{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":14,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":14,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"32813abe1187","filters":{"venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"}},"results":[{"id":"W4313525308","doi":"10.1109/bibm55620.2022.9995552","title":"Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Robustness (evolution); Computer science; Algorithm; Band-pass filter; Passband; QRS complex; Noise (video); Artificial intelligence; Pattern recognition (psychology); Signal processing; Noise reduction; Detector; Filter (signal processing); Reduction (mathematics); Speech recognition; Mathematics; Telecommunications; Engineering; Computer vision; Electronic engineering","authors":[{"name":"Md Niaz Imtiaz","is_ca":true},{"name":"Naimul Khan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07299498389888334,"gpt":0.3093321178113743,"spread":0.2363371339124909,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006381227,0.0002297294,0.0004170056,0.001288352,0.0001436851,0.00006479966,0.0002960647,0.0000629355,0.0004070521],"category_scores_gemma":[0.00007866636,0.0001875501,0.00009271414,0.0008551674,0.00007636596,0.00009211729,0.0001563962,0.0004741013,0.00002895095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001909356,"about_ca_system_score_gemma":0.000110559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001602648,"about_ca_topic_score_gemma":0.00001157494,"domain_scores_codex":[0.9977971,0.00004053946,0.0006229321,0.0002980218,0.0009462519,0.0002951596],"domain_scores_gemma":[0.999166,0.00006336418,0.000170158,0.0002453816,0.0001422991,0.0002127612],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003380445,0.003771042,0.0311912,0.001332278,0.002247096,0.0005763895,0.01591792,0.007722889,0.07083271,0.006151323,0.06106701,0.7958097],"study_design_scores_gemma":[0.004576718,0.002741795,0.006491049,0.0005257754,0.000167525,0.0002769579,0.01110055,0.9485337,0.00161917,0.0004251757,0.02284183,0.0006998258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8316488,0.0002916516,0.025618,0.02035641,0.002737872,0.001672077,0.0004336218,0.0002299199,0.1170117],"genre_scores_gemma":[0.9895211,0.0002210974,0.005811885,0.001884563,0.0002734521,0.0001144918,0.0002090447,0.00001719731,0.001947122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9408107,"threshold_uncertainty_score":0.764807,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313452391","doi":"10.1109/bibm55620.2022.9995562","title":"S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Protein Data Bank (RCSB PDB); Protein Data Bank; Computer science; Protein structure; Artificial intelligence; Pattern recognition (psychology); Naive Bayes classifier; Protein structure prediction; Computational biology; Biology; Support vector machine","authors":[{"name":"M. Saqib Nawaz","is_ca":false},{"name":"Philippe Fournier‐Viger","is_ca":false},{"name":"Yulin He","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04059322901779817,"gpt":0.3235066638355676,"spread":0.2829134348177694,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003706057,0.0001814503,0.0002380195,0.0005092703,0.0001328407,0.00004570787,0.0003004982,0.00007735203,0.0001259821],"category_scores_gemma":[0.00007092216,0.0001545074,0.00006962903,0.0003571705,0.000188933,0.00001573255,0.0002136303,0.0002164252,0.000002167279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002443897,"about_ca_system_score_gemma":0.00005735936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004147296,"about_ca_topic_score_gemma":0.00002113201,"domain_scores_codex":[0.9985653,0.00004375173,0.0005358846,0.000199513,0.0005019837,0.0001536097],"domain_scores_gemma":[0.9990923,0.00001619079,0.0004352535,0.0002573788,0.0001441289,0.00005476913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000308503,0.0001235751,0.006038321,0.0001839452,0.0008494697,0.000002579747,0.0008916143,0.0001059859,0.9508271,0.01710303,0.003715961,0.01984989],"study_design_scores_gemma":[0.004065949,0.004663632,0.04748738,0.0001170722,0.0005336719,0.0001383097,0.005199781,0.7080713,0.1629418,0.002471364,0.06304567,0.001264114],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854673,0.00005981852,0.00561377,0.001299164,0.0002713788,0.0003199332,0.000281448,0.00001631697,0.006670898],"genre_scores_gemma":[0.9956064,0.000141722,0.002619527,0.0004842299,0.00007093365,0.00003196561,0.0007361679,0.000009511725,0.0002995818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7878854,"threshold_uncertainty_score":0.6300629,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313527334","doi":"10.1109/bibm55620.2022.9995592","title":"Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Big data; Pandemic; Informatics; Computer science; Health informatics; Data science; Health care; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Data mining; Medicine; Engineering; Disease; Infectious disease (medical specialty); Political science","authors":[{"name":"Carson K. Leung","is_ca":true},{"name":"Evan W.R. Madill","is_ca":true},{"name":"Nguyen Tran","is_ca":true},{"name":"Christine Y. Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2455717761894158,"gpt":0.4181600665094102,"spread":0.1725882903199944,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001879781,0.000344791,0.0005608632,0.001168278,0.000555005,0.0001078704,0.0007614005,0.00009098763,0.0009745177],"category_scores_gemma":[0.0006279952,0.0002932846,0.00007322599,0.0006210924,0.0002211715,0.0002086989,0.000620253,0.001059513,0.0000690932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008594688,"about_ca_system_score_gemma":0.001156009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003517153,"about_ca_topic_score_gemma":0.00004184686,"domain_scores_codex":[0.9962341,0.0000961512,0.001230789,0.0003563178,0.001649715,0.0004329575],"domain_scores_gemma":[0.9973829,0.0004316355,0.0007456874,0.0007058486,0.0001570113,0.0005768531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001991592,0.001568214,0.02672154,0.002284947,0.000876529,0.0001592001,0.01637225,0.001911299,0.001260204,0.005321676,0.364671,0.5768616],"study_design_scores_gemma":[0.003659808,0.003084913,0.0008014296,0.0003309338,0.00006185246,0.0005451501,0.004483876,0.3723531,0.00002235742,0.0001983111,0.6140859,0.0003723147],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1792289,0.0006532635,0.05783906,0.7195249,0.01219862,0.004994852,0.004347586,0.001390939,0.01982184],"genre_scores_gemma":[0.7925802,0.002885075,0.001582033,0.1957116,0.0005655399,0.00009133176,0.005364344,0.0000462332,0.001173582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6133513,"threshold_uncertainty_score":0.999952,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313526674","doi":"10.1109/bibm55620.2022.9995306","title":"Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Extracellular vesicles in disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Science Basic Research Program of Shaanxi Province; Science and Engineering Research Council; Natural Science Foundation of Shaanxi Province; China Postdoctoral Science Foundation","keywords":"Mechanism (biology); Computer science; Sequence (biology); Embedding; Artificial intelligence; Deep learning; Chemistry; Physics","authors":[{"name":"Yajun Liu","is_ca":false},{"name":"Yulian Ding","is_ca":true},{"name":"Aimin Li","is_ca":false},{"name":"Rong Fei","is_ca":false},{"name":"Fang‐Xiang Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03893092741826702,"gpt":0.2871037735939953,"spread":0.2481728461757283,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003095377,0.000144787,0.0001286889,0.000207434,0.000161508,0.00002498861,0.0002011203,0.00005258707,0.0000973838],"category_scores_gemma":[0.00005363654,0.0001255305,0.00005550211,0.0001090648,0.00009274104,0.00001363785,0.00006397194,0.0001491434,0.000001155379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000426243,"about_ca_system_score_gemma":0.00006956924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000432779,"about_ca_topic_score_gemma":0.000001767033,"domain_scores_codex":[0.9988308,0.00003459455,0.0003133301,0.0002111404,0.0004625834,0.0001475433],"domain_scores_gemma":[0.9993251,0.00002703207,0.0002657282,0.0001544833,0.0001558965,0.00007176095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001311863,0.000260344,0.001259225,0.0001666813,0.0001423434,0.000008512797,0.0001972934,0.004297256,0.9760597,0.006828716,0.0003662431,0.009101758],"study_design_scores_gemma":[0.002216242,0.004708618,0.0004454075,0.0001232669,0.00004474163,0.00002783741,0.001273036,0.9479276,0.04062751,0.0003842936,0.002019797,0.0002016471],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8440157,0.00004892141,0.150926,0.0007673872,0.0007388569,0.0006338495,0.0005225126,0.00003144406,0.00231533],"genre_scores_gemma":[0.9952307,0.00005706894,0.002784124,0.0002928944,0.0001088364,0.00008429521,0.00117137,0.00001407471,0.0002565719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9436303,"threshold_uncertainty_score":0.5118984,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313525848","doi":"10.1109/bibm55620.2022.9994928","title":"Hierarchical