{"meta":{"query_hash":"6ebfecb0349d","filters":{"venue":"Computers & Security"},"cohort_total":77,"direct_labels_cover":0,"predictions_cover":77,"exported":77,"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/6ebfecb0349d","api":"https://metacan.xera.ac/api/v1/cohort?venue=Computers+%26+Security"},"results":[{"id":"W1965569486","doi":"10.1016/j.cose.2011.11.003","title":"A new robust adjustable logo watermarking scheme","year":2011,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Council of Scientific and Industrial Research, India","keywords":"Digital watermarking; Watermark; Computer science; Singular value decomposition; Robustness (evolution); Artificial intelligence; Embedding; Image (mathematics); Computer vision; Authentication (law); Algorithm; Pattern recognition (psychology); Theoretical computer science; Computer security","score_opus":0.030625478073763316,"score_gpt":0.22513410617438567,"score_spread":0.19450862810062236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965569486","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067198416,0.00028956527,0.9863076,0.00016110847,0.00079849444,0.00020571474,0.0000015788389,0.0013482452,0.004167811],"genre_scores_gemma":[0.4045763,0.000030507443,0.594845,0.00034941587,0.00011300004,0.000008130152,0.0000035541968,0.000014934934,0.0000591522],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980456,0.00008586631,0.00032385963,0.00065934716,0.00025276383,0.0006325551],"domain_scores_gemma":[0.9985138,0.0000425963,0.00013601125,0.0009712983,0.0000789362,0.00025731148],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002668703,0.0003004526,0.00029310217,0.00021020665,0.00022766486,0.00016838707,0.0018522958,0.00012627355,0.000024760391],"category_scores_gemma":[0.000007976364,0.0002826084,0.00016839369,0.00050399295,0.00007423968,0.001161662,0.0009230737,0.0003275822,0.000025582127],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019397573,0.0008803313,0.015883451,0.0003425292,0.0003460958,0.0013710888,0.045096524,0.00019435502,0.002285093,0.48750344,0.091828234,0.35407487],"study_design_scores_gemma":[0.002530916,0.00077984756,0.010154902,0.00066211435,0.00005667616,0.0005709169,0.00009881039,0.122657634,0.083818,0.64839524,0.1268961,0.003378812],"about_ca_topic_score_codex":0.00015241772,"about_ca_topic_score_gemma":0.000010063629,"teacher_disagreement_score":0.39785644,"about_ca_system_score_codex":0.000044867844,"about_ca_system_score_gemma":0.00006070898,"threshold_uncertainty_score":0.9999626},"labels":[],"label_agreement":null},{"id":"W1981847674","doi":"10.1016/s0167-4048(00)88612-5","title":"Information Systems Risk Management: Key Concepts and Business Processes","year":2000,"lang":"en","type":"article","venue":"Computers & Security","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":84,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"PricewaterhouseCoopers (Canada)","funders":"","keywords":"Computer science; Key (lock); Risk management; Task (project management); Risk analysis (engineering); Knowledge management; Process management; Management science; Data science; Business; Computer security; Engineering; Systems engineering","score_opus":0.015920056745476086,"score_gpt":0.2382350769441699,"score_spread":0.2223150201986938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981847674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79581,0.004798187,0.0363101,0.0009439192,0.004260586,0.002197322,0.00013056601,0.0013546197,0.1541947],"genre_scores_gemma":[0.9968637,0.0011988081,0.00023945734,0.0007530515,0.00062227616,0.000023497718,0.0002023825,0.00001400871,0.000082767416],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998861,0.000012031546,0.00033025802,0.00024542416,0.00026871392,0.0002825593],"domain_scores_gemma":[0.999129,0.000032432017,0.00020937629,0.0002819425,0.00032670982,0.000020533475],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00020558643,0.00022905465,0.00021747909,0.00017328082,0.00028328606,0.0010639182,0.00037345174,0.00007548246,0.00016495067],"category_scores_gemma":[0.00003573862,0.00020978338,0.000025521571,0.00089428434,0.000114119466,0.0043671746,0.00021754851,0.0001267899,0.0005220841],"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":[0.00024907535,0.00033828514,0.025316969,0.014472224,0.0002034862,0.0000411324,0.0020740456,0.0034790647,0.0000017457882,0.07661604,0.08657752,0.7906304],"study_design_scores_gemma":[0.00047171593,0.000005536743,0.012892837,0.00032071696,0.00007566562,0.000011117527,0.00023100109,0.016268397,0.0000032804835,0.0023178246,0.9670054,0.0003965449],"about_ca_topic_score_codex":0.0007184787,"about_ca_topic_score_gemma":0.000026334825,"teacher_disagreement_score":0.88042784,"about_ca_system_score_codex":0.000016384203,"about_ca_system_score_gemma":0.000013710131,"threshold_uncertainty_score":0.99997306},"labels":[],"label_agreement":null},{"id":"W1985820243","doi":"10.1016/j.cose.2009.02.003","title":"New aspect-oriented constructs for security hardening concerns","year":2009,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Correctness; Programming language; Graph; Control flow; Theoretical computer science","score_opus":0.02714622504838259,"score_gpt":0.2975025793651705,"score_spread":0.2703563543167879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985820243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052286712,0.0004974557,0.9883933,0.0009614622,0.002629191,0.00042603575,0.000010410503,0.0016421993,0.00021128549],"genre_scores_gemma":[0.26190084,0.00001252475,0.7372526,0.00041869804,0.00036263248,0.000006741912,0.0000073321835,0.000013555956,0.000025079593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99780166,0.000116450974,0.0003685955,0.00077616674,0.00029425832,0.000642883],"domain_scores_gemma":[0.9976422,0.0009349777,0.00016576592,0.0008049081,0.00017036214,0.00028176306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041281056,0.0003451649,0.00047252956,0.00012752024,0.00019250593,0.0001693119,0.0012348864,0.00013706775,0.000005894087],"category_scores_gemma":[0.00051106844,0.0003705324,0.00017557602,0.0004445248,0.0000768007,0.0006073572,0.00031413845,0.00034645165,0.000009769358],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008514636,0.00013200247,0.0002839152,0.00010786257,0.00012608088,0.00011724771,0.011034189,0.015942223,0.0008811189,0.6919971,0.04148802,0.2378051],"study_design_scores_gemma":[0.0030439678,0.0007833716,0.0016634801,0.00017646073,0.000034789176,0.00014512852,0.00009360858,0.11072067,0.009792828,0.82081,0.051275797,0.001459889],"about_ca_topic_score_codex":0.000012784853,"about_ca_topic_score_gemma":0.0000032738692,"teacher_disagreement_score":0.25667217,"about_ca_system_score_codex":0.00013321027,"about_ca_system_score_gemma":0.00015113859,"threshold_uncertainty_score":0.99987465},"labels":[],"label_agreement":null},{"id":"W2012759515","doi":"10.1016/j.cose.2011.07.004","title":"Estimating