt":0.296417865075935,"score_spread":0.2693055957231337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205677568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94759226,0.0016384786,0.034787733,0.000494511,0.000055462537,0.00012055838,0.003250769,0.0010249309,0.011035294],"genre_scores_gemma":[0.9734071,0.000917251,0.020933842,0.00010500615,0.000019642563,0.00010936211,0.0033585294,0.00007693279,0.0010724271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998109,0.000077282886,0.000010979823,0.000030254198,0.00003527292,0.0000353948],"domain_scores_gemma":[0.99970895,0.00020218606,0.00002700752,0.00001387653,0.000024474164,0.000023533024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048049644,0.00084049365,0.0012573166,0.00094772247,0.00047653995,0.0007434379,0.0007385954,0.0005676096,0.0037864663],"category_scores_gemma":[0.0007663099,0.0002865884,0.0015432532,0.0006243045,0.00016867189,0.0004544082,0.00046622855,0.000367355,0.00032821795],"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":[0.00043451643,0.000315141,0.015564232,0.00039318798,0.000506848,0.00042249035,0.000047383008,0.9611845,0.0070606847,0.0041675544,0.0010525091,0.008850899],"study_design_scores_gemma":[0.000039417686,0.0001571542,0.0013547686,0.000008817716,0.0001029224,0.000044432552,0.000029990486,0.99551696,0.0010243735,0.000963279,0.00075069943,0.0000071649656],"about_ca_topic_score_codex":0.006750621,"about_ca_topic_score_gemma":0.008361639,"teacher_disagreement_score":0.006750621,"about_ca_system_score_codex":0.00039973788,"about_ca_system_score_gemma":0.001084264,"threshold_uncertainty_score":0.013422668},"labels":[],"label_agreement":null},{"id":"W4212863275","doi":"10.3389/fbinf.2022.781949","title":"Visualizing RNA Structures by SAXS-Driven MD Simulations","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"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":"York University; New York University Abu Dhabi","keywords":"Riboswitch; Molecular dynamics; RNA; Small-angle X-ray scattering; Biomolecule; Chemical physics; Molecule; Chemistry; Nucleic acid structure; Crystallography; Scattering; Physics; Computational chemistry; Non-coding RNA; Biochemistry","score_opus":0.008062234504446625,"score_gpt":0.23905473711084488,"score_spread":0.23099250260639825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212863275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70176065,0.00067106134,0.27031565,0.000803289,0.00013199662,0.0003133936,0.0039261607,0.004030856,0.018046962],"genre_scores_gemma":[0.86010283,0.00066308945,0.13302253,0.00018389795,0.00002899019,0.00049909647,0.002352045,0.00045778987,0.0026897416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998666,0.000033565255,0.000009227805,0.000019953019,0.000051262086,0.00001944174],"domain_scores_gemma":[0.99979705,0.00008515354,0.000026136884,0.000022097734,0.00003688638,0.000032817155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029928715,0.0006055699,0.00055400893,0.00044185336,0.00037688517,0.0006706754,0.0008712806,0.0009317132,0.0025926977],"category_scores_gemma":[0.0005897175,0.00039912842,0.00040743948,0.00041172892,0.00041181783,0.00039859745,0.00042712525,0.00074252096,0.00043303028],"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":[0.00009163121,0.00010050925,0.0009907145,0.00008088162,0.000053129123,0.00013544438,0.00008231884,0.95731354,0.031190393,0.0042770486,0.0008897046,0.004794702],"study_design_scores_gemma":[0.00001068572,0.0000065640343,0.00013191548,0.0000031665345,0.0000018182872,0.000004507877,0.0000056242966,0.99646205,0.0023933896,0.00048175984,0.0004932223,0.0000052687897],"about_ca_topic_score_codex":0.003059041,"about_ca_topic_score_gemma":0.0023091962,"teacher_disagreement_score":0.003059041,"about_ca_system_score_codex":0.00087237544,"about_ca_system_score_gemma":0.00068295054,"threshold_uncertainty_score":0.008673489},"labels":[],"label_agreement":null},{"id":"W4281940572","doi":"10.3389/fbinf.2022.896295","title":"ContactPFP: Protein Function Prediction Using Predicted Contact Information","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"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":"Institute of Genetics; National Science Foundation; National Institutes of Health; Division of Civil, Mechanical and Manufacturing Innovation; National Institute of General Medical Sciences; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Protein function prediction; Computer science; Protein structure prediction; Computational biology; Protein function; Structural genomics; Function (biology); Data mining; Sequence (biology); Protein structure database; Protein structure; Bioinformatics; Artificial intelligence; Biology; Gene; Genetics; Sequence database","score_opus":0.004371013926252199,"score_gpt":0.19133590526396016,"score_spread":0.18696489133770797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281940572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26664424,0.0039383625,0.57526106,0.0004762202,0.00028509696,0.00063539733,0.022487098,0.12160178,0.008670758],"genre_scores_gemma":[0.6345051,0.0016440762,0.310732,0.00023703197,0.00010760507,0.0006891984,0.046801798,0.0021373036,0.0031458093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922156,0.00008075536,0.000056300032,0.00019281777,0.0003728107,0.0000756301],"domain_scores_gemma":[0.99891245,0.0005204117,0.0001338108,0.00016672154,0.00019876126,0.00006784902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093706325,0.001600911,0.0012473458,0.0022993071,0.00082990073,0.00080958265,0.0014799301,0.0015202087,0.0044746674],"category_scores_gemma":[0.004296323,0.0004346282,0.0011732202,0.0017900228,0.00034977772,0.0016692438,0.001274949,0.0009996364,0.0020527334],"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":[0.0035189542,0.001051772,0.056766376,0.004041816,0.0009044328,0.0031020076,0.00055261934,0.14633587,0.13541318,0.014598201,0.12865797,0.5050568],"study_design_scores_gemma":[0.00015755305,0.0003128808,0.0113745965,0.00006481219,0.00010519214,0.0014308165,0.00008107423,0.9012128,0.06104446,0.005301777,0.018813057,0.00010100228],"about_ca_topic_score_codex":0.0022928538,"about_ca_topic_score_gemma":0.0023793094,"teacher_disagreement_score":0.0044746674,"about_ca_system_score_codex":0.00062919495,"about_ca_system_score_gemma":0.00092168525,"threshold_uncertainty_score":0.014969289},"labels":[],"label_agreement":null},{"id":"W4293770727","doi":"10.3389/fbinf.2022.960889","title":"Sparse bayesian learning for genomic selection in yeast","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Machine learning; Bayesian probability; Artificial intelligence; Trait; Ranking (information retrieval); Selection (genetic algorithm); Computer science; Relevance (law); Heritability; Biology; Evolutionary