{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"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":"5fc04119aabf","filters":{"venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004."}},"results":[{"id":"W2134563544","doi":"10.1109/csb.2004.1332434","title":"SPIDER: software for protein identification from sequence tags with de novo sequencing error","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":35,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Software; Identification (biology); Sequence (biology); Computer science; Computational biology; Protein sequencing; DNA sequencing; Sequence assembly; Peptide sequence; Biology; Genetics; Programming language; DNA; Gene; Transcriptome","authors":[{"name":"Yonghua Han","is_ca":true},{"name":"Bin Ma","is_ca":true},{"name":"Kaizhong Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03549132856981759,"gpt":0.2753646704719921,"spread":0.2398733419021745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003929963,0.0006028618,0.00057123,0.0002152801,0.0006004092,0.0006033154,0.0007156718,0.0003808161,0.00004030722],"category_scores_gemma":[0.0001750104,0.0005872151,0.0001316377,0.0005585771,0.0002311394,0.001116765,0.00005536058,0.0004623245,0.00006974686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001516228,"about_ca_system_score_gemma":0.001473023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002632054,"about_ca_topic_score_gemma":0.00002638099,"domain_scores_codex":[0.996388,0.000007550624,0.00143119,0.0006615795,0.0007390354,0.00077263],"domain_scores_gemma":[0.9959291,0.00007501566,0.00132868,0.0003608832,0.001999194,0.0003071423],"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":[0.000341305,0.0006240064,0.001543005,0.005684847,0.0004731579,0.00001784665,0.005310812,0.7151078,0.1658207,0.0994555,0.001930192,0.003690794],"study_design_scores_gemma":[0.00654508,0.0004650761,0.0001266928,0.005879856,0.0003146405,0.0006206707,0.0042498,0.5761903,0.2191181,0.1781784,0.004721899,0.003589453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1109267,0.0001677286,0.883888,0.0001988186,0.00008158712,0.0021153,0.0008897309,0.000693427,0.001038732],"genre_scores_gemma":[0.5757914,0.000007499173,0.4211148,0.00009013346,0.0002122873,0.001664692,0.0006309094,0.00006907307,0.0004191812],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4648647,"threshold_uncertainty_score":0.9996579,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2118303822","doi":"10.1109/csb.2004.1332443","title":"An algorithm for detecting homologues of known structured rnas in genomes","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University; Western University","funders":"","keywords":"RNA; Computational biology; Nucleic acid secondary structure; Structural motif; Genome; Nucleic acid structure; Sequence (biology); Base pair; Sequence alignment; Structural alignment; Algorithm; Biology; Genetics; Computer science; DNA; Gene; Peptide sequence","authors":[{"name":"Shu‐Yun Le","is_ca":false},{"name":"Jacob V. Maizel","is_ca":false},{"name":"Kaizhong Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01709846195398416,"gpt":0.2513772207806546,"spread":0.2342787588266704,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006159106,0.0004170882,0.0005476694,0.0003253334,0.0001869657,0.0001696369,0.0004949442,0.0003944202,0.000008210106],"category_scores_gemma":[0.00008656455,0.0004036238,0.0001470159,0.0003051489,0.00013012,0.00008362984,0.00004488088,0.0001600967,0.000009313094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001199553,"about_ca_system_score_gemma":0.0004777489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000745083,"about_ca_topic_score_gemma":0.00003424077,"domain_scores_codex":[0.9974369,0.00002711283,0.001139078,0.000430797,0.0004258723,0.0005401741],"domain_scores_gemma":[0.9979547,0.00003353608,0.0007036307,0.0002095752,0.0009231181,0.0001754812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005430132,0.000924604,0.00189664,0.003630946,0.0007800696,0.00001139513,0.006051066,0.433923,0.340017,0.01776099,0.0009849786,0.1934762],"study_design_scores_gemma":[0.009286799,0.003834909,0.001007184,0.001371808,0.0001648544,0.0004334194,0.005751414,0.4398607,0.5019461,0.02893881,0.00459354,0.002810426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2036526,0.002093461,0.7905094,0.0000366649,0.0006427602,0.001923408,0.0003054044,0.00007557911,0.0007607313],"genre_scores_gemma":[0.8677081,0.000042698,0.1313646,0.00004985779,0.0002947558,0.0001610576,0.0002519285,0.00004230126,0.00008472335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6640555,"threshold_uncertainty_score":0.9998416,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1521742603","doi":"10.1109/csb.2004.1332444","title":"Inverse