botnet virulence within mathematical models of botnet propagation dynamics","year":2011,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Botnet; Computer science; Artificial intelligence; The Internet; World Wide Web","score_opus":0.02477124696534392,"score_gpt":0.22886933713980429,"score_spread":0.20409809017446037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012759515","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.106372885,0.00004281993,0.8907517,0.00007647674,0.0008178835,0.00026426822,0.0000034957047,0.00024003923,0.0014304193],"genre_scores_gemma":[0.7023834,0.000004297083,0.2974399,0.00008105916,0.00006366204,0.000008919342,0.000003987169,0.000009403069,0.0000053553777],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802995,0.00014784261,0.00061310886,0.00048316747,0.00041538267,0.00031052265],"domain_scores_gemma":[0.9985741,0.00009328646,0.00037836286,0.0006620682,0.0001615682,0.00013066392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065104745,0.00021949646,0.00031654985,0.00012097295,0.0001594738,0.00008574928,0.0009776016,0.00013259136,0.000020546271],"category_scores_gemma":[0.000040183382,0.00021333066,0.00009862203,0.00044760754,0.00014506001,0.0010953723,0.0004848675,0.00030897532,0.000022136155],"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":[0.000040421914,0.00047185522,0.00007678749,0.00027894802,0.00004051161,0.000019496107,0.020268848,0.019324047,0.00012875206,0.9305564,0.00050758803,0.028286342],"study_design_scores_gemma":[0.0001409063,0.00013265992,0.000059387745,0.00011536975,0.000007223741,0.00003045074,0.000020479149,0.70080155,0.0010629019,0.29746214,0.0000063965053,0.00016049517],"about_ca_topic_score_codex":0.00006737668,"about_ca_topic_score_gemma":0.000021892825,"teacher_disagreement_score":0.68147755,"about_ca_system_score_codex":0.00007365726,"about_ca_system_score_gemma":0.000058882662,"threshold_uncertainty_score":0.8699372},"labels":[],"label_agreement":null},{"id":"W2013611975","doi":"10.1016/j.cose.2012.02.006","title":"Dynamic risk-based decision methods for access control systems","year":2012,"lang":"en","type":"article","venue":"Computers & Security","topic":"Access Control and Trust","field":"Social Sciences","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada; CA Technologies","keywords":"Computer science; Access control; Control (management); Computer security; Resource (disambiguation); Risk analysis (engineering); Order (exchange); Role-based access control; Computer network; Artificial intelligence; Business","score_opus":0.026364572328360605,"score_gpt":0.40949628202248667,"score_spread":0.3831317096941261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013611975","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0279173,0.0023683612,0.963133,0.0004346202,0.004239673,0.0010003176,0.0000435185,0.00017015741,0.0006930885],"genre_scores_gemma":[0.9742221,0.000031527434,0.024765927,0.00030405752,0.00054143195,0.000095763986,0.000008626358,0.000015991702,0.000014535003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976812,0.0008510542,0.0002977961,0.00028059588,0.0002754172,0.00061393017],"domain_scores_gemma":[0.9961393,0.002853406,0.00025393942,0.00027425025,0.00018119383,0.00029793452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003069182,0.00017031116,0.00036440394,0.00009270459,0.0007709683,0.00036598823,0.0007536796,0.00015752207,0.000023508037],"category_scores_gemma":[0.00048868044,0.00015108757,0.00019116128,0.00023134664,0.00014992907,0.0006705152,0.00007668118,0.00017213155,0.000015020213],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00067316595,0.00083366706,0.09441034,0.00020468274,0.00032660406,0.0000042204542,0.008945093,0.00568264,0.000032033215,0.0619363,0.008481701,0.8184696],"study_design_scores_gemma":[0.003909934,0.000077696415,0.022747425,0.00007561298,0.00018822053,0.0000010141786,0.00033640477,0.7485328,0.000014421943,0.01330332,0.21027485,0.0005383489],"about_ca_topic_score_codex":0.0015853029,"about_ca_topic_score_gemma":0.0002908153,"teacher_disagreement_score":0.94630486,"about_ca_system_score_codex":0.0001495182,"about_ca_system_score_gemma":0.00010969802,"threshold_uncertainty_score":0.61611724},"labels":[],"label_agreement":null},{"id":"W2025087771","doi":"10.1016/j.cose.2006.10.001","title":"SVision: A novel visual network-anomaly identification technique","year":2006,"lang":"en","type":"article","venue":"Computers & Security","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Defense Advanced Research Projects Agency","keywords":"Computer science; Denial-of-service attack; Intrusion detection system; Roaming; Network security; Data mining; Identification (biology); Visualization; Anomaly detection; Set (abstract data type); Computer network; Computer security; The Internet; World Wide