biology","score_opus":0.008078267660177099,"score_gpt":0.20273250425302541,"score_spread":0.1946542365928483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293770727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03745589,0.00071297435,0.9600774,0.00043368642,0.000026387297,0.000032818043,0.00020182789,0.0005645332,0.0004945123],"genre_scores_gemma":[0.6712186,0.0011000079,0.32244623,0.00041901413,0.00015224669,0.00024964227,0.0017611887,0.0001545173,0.0024985939],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889296,0.0005961673,0.000050892686,0.00017767849,0.0001990254,0.00008337262],"domain_scores_gemma":[0.9937983,0.004809692,0.00030307847,0.0002555723,0.00067507604,0.00015827296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037661952,0.0007322807,0.0016062602,0.0009832962,0.00041080275,0.0010240527,0.001408297,0.0010757385,0.0010986191],"category_scores_gemma":[0.012719683,0.00048795991,0.0008379295,0.0011458494,0.0008124545,0.0013576968,0.0012004742,0.0016625467,0.00039163145],"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":[0.00016065943,0.00005727494,0.0024126384,0.00006270835,0.000079794525,0.000043094256,0.00007912742,0.8984512,0.000966239,0.009350681,0.0011436836,0.08719291],"study_design_scores_gemma":[0.0000091284255,0.000008800482,0.0001455584,0.00000386558,0.0000040721256,0.0000046283026,0.0000047116278,0.9923856,0.00016150768,0.007148798,0.00011974484,0.0000035881112],"about_ca_topic_score_codex":0.010199317,"about_ca_topic_score_gemma":0.008967472,"teacher_disagreement_score":0.010199317,"about_ca_system_score_codex":0.001035224,"about_ca_system_score_gemma":0.001305338,"threshold_uncertainty_score":0.020279884},"labels":[],"label_agreement":null},{"id":"W4297394736","doi":"10.3389/fbinf.2022.954529","title":"Predicting liver cancer on epigenomics data using machine learning","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Epigenetics and DNA Methylation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Epigenomics; Liver cancer; Epigenetics; DNA methylation; Computational biology; Biology; Feature selection; Cancer; Histone; Genome; Hepatocellular carcinoma; Computer science; Gene; Bioinformatics; Artificial intelligence; Genetics; Gene expression","score_opus":0.03194714082554689,"score_gpt":0.2715499766577785,"score_spread":0.23960283583223163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297394736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89266616,0.002482819,0.08470852,0.0008534002,0.000088397406,0.00016520527,0.015947532,0.0015274482,0.0015605034],"genre_scores_gemma":[0.95130205,0.00056716247,0.031668615,0.00011233217,0.000060750845,0.0001021523,0.015385078,0.00002881217,0.00077297934],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996563,0.00011767622,0.00003242302,0.00009652845,0.000052056956,0.00004499053],"domain_scores_gemma":[0.9986224,0.000845643,0.00016302285,0.00009870914,0.0002210631,0.00004918136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001102438,0.0005688437,0.000662717,0.002334103,0.00017336867,0.0005051729,0.00038618507,0.0006271038,0.0007775683],"category_scores_gemma":[0.003040242,0.00016176084,0.0007601854,0.0013968864,0.00015009438,0.0003605031,0.00036851218,0.0005924733,0.0004666939],"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":[0.0016403092,0.0005926885,0.5168332,0.00051761937,0.00067500275,0.0012097801,0.00012207653,0.22054788,0.016024495,0.0006672317,0.007696826,0.23347294],"study_design_scores_gemma":[0.00003580591,0.00021342216,0.12129726,0.00004843274,0.00011635108,0.000510516,0.00010568578,0.86527824,0.0077688764,0.0020327524,0.0025626086,0.000030073832],"about_ca_topic_score_codex":0.004295638,"about_ca_topic_score_gemma":0.005065328,"teacher_disagreement_score":0.004295638,"about_ca_system_score_codex":0.00038934936,"about_ca_system_score_gemma":0.0003496784,"threshold_uncertainty_score":0.008541286},"labels":[],"label_agreement":null},{"id":"W4307552292","doi":"10.3389/fbinf.2022.968327","title":"A brief survey of tools for genomic regions enrichment analysis","year":2022,"lang":"en","type":"review","venue":"Frontiers in Bioinformatics","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"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 Toronto","funders":"","keywords":"KEGG; Computational biology; Biology; Gene; Gene ontology; Gene Annotation; Set (abstract data type); Computer science; Genetics; Genome","score_opus":0.04449624855830717,"score_gpt":0.29084691903694365,"score_spread":0.24635067047863649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307552292","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041781066,0.9736953,0.019016558,0.0006351739,0.00045957425,0.000076070915,0.00073709403,0.0008861321,0.0040763966],"genre_scores_gemma":[0.001954762,0.9658639,0.02625734,0.0009241446,0.0004436668,0.00018017025,0.001859752,0.00021054677,0.0023056788],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991547,0.00019482474,0.00011030623,0.00017751908,0.00030100087,0.00006165482],"domain_scores_gemma":[0.99822015,0.0011777396,0.000112717935,0.00007513812,0.00034308332,0.00007118013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018194001,0.0018492066,0.0018203475,0.0071237213,0.00033760746,0.0013317036,0.002111823,0.001096226,0.0075436956],"category_scores_gemma":[0.0031896057,0.0008534311,0.0013170526,0.0073603718,0.00055686693,0.0021464843,0.00083492516,0.0022233836,0.009235779],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009615724,0.00007774992,0.0002789517,0.0161237,0.0001559267,0.0001685648,0.000074196636,0.0007314772,0.0037969577,0.004368571,0.040964693,0.933163],"study_design_scores_gemma":[0.000027355325,0.000114101684,0.0011946863,0.0030853648,0.00019916073,0.001285915,0.00004590943,0.0006447025,0.0036214753,0.0039825,0.9857217,0.00007709264],"about_ca_topic_score_codex":0.001388288,"about_ca_topic_score_gemma":0.001226861,"teacher_disagreement_score":0.0075436956,"about_ca_system_score_codex":0.00084428716,"about_ca_system_score_gemma":0.0012989133,"threshold_uncertainty_score":0.02523613},"labels":[],"label_agreement":null},{"id":"W4312187757","doi":"10.3389/fbinf.2022.984807","title":"Algorithms to anonymize structured medical and healthcare data: A systematic review","year":2022,"lang":"en","type":"review","venue":"Frontiers in Bioinformatics","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":18,"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 Ottawa","funders":"Aalborg Universitet","keywords":"Health care; Computer science; Data science; Internet privacy; Political science","score_opus":0.06932239267252895,"score_gpt":0.34527814705365056,"score_spread":0.27595575438112163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312187757","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014331929,0.98084587,0.009402035,0.0017703148,0.00039672042,0.0038774002,0.0013141636,0.00009905826,0.000861173],"genre_scores_gemma":[0.04108492,0.88791096,0.054387566,0.002139394,0.00042116814,0.012409156,0.0013612815,0.00005714988,0.00022841035],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.84900916,0.0944672,0.03456493,0.004864286,0.016212815,0.00088152353],"domain_scores_gemma":[0.64682317,0.2927053,0.03603216,0.009481651,0.01438209,0.00057565415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10322214,0.0021732212,0.0073749903,0.015059877,0.0011064954,0.0054236576,0.002781794,0.002108636,0.004510518],"category_scores_gemma":[0.30177897,0.0014243455,0.012486512,0.013931996,0.0024411203,0.008156592,0.003553615,0.002476023,0.00061430596],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031621158,0.000031086132,0.001155401,0.8489426,0.0086435815,0.000037419515,0.00049540785,0.00063847326,0.00009179396,0.0025486825,0.0030771005,0.13402225],"study_design_scores_gemma":[0.0005137961,0.0002934938,0.0022057383,0.9282169,0.027629793,0.00023088939,0.00043610047,0.0007329264,0.00039614883,0.004382008,0.034853812,0.000108297136],"about_ca_topic_score_codex":0.003713359,"about_ca_topic_score_gemma":0.0065585524,"teacher_disagreement_score":0.10322214,"about_ca_system_score_codex":0.005166986,"about_ca_system_score_gemma":0.021022277,"threshold_uncertainty_score":0.54589736},"labels":[],"label_agreement":null},{"id":"W4367317956","doi":"10.3389/fbinf.2023.1127661","title":"Application of annotation-agnostic RNA sequencing data analysis tools for biomarker discovery in liquid biopsy","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Stan Cassidy Foundation; Dr. Georges-L.