Categorical Generative Modeling for Multi-omics Cancer Subtyping","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Bioscience Database Center; Ministry of Education","keywords":"Subtyping; Overfitting; Categorical variable; Computer science; Machine learning; Generative grammar; Generative model; Artificial intelligence; Cancer; Data mining; Biology; Artificial neural network","authors":[{"name":"Ziwei Yang","is_ca":false},{"name":"Lingwei Zhu","is_ca":true},{"name":"Chen Li","is_ca":false},{"name":"Zheng Chen","is_ca":false},{"name":"Naoki Ono","is_ca":false},{"name":"Md. Altaf‐Ul‐Amin","is_ca":false},{"name":"Shigehiko Kanaya","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07809674224255929,"gpt":0.3308368585973386,"spread":0.2527401163547793,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003677204,0.0002207847,0.0002197744,0.000179951,0.0003319246,0.0000704941,0.0003450801,0.0001051456,0.00008874887],"category_scores_gemma":[0.00002739016,0.0001924731,0.00008887352,0.0001170657,0.0001020704,0.00001415924,0.0002340803,0.0002894851,0.000002440254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007437357,"about_ca_system_score_gemma":0.0001563577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003285521,"about_ca_topic_score_gemma":0.00002102185,"domain_scores_codex":[0.9986006,0.00002471541,0.0005094934,0.0002568228,0.000315722,0.0002926294],"domain_scores_gemma":[0.9993172,0.00002003078,0.0001858114,0.0001800528,0.0001628694,0.0001340564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005653187,0.001763873,0.001447228,0.0007590853,0.003107378,0.00003493976,0.01066233,0.08759116,0.2642947,0.2000897,0.1011607,0.3234357],"study_design_scores_gemma":[0.001390187,0.0005058072,0.00001625696,0.00001587849,0.00002149108,0.00002680989,0.0009410495,0.98116,0.0007982572,0.0009069255,0.01395559,0.0002617197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1990553,0.0006855903,0.7822874,0.007811602,0.004281049,0.001320905,0.001662788,0.00004770633,0.00284769],"genre_scores_gemma":[0.9789554,0.00153694,0.01199419,0.003689465,0.0008348604,0.000306819,0.001710789,0.00003017211,0.0009413481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8935689,"threshold_uncertainty_score":0.7848824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313525682","doi":"10.1109/bibm55620.2022.9994849","title":"Deep Learning Based Parametrization of Diffeomorphic Image Registration for the Application of Cardiac Image Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Alberta Hospital Edmonton","funders":"","keywords":"Artificial intelligence; Segmentation; Hausdorff distance; Diffeomorphism; Computer vision; Computer science; Deep learning; Parametrization (atmospheric modeling); Image segmentation; Transformation (genetics); Image registration; Pattern recognition (psychology); Rigid transformation; Image (mathematics); Mathematics","authors":[{"name":"Ameneh Sheikhjafari","is_ca":false},{"name":"Deepa Krishnaswamy","is_ca":true},{"name":"Michelle Noga","is_ca":true},{"name":"Nilanjan Ray","is_ca":true},{"name":"Kumaradevan Punithakumar","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03050761723164368,"gpt":0.3091556862894007,"spread":0.278648069057757,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009120333,0.000118155,0.0001826948,0.0003641879,0.0001732955,0.00006595239,0.0004898681,0.00003384131,0.00006692837],"category_scores_gemma":[0.000172927,0.00009248901,0.00006141833,0.0004855748,0.0001794165,0.000315524,0.00008770313,0.0001579054,0.000001129648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006499491,"about_ca_system_score_gemma":0.00006691222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003950049,"about_ca_topic_score_gemma":0.00000145038,"domain_scores_codex":[0.9982249,0.00007395196,0.0006067132,0.0001741127,0.0008098356,0.0001104591],"domain_scores_gemma":[0.9982623,0.0003461135,0.0007429156,0.0002288079,0.0003746717,0.00004514198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001645013,0.0002766347,0.0003391879,0.0003534821,0.000142223,8.539807e-7,0.002423919,0.0007705179,0.4672322,0.02668837,0.002018724,0.4995894],"study_design_scores_gemma":[0.0005204054,0.0005163318,0.0003695201,0.00002548595,0.00002246535,0.000002085261,0.001091232,0.9402202,0.0561292,0.0006592794,0.0003510801,0.00009266063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001696348,0.00003109896,0.9949726,0.001864237,0.000308784,0.0007022687,0.00006718984,0.00004057543,0.0003168374],"genre_scores_gemma":[0.8197433,0.0003354075,0.177898,0.0005395996,0.00007348589,0.0004684602,0.0008371325,0.00001224291,0.00009242595],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9394497,"threshold_uncertainty_score":0.3771592,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313413122","doi":"10.1109/bibm55620.2022.9995697","title":"Defensive