Web","score_opus":0.01084682031874227,"score_gpt":0.2745660951248051,"score_spread":0.26371927480606283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025087771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022070752,0.000074156385,0.9951088,0.0005033668,0.0006086629,0.00022556685,0.0000075196363,0.00043924025,0.0008256061],"genre_scores_gemma":[0.93519783,0.000008765668,0.0633035,0.0006877332,0.0004840415,0.000016810447,0.00012768802,0.0000147905,0.0001588433],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983016,0.0000695029,0.00043597832,0.0005323813,0.00033836634,0.00032217527],"domain_scores_gemma":[0.9988715,0.00006160549,0.00019602002,0.00062743016,0.00014935764,0.000094053634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047058958,0.00018224132,0.00019341205,0.00012357639,0.00020770503,0.00049742544,0.0010174443,0.00009163287,0.00001169778],"category_scores_gemma":[0.000014750792,0.00018963925,0.00009506434,0.0008393945,0.000058128673,0.0006433487,0.0004748287,0.0001419176,0.00007148884],"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":[0.0000072822772,0.0008828216,0.0017487714,0.00005378624,0.000032909873,0.000030698902,0.00028668685,0.0028473472,0.003189026,0.8529083,0.13028432,0.0077280067],"study_design_scores_gemma":[0.00042650005,0.00006498479,0.009889774,0.000055148717,0.000013292692,0.00004085197,0.0000055866503,0.8983686,0.0014348106,0.023987826,0.06526233,0.00045027566],"about_ca_topic_score_codex":0.000067930596,"about_ca_topic_score_gemma":0.000022583254,"teacher_disagreement_score":0.93299073,"about_ca_system_score_codex":0.00005352109,"about_ca_system_score_gemma":0.000059670147,"threshold_uncertainty_score":0.77332646},"labels":[],"label_agreement":null},{"id":"W2060821931","doi":"10.1016/j.cose.2013.03.010","title":"A framework for risk assessment in access control systems","year":2013,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Access control; Context (archaeology); Risk assessment; Risk analysis (engineering); Situation awareness; Computer security; Object (grammar); Judgement; Security controls; Control (management); Artificial intelligence","score_opus":0.014538191734132035,"score_gpt":0.2907338934811638,"score_spread":0.2761957017470318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060821931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026339907,0.00009924626,0.9676877,0.0016067579,0.0018606768,0.0013875212,0.000015461546,0.00021263222,0.00079005933],"genre_scores_gemma":[0.95729697,0.00001402634,0.040802218,0.0014149022,0.0001290724,0.0003229152,0.000006595172,0.000008129258,0.0000051897455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981785,0.00018461228,0.00049965066,0.0003785112,0.00029895714,0.00045980015],"domain_scores_gemma":[0.99823785,0.00046250675,0.00026801217,0.0006430698,0.00022894505,0.00015962156],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00063302723,0.00020633567,0.00032879531,0.00016110126,0.00016990092,0.0013061609,0.001570105,0.00014062978,0.000015351337],"category_scores_gemma":[0.000059563994,0.00019388927,0.00011571776,0.00036853531,0.00003683916,0.0020883044,0.00032575676,0.00043778168,0.00005859361],"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":[0.000008979129,0.00021597104,0.007989399,0.00012608708,0.00004529106,0.0000043053415,0.003839892,0.002423454,0.0000022605084,0.96124846,0.0115213245,0.012574591],"study_design_scores_gemma":[0.0010500692,0.00006031473,0.02538842,0.00006197945,0.000004962695,0.000004517384,0.00005772199,0.87087625,0.000007956615,0.09751545,0.0047200895,0.00025226743],"about_ca_topic_score_codex":0.0005234192,"about_ca_topic_score_gemma":0.0000240131,"teacher_disagreement_score":0.930957,"about_ca_system_score_codex":0.00012850371,"about_ca_system_score_gemma":0.000103351726,"threshold_uncertainty_score":0.9997306},"labels":[],"label_agreement":null},{"id":"W2063461959","doi":"10.1016/j.cose.2005.09.007","title":"Timing is everything","year":2005,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vulnerability (computing); Computer science; Strengths and weaknesses; Software; Computer security; Work (physics); Internet privacy; Data science; Psychology; Operating system","score_opus":0.014137988401936297,"score_gpt":0.24003401272608527,"score_spread":0.22589602432414896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063461959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061360274,0.00029905874,0.8974271,0.012986517,0.0016444265,0.00024129654,0.0000044877547,0.0009910595,0.025045777],"genre_scores_gemma":[0.93096036,0.000012572405,0.05638924,0.012292098,0.0002507787,0.0000037932216,0.0000036227457,0.000006937915,0.00008060464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986169,0.000047587757,0.0003092041,0.00032000834,0.00033952593,0.00036682552],"domain_scores_gemma":[0.99899614,0.000053924356,0.000108839886,0.00059710606,0.000091651746,0.00015230985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003152681,0.0001726617,0.00016758952,0.00011158456,0.0002460668,0.00034231908,0.0010740716,0.00007780763,0.00006575014],"category_scores_gemma":[0.000011234168,0.00017759975,0.000111178844,0.0002991547,0.000034732362,0.001823855,0.0004930494,0.00025799006,0.00057582936],"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":[0.000008403735,0.0002343387,0.00041670504,0.00006653549,0.00005675811,0.00002493831,0.102651566,0.0005482467,0.000045311597,0.34739232,0.19700643,0.35154843],"study_design_scores_gemma":[0.000421877,0.000026992298,0.0007930419,0.000030578918,0.000004083466,0.000049634924,0.000048090023,0.6242001,0.0012210235,0.0050131353,0.36782727,0.0003642123],"about_ca_topic_score_codex":0.000020402305,"about_ca_topic_score_gemma":0.000007750208,"teacher_disagreement_score":0.86960006,"about_ca_system_score_codex":0.00008467672,"about_ca_system_score_gemma":0.000053860644,"threshold_uncertainty_score":0.7401313},"labels":[],"label_agreement":null},{"id":"W2087072724","doi":"10.1016/j.cose.2008.04.003","title":"An aspect-oriented approach for the systematic security hardening of code","year":2008,"lang":"en","type":"article","venue":"Computers & Security","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Software security assurance; Hardening (computing); Security testing; Computer security; Leverage (statistics); Security information and event management; Security service; Information security; Cloud computing security; Artificial intelligence; Operating