-Dumont University Hospital Centre; Atlantic Cancer Research Institute; Vitalité Health Network; Beatrice Hunter Cancer Research Institute; Université de Moncton","funders":"","keywords":"Computational biology; Annotation; RNA; Biomarker discovery; Computer science; Sequence analysis; Biology; Bioinformatics; Proteomics; Gene; Genetics","score_opus":0.0274389689924735,"score_gpt":0.28969588594955226,"score_spread":0.26225691695707876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367317956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069788165,0.0012488771,0.88851076,0.0007728607,0.00019214336,0.00037650045,0.011328548,0.026130306,0.0016518055],"genre_scores_gemma":[0.17425157,0.0006537312,0.8090998,0.00068287126,0.000101629674,0.0006985363,0.010930015,0.0023332778,0.0012485606],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99636555,0.001052351,0.00042720584,0.0011103757,0.0009271682,0.000117321506],"domain_scores_gemma":[0.9898665,0.0060475455,0.0014679048,0.0010802724,0.0012748672,0.00026287255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064348034,0.0012777523,0.0008391892,0.003539845,0.00078887644,0.0023707603,0.00084988115,0.0010635691,0.0020285153],"category_scores_gemma":[0.014829852,0.0004635462,0.0011320636,0.0019162394,0.00067768263,0.0013256983,0.0010845688,0.0013984299,0.0018419685],"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":[0.0025899438,0.0003562236,0.032168023,0.0028287836,0.00082518806,0.0016754667,0.0015996123,0.01711893,0.5551277,0.0070395228,0.011229258,0.36744133],"study_design_scores_gemma":[0.00015347333,0.00061517797,0.03970281,0.00058066845,0.0003301684,0.001994557,0.00088657887,0.36594766,0.49558368,0.03699819,0.05685233,0.00035466652],"about_ca_topic_score_codex":0.0007891249,"about_ca_topic_score_gemma":0.0017957952,"teacher_disagreement_score":0.0064348034,"about_ca_system_score_codex":0.0007590337,"about_ca_system_score_gemma":0.0013005022,"threshold_uncertainty_score":0.034030855},"labels":[],"label_agreement":null},{"id":"W4367321738","doi":"10.3389/fbinf.2023.1162723","title":"The HRA Organ Gallery affords immersive superpowers for building and exploring the Human Reference Atlas with virtual reality","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"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":"National Institutes of Health; McGill University","keywords":"Computer science; Virtual reality; Human–computer interaction; Atlas (anatomy); Context (archaeology); Visualization; Computer graphics (images); Artificial intelligence; Geography","score_opus":0.03242041294152566,"score_gpt":0.2529991095080523,"score_spread":0.22057869656652668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367321738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047895484,0.002833351,0.82175565,0.002537814,0.00084801915,0.0009014221,0.017682938,0.04510012,0.060445193],"genre_scores_gemma":[0.25335288,0.0028325508,0.6993236,0.0018672016,0.00040905533,0.0016426018,0.01294091,0.011394926,0.01623632],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993305,0.0002098346,0.000036866248,0.00011657911,0.00019275754,0.00011348264],"domain_scores_gemma":[0.998171,0.0007480415,0.000068705274,0.0005530824,0.00012514064,0.00033407361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017008237,0.0012273261,0.00066065113,0.001821444,0.00080319063,0.0033218863,0.0016485018,0.0010363602,0.03167513],"category_scores_gemma":[0.0035928104,0.0009507155,0.0019765499,0.0007674427,0.00075425045,0.0023236985,0.007996066,0.0014243434,0.007289903],"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":[0.0022155922,0.00046472705,0.007735496,0.0025393704,0.00048557765,0.0024902734,0.007062052,0.02513546,0.11012724,0.041259848,0.3090457,0.49143866],"study_design_scores_gemma":[0.00041208218,0.0007808507,0.021385102,0.00072479143,0.00029609245,0.0047732196,0.0019894477,0.047489677,0.035971962,0.050412133,0.835133,0.000631581],"about_ca_topic_score_codex":0.0015122197,"about_ca_topic_score_gemma":0.004206564,"teacher_disagreement_score":0.03167513,"about_ca_system_score_codex":0.00037473973,"about_ca_system_score_gemma":0.0008371921,"threshold_uncertainty_score":0.105963886},"labels":[],"label_agreement":null},{"id":"W4378072319","doi":"10.3389/fbinf.2023.1163430","title":"Bayesian networks and imaging-derived phenotypes highlight the role of fat deposition in COVID-19 hospitalisation risk","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Liver Disease Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"European Commission; Royal Commission for the Exhibition of 1851","keywords":"Adipose tissue; Obesity; Coronavirus disease 2019 (COVID-19); Medicine; Causality (physics); Risk factor; Internal medicine; Disease","score_opus":0.004697105803108682,"score_gpt":0.22189332250445862,"score_spread":0.21719621670134995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378072319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39775348,0.0032639753,0.5849863,0.004610348,0.00012758741,0.00017331263,0.0026184618,0.00037384688,0.0060926685],"genre_scores_gemma":[0.95893437,0.0012385547,0.036885373,0.00029694173,0.0000918283,0.0001406789,0.0009917537,0.000055059816,0.0013654833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684453,0.002132575,0.00011404389,0.00056878594,0.00019741227,0.00014261743],"domain_scores_gemma":[0.9733646,0.022916479,0.0021890646,0.00055722694,0.00063530257,0.00033735033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008647844,0.0009241135,0.00089511566,0.0017811346,0.0006497283,0.0023846747,0.0012578119,0.0014448294,0.004052896],"category_scores_gemma":[0.04090411,0.0007108357,0.0014172292,0.0014899266,0.00116426,0.0019384125,0.0015005841,0.0023143818,0.00031486692],"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":[0.000787846,0.00016703906,0.22149456,0.00050468446,0.0011925221,0.0009217426,0.00094121945,0.61963284,0.0016533777,0.08708933,0.0032128163,0.06240202],"study_design_scores_gemma":[0.00005397542,0.00009996792,0.0400413,0.00019773575,0.00024835276,0.0003792956,0.00018074269,0.82207,0.0003075566,0.13400567,0.0023477774,0.000067577006],"about_ca_topic_score_codex":0.023093691,"about_ca_topic_score_gemma":0.01710899,"teacher_disagreement_score":0.023093691,"about_ca_system_score_codex":0.0015868782,"about_ca_system_score_gemma":0.0012097446,"threshold_uncertainty_score":0.045918584},"labels":[],"label_agreement":null},{"id":"W4378219828","doi":"10.3389/fbinf.2023.1153800","title":"ModEx: a general purpose computer model exploration system","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canadian Institutes of Health Research; Genome British Columbia","keywords":"Computer science; Flexibility (engineering); Workflow; Interface (matter); Key (lock); Variety (cybernetics); Data mining; Machine learning; Parameter space; Data exploration; Visualization; Artificial intelligence; Human–computer interaction; Software engineering; Theoretical computer science; Database; Operating