Adversarial Training for Enhancing Robustness of ECG based User Identification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hanyang University","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Identification (biology); Wearable computer; Machine learning; Wearable technology; Noise (video); Gaussian noise; Adversarial system; Noise measurement; Data mining; Pattern recognition (psychology); Noise reduction; Embedded system","authors":[{"name":"Hongbi Jeong","is_ca":true},{"name":"Junggab Son","is_ca":false},{"name":"Hyunbum Kim","is_ca":false},{"name":"Kyungtae Kang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08006396738555416,"gpt":0.3304434298012353,"spread":0.2503794624156811,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000548237,0.0001386391,0.0003075975,0.0005573891,0.0001615354,0.00002771962,0.0001525602,0.00004787846,0.0002347917],"category_scores_gemma":[0.0001495139,0.0001203321,0.0001099225,0.0002867437,0.00008667986,0.00008834065,0.00004049288,0.0001805133,0.000001779263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008773838,"about_ca_system_score_gemma":0.0001640283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002620483,"about_ca_topic_score_gemma":0.000007238968,"domain_scores_codex":[0.9984366,0.00002130936,0.0005957106,0.0001770538,0.0006105292,0.0001587501],"domain_scores_gemma":[0.9988754,0.0001288111,0.0003681592,0.0001636405,0.000376675,0.00008729112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00738156,0.00204453,0.0110371,0.003126228,0.003611423,0.000064306,0.01742663,0.01847424,0.6217051,0.01095309,0.02038821,0.2837875],"study_design_scores_gemma":[0.003052204,0.0008639474,0.0007957117,0.0002539683,0.0002215278,0.00002186154,0.01121576,0.971772,0.009417824,0.00008898433,0.002112919,0.0001833087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7629485,0.00005987228,0.2217942,0.007103026,0.004714622,0.0009247202,0.0006104338,0.00007712498,0.001767439],"genre_scores_gemma":[0.9939437,0.00004190647,0.004254266,0.0003241447,0.0003378234,0.00006367484,0.0005035233,0.0000113925,0.0005195968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9532977,"threshold_uncertainty_score":0.4907001,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313526690","doi":"10.1109/bibm55620.2022.9995689","title":"Molecular Property Prediction based on Bimodal Supervised Contrastive Learning","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba; Artificial Intelligence in Medicine (Canada)","funders":"Mitacs","keywords":"Computer science; Graph; Artificial intelligence; Molecular graph; Machine learning; Theoretical computer science; Convolutional neural network; Encoding (memory); Natural language processing; Pattern recognition (psychology)","authors":[{"name":"Yan Sun","is_ca":true},{"name":"Mohaiminul Islam","is_ca":true},{"name":"Ehsan Zahedi","is_ca":true},{"name":"Mélaine A. Kuenemann","is_ca":false},{"name":"Hassan Chouaib","is_ca":true},{"name":"Pingzhao Hu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03755818534565528,"gpt":0.2885960957091557,"spread":0.2510379103635004,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007266715,0.0002195341,0.0002103663,0.0006220949,0.0002906455,0.0001826162,0.0006669986,0.00004291946,0.0002189671],"category_scores_gemma":[0.0001166428,0.0001637607,0.00006918701,0.0005027237,0.00009661822,0.0003140319,0.0002476294,0.0004666858,0.00001439398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001752268,"about_ca_system_score_gemma":0.0002369531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000026824,"about_ca_topic_score_gemma":9.441533e-7,"domain_scores_codex":[0.9975132,0.0001757229,0.0004492616,0.0003420185,0.001288423,0.0002313135],"domain_scores_gemma":[0.9990124,0.0002194498,0.0002041089,0.0002229822,0.0002141246,0.0001270118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001006506,0.00118379,0.001718697,0.0001607274,0.0003811527,0.000177931,0.005306547,0.4349277,0.01862337,0.1909974,0.005509691,0.3400066],"study_design_scores_gemma":[0.001232947,0.001242854,0.0008541527,0.00005892098,0.000008920477,0.00002273806,0.0004037485,0.9905353,0.0005588091,0.0009899939,0.003898864,0.000192747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08920592,0.0000228392,0.8670023,0.0148631,0.00277146,0.0007107608,0.0002733938,0.0002296992,0.02492052],"genre_scores_gemma":[0.9894166,0.00002340202,0.007362683,0.002557456,0.00009384236,0.00008396171,0.0002186572,0.00001148336,0.0002319003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9002107,"threshold_uncertainty_score":0.6677969,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313527435","doi":"10.1109/bibm55620.2022.9995066","title":"Unseen