system","score_opus":0.03443803637260593,"score_gpt":0.266494874496235,"score_spread":0.23205683812362907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087072724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034696966,0.000691336,0.96168995,0.00023292618,0.0009371381,0.0011659373,0.000014844943,0.00033785822,0.00023302744],"genre_scores_gemma":[0.9165872,0.000022091686,0.08284151,0.00021293195,0.00021702098,0.00007984801,0.000016574877,0.000016841685,0.0000059935396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971947,0.00035449833,0.0007499369,0.0007211058,0.0005383655,0.00044139024],"domain_scores_gemma":[0.9963914,0.0009781218,0.0004954001,0.0015831437,0.00039554323,0.00015639544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012988119,0.0002815424,0.0005587859,0.00012842106,0.00079774193,0.00014085915,0.0024587496,0.00011899468,0.0000027566534],"category_scores_gemma":[0.00013883664,0.00022925633,0.00025208978,0.0006438864,0.00023657709,0.00049768237,0.00034530254,0.00030934048,0.0000043600207],"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":[0.00012099101,0.0025788762,0.0024677159,0.015296783,0.0006600659,0.000034625384,0.16103199,0.017578805,0.00047624588,0.78770214,0.010600874,0.0014508575],"study_design_scores_gemma":[0.0005385778,0.00013579121,0.00054806727,0.00024273747,0.000033338725,0.000111697525,0.00037811106,0.993974,0.0007259071,0.0025007143,0.0005278485,0.00028322986],"about_ca_topic_score_codex":0.000043269516,"about_ca_topic_score_gemma":0.000004395713,"teacher_disagreement_score":0.9763952,"about_ca_system_score_codex":0.00007145733,"about_ca_system_score_gemma":0.00011772361,"threshold_uncertainty_score":0.9348802},"labels":[],"label_agreement":null},{"id":"W2092919558","doi":"10.1016/j.cose.2012.02.005","title":"Systematically breaking and fixing OpenID security: Formal analysis, semi-automated empirical evaluation, and practical countermeasures","year":2012,"lang":"en","type":"article","venue":"Computers & Security","topic":"Web Application Security Vulnerabilities","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; University of British Columbia","funders":"","keywords":"Computer science; Single sign-on; Computer security; Protocol (science); Password; Security analysis; Authentication (law); Scalability; World Wide Web; Database","score_opus":0.03740046871827278,"score_gpt":0.35394384000055357,"score_spread":0.31654337128228077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092919558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66066664,0.0011691897,0.33277124,0.0025363602,0.00035017548,0.0010760063,0.000014006257,0.0008377172,0.0005786539],"genre_scores_gemma":[0.98998934,0.00001637631,0.00886463,0.00082782784,0.0001697305,0.00009085816,0.000021507043,0.000017524639,0.0000022268275],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9954667,0.0009618242,0.00081789744,0.00080540375,0.001170717,0.0007774508],"domain_scores_gemma":[0.99655795,0.0011396651,0.0003384876,0.0009146001,0.00054869737,0.00050057797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0049863444,0.00038205233,0.00071366905,0.0003165252,0.00047684758,0.00096878613,0.0005275336,0.000202829,0.00001917112],"category_scores_gemma":[0.00044063345,0.00036409154,0.00013175211,0.0009276709,0.00026104777,0.0028495053,0.0011074716,0.0004240071,0.000016723649],"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":[0.00016788572,0.0027037861,0.40158564,0.0038867067,0.0042172675,0.0000795408,0.17902498,0.0011367988,0.00040252972,0.36852658,0.029469335,0.008798939],"study_design_scores_gemma":[0.0006387564,0.000056523793,0.037975006,0.000089831905,0.00037018166,0.00035949054,0.00031480775,0.95333475,0.000078644094,0.004493237,0.0018149364,0.00047384942],"about_ca_topic_score_codex":0.000051360963,"about_ca_topic_score_gemma":0.00003923716,"teacher_disagreement_score":0.9521979,"about_ca_system_score_codex":0.0001694263,"about_ca_system_score_gemma":0.00016906975,"threshold_uncertainty_score":0.9998811},"labels":[],"label_agreement":null},{"id":"W2116499651","doi":"10.1016/j.cose.2009.01.007","title":"A robust software watermarking for copyright protection","year":2009,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"University of Sharjah","keywords":"Computer science; Watermark; Digital watermarking; Software; Information hiding; Redundancy (engineering); Data structure; Tree (set theory); Checksum; Data mining; Computer security; Theoretical computer science; Operating system; Image (mathematics); Artificial intelligence; Mathematics","score_opus":0.022510081627130606,"score_gpt":0.2431009226350347,"score_spread":0.22059084100790408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116499651","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019404929,0.000056689234,0.9930936,0.0009882846,0.00070166163,0.0008668169,0.0000032637643,0.0022724702,0.00007673108],"genre_scores_gemma":[0.4765536,0.000005413434,0.5224966,0.0006492457,0.00016844622,0.00008729026,0.000004856543,0.000011440153,0.000023173518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985279,0.000053249496,0.00026286105,0.00057768746,0.00019897558,0.0003793273],"domain_scores_gemma":[0.99897146,0.000064397944,0.00013791394,0.0005538707,0.00017046838,0.0001019047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023764987,0.00021234964,0.00021543047,0.00015667618,0.00030572998,0.0001958708,0.0007478221,0.00010713731,0.0000030668527],"category_scores_gemma":[0.00004420714,0.00020830234,0.00012696734,0.0003217,0.000033378386,0.00082138367,0.0001526932,0.00021201676,0.0000075913917],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008480917,0.00026839576,0.000034335077,0.00011847511,0.000029912579,0.00003392941,0.0017611274,0.0017593963,0.0025105162,0.01700792,0.011336147,0.96505505],"study_design_scores_gemma":[0.0013056068,0.0013735454,0.0008791458,0.000189697,0.000015928283,0.00018682363,0.000010810088,0.4643657,0.099175796,0.3260378,0.10533558,0.0011235812],"about_ca_topic_score_codex":0.000007818098,"about_ca_topic_score_gemma":0.0000038052021,"teacher_disagreement_score":0.96393144,"about_ca_system_score_codex":0.00012989102,"about_ca_system_score_gemma":0.000030721687,"threshold_uncertainty_score":0.84943235},"labels":[],"label_agreement":null},{"id":"W2137055246","doi":"10.1016/j.cose.2009.01.006","title":"Reducing threats from flawed security APIs: The banking PIN case","year":2009,"lang":"en","type":"article","venue":"Computers & Security","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Canada Research Chairs; Universität