protein folding in 2D HP model","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Fold (higher-order function); Inverse; Protein folding; Protein structure; Computer science; Protein structure prediction; Sequence (biology); Stability (learning theory); Protein design; Folding (DSP implementation); Computational biology; Algorithm; Mathematics; Chemistry; Biology; Engineering; Biochemistry; Machine learning; Geometry","authors":[{"name":"Arvind Gupta","is_ca":true},{"name":"Ján Maňuch","is_ca":true},{"name":"Ladislav Stacho","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01500487766725089,"gpt":0.234366057293783,"spread":0.2193611796265321,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004560095,0.0005611026,0.0005146639,0.0003759533,0.0002240212,0.0003072262,0.0005539361,0.0004976947,0.000007927952],"category_scores_gemma":[0.0001029555,0.0005593986,0.0001538273,0.0005017096,0.0001719978,0.0001280252,0.0001223842,0.0003853141,0.00006435798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003120061,"about_ca_system_score_gemma":0.0009549056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001003656,"about_ca_topic_score_gemma":0.00007802436,"domain_scores_codex":[0.9968362,0.00001816356,0.001173167,0.0005460423,0.0006769242,0.0007494516],"domain_scores_gemma":[0.9981691,0.00001003743,0.0005490943,0.0002556285,0.0007433158,0.0002727877],"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":[0.0001058738,0.0001316809,0.0005332436,0.0006456805,0.00009505433,0.000007233007,0.0007984311,0.9621003,0.01263125,0.02063337,0.001762945,0.0005548939],"study_design_scores_gemma":[0.003486605,0.0003463427,0.0001603873,0.0007063511,0.0000384905,0.0001521438,0.0007072112,0.9697884,0.005134145,0.01720151,0.001081901,0.001196549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.499614,0.0008451838,0.4845792,0.0001811254,0.0005412147,0.002644432,0.0001795017,0.0001693581,0.01124599],"genre_scores_gemma":[0.9574536,0.0000273121,0.04089014,0.0002509349,0.0002512223,0.000190538,0.0002838937,0.00005416549,0.000598186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4578396,"threshold_uncertainty_score":0.9996858,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147960554","doi":"10.1109/csb.2004.1332487","title":"Exploring the use of stem-loop characteristics for pinpointing structural RNA genes","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Loop (graph theory); Computational biology; Metric (unit); Stem-loop; Gene; RNA; Biology; Stem cell; Computer science; Genetics; Mathematics; Engineering; Combinatorics","authors":[{"name":"Kirt M. Noël","is_ca":true},{"name":"Kay C. Wiese","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09738166893902384,"gpt":0.2539932195143363,"spread":0.1566115505753125,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004951665,0.0004481153,0.0005103631,0.0001635882,0.0004077223,0.0003912837,0.0004999883,0.0001973416,0.000005173056],"category_scores_gemma":[0.0001308353,0.0003594253,0.0002100529,0.0002502453,0.0001624725,0.000157945,0.00008540908,0.0001589588,0.00001405356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008558174,"about_ca_system_score_gemma":0.000367289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004490983,"about_ca_topic_score_gemma":0.000004472782,"domain_scores_codex":[0.9972466,0.0000260436,0.001260405,0.0003722694,0.000552148,0.0005424905],"domain_scores_gemma":[0.9971896,0.00007735076,0.0009834005,0.0002528187,0.001343868,0.0001529357],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001005225,0.0003835644,0.002493383,0.007530276,0.001706558,0.000007068597,0.004713906,0.4333455,0.3796008,0.06348144,0.004076229,0.1016561],"study_design_scores_gemma":[0.007748934,0.002422018,0.002530909,0.003606074,0.0005646436,0.0005509452,0.005104043,0.4002294,0.5426335,0.004553765,0.02571865,0.004337152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7494052,0.0005771506,0.2465698,0.0001129843,0.001130524,0.001667873,0.0002792073,0.00006445498,0.0001928159],"genre_scores_gemma":[0.9720389,0.00009741059,0.02626073,0.0001183153,0.0005111841,0.0002895501,0.0001944705,0.00005852845,0.000430876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2226337,"threshold_uncertainty_score":0.9998858,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158541923","doi":"10.1109/csb.2004.1332438","title":"Multiple RNA