Epitope-TCR Interaction Prediction based on Amino Acid Physicochemical Properties","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"vaccines and immunoinformatics approaches","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Science and Engineering Research Council","keywords":"Epitope; T-cell receptor; Computational biology; Amino acid; Computer science; Artificial intelligence; Sequence (biology); Product (mathematics); Epitope mapping; T cell; Antigen; Chemistry; Biology; Mathematics; Biochemistry; Immune system; Genetics","authors":[{"name":"Rawshon Raha","is_ca":true},{"name":"Yulian Ding","is_ca":true},{"name":"Qiang Liu","is_ca":true},{"name":"Fang‐Xiang Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0403913250873883,"gpt":0.2643326205174773,"spread":0.223941295430089,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002651532,0.0002448687,0.0001980414,0.0002572749,0.0002238404,0.00008928341,0.0003327684,0.00008228776,0.0002067571],"category_scores_gemma":[0.00004197098,0.0001967426,0.00008490762,0.000157183,0.00009195066,0.00003404778,0.0001688749,0.0003062104,0.00001592959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000730889,"about_ca_system_score_gemma":0.00007997502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001504774,"about_ca_topic_score_gemma":0.000001581864,"domain_scores_codex":[0.998471,0.00003439121,0.0004926366,0.0002424952,0.0005423999,0.000217053],"domain_scores_gemma":[0.9992449,0.00001378127,0.0002468025,0.0002797528,0.0001278727,0.000086848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001748934,0.0007191529,0.0006354616,0.0001921171,0.0003032956,0.00000460707,0.0009788568,0.001210593,0.9218174,0.00146218,0.02889151,0.04203594],"study_design_scores_gemma":[0.00286995,0.004077998,0.000651155,0.000174808,0.0000505494,0.00007397957,0.004043792,0.7736658,0.1577898,0.0001251328,0.05592785,0.0005491583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712955,0.0001344258,0.002794098,0.004590257,0.002405474,0.0007005084,0.0005521028,0.00006520422,0.01746239],"genre_scores_gemma":[0.9955059,0.0002495358,0.0002970798,0.001587861,0.0003226341,0.0001063529,0.001453418,0.00001698678,0.0004602299],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7724552,"threshold_uncertainty_score":0.8022929,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313525854","doi":"10.1109/bibm55620.2022.9994977","title":"A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studies","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Morphometrics; Association (psychology); Inclusion (mineral); Medical imaging; Computer science; Artificial intelligence; Computational biology; Biology; Geology; Zoology; Psychology; Mineralogy","authors":[{"name":"Gabrielle Dagasso","is_ca":true},{"name":"Matthias Wilms","is_ca":true},{"name":"Nils D. Forkert","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04628372351734753,"gpt":0.3326293342578998,"spread":0.2863456107405522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001462307,0.0001810992,0.0004226692,0.0003099927,0.000325723,0.00001479401,0.0003390634,0.0001026891,0.00005391401],"category_scores_gemma":[0.003355438,0.0001536112,0.00008360219,0.0002344481,0.0001511796,0.000009812807,0.0007994754,0.0001709647,8.785422e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001904743,"about_ca_system_score_gemma":0.0001209081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003510591,"about_ca_topic_score_gemma":0.000006713393,"domain_scores_codex":[0.9980201,0.00008563649,0.0007454095,0.0002537926,0.0006903691,0.0002047474],"domain_scores_gemma":[0.9980144,0.0004767283,0.0006786425,0.0001645621,0.0005831087,0.00008255909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002388936,0.002496101,0.4297687,0.001651659,0.012213,0.00002691892,0.02983155,0.001079419,0.1016306,0.003436967,0.2683875,0.1470888],"study_design_scores_gemma":[0.01176962,0.004226553,0.04413919,0.0003136326,0.0006120898,0.00003363926,0.0738419,0.7380786,0.00333461,0.00869975,0.1131613,0.00178912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9159041,0.003638712,0.05968744,0.01544359,0.002344075,0.0007776872,0.001509068,0.0000254159,0.0006698912],"genre_scores_gemma":[0.9809543,0.006453109,0.006157667,0.003224684,0.0003220509,0.000123279,0.002530216,0.00001385715,0.0002208641],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7369992,"threshold_uncertainty_score":0.6264082,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313452864","doi":"10.1109/bibm55620.2022.9995196","title":"A