system; Mathematics","score_opus":0.02890042162781355,"score_gpt":0.2599262772497422,"score_spread":0.23102585562192865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378219828","genre_codex":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004826673,0.00024046715,0.4810073,0.000167014,0.00006185386,0.00023647213,0.006210593,0.5005221,0.006727527],"genre_scores_gemma":[0.16167001,0.00092521834,0.7172314,0.0010466296,0.00013472013,0.002554359,0.02808522,0.06830257,0.020049853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995702,0.00007613717,0.000033281984,0.00011840542,0.00016093127,0.00004103346],"domain_scores_gemma":[0.9988913,0.00056752574,0.00006256405,0.0002452544,0.00015663562,0.00007690367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001196032,0.001619308,0.0008782148,0.0014428432,0.0004015451,0.0017687214,0.0026507077,0.00089715683,0.049304765],"category_scores_gemma":[0.0033140697,0.0009269314,0.0011821841,0.0007477914,0.00030478468,0.0022782248,0.0023597,0.0013009618,0.012474535],"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":[0.0023851057,0.00031387937,0.0052868365,0.0015967451,0.00032984494,0.0007992803,0.0008053264,0.031601734,0.050731394,0.01780111,0.39991993,0.4884289],"study_design_scores_gemma":[0.00079742365,0.0002695758,0.003427605,0.00027598068,0.00012838897,0.00074539345,0.00013600456,0.65023935,0.0421765,0.03524862,0.266259,0.00029611582],"about_ca_topic_score_codex":0.0022404224,"about_ca_topic_score_gemma":0.0020789318,"teacher_disagreement_score":0.049304765,"about_ca_system_score_codex":0.00059867674,"about_ca_system_score_gemma":0.0008982941,"threshold_uncertainty_score":0.1649409},"labels":[],"label_agreement":null},{"id":"W4381429387","doi":"10.3389/fbinf.2023.1199675","title":"Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Nematode management and characterization studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada; Grain Farmers of Ontario","keywords":"Soybean cyst nematode; Biology; Gene; Proteome; Computational biology; In silico; Genetics; Pipeline (software); Ribosomal protein; Protein–protein interaction; Bioinformatics; Computer science","score_opus":0.0655055117271323,"score_gpt":0.27095639023908197,"score_spread":0.20545087851194965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381429387","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5705274,0.0025784168,0.20189376,0.001819876,0.00013007568,0.0011012382,0.16944069,0.048204232,0.0043043448],"genre_scores_gemma":[0.44458064,0.0007856846,0.2769307,0.00053575664,0.000042693166,0.00093052926,0.2733904,0.0006181349,0.0021855359],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995378,0.0000467418,0.00004774003,0.00020810944,0.0001134221,0.0000461363],"domain_scores_gemma":[0.9992499,0.00030574386,0.00011852077,0.00006721594,0.00016286832,0.00009582364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080581784,0.0012879235,0.0008771865,0.0027140935,0.0009909194,0.0008879374,0.0010857519,0.0007001757,0.0018010802],"category_scores_gemma":[0.0015768898,0.00043457272,0.0017360233,0.0017936279,0.00022671586,0.0007857509,0.0008261096,0.00083117606,0.0011032326],"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":[0.003936605,0.002222474,0.20385931,0.003558562,0.0014722074,0.0058308453,0.0009568966,0.058836743,0.3554877,0.0040615043,0.06858491,0.2911923],"study_design_scores_gemma":[0.00044564105,0.0008560131,0.11990964,0.00012904024,0.0006568791,0.0014165711,0.0008803822,0.74309087,0.08608162,0.010402608,0.035994194,0.00013657601],"about_ca_topic_score_codex":0.004545654,"about_ca_topic_score_gemma":0.009140746,"teacher_disagreement_score":0.004545654,"about_ca_system_score_codex":0.00060234265,"about_ca_system_score_gemma":0.0015723656,"threshold_uncertainty_score":0.009038389},"labels":[],"label_agreement":null},{"id":"W4385724762","doi":"10.3389/fbinf.2023.1211819","title":"Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Dimensionality reduction; Multidimensional scaling; Outlier; Computer science; Nonlinear dimensionality reduction; Anomaly detection; Visualization; Pattern recognition (psychology); Embedding; Clustering high-dimensional data; Data mining; Dimension (graph theory); Data point; Artificial intelligence; Curse of dimensionality; Mathematics; Machine learning; Cluster analysis","score_opus":0.010245262593674668,"score_gpt":0.27786250480765307,"score_spread":0.2676172422139784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385724762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053136824,0.00010698339,0.9936986,0.00007795293,0.000027022383,0.00002667554,0.0001420947,0.00048127308,0.00012560877],"genre_scores_gemma":[0.075933546,0.00025918634,0.92111844,0.000057201272,0.000054503507,0.00023766409,0.0014550362,0.00027806184,0.0006064317],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963586,0.0011100817,0.00035678208,0.0007833603,0.0012267525,0.00016448743],"domain_scores_gemma":[0.9936847,0.002261389,0.0008037511,0.0015139927,0.0015221956,0.00021397162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032427607,0.0015735308,0.0013596604,0.0028949124,0.0007926244,0.0018038625,0.0013677186,0.0009835749,0.001235825],"category_scores_gemma":[0.016829774,0.0006049117,0.0016846916,0.0030917868,0.001363582,0.0019896918,0.0032504005,0.0026407007,0.0010111078],"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":[0.00041647177,0.00017751472,0.0054591945,0.00065121276,0.00037514375,0.00030042767,0.0009875129,0.31891343,0.048295654,0.06649782,0.00987909,0.5480465],"study_design_scores_gemma":[0.000015462765,0.000049165454,0.00082500756,0.000029353527,0.000017243512,0.00011746842,0.000085342,0.9580266,0.010789336,0.025459182,0.004536434,0.000049455557],"about_ca_topic_score_codex":0.0018013881,"about_ca_topic_score_gemma":0.0023004834,"teacher_disagreement_score":0.0032427607,"about_ca_system_score_codex":0.0007615163,"about_ca_system_score_gemma":0.0016760038,"threshold_uncertainty_score":0.017149568},"labels":[],"label_agreement":null},{"id":"W4387500504","doi":"10.3389/fbinf.2023.1275787","title":"DeepRaccess: high-speed RNA accessibility prediction using deep learning","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"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":"Japan Society for the Promotion of Science; Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"RNA; Computer science; Translation (biology); Artificial intelligence; Software; Feature (linguistics); Source code; Transcriptome; Deep learning; Computational biology; Nucleic acid secondary structure; Messenger RNA; Data mining; Biology; Genetics; Gene; Gene expression","score_opus":0.015199957893944892,"score_gpt":0.25436085475858183,"score_spread":0.23916089686463693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387500504","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08154939,0.001611317,0.8084087,0.00054116943,0.00025904944,0.00018071152,0.0067924764,0.09506632,0.005590931],"genre_scores_gemma":[0.48494294,0.0012019529,0.47389904,0.0009146876,0.00012862905,0.00064592727,0.025179712,0.0050804885,0.00800671],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971086,0.00003981633,0.000017363023,0.000104272774,0.00008338234,0.00004419366],"domain_scores_gemma":[0.99936455,0.00030777656,0.00005862401,0.00009417967,0.000121376346,0.000053512784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057874917,0.0016606736,0.0011027992,0.00065479585,0.0004779657,0.0011249965,0.0021914272,0.0014459953,0.0065568713],"category_scores_gemma":[0.002197089,0.0009197009,0.0014935945,0.0006235737,0.0005046146,0.0015807548,0.0012269901,0.0030913462,0.0028440794],"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":[0.0006429249,0.0004944434,0.007779165,0.00095266796,0.00065636646,0.0004885385,0.000226776,0.6204589,0.06353462,0.013539717,0.06418376,0.22704214],"study_design_scores_gemma":[0.000019675308,0.000032484484,0.0002996859,0.000009309622,0.00001156087,0.000028926372,0.0000070165174,0.9885224,0.0061175316,0.0027810808,0.0021546232,0.00001574607],"about_ca_topic_score_codex":0.0067891413,"about_ca_topic_score_gemma":0.009877817,"teacher_disagreement_score":0.0067891413,"about_ca_system_score_codex":0.0009552385,"about_ca_system_score_gemma":0.0014732112,"threshold_uncertainty_score":0.021934927},"labels":[],"label_agreement":null},{"id":"W4392037827","doi":"10.3389/fbinf.2023.1285828","title":"Posterior