structure alignment","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"RNA and protein synthesis mechanisms","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":"Western University","funders":"","keywords":"RNA; Nucleic acid structure; Computer science; Structural alignment; Computational biology; Algorithm; Theoretical computer science; Sequence alignment; Biology; Genetics; Gene","authors":[{"name":"Zhuozhi Wang","is_ca":false},{"name":"Kaizhong Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01450184238215837,"gpt":0.226121016449254,"spread":0.2116191740670956,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003812761,0.0006080282,0.0005160512,0.0002179111,0.0003678564,0.0004037319,0.0006119809,0.0004789722,0.00005327873],"category_scores_gemma":[0.0001112745,0.0005658798,0.0001885825,0.0003072518,0.0001461711,0.00008174696,0.0001001125,0.0002408117,0.000162488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002055337,"about_ca_system_score_gemma":0.0005316193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006604711,"about_ca_topic_score_gemma":0.00001752992,"domain_scores_codex":[0.9967144,0.00002641745,0.001089901,0.0005625676,0.0008837597,0.0007230102],"domain_scores_gemma":[0.9978437,0.00002447086,0.0006414652,0.000296893,0.0008509698,0.0003424359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000468581,0.0006427594,0.001580676,0.002124228,0.001053931,0.00002151489,0.002643614,0.5368868,0.3820114,0.03601229,0.02932426,0.007229913],"study_design_scores_gemma":[0.01640476,0.002962589,0.001177889,0.002448195,0.000398375,0.001272732,0.004770307,0.1497121,0.7007313,0.03453375,0.07877095,0.006817033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3986867,0.003117753,0.5772738,0.0004506284,0.002819641,0.003755258,0.0008149827,0.0003465603,0.01273465],"genre_scores_gemma":[0.9747626,0.00004943748,0.02304636,0.000300353,0.000565941,0.000111752,0.0004035363,0.00006148964,0.0006984919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5760759,"threshold_uncertainty_score":0.9996793,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4231152591","doi":"10.1109/csb.2004.1332515","title":"A genetic algorithm for inferring time delays in gene regulatory networks","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"Gene Regulatory Network Analysis","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":"University of Saskatchewan","funders":"","keywords":"Gene regulatory network; Computer science; Boolean network; Genetic algorithm; Algorithm; Genetic network; Gene; Boolean function; Machine learning; Biology; Gene expression; Genetics","authors":[{"name":"Fang‐Xiang Wu","is_ca":true},{"name":"A.J. Kusalik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01006024503244255,"gpt":0.2195745500883833,"spread":0.2095143050559408,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006685588,0.0006589794,0.0007359606,0.0004735365,0.0002915138,0.0003023606,0.000588132,0.0005639122,0.00001341536],"category_scores_gemma":[0.00004955091,0.0007047743,0.0002979859,0.0007033225,0.0001818893,0.00007723498,0.0001156357,0.000273579,0.00006540121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003724741,"about_ca_system_score_gemma":0.0006626358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004597349,"about_ca_topic_score_gemma":0.00001909632,"domain_scores_codex":[0.9960725,0.00003025766,0.001567789,0.0007019958,0.0006558665,0.0009715339],"domain_scores_gemma":[0.9974551,0.00003092741,0.000719585,0.0003496638,0.001102313,0.000342446],"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":[0.00003871028,0.0001144537,0.0006792514,0.0001626408,0.0002285318,0.000003840207,0.0001849611,0.9901751,0.001419884,0.0003733183,0.002275661,0.004343646],"study_design_scores_gemma":[0.002549325,0.0002721552,0.001592511,0.0002901898,0.0001090976,0.0001653313,0.0001538558,0.9906009,0.001468558,0.000863034,0.001011234,0.0009238276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1565141,0.002885398,0.8376089,0.00004045631,0.0004625509,0.00155946,0.00009414116,0.0001057175,0.0007292118],"genre_scores_gemma":[0.8944907,0.0000783675,0.1029382,0.0001716665,0.0009111732,0.0002941993,0.0005517798,0.00009892101,0.0004649481],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7379766,"threshold_uncertainty_score":0.9995403,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4232462293","doi":"10.1109/csb.2004.1332534","title":"Finding cancer biomarkers from mass spectrometry data by decision lists","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"Metabolomics and Mass Spectrometry