Siegen","keywords":"Computer security; Computer science; Business; Internet privacy","score_opus":0.02048427071776934,"score_gpt":0.2584492219146772,"score_spread":0.23796495119690783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137055246","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82462794,0.0029682438,0.14928257,0.015219652,0.0036620384,0.0010618807,0.000029886112,0.0012991888,0.0018485984],"genre_scores_gemma":[0.99480873,0.000034716028,0.0022602659,0.0023344923,0.0004941782,0.000012914592,0.000017262868,0.000015306867,0.000022121347],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670905,0.0005114366,0.0006277345,0.000983063,0.0005602831,0.00060843414],"domain_scores_gemma":[0.99700457,0.00037142847,0.00027060113,0.0019386165,0.00015526182,0.00025951682],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008505568,0.000384614,0.00043168888,0.00013122855,0.0007084002,0.00089090865,0.0021026388,0.00015775964,0.000032682525],"category_scores_gemma":[0.000043877368,0.0003139128,0.00023804467,0.0006600791,0.00011045653,0.0007473695,0.00044905883,0.0005496848,0.000107711814],"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":[0.000042541098,0.0009929873,0.00057061494,0.00008500434,0.0002677699,0.002696573,0.71551305,0.00009985238,0.0007796864,0.14923008,0.027926259,0.101795584],"study_design_scores_gemma":[0.0010790183,0.0001435242,0.0017270766,0.00021184652,0.000049741957,0.00132975,0.00055233494,0.7300198,0.0011144811,0.25507507,0.007838419,0.00085892156],"about_ca_topic_score_codex":0.0006763741,"about_ca_topic_score_gemma":0.00013026784,"teacher_disagreement_score":0.72991997,"about_ca_system_score_codex":0.000119301156,"about_ca_system_score_gemma":0.00008878688,"threshold_uncertainty_score":0.9999313},"labels":[],"label_agreement":null},{"id":"W2166825417","doi":"10.1016/j.cose.2006.08.003","title":"A privacy-preserving clustering approach toward secure and effective data analysis for business collaboration","year":2006,"lang":"en","type":"article","venue":"Computers & Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Computer security; Artificial intelligence","score_opus":0.02851437046450045,"score_gpt":0.27683647893019003,"score_spread":0.24832210846568958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166825417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025123715,0.00049278437,0.96428627,0.00749181,0.00037056103,0.0010977684,0.00021301488,0.0008211616,0.00010294355],"genre_scores_gemma":[0.5053812,0.000018222614,0.49387908,0.00006565395,0.000107942185,0.00006583408,0.0004660555,0.000013908971,0.0000020692064],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697274,0.00016382024,0.00041322008,0.0015715051,0.00036539874,0.0005133015],"domain_scores_gemma":[0.98885614,0.0004566694,0.00026096182,0.010011649,0.0003309012,0.00008366468],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00089331745,0.0003425275,0.00052845373,0.0004243903,0.00030354827,0.0008780268,0.02442178,0.0002067366,0.000001119412],"category_scores_gemma":[0.003393129,0.00035035322,0.00007561198,0.0030769873,0.00013186749,0.0021583226,0.12242303,0.00025611918,0.0000012414873],"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":[0.00032941825,0.0017619156,0.039299615,0.00493178,0.0035413844,0.0001140793,0.00727914,0.023795562,0.0012711926,0.019533323,0.8117143,0.08642831],"study_design_scores_gemma":[0.0005724744,0.000037594495,0.013433151,0.000036340756,0.00012048597,0.00001085127,0.000043854423,0.93058115,0.00012525152,0.052749746,0.001916586,0.00037250324],"about_ca_topic_score_codex":0.0002766301,"about_ca_topic_score_gemma":0.00012737674,"teacher_disagreement_score":0.9067856,"about_ca_system_score_codex":0.00012706056,"about_ca_system_score_gemma":0.0000738782,"threshold_uncertainty_score":0.99989486},"labels":[],"label_agreement":null},{"id":"W2168705386","doi":"10.1016/j.cose.2010.11.004","title":"Extending the enforcement power of truncation monitors using static analysis","year":2010,"lang":"en","type":"article","venue":"Computers & Security","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; A priori and a posteriori; Truncation (statistics); Intuition; Enforcement; Alphabet; Static analysis; Computer security; Set (abstract data type); Range (aeronautics); Law; Programming language; Machine learning","score_opus":0.017786227151288896,"score_gpt":0.2854726528457654,"score_spread":0.2676864256944765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168705386","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46443778,0.000030592502,0.53420013,0.00018812317,0.00085544185,0.00011633744,0.0000015835354,0.000059332124,0.00011067196],"genre_scores_gemma":[0.9580265,0.000004268101,0.041727986,0.00013824156,0.000085798696,0.000003996572,0.0000045699007,0.0000060236794,0.0000025910595],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983779,0.00012910867,0.00045477354,0.00037608662,0.000403612,0.00025851498],"domain_scores_gemma":[0.9981117,0.00032610018,0.00035828789,0.00094087335,0.00018151931,0.00008149032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000803728,0.00015671099,0.00023349756,0.0002540988,0.00031802728,0.0001832276,0.0012170852,0.00006442899,0.000031123807],"category_scores_gemma":[0.000049488066,0.00013033609,0.00018349507,0.0013503672,0.00011440263,0.0003522043,0.0003200648,0.00029344516,0.0000047947324],"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":[0.0000215398,0.000585615,0.023320647,0.00012729765,0.0011313747,0.000009627556,0.06936303,0.052705243,0.007641443,0.8131309,0.00077477156,0.031188488],"study_design_scores_gemma":[0.00015164676,0.00002858129,0.010662569,0.00001755615,0.00007023409,0.000006109867,0.00019064853,0.9810598,0.0018052319,0.0050160815,0.00081915845,0.00017241281],"about_ca_topic_score_codex":0.00016421222,"about_ca_topic_score_gemma":0.000012647575,"teacher_disagreement_score":0.9283545,"about_ca_system_score_codex":0.00004795715,"about_ca_system_score_gemma":0.000070174836,"threshold_uncertainty_score":0.53149515},"labels":[],"label_agreement":null},{"id":"W2170284429","doi":"10.1016/s0167-4048(00)05016-1","title":"Canadian Credit Card Conundrum Caused by a