{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":3,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":3,"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":"1412e4ccc0ff","filters":{"venue":"VNU Journal of Science Computer Science and Communication Engineering"}},"results":[{"id":"W3157414336","doi":"10.25073/2588-1086/vnucsce.237","title":"Single Concatenated Input is Better than Indenpendent Multiple-input for CNNs to Predict Chemical-induced Disease Relation from Literature","year":2020,"lang":"en","type":"article","venue":"VNU Journal of Science Computer Science and Communication Engineering","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Foundation for Science and Technology Development","keywords":"Concatenation (mathematics); Computer science; Biomedical text mining; Convolutional neural network; Benchmark (surveying); Relation (database); Named-entity recognition; Artificial intelligence; Natural language processing; Data mining; Text mining; Mathematics; Task (project management); Cartography","authors":[{"name":"Bui Manh Thang","is_ca":false},{"name":"Đặng Thanh Hải","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02200202434629777,"gpt":0.2493643346008226,"spread":0.2273623102545248,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005185233,0.00009880227,0.0001174676,0.0001224066,0.000161805,0.0001662914,0.0009131869,0.00005803305,6.437002e-7],"category_scores_gemma":[0.0006242953,0.00008189855,0.0000387888,0.0005707611,0.0003390537,0.00006636347,0.0003645032,0.0001450352,4.968384e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003183306,"about_ca_system_score_gemma":0.0001413576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001822754,"about_ca_topic_score_gemma":2.018002e-7,"domain_scores_codex":[0.9989401,0.00001420347,0.0002400757,0.0002577947,0.0003585977,0.0001891992],"domain_scores_gemma":[0.9988122,0.00004815475,0.0001192527,0.0002628004,0.0004065711,0.0003510212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003333576,0.00002227231,0.001266838,0.000008155669,0.000008518776,0.000001047854,0.001178986,0.0004843889,0.9692858,0.000009450673,0.0001147316,0.02758645],"study_design_scores_gemma":[0.001006045,0.0007187075,0.04145143,0.0003266603,0.00002413303,0.00001946596,0.0001273687,0.3787646,0.5734277,0.00009228307,0.003709828,0.0003317715],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.844416,0.0004750988,0.1507232,0.004138832,0.00013281,0.000091227,0.000008264758,0.00001211043,0.000002499586],"genre_scores_gemma":[0.9522985,0.00004120322,0.04642971,0.001078918,0.0001346825,0.00000373979,0.000007082266,0.000005008856,0.000001186293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3958581,"threshold_uncertainty_score":0.3339726,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3157716736","doi":"10.25073/2588-1086/vnucsce.240","title":"Performance of Orthogonal Frequency Division Multiplexing Based Advanced Encryption Standard","year":2020,"lang":"en","type":"article","venue":"VNU Journal of Science Computer Science and Communication Engineering","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Orthogonal frequency-division multiplexing; Computer science; Encryption; Advanced Encryption Standard; Computer network; Physical layer; Wireless; Telecommunications; Channel (broadcasting)","authors":[{"name":"Duc-Tai Truong","is_ca":false},{"name":"Quoc-Tuan Nguyen","is_ca":false},{"name":"Thai-Mai Thi Dinh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01175622558245885,"gpt":0.2189947644187108,"spread":0.2072385388362519,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00303349,0.0001249589,0.0002040861,0.0005825461,0.0004688577,0.0002251217,0.002898311,0.00002369478,0.000001854802],"category_scores_gemma":[0.0002195906,0.0001108183,0.00005751702,0.002618257,0.0009262541,0.003470569,0.0005558726,0.0002298527,5.267097e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005634777,"about_ca_system_score_gemma":0.0004249514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.457803e-7,"about_ca_topic_score_gemma":9.033836e-8,"domain_scores_codex":[0.9979204,0.0000378763,0.0004638895,0.0002676206,0.001048044,0.0002621885],"domain_scores_gemma":[0.9978258,0.0001328065,0.0003595059,0.0004721085,0.0009679746,0.0002418285],"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":[0.0000438886,0.0000635977,0.005746847,0.00008194289,0.000009062493,0.000003280478,0.003649236,0.2388574,0.4032784,0.04642419,0.000005114578,0.301837],"study_design_scores_gemma":[0.0003367453,0.0005558439,0.01404241,0.0002058923,0.000002774321,0.00001570824,0.0000317697,0.9625822,0.02166202,0.0003699237,0.00005681137,0.0001379362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4872519,0.0001264123,0.5120692,0.0003551786,0.000114751,0.00004034776,4.456451e-7,0.00002333663,0.00001842427],"genre_scores_gemma":[0.704194,0.00005845057,0.2956319,0.00009292292,0.00001891314,8.601458e-7,1.285423e-7,0.000002712022,7.263373e-8],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7237248,"threshold_uncertainty_score":0.5385832,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3157027354","doi":"10.25073/2588-1086/vnucsce.231","title":"Adaptation in Statistical Machine Translation for Low-resource Domains in English-Vietnamese Language","year":2020,"lang":"en","type":"article","venue":"VNU Journal of Science Computer Science and Communication Engineering","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Machine translation; Vietnamese; Artificial intelligence; Natural language processing; Computer science; Evaluation of machine translation; Phrase; Domain adaptation; Domain (mathematical analysis); Example-based machine translation; Machine translation software usability; Translation (biology); Baseline (sea); Classifier (UML); Linguistics; Mathematics; Philosophy","authors":[{"name":"Nghia-Luan Pham","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01327179269771563,"gpt":0.2582265781423414,"spread":0.2449547854446258,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002653978,0.0001003295,0.0001661337,0.0006228337,0.0001407325,0.0003212516,0.001943821,0.00002918418,4.270635e-7],"category_scores_gemma":[0.0005528044,0.0000907032,0.0000206003,0.002306024,0.0003433606,0.002419735,0.0002511051,0.0002737008,1.578381e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001253532,"about_ca_system_score_gemma":0.0002821553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008652472,"about_ca_topic_score_gemma":0.000005841622,"domain_scores_codex":[0.9985257,0.00003512541,0.000399001,0.0002466367,0.0005458107,0.0002477729],"domain_scores_gemma":[0.9989003,0.0002022122,0.000157344,0.0002571244,0.0003382019,0.0001448277],"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":[0.00003839134,0.0001020077,0.0002792894,0.0001102657,0.000003687013,0.00002169225,0.08570358,0.05474656,0.04739178,0.02778095,0.0000246402,0.7837971],"study_design_scores_gemma":[0.0003752036,0.0001037056,0.0005123666,0.0001172149,0.00000139742,0.00001444628,0.0002206402,0.9948115,0.003107765,0.0005408106,0.00008776825,0.0001071217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0359651,0.00183708,0.960532,0.00139358,0.00006550109,0.0001426375,0.00000114301,0.00005296485,0.000009987064],"genre_scores_gemma":[0.5469753,0.00002501095,0.4528528,0.0001225308,0.00001867784,0.000002586833,4.349335e-7,0.000002520109,1.252995e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.940065,"threshold_uncertainty_score":0.3698769,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}