single cell potency inference method based on the local cell-specific network entropy","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Inference; Computer science; Entropy (arrow of time); Potency; Artificial intelligence; Chemistry; Physics; Thermodynamics","authors":[{"name":"Ziwei Xu","is_ca":false},{"name":"Ruiqing Zheng","is_ca":false},{"name":"Yuxuan Chen","is_ca":false},{"name":"Edwin Wang","is_ca":true},{"name":"Min Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03508104029617786,"gpt":0.2653878979482166,"spread":0.2303068576520387,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004916819,0.0002612369,0.000203242,0.0001504399,0.0003206417,0.00008976712,0.0005436142,0.00009225626,0.000683812],"category_scores_gemma":[0.00002085762,0.0001896654,0.00009925944,0.0002316051,0.000174209,0.000009652671,0.0001310378,0.0003630717,0.00001492879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006173716,"about_ca_system_score_gemma":0.0001023122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003373136,"about_ca_topic_score_gemma":0.000005441795,"domain_scores_codex":[0.998247,0.00008902931,0.0004402282,0.0003099645,0.0006073999,0.0003064059],"domain_scores_gemma":[0.9990999,0.0001078995,0.0002162299,0.0003322256,0.0001290953,0.0001146334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002337097,0.002172438,0.001239968,0.000175626,0.0002297007,0.0000490536,0.001315305,0.01721313,0.7727871,0.02203865,0.1164364,0.06400555],"study_design_scores_gemma":[0.003313907,0.00555286,0.0002406837,0.0001044604,0.00004886373,0.00002862528,0.002306102,0.6780418,0.06328318,0.001279452,0.2450144,0.0007856021],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2821479,0.0008639894,0.5918563,0.01662347,0.007864142,0.001738285,0.0009955701,0.0001173,0.09779307],"genre_scores_gemma":[0.9910331,0.0003739793,0.002646931,0.004240429,0.0003614074,0.00004974374,0.0004368752,0.00002009073,0.0008374117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7095039,"threshold_uncertainty_score":0.773433,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313527288","doi":"10.1109/bibm55620.2022.9995336","title":"Depth Encoding for Neonatal Patient Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Encoding (memory); Computer science; Segmentation; Computer vision; Artificial intelligence; Image segmentation","authors":[{"name":"Yasmina Souley Dosso","is_ca":true},{"name":"Kim Greenwood","is_ca":true},{"name":"JoAnn Harrold","is_ca":true},{"name":"James R. Green","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05224953634822624,"gpt":0.3095678349085033,"spread":0.257318298560277,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001783505,0.0001412995,0.0001278358,0.0002781984,0.0003407246,0.0000944733,0.000556831,0.0000237244,0.00008222851],"category_scores_gemma":[0.00001878524,0.000126199,0.00004190681,0.0003297199,0.00005523519,0.0003472945,0.0002579902,0.0001602587,0.000007856757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001084556,"about_ca_system_score_gemma":0.00004826306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005898302,"about_ca_topic_score_gemma":0.00000412429,"domain_scores_codex":[0.9986271,0.00001755072,0.0003849608,0.0002372808,0.0005395438,0.0001936115],"domain_scores_gemma":[0.9992321,0.0001101632,0.0002467881,0.0002081901,0.0001146229,0.00008818453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009116039,0.0001386947,0.0001179088,0.00004168335,0.00007236793,0.00000855265,0.003253295,0.001083394,0.008491054,0.2932721,0.01380275,0.6796271],"study_design_scores_gemma":[0.0009875668,0.0008599694,0.00009192609,0.000027969,0.000009114614,0.00006570255,0.001709713,0.9358776,0.002611014,0.007049511,0.05044648,0.0002634419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01684895,0.00006446446,0.9588876,0.01350244,0.002341944,0.001019358,0.0002619653,0.0001455507,0.006927721],"genre_scores_gemma":[0.9414593,0.000191538,0.05319045,0.003797723,0.0001667103,0.0005121137,0.0003666864,0.00001215801,0.0003033383],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9347942,"threshold_uncertainty_score":0.5146246,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313452912","doi":"10.1109/bibm55620.2022.9995700","title":"Private