Cracker","year":2000,"lang":"en","type":"article","venue":"Computers & Security","topic":"Ion Transport and Channel Regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Aquaporin; Abiotic component; Water transport; Photosynthesis; Carbon dioxide; Cell biology; Membrane; Biophysics; Biology; Abiotic stress; Chemistry; Botany; Biochemistry; Water flow; Ecology; Environmental science; Gene; Environmental engineering","score_opus":0.003822070411061139,"score_gpt":0.19423257537650943,"score_spread":0.19041050496544829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170284429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99590445,0.0003908283,0.00042621896,0.0007813793,0.00038034667,0.00013102942,0.000114615184,0.000022628776,0.0018485251],"genre_scores_gemma":[0.9964313,0.000059911923,0.000052618205,0.0007543191,0.00035390208,0.000005676691,0.0012140987,0.000014989484,0.0011132209],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.999211,0.000024980935,0.00012932917,0.00026780655,0.00009411689,0.0002727746],"domain_scores_gemma":[0.9995041,0.00000288172,0.000022752645,0.00021833894,0.000044682027,0.00020727192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000057785135,0.00012906875,0.00010980498,0.000026548903,0.000097499775,0.000025861335,0.00014024478,0.00014787738,0.00027311494],"category_scores_gemma":[0.0000017565967,0.00013948916,0.000075831456,0.00005887753,0.000044352746,0.0000042393626,0.000010048001,0.00007323834,0.000041659056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001349869,0.00015673682,0.008051501,0.00005243001,0.00020494635,0.00006481936,0.0011597708,0.0007248714,0.029100817,0.00012684631,0.9410859,0.01913642],"study_design_scores_gemma":[0.00064569444,0.000115419214,0.006172912,0.000011746219,0.000019558838,0.000009649411,0.000028533468,0.0010113477,0.01466881,0.00016774684,0.97681826,0.0003303178],"about_ca_topic_score_codex":0.00748876,"about_ca_topic_score_gemma":0.019864062,"teacher_disagreement_score":0.035732407,"about_ca_system_score_codex":0.00003365698,"about_ca_system_score_gemma":0.00009951452,"threshold_uncertainty_score":0.9991205},"labels":[],"label_agreement":null},{"id":"W2208596391","doi":"10.1016/j.cose.2015.11.001","title":"Taxonomy of information security risk assessment (ISRA)","year":2015,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Standards and Technology; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Taxonomy (biology); Asset (computer security); Risk assessment; Computer science; Risk analysis (engineering); IT risk management; Information security; Information security management; Security management; Risk management; Threat; Factor analysis of information risk; Knowledge management; Standard of Good Practice; Security information and event management; Computer security; Risk management information systems; Information system; Business; Management information systems; Security service; Cloud computing security; Finance; Engineering","score_opus":0.017636479221386822,"score_gpt":0.2431691983509228,"score_spread":0.22553271912953599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2208596391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045331463,0.00007131795,0.9202924,0.0008261911,0.0018744383,0.00063065457,0.000040446048,0.00039033397,0.030542755],"genre_scores_gemma":[0.9519373,0.000016862728,0.04721311,0.00067991565,0.00007347448,0.000030097195,0.000040150724,0.0000049645464,0.000004091286],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772096,0.00021861862,0.00073779654,0.000255656,0.00070037605,0.0003665851],"domain_scores_gemma":[0.99748874,0.00008733869,0.000630621,0.00086288847,0.0006077393,0.0003226705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012377822,0.00024528248,0.00034185094,0.00023812822,0.00014404177,0.0003039213,0.0013020654,0.0001277714,0.0000139087515],"category_scores_gemma":[0.00007343966,0.00024438347,0.00015078843,0.00056148425,0.00009670113,0.004280565,0.00070845825,0.00040513164,0.000114966315],"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":[0.000041569685,0.0005035299,0.008976227,0.0001983423,0.00012462841,0.000008422066,0.041137088,0.0011169396,0.0000037308355,0.7914087,0.09666266,0.059818145],"study_design_scores_gemma":[0.0024412926,0.00031947216,0.01142715,0.00005134252,0.000024783034,0.000032399123,0.00066599646,0.6283551,0.0005293675,0.053832058,0.30163273,0.00068830495],"about_ca_topic_score_codex":0.00022246248,"about_ca_topic_score_gemma":0.000020119633,"teacher_disagreement_score":0.90660584,"about_ca_system_score_codex":0.00017227586,"about_ca_system_score_gemma":0.00035324797,"threshold_uncertainty_score":0.9965669},"labels":[],"label_agreement":null},{"id":"W2476948659","doi":"10.1016/j.cose.2019.03.001","title":"Distributed, end-to-end verifiable, and privacy-preserving internet voting systems","year":2019,"lang":"en","type":"article","venue":"Computers & Security","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"European Research Council","keywords":"Verifiable secret sharing; Computer science; Outsourcing; Voting; Computer security; Asynchronous communication; Electronic voting; The Internet; Computer network; World Wide Web; Programming language","score_opus":0.009749343280490555,"score_gpt":0.22013317704504431,"score_spread":0.21038383376455377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2476948659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38189447,0.00033875313,0.61513376,0.00032365564,0.0014107698,0.00028900607,0.000010591696,0.00023138999,0.00036759948],"genre_scores_gemma":[0.9940717,0.000006065295,0.0052178367,0.00029326108,0.00021155918,0.000008654081,0.00002944777,0.000021679296,0.00013978191],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969861,0.00019571232,0.00058648636,0.0010413013,0.00050336885,0.0006870493],"domain_scores_gemma":[0.9983913,0.00026005702,0.00023140307,0.00064659026,0.0001591609,0.00031152653],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007914641,0.00036439803,0.0005690861,0.00019067273,0.00013847786,0.0010433539,0.0021978994,0.00012679733,0.000044766926],"category_scores_gemma":[0.00007563035,0.0003504022,0.00015420844,0.00052551914,0.000048392467,0.00065552566,0.0027578133,0.00041889813,0.00014315649],"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":[0.000049518596,0.00029581002,0.013374067,0.00072094286,0.00056943274,0.00019622393,0.019310193,0.058946792,0.00022942448,0.85907745,0.03518895,0.012041203],"study_design_scores_gemma":[0.00033753368,0.00010442469,0.0010184462,0.00027671026,0.000017270053,0.00004782178,0.000118314456,0.9828025,0.00005223186,0.00004261271,0.014777808,0.0004043327],"about_ca_topic_score_codex":0.00028081142,"about_ca_topic_score_gemma":0.00003746041,"teacher_disagreement_score":0.9238557,"about_ca_system_score_codex":0.000119007134,"about_ca_system_score_gemma":0.00004562362,"threshold_uncertainty_score":0.9999937},"labels":[],"label_agreement":null},{"id":"W2562513205","doi":"10.1016/j.cose.2016.12.006","title":"Practical-oriented