inference of Hi-C contact frequency through sampling","year":2024,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Downstream (manufacturing); Sampling (signal processing); Posterior probability; Computer science; Biological system; Algorithm; Mathematics; Artificial intelligence; Biology; Engineering; Bayesian probability; Computer vision","score_opus":0.011511979321712176,"score_gpt":0.25795899176719606,"score_spread":0.2464470124454839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392037827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10453074,0.0003933194,0.8888533,0.00019256237,0.000047015506,0.00011091545,0.0018942833,0.0023297125,0.001648229],"genre_scores_gemma":[0.7238642,0.0004385534,0.25817636,0.00041243643,0.00018209833,0.00039521014,0.012820138,0.0011561827,0.0025549426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718225,0.0008461647,0.0001155942,0.0009709508,0.0006376024,0.00024749176],"domain_scores_gemma":[0.97416085,0.019630639,0.0012199553,0.0030111955,0.0015166149,0.0004607636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074840374,0.000917853,0.0013258827,0.0020193108,0.0009796624,0.0024397387,0.0023672048,0.0012659072,0.0044282624],"category_scores_gemma":[0.039894536,0.0008969261,0.0011629985,0.0015864326,0.0019215965,0.002179432,0.0020183097,0.0027491264,0.0013332772],"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":[0.0021404335,0.0002773442,0.10714012,0.00095309166,0.00095531205,0.0009409328,0.0013045196,0.5185818,0.028938597,0.1514018,0.018293604,0.16907239],"study_design_scores_gemma":[0.00006377669,0.0000582486,0.0065283487,0.000057717974,0.000083464176,0.000184725,0.00007356049,0.9324948,0.0069845286,0.05042919,0.0029772802,0.000064202286],"about_ca_topic_score_codex":0.00764059,"about_ca_topic_score_gemma":0.0071287933,"teacher_disagreement_score":0.00764059,"about_ca_system_score_codex":0.0013236073,"about_ca_system_score_gemma":0.0019878035,"threshold_uncertainty_score":0.03957981},"labels":[],"label_agreement":null},{"id":"W4399868446","doi":"10.3389/fbinf.2024.1328714","title":"Bioinformatics proficiency among African students","year":2024,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"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 Guelph","funders":"","keywords":"Context (archaeology); Diversity (politics); Literacy; Scientific literacy; Knowledge management; Medical education; Computer science; Medicine; Political science; Science education; Mathematics education; Sociology; Psychology; Biology; Pedagogy","score_opus":0.01049272390913672,"score_gpt":0.2776126379492186,"score_spread":0.26711991404008184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399868446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9869816,0.00091503194,0.0002570552,0.0038183534,0.0001046157,0.000037333906,0.00027814845,0.000018152388,0.007589739],"genre_scores_gemma":[0.9960056,0.0008303224,0.00016176047,0.0008362348,0.000025703413,0.000034490164,0.00013030622,0.0000075051153,0.0019681284],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99874145,0.00023473814,0.0000939364,0.00018304015,0.00033822958,0.00040860966],"domain_scores_gemma":[0.9933555,0.0015737022,0.0013968727,0.00017402001,0.0009924121,0.0025075392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648892,0.00019487945,0.00040912165,0.0016501201,0.0018222772,0.0033788825,0.00042869837,0.000882439,0.010984482],"category_scores_gemma":[0.0128847575,0.00020683503,0.00031701094,0.0014337971,0.0012609684,0.0018592267,0.0018826838,0.0012113998,0.0016316577],"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":[0.00012897071,0.0006373965,0.8709653,0.0002685047,0.000035077825,0.0010562007,0.041309517,0.00012468101,0.0019828188,0.0030137838,0.00841438,0.072063446],"study_design_scores_gemma":[0.000030029038,0.0007167599,0.8307432,0.001066176,0.00008128923,0.0025950088,0.08443559,0.0008609189,0.0015262685,0.0039839423,0.07384597,0.000114842675],"about_ca_topic_score_codex":0.003415698,"about_ca_topic_score_gemma":0.0026308093,"teacher_disagreement_score":0.010984482,"about_ca_system_score_codex":0.00065284513,"about_ca_system_score_gemma":0.0020594373,"threshold_uncertainty_score":0.03674674},"labels":[],"label_agreement":null},{"id":"W4400218314","doi":"10.3389/fbinf.2024.1391086","title":"Maximum-scoring path sets on pangenome graphs of constant treewidth","year":2024,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Advanced Graph Theory Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"HORIZON EUROPE Marie Sklodowska-Curie Actions; European Commission; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Natural Sciences and Engineering Research Council of Canada; Agentúra Ministerstva Školstva, Vedy, Výskumu a Športu SR; National Institutes of Health; National Human Genome Research Institute; Agentúra na Podporu Výskumu a Vývoja","keywords":"Treewidth; Constant (computer programming); Path (computing); Mathematics; Combinatorics; Partial k-tree; Pathwidth; 1-planar graph; Graph; Computer science; Chordal graph","score_opus":0.0175788546057046,"score_gpt":0.26583076268259626,"score_spread":0.24825190807689165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400218314","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1524564,0.000583654,0.8412794,0.00065151986,0.000029620018,0.00028257907,0.0012456189,0.0012135137,0.0022577185],"genre_scores_gemma":[0.26029655,0.0005119554,0.7314967,0.0002045734,0.000048555517,0.00039897716,0.0038775166,0.00042796024,0.002737269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989497,0.00027771178,0.000062551786,0.00039087108,0.00021692211,0.00010220705],"domain_scores_gemma":[0.99346364,0.004512753,0.00068977266,0.0006586789,0.0004004727,0.00027465296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013920856,0.0013273383,0.001372606,0.0023638166,0.0010662798,0.0013958346,0.0020508196,0.001670845,0.0035411504],"category_scores_gemma":[0.01035731,0.0010189717,0.0012982702,0.002950564,0.0012602288,0.004919608,0.0026260342,0.0017571746,0.00061073725],"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":[0.00066449994,0.00035045922,0.00611619,0.0013492381,0.00024416696,0.0006212047,0.0010031622,0.50325435,0.02454758,0.13699065,0.011083685,0.31377476],"study_design_scores_gemma":[0.0000756053,0.00012068149,0.0009881736,0.00006385515,0.000048633414,0.00035102287,0.00021644223,0.6181858,0.006144991,0.36975887,0.0040206397,0.000025229047],"about_ca_topic_score_codex":0.0011769285,"about_ca_topic_score_gemma":0.0018103393,"teacher_disagreement_score":0.0035411504,"about_ca_system_score_codex":0.000953331,"about_ca_system_score_gemma":0.0008872506,"threshold_uncertainty_score":0.011846304},"labels":[],"label_agreement":null},{"id":"W4401745183","doi":"10.3389/fbinf.2024.1353807","title":"Design