Federated Framework for Health Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor; University of Manitoba","funders":"","keywords":"Differential privacy; Computer science; Raw data; Architecture; Federated learning; Information privacy; Private information retrieval; Layer (electronics); Information sensitivity; Data modeling; Noise (video); Data mining; Computer security; Distributed computing; Artificial intelligence; Database","authors":[{"name":"Tanzir Ul Islam","is_ca":true},{"name":"Noman Mohammed","is_ca":true},{"name":"Dima Alhadidi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.130495123056648,"gpt":0.372480963103445,"spread":0.241985840046797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001086488,0.0001990003,0.0002586133,0.0004374864,0.0004730854,0.0003063383,0.01620391,0.0000667258,0.0001404565],"category_scores_gemma":[0.002352039,0.0001721182,0.00003005352,0.0005391717,0.0001258561,0.0005745872,0.02916514,0.0004554503,0.00001130734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001359933,"about_ca_system_score_gemma":0.0001987932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002656795,"about_ca_topic_score_gemma":0.000004147176,"domain_scores_codex":[0.9978006,0.00004004781,0.0005813589,0.0004551935,0.0007783396,0.0003443944],"domain_scores_gemma":[0.9961464,0.0002136915,0.0004034495,0.003008122,0.0001140735,0.0001142551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006730668,0.0001529878,0.00008611859,0.00009687875,0.0001209836,0.00000878498,0.0003080852,0.00000627433,0.0002300092,0.2557176,0.6089492,0.1342558],"study_design_scores_gemma":[0.0005386294,0.0005590191,0.0000642661,0.00008096023,0.000003859731,0.00002983741,0.0002990443,0.8148949,0.0001039908,0.128757,0.05447659,0.0001919681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0011421,0.00007660126,0.7804096,0.2129916,0.002201351,0.0004722002,0.001546122,0.0002848328,0.0008755374],"genre_scores_gemma":[0.1571212,0.001188957,0.8235603,0.01450302,0.0002574997,0.0001855894,0.00294721,0.00002652583,0.0002096836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8148886,"threshold_uncertainty_score":0.9891189,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313413436","doi":"10.1109/bibm55620.2022.9995071","title":"EMDS: predicting essential miRNAs based on deep learning and sequences","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Subsequence; Artificial intelligence; Computer science; Longest common subsequence problem; Support vector machine; Convolutional neural network; Deep learning; Machine learning; Artificial neural network; Pattern recognition (psychology); Algorithm; Mathematics","authors":[{"name":"Cheng Yan","is_ca":false},{"name":"Guihua Duan","is_ca":false},{"name":"Fang‐Xiang Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01674005353689494,"gpt":0.2797197553463578,"spread":0.2629797018094629,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000297326,0.0001602944,0.0001262865,0.0002242918,0.000249166,0.00006803616,0.0001739626,0.00005894043,0.0003380231],"category_scores_gemma":[0.00006875023,0.000143761,0.00004009063,0.0001154951,0.0001354489,0.00001275678,0.0001255514,0.0001959506,0.000003404499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003665291,"about_ca_system_score_gemma":0.00006797952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001493875,"about_ca_topic_score_gemma":0.000005867475,"domain_scores_codex":[0.9987906,0.00005048772,0.0003010186,0.0002361432,0.0004590496,0.0001626987],"domain_scores_gemma":[0.9994642,0.00002899688,0.0002034096,0.0001181933,0.000089478,0.00009572948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001605268,0.0004938341,0.01740682,0.0003492057,0.0004960268,0.00005079903,0.001406909,0.004555511,0.8859811,0.004175772,0.005331006,0.07814778],"study_design_scores_gemma":[0.002345299,0.002292485,0.003163323,0.0001481811,0.00005194252,0.00008013986,0.00274781,0.9475127,0.01554353,0.0002465692,0.02538967,0.000478325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862497,0.0001961078,0.00309255,0.00202298,0.0008557544,0.0003076105,0.0001536449,0.00003375371,0.007087837],"genre_scores_gemma":[0.9975911,0.0002196424,0.0004408342,0.0005848149,0.0002149268,0.00003126291,0.0006422295,0.00001107048,0.0002641402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9429572,"threshold_uncertainty_score":0.5862402,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}