protocols for privacy-preserving outsourced big data analysis: Challenges and future research directions","year":2016,"lang":"en","type":"article","venue":"Computers & Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; China Postdoctoral Science Foundation","keywords":"Outsourcing; Big data; Computer science; Computer security; Surprise; Variety (cybernetics); Cryptography; Key (lock); Data science; Information privacy; State (computer science); Cloud computing; Government (linguistics); Internet privacy; Business; Data mining","score_opus":0.2248470047818129,"score_gpt":0.41057469283343884,"score_spread":0.18572768805162593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562513205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008129107,0.0012852064,0.7452206,0.24421667,0.0006537026,0.0062522343,0.00015848443,0.0012195287,0.00018068323],"genre_scores_gemma":[0.04612971,0.0036306027,0.943052,0.00019026444,0.0015353805,0.0052687973,0.00009156673,0.000060679653,0.000040989707],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99508864,0.0006151647,0.00049843325,0.0020863935,0.0007816204,0.0009297762],"domain_scores_gemma":[0.97545356,0.0024082412,0.00023605916,0.02114836,0.00049881137,0.00025498882],"candidate_categories":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0035106929,0.00032539084,0.00051694235,0.0007376556,0.00056123576,0.00039812565,0.029584391,0.00027984972,0.000005377387],"category_scores_gemma":[0.022479242,0.00024757054,0.000113564565,0.0017712013,0.00036101777,0.0018294534,0.1669608,0.00056756794,0.000009763891],"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":[0.000095497235,0.00053972384,0.0007188369,0.00028689756,0.00091604755,0.000036958274,0.0016631595,6.89564e-7,0.00027964494,0.030587178,0.31697384,0.64790154],"study_design_scores_gemma":[0.0011976467,0.00027374044,0.0033216358,0.000228894,0.00009856402,0.00002649556,0.000256575,0.12791403,0.00046448846,0.14974214,0.7159095,0.0005662892],"about_ca_topic_score_codex":0.000036585134,"about_ca_topic_score_gemma":0.0001253303,"teacher_disagreement_score":0.64733523,"about_ca_system_score_codex":0.00013177631,"about_ca_system_score_gemma":0.00015613198,"threshold_uncertainty_score":0.9999977},"labels":[],"label_agreement":null},{"id":"W2748696935","doi":"10.1016/j.cose.2017.08.005","title":"Survey of publicly available reports on advanced persistent threat actors","year":2017,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Espionage; Hacker; Globe; Political science; Public relations; State (computer science); The Internet; Computer security; Internet privacy; Business; Computer science; Law; Psychology; World Wide Web","score_opus":0.03293520642325198,"score_gpt":0.2595525260668101,"score_spread":0.22661731964355813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748696935","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9311114,0.00018495387,0.008617405,0.0011209325,0.004468671,0.0005018031,0.000026218266,0.0003226345,0.05364597],"genre_scores_gemma":[0.9978677,0.000018899167,0.001534673,0.00034109576,0.00003214601,0.000005536648,0.000020552989,0.000008055931,0.00017132862],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980302,0.00010891921,0.00048851164,0.0005080248,0.00049842714,0.00036594615],"domain_scores_gemma":[0.99619067,0.0001013343,0.0006889447,0.0024347992,0.00039514725,0.00018909118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010216092,0.00022163021,0.00034094436,0.00011065665,0.00049233466,0.0005275172,0.0016764377,0.000097475466,0.00005683055],"category_scores_gemma":[0.00020446166,0.00020714359,0.00019098146,0.00015239975,0.00013131507,0.0013684466,0.0007283689,0.00022979954,0.0000883326],"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":[0.00033232948,0.0030543802,0.35021836,0.0005876512,0.0005664856,0.0004493738,0.031972796,0.0011756597,0.00019622962,0.1495681,0.2978691,0.16400957],"study_design_scores_gemma":[0.002357518,0.0009859947,0.8066759,0.00027312327,0.00002877675,0.00012831343,0.0001183154,0.112456866,0.0046419706,0.0037325313,0.06704596,0.0015547103],"about_ca_topic_score_codex":0.0009889603,"about_ca_topic_score_gemma":0.0003194327,"teacher_disagreement_score":0.45645759,"about_ca_system_score_codex":0.0000771518,"about_ca_system_score_gemma":0.00013290854,"threshold_uncertainty_score":0.844707},"labels":[],"label_agreement":null},{"id":"W2770593179","doi":"10.1016/j.cose.2017.10.014","title":"Privacy preserving fine-grained location-based access control for mobile cloud","year":2017,"lang":"en","type":"article","venue":"Computers & Security","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université de Montréal","funders":"","keywords":"Computer science; Encryption; Cloud computing; Access control; Computer security; Mobile cloud computing; Anonymity; Proxy re-encryption; Server; Attribute-based encryption; Outsourcing; Mobile device; Computer network; Mobile computing; Public-key cryptography; World Wide