principles for molecular animation","year":2024,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; National Science Foundation","keywords":"Animation; Function (biology); Computer science; Flexibility (engineering); Agency (philosophy); Visualization; Set (abstract data type); Design elements and principles; Molecular graphics; Human–computer interaction; Motion (physics); Data science; Nanotechnology; Computer graphics; Epistemology; Artificial intelligence; Computer graphics (images); Biology","score_opus":0.02492711352337584,"score_gpt":0.28274939988286174,"score_spread":0.2578222863594859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401745183","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009011948,0.00085645716,0.9629395,0.0022021467,0.00047019936,0.0002703261,0.00010918547,0.0012107957,0.031040175],"genre_scores_gemma":[0.052238796,0.0014655482,0.9190584,0.0012206895,0.00029818408,0.0021162967,0.00022746735,0.0010270411,0.022347597],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9955236,0.0017693172,0.0003550574,0.0005718345,0.0014998899,0.00028032842],"domain_scores_gemma":[0.99463475,0.0025890633,0.0003279905,0.00097634067,0.0012232412,0.0002485739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006252534,0.0015601323,0.0007233587,0.0017525367,0.0026091062,0.0054514874,0.0031415143,0.003772848,0.021044934],"category_scores_gemma":[0.015091547,0.001243955,0.0018193447,0.0010092965,0.007153124,0.004979159,0.0039109746,0.004790111,0.008187798],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024248297,0.000017487175,0.000101161735,0.00020929001,0.0000122598885,0.000057710295,0.0004190749,0.00661146,0.0012765921,0.96426547,0.0055410312,0.021464247],"study_design_scores_gemma":[0.00007763663,0.000067312554,0.00009057756,0.00027186642,0.000029256686,0.00028342885,0.00014030011,0.042701658,0.0035802927,0.62671405,0.32599002,0.00005361182],"about_ca_topic_score_codex":0.0015439498,"about_ca_topic_score_gemma":0.0012677262,"teacher_disagreement_score":0.021044934,"about_ca_system_score_codex":0.0024115178,"about_ca_system_score_gemma":0.0019754271,"threshold_uncertainty_score":0.070402324},"labels":[],"label_agreement":null},{"id":"W4406774579","doi":"10.3389/fbinf.2024.1489704","title":"A novel lossless encoding algorithm for data compression–genomics data as an exemplar","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Data compression; Lossless compression; Encoding (memory); Benchmark (surveying); Entropy encoding; Algorithm; Data mining; Compression (physics); Entropy (arrow of time); Bin; Artificial intelligence","score_opus":0.04953632431663978,"score_gpt":0.3138358221403895,"score_spread":0.26429949782374973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406774579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011921725,0.0008980245,0.98371375,0.0003778188,0.00020404374,0.00008369315,0.00018965699,0.0010084934,0.0016029191],"genre_scores_gemma":[0.11263906,0.0011339828,0.8786436,0.00037776705,0.00020474983,0.00020667832,0.0011128347,0.00017642697,0.0055049267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934834,0.00008093153,0.000056132714,0.00010525356,0.00036436002,0.000045001983],"domain_scores_gemma":[0.9992156,0.00025975084,0.00006941418,0.00017160902,0.00025401768,0.00002955329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062536664,0.00071840105,0.0005743508,0.001163291,0.0004236911,0.0010315434,0.00093961076,0.0008527369,0.0019808675],"category_scores_gemma":[0.0027323451,0.00020295811,0.00042752587,0.0016912364,0.0006379587,0.001735568,0.0008922784,0.0013299393,0.0015733354],"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":[0.00040123085,0.0001337253,0.00074391987,0.00026483877,0.00004359685,0.00028977345,0.00019941723,0.03546631,0.09754638,0.027584298,0.008793931,0.82853264],"study_design_scores_gemma":[0.00007488942,0.0003933581,0.00095151045,0.00009251121,0.000042787415,0.0020154286,0.00011521598,0.78225815,0.16073656,0.017588826,0.035666663,0.000064152366],"about_ca_topic_score_codex":0.0006689076,"about_ca_topic_score_gemma":0.0006583165,"teacher_disagreement_score":0.0019808675,"about_ca_system_score_codex":0.00044799838,"about_ca_system_score_gemma":0.00066194724,"threshold_uncertainty_score":0.0066266656},"labels":[],"label_agreement":null},{"id":"W4408535953","doi":"10.3389/fbinf.2025.1484113","title":"Using short-read 16S rRNA sequencing of multiple variable regions to generate high-quality results to a species level","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health","keywords":"16S ribosomal RNA; Computational biology; Quality (philosophy); Variable (mathematics); Biology; Ribosomal RNA; Computer science; Genetics; Evolutionary biology; Gene; Mathematics; Physics","score_opus":0.08336275701906307,"score_gpt":0.32475862999787863,"score_spread":0.24139587297881557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408535953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15697916,0.0046225595,0.82508314,0.00066737464,0.0006480741,0.00079511985,0.0031981203,0.0042115324,0.003794996],"genre_scores_gemma":[0.18020096,0.0024103075,0.80773324,0.0007081167,0.0002144569,0.0007634631,0.0048890417,0.0007117368,0.0023686027],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.991289,0.0026785305,0.000642022,0.0022197359,0.0028900665,0.00028051838],"domain_scores_gemma":[0.99101627,0.0026176628,0.002234739,0.0011173296,0.0027582257,0.00025578795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073902635,0.001740489,0.0014510661,0.0017134692,0.0005880276,0.0021013753,0.0012091846,0.0013271715,0.0030596822],"category_scores_gemma":[0.009819808,0.00072620925,0.0019444645,0.0014839395,0.00086902326,0.0014448907,0.001621022,0.0012424516,0.0036738527],"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":[0.000497572,0.00021588607,0.014337381,0.0025787642,0.00046779538,0.00033785964,0.00036268006,0.003203271,0.86513245,0.0010891765,0.0021629732,0.10961415],"study_design_scores_gemma":[0.000072504474,0.0011182844,0.022166006,0.0005784663,0.00030822057,0.001090927,0.00021614296,0.021787476,0.9247271,0.0027605232,0.024993189,0.00018110385],"about_ca_topic_score_codex":0.0005324409,"about_ca_topic_score_gemma":0.0015162476,"teacher_disagreement_score":0.0073902635,"about_ca_system_score_codex":0.0005218136,"about_ca_system_score_gemma":0.0011827721,"threshold_uncertainty_score":0.039083898},"labels":[],"label_agreement":null},{"id":"W4409295349","doi":"10.3389/fbinf.2025.1585717","title":"A cost and community perspective on the barriers to microbiome data reuse","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Research Data Management Practices","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; University of Calgary","funders":"Pacific Northwest National Laboratory; Biological and Environmental Research; Los Alamos National Laboratory; Lawrence Berkeley National Laboratory; Office of Science; U.S. Department of Energy","keywords":"Perspective (graphical); Reuse; Microbiome; Data science; Sociology; Knowledge management; Management science; Engineering ethics; Computer science; Engineering; Ecology; Biology; Bioinformatics; Artificial