Studies","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 Waterloo","funders":"","keywords":"USable; Computer science; Biomarker; Cancer; Support vector machine; Machine learning; Artificial intelligence; Biomarker discovery; Computational biology; Data mining; Proteomics; Medicine; Internal medicine; Biology","authors":[{"name":"Jian Liu","is_ca":true},{"name":"Ming Li","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02750900642402098,"gpt":0.2791460468051998,"spread":0.2516370403811788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006002749,0.0006612463,0.0007168544,0.0003934941,0.0004212044,0.000556714,0.001215316,0.0003966338,0.0000729743],"category_scores_gemma":[0.0002241493,0.0006275757,0.0001517204,0.0007487399,0.0002041051,0.0001441591,0.0003229655,0.0003191721,0.00009702516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003109839,"about_ca_system_score_gemma":0.0005671763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004539197,"about_ca_topic_score_gemma":0.00005650948,"domain_scores_codex":[0.9960187,0.00002151778,0.001263365,0.000926998,0.0009528282,0.0008165761],"domain_scores_gemma":[0.9975318,0.00006290343,0.0007990061,0.0005407835,0.000732719,0.0003328282],"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":[0.001060836,0.001192356,0.01725409,0.001714342,0.006307138,0.00003124657,0.001733943,0.08047397,0.2939047,0.01107033,0.5747535,0.01050355],"study_design_scores_gemma":[0.03580841,0.003051633,0.01216601,0.005349092,0.001903526,0.0005268016,0.009853355,0.5031186,0.1002856,0.03950967,0.2741484,0.01427887],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3101103,0.02146427,0.6510218,0.0004853402,0.003175369,0.00169777,0.005374219,0.0002075794,0.006463299],"genre_scores_gemma":[0.924337,0.001242763,0.06975898,0.00025445,0.0007778313,0.00008567396,0.002974011,0.00007845629,0.0004908781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6142266,"threshold_uncertainty_score":0.9996176,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4252154226","doi":"10.1109/csb.2004.1332458","title":"State-space model for gene regulatory networks with time delays","year":2004,"lang":"en","type":"article","venue":"Proceedings. 2004 IEEE Computational Systems Bioinformatics Conference, 2004. CSB 2004.","topic":"Gene Regulatory Network Analysis","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":"","keywords":"Expression (computer science); Gene regulatory network; Computer science; Dynamic Bayesian network; Principal component analysis; Bayesian network; State space; State-space representation; Probabilistic logic; State (computer science); Component (thermodynamics); Bayesian information criterion; State variable; Data mining; Gene; Gene expression; Mathematics; Artificial intelligence; Algorithm; Statistics; Genetics; Biology","authors":[{"name":"Fang‐Xiang Wu","is_ca":true},{"name":"A.J. Kusalik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0113120542123053,"gpt":0.2129494918690172,"spread":0.2016374376567119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005824015,0.0007186236,0.0007128333,0.0002856558,0.0004247998,0.0003499622,0.0005708719,0.0004371248,0.000008527162],"category_scores_gemma":[0.00003227005,0.0006698814,0.0002629427,0.000533465,0.0002516561,0.00009409787,0.00008746762,0.0002440126,0.00005553169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002845217,"about_ca_system_score_gemma":0.0009704525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001899616,"about_ca_topic_score_gemma":0.0000203371,"domain_scores_codex":[0.9963727,0.00002030959,0.001157357,0.0007117674,0.0007929095,0.0009449656],"domain_scores_gemma":[0.996412,0.00002471788,0.0008506899,0.0003954122,0.001895008,0.0004221079],"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":[0.0001657757,0.00009499797,0.0001546876,0.0001916889,0.0003803807,0.000001590624,0.0002722642,0.9873775,0.001964024,0.001043206,0.008064779,0.0002891402],"study_design_scores_gemma":[0.002242263,0.0003763105,0.00009477994,0.0002080924,0.0001657197,0.0001157355,0.0001406856,0.9917403,0.002311894,0.0009374403,0.0007574087,0.0009093369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1004672,0.001332351,0.8953412,0.0000825032,0.0002053691,0.001381215,0.000156525,0.000129347,0.0009043193],"genre_scores_gemma":[0.9285922,0.00005196115,0.06710542,0.0001896875,0.0004688521,0.0002253393,0.0008822373,0.0001140326,0.002370288],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8282358,"threshold_uncertainty_score":0.9995753,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}