Web; Business","score_opus":0.023213549087158634,"score_gpt":0.3018504791962753,"score_spread":0.27863693010911667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770593179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030537099,0.0002474256,0.96335804,0.0022933555,0.0017199126,0.0012545042,0.000112339934,0.0003323783,0.00014494976],"genre_scores_gemma":[0.9695628,0.0000053997887,0.02891564,0.000742875,0.000458796,0.00023124939,0.0000633317,0.000016744743,0.0000031119084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977827,0.00010739816,0.00039069718,0.00083816494,0.0003311389,0.0005498757],"domain_scores_gemma":[0.99532795,0.0006063437,0.00040758407,0.003037062,0.00038316398,0.00023789916],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.00064851384,0.00030301514,0.00038200952,0.0001397996,0.0011597708,0.0019895057,0.006734409,0.0001243421,0.00001351748],"category_scores_gemma":[0.0003129423,0.00030547634,0.00025042403,0.00025543506,0.00017699317,0.0019070626,0.0012917958,0.0002247569,0.000009600251],"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":[0.0007247956,0.0031398656,0.045003433,0.0021051813,0.00056410173,0.00008952329,0.007702374,0.011065266,0.00037584023,0.59799623,0.2388183,0.092415094],"study_design_scores_gemma":[0.0040308307,0.00023538082,0.016355641,0.00012236908,0.000033528722,0.0000038822063,0.000007961374,0.8607052,0.0006790521,0.06829601,0.04889564,0.00063452224],"about_ca_topic_score_codex":0.00018705464,"about_ca_topic_score_gemma":0.00010572081,"teacher_disagreement_score":0.93902576,"about_ca_system_score_codex":0.000042304597,"about_ca_system_score_gemma":0.00018509844,"threshold_uncertainty_score":0.99993974},"labels":[],"label_agreement":null},{"id":"W2789617153","doi":"10.1016/j.cose.2018.02.018","title":"An efficient intrusion detection in resource-constrained mobile ad-hoc networks","year":2018,"lang":"en","type":"article","venue":"Computers & Security","topic":"Mobile Ad Hoc Networks","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Communication source; Node (physics); Intrusion detection system; Mobile ad hoc network; Wireless ad hoc network; Game theory; Resource (disambiguation); Computer network; Scheme (mathematics); Bayesian game; Bayesian probability; Distributed computing; Computer security; Artificial intelligence; Repeated game; Wireless; Network packet","score_opus":0.0057128601128184735,"score_gpt":0.22688577565410006,"score_spread":0.2211729155412816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789617153","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38049915,0.0005055474,0.6164769,0.00006921979,0.0013262,0.0004818302,9.371827e-7,0.00040377892,0.00023642747],"genre_scores_gemma":[0.98894274,0.000033707165,0.00978413,0.0005151584,0.00063611876,0.000053178446,0.000007870832,0.000022938882,0.000004143419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970876,0.000382399,0.00048433305,0.0009856513,0.0003545986,0.0007054184],"domain_scores_gemma":[0.9980138,0.00016512543,0.00017077934,0.0012537813,0.00011851055,0.00027798733],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00086715794,0.00030618973,0.00032254215,0.00022484799,0.00027460026,0.00026316493,0.0014057587,0.00023120493,0.0000251477],"category_scores_gemma":[0.000015337673,0.00031817704,0.00009650179,0.0010827953,0.00025215623,0.0003528072,0.0006349586,0.0005148609,0.000042001535],"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":[0.00008408886,0.00041687186,0.00013672742,0.0000136638655,0.000013402642,0.00005540553,0.0043194857,0.117619865,0.00028389163,0.0013511898,0.0010167467,0.8746887],"study_design_scores_gemma":[0.0006406551,0.0007329021,0.001377898,0.00006321213,0.000004136618,0.00003499701,0.000047243746,0.9799759,0.00043577846,0.0006155839,0.015728114,0.00034358262],"about_ca_topic_score_codex":0.000016831733,"about_ca_topic_score_gemma":0.0002224432,"teacher_disagreement_score":0.87434506,"about_ca_system_score_codex":0.00019791718,"about_ca_system_score_gemma":0.000053065316,"threshold_uncertainty_score":0.99992704},"labels":[],"label_agreement":null},{"id":"W2797835509","doi":"10.1016/j.cose.2018.01.017","title":"If-transpiler: Inlining of hybrid flow-sensitive security monitor for JavaScript","year":2018,"lang":"en","type":"article","venue":"Computers & Security","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"JavaScript; Computer science; Unobtrusive JavaScript; Compiler; Source code; Web application; Computer security; Programming language; Rich Internet application; Operating system","score_opus":0.019626182010947298,"score_gpt":0.26642535161458086,"score_spread":0.24679916960363357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797835509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20407687,0.00012858026,0.7903736,0.0005078505,0.0036464927,0.0005092204,0.000058373163,0.0003375319,0.0003614817],"genre_scores_gemma":[0.8881795,0.000013601857,0.10995513,0.0005302031,0.0012473443,0.000021326614,0.000024337287,0.000022702843,0.0000058954856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99697477,0.0001979957,0.0007751702,0.00096127595,0.00045240502,0.00063838594],"domain_scores_gemma":[0.9967958,0.00063464185,0.00039334066,0.001053671,0.00089558924,0.00022699923],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009359094,0.0003628461,0.00054985395,0.00021390001,0.0005818746,0.00025187168,0.0015637554,0.00012326156,0.000009369237],"category_scores_gemma":[0.0001279283,0.00040440325,0.00032047543,0.0005466111,0.00045656634,0.00062482746,0.00045220598,0.00033621458,0.000022753862],"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":[0.00086750794,0.0034631323,0.0032281813,0.0018565299,0.0013537238,0.0001624728,0.24888657,0.0030298685,0.005059098,0.33948386,0.11050176,0.2821073],"study_design_scores_gemma":[0.0010352578,0.00043814635,0.0004937504,0.00017541114,0.000028092123,0.0000374882,0.00015542803,0.9359667,0.02779898,0.019773366,0.013587161,0.0005102293],"about_ca_topic_score_codex":0.00006012912,"about_ca_topic_score_gemma":0.000009243522,"teacher_disagreement_score":0.93293685,"about_ca_system_score_codex":0.000086449494,"about_ca_system_score_gemma":0.00016574103,"threshold_uncertainty_score":0.9998408},"labels":[],"label_agreement":null},{"id":"W2922208991","doi":"10.1016/j.cose.2019.03.005","title":"An evaluation framework for network security visualizations","year":2019,"lang":"en","type":"article","venue":"Computers & Security","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Visualization; Network security; Categorization; Taxonomy (biology); Ranking (information retrieval); Data science; Data mining