intelligence","score_opus":0.09297826533589958,"score_gpt":0.3635733126563203,"score_spread":0.2705950473204207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409295349","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":"reproducibility","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":"reproducibility","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4458167,0.0068660467,0.07304903,0.437942,0.00065030134,0.00077776384,0.0011851037,0.00060001225,0.033113018],"genre_scores_gemma":[0.93705684,0.0015610359,0.047452025,0.010499292,0.00039677153,0.00062756677,0.00048194276,0.0002594932,0.0016649803],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.66237414,0.2269426,0.02826766,0.012324101,0.055868685,0.014222861],"domain_scores_gemma":[0.23301543,0.54427755,0.070723735,0.047650553,0.082467385,0.021865426],"candidate_categories":["metaresearch","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.26991305,0.00073623593,0.0011059205,0.009082758,0.008011221,0.020544194,0.006062568,0.004373518,0.0064484477],"category_scores_gemma":[0.52828443,0.0012489234,0.0013855988,0.015768649,0.011733459,0.024672603,0.017856799,0.0046893344,0.0009910341],"study_design_candidate":"qualitative","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":[0.00070320076,0.00062830804,0.32584292,0.0037964946,0.00065279886,0.0015374016,0.10068009,0.0049587763,0.004875704,0.11499433,0.032674033,0.40865594],"study_design_scores_gemma":[0.0002481096,0.0015692185,0.17503831,0.0076255878,0.0005120915,0.0038136228,0.28027582,0.016425293,0.007433984,0.26453197,0.241568,0.0009581076],"about_ca_topic_score_codex":0.020297851,"about_ca_topic_score_gemma":0.019560128,"teacher_disagreement_score":0.99393743,"about_ca_system_score_codex":0.010676196,"about_ca_system_score_gemma":0.03013309,"threshold_uncertainty_score":0.9003272},"labels":[],"label_agreement":null},{"id":"W4409557213","doi":"10.3389/fbinf.2025.1539936","title":"Classification of collagen remodeling in asthma using second-harmonic generation imaging, supervised machine learning and texture-based analysis","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Cancer Cells and Metastasis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Ottawa Hospital; University of British Columbia; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Excellence Research Chairs, Government of Canada; Canadian Allergy, Asthma and Immunology Foundation","keywords":"Extracellular matrix; Dimensionality reduction; Artificial intelligence; Biomedical engineering; Pathology; Pattern recognition (psychology); Materials science; Computer science; Medicine; Biology; Cell biology","score_opus":0.019511327520946375,"score_gpt":0.27714994292114165,"score_spread":0.2576386154001953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409557213","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7948744,0.0007010939,0.20073172,0.00017353009,0.000035568508,0.00017530908,0.0006851235,0.0014240334,0.0011991529],"genre_scores_gemma":[0.89725256,0.00019966914,0.10038401,0.000037298178,0.000023956238,0.00010973276,0.00087275583,0.00005355879,0.001066457],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973327,0.0000605696,0.000019601572,0.00007444243,0.00007682549,0.000035270146],"domain_scores_gemma":[0.9994361,0.00023216692,0.000092850605,0.000058522175,0.00014681343,0.00003366381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009291782,0.00034077745,0.00034969806,0.0011207307,0.00014007068,0.0004160931,0.00029807095,0.0003911766,0.00061764615],"category_scores_gemma":[0.0014203875,0.0001161094,0.00041826704,0.0003993739,0.00021544842,0.00020563767,0.00025574077,0.0003565115,0.00029549643],"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":[0.0013854866,0.0005332749,0.08233768,0.00031360707,0.00019445176,0.00036063368,0.00030281092,0.064168684,0.25431263,0.0008528708,0.0026564454,0.59258145],"study_design_scores_gemma":[0.000023667375,0.0003320901,0.078519516,0.000020614349,0.000049199873,0.00039950927,0.0000954262,0.87024224,0.048495084,0.0008619268,0.0009307047,0.00002989997],"about_ca_topic_score_codex":0.0017111494,"about_ca_topic_score_gemma":0.0023782672,"teacher_disagreement_score":0.0017111494,"about_ca_system_score_codex":0.00022858666,"about_ca_system_score_gemma":0.00030474053,"threshold_uncertainty_score":0.0049139857},"labels":[],"label_agreement":null},{"id":"W4409710424","doi":"10.3389/fbinf.2025.1574359","title":"Artificial intelligence in variant calling: a review","year":2025,"lang":"en","type":"review","venue":"Frontiers in Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Genome Canada","keywords":"Indel; Genomics; Computer science; Artificial intelligence; Scalability; Computational biology; DNA sequencing; Data science; Single-nucleotide polymorphism; Biology; Genetics; Genome; Gene","score_opus":0.028457972521385308,"score_gpt":0.30346485928799205,"score_spread":0.27500688676660673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409710424","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000058709753,0.99833626,0.0004621338,0.0003531539,0.00015494075,0.000005834524,0.000025876585,0.000011650417,0.00059154787],"genre_scores_gemma":[0.0004440746,0.998247,0.00066210283,0.00023697116,0.00016219131,0.000009644188,0.000044901793,0.0000039613165,0.00018907912],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994524,0.00014419355,0.00009238275,0.00010486572,0.00017297847,0.000033140263],"domain_scores_gemma":[0.99682295,0.0025233186,0.00016951222,0.000061179984,0.00034838056,0.00007461231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017801488,0.0010596118,0.0017244198,0.0031244028,0.0003269073,0.0016051128,0.0015414989,0.001597046,0.00406083],"category_scores_gemma":[0.0036079371,0.00047341886,0.0010410616,0.0044077104,0.0008483402,0.0020519004,0.00085341075,0.0021223107,0.0022179678],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048153823,0.000049150043,0.00021746491,0.02453401,0.00016285724,0.00012227947,0.00006517884,0.0007450745,0.0004916284,0.0060909824,0.021245992,0.9462273],"study_design_scores_gemma":[0.000021387963,0.00009743029,0.00088777306,0.012528864,0.00030098806,0.0010311177,0.00007079847,0.0003418506,0.0004812304,0.007175679,0.9770089,0.000054047418],"about_ca_topic_score_codex":0.0021428086,"about_ca_topic_score_gemma":0.002286006,"teacher_disagreement_score":0.00406083,"about_ca_system_score_codex":0.0008667322,"about_ca_system_score_gemma":0.0020538657,"threshold_uncertainty_score":0.013584852},"labels":[],"label_agreement":null},{"id":"W4413998513","doi":"10.3389/fbinf.2025.1577324","title":"A novel linear indexing method for strings under all internal nodes in a suffix tree","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"University of Connecticut","keywords":"Search engine indexing; Suffix; Generalized suffix tree; Computer science; Tree (set theory); Compressed suffix array; Suffix tree; Theoretical computer science; Algorithm; Mathematics; Artificial intelligence; Data structure; Combinatorics; Programming language","score_opus":0.019749279133473994,"score_gpt":0.30205323189460714,"score_spread":0.2823039527611331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413998513","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004277341,0.0002883412,0.99168175,0.00010623201,0.00013389968,0.00009847265,0.00029549276,0.0018405371,0.0012778459],"genre_scores_gemma":[0.025965992,0.00031134815,0.96912473,0.00010515665,0.00015688763,0.00018746253,0.0012635598,0.00030624008,0.0025785505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982893,0.00017165345,0.00023914731,0.0003944249,0.000773414,0.00013196531],"domain_scores_gemma":[0.99730515,0.0007505404,0.0002530359,0.00073882047,0.0008117823,0.00014067514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008759871,0.00078477553,0.0013191333,0.0026455203,0.0012713489,0.0022112539,0.0017873257,0.0010102787,0.0050235637],"category_scores_gemma":[0.0056971516,0.00046329421,0.0009434853,0.005278647,0.0008630284,0.0055316985,0.0023157662,0.0014040398,0.0053704362],"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":[0.00029406237,0.00018950131,0.000975518,0.00042845638,0.000052716896,0.00018620734,0.00046489327,0.010488418,0.05486055,0.049132846,0.013626072,0.8693008],"study_design_scores_gemma":[0.00026785265,0.00079330185,0.0013551768,0.00016314384,0.00015607712,0.0025053169,0.0005768951,0.659881,0.10610722,0.13076426,0.09720081,0.00022892523],"about_ca_topic_score_codex":0.0014491065,"about_ca_topic_score_gemma":0.0020152933,"teacher_disagreement_score":0.0050235637,"about_ca_system_score_codex":0.0006898725,"about_ca_system_score_gemma":0.0026773938,"threshold_uncertainty_score":0.01680553},"labels":[],"label_agreement":null},{"id":"W4414426256","doi":"10.3389/fbinf.2025.1645785","title":"Extracting a COVID-19 signature from a multi-omic dataset","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Engineering Link (Canada); Mila - Quebec Artificial Intelligence Institute; Université Laval","funders":"Canadian Institutes of Health Research; Ministère de la Santé et des Services sociaux; Ministère de la Santé; Génome Québec; Public Health Agency; Public Health Agency of Canada","keywords":"Signature (topology); Pattern recognition (psychology); Path (computing); Biomarker; Feature extraction; Work (physics)","score_opus":0.010564276815481556,"score_gpt":0.29788201329463926,"score_spread":0.2873177364791577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414426256","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74323416,0.003151192,0.14686526,0.0019552824,0.00016706092,0.00092677044,0.09665005,0.0035550285,0.003495213],"genre_scores_gemma":[0.7469919,0.0005944926,0.14555028,0.00041239365,0.0000885614,0.0003008709,0.10480928,0.0001202008,0.0011319383],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988582,0.0001452288,0.0001222957,0.00030898105,0.00043346558,0.00013189705],"domain_scores_gemma":[0.9977602,0.0005344292,0.0003797433,0.00037564914,0.0007889029,0.00016100319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018099112,0.0011236003,0.0008553903,0.0036141188,0.0007327149,0.0015713972,0.00090633344,0.0007180082,0.0012346237],"category_scores_gemma":[0.004406068,0.00016691312,0.00094471086,0.0032388575,0.00043627867,0.0006333107,0.0011534288,0.00087205926,0.00076050207],"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":[0.0014671964,0.0006107217,0.45422858,0.0018770837,0.0013929467,0.0017415754,0.0003227829,0.038228862,0.15558606,0.0021326214,0.02797224,0.31443927],"study_design_scores_gemma":[0.00020805662,0.00066884357,0.4121236,0.0004412168,0.000662938,0.00211648,0.0005875257,0.42815158,0.10134187,0.009964929,0.04346159,0.00027136938],"about_ca_topic_score_codex":0.025690984,"about_ca_topic_score_gemma":0.040196154,"teacher_disagreement_score":0.025690984,"about_ca_system_score_codex":0.0017551432,"about_ca_system_score_gemma":0.0033408597,"threshold_uncertainty_score":0.05108285},"labels":[],"label_agreement":null},{"id":"W4415685441","doi":"10.3389/fbinf.2025.1693343","title":"The importance of democratized resources in early-career training for bioimage analysts and bioimaging scientists","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"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","funders":"Chan Zuckerberg Initiative; McGill University","keywords":"Field (mathematics); Training (meteorology); Emerging technologies; Variety (cybernetics); Coronavirus disease 2019 (COVID-19)","score_opus":0.009155576486617627,"score_gpt":0.26132341735227543,"score_spread":0.2521678408656578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415685441","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062448747,0.009396083,0.11215267,0.67924327,0.008896507,0.0014528523,0.0022808148,0.0070852884,0.117043756],"genre_scores_gemma":[0.3193803,0.012491011,0.4583976,0.10851552,0.0064758724,0.007138233,0.0051583867,0.0040969993,0.07834605],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.96872634,0.019869635,0.0014932884,0.002335278,0.004197027,0.0033784483],"domain_scores_gemma":[0.75991195,0.11011517,0.010531969,0.022615986,0.021538578,0.075286254],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.05597614,0.0008125613,0.0011866349,0.0039745118,0.00885337,0.017589485,0.005786292,0.005085301,0.06030057],"category_scores_gemma":[0.14560603,0.0010390413,0.0009959989,0.0031740163,0.004403147,0.031352483,0.029099531,0.009517157,0.030348433],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024285875,0.0010881429,0.009036507,0.0017790467,0.000042354808,0.0012498477,0.037805017,0.00048897153,0.0033311606,0.039244667,0.37674522,0.5289462],"study_design_scores_gemma":[0.00010937027,0.00022520756,0.004828315,0.0027787033,0.000023360966,0.0007166025,0.032203358,0.0005363362,0.0015732754,0.06900945,0.8878489,0.00014710952],"about_ca_topic_score_codex":0.0013426291,"about_ca_topic_score_gemma":0.0031205541,"teacher_disagreement_score":0.9942137,"about_ca_system_score_codex":0.0030312368,"about_ca_system_score_gemma":0.03252666,"threshold_uncertainty_score":0.29603362},"labels":[],"label_agreement":null},{"id":"W4416160833","doi":"10.3389/fbinf.2025.1636240","title":"Comprehensive analysis of multi-omics vaccine response data using MOFA and Stabl algorithms","year":2025,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute of Infection and Immunity","funders":"","keywords":"Key (lock); Code (set theory); Basis (linear algebra); Source code","score_opus":0.033080976233085764,"score_gpt":0.29346315126198563,"score_spread":0.2603821750288999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416160833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32425585,0.0064013246,0.60830164,0.0018888564,0.00026873176,0.0005644189,0.031656068,0.02478674,0.0018764321],"genre_scores_gemma":[0.37614572,0.0009903411,0.5631486,0.0007204954,0.00016402183,0.0008774405,0.05547403,0.0007965275,0.0016828077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792576,0.00078813214,0.00016744014,0.000589678,0.00032780718,0.00020102249],"domain_scores_gemma":[0.9939964,0.0043344293,0.00046039984,0.00043431539,0.00064612407,0.00012841032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007899317,0.0026215557,0.0020487958,0.006298497,0.0013269243,0.0027507609,0.0013349773,0.0018689326,0.0028116019],"category_scores_gemma":[0.0137620205,0.0006088089,0.005591534,0.0029242781,0.00050716597,0.0011344359,0.0011427324,0.0016619769,0.0016427408],"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":[0.0030006468,0.0010221811,0.13195454,0.0014440734,0.0043666516,0.0005989309,0.00035535858,0.36948684,0.025435194,0.0040816753,0.026537195,0.4317167],"study_design_scores_gemma":[0.00008786971,0.00030934357,0.01584798,0.00007800001,0.00029052413,0.00023242571,0.00010197937,0.9639004,0.004706228,0.00578893,0.008588671,0.00006768368],"about_ca_topic_score_codex":0.011496319,"about_ca_topic_score_gemma":0.011151731,"teacher_disagreement_score":0.011496319,"about_ca_system_score_codex":0.0015753298,"about_ca_system_score_gemma":0.002280043,"threshold_uncertainty_score":0.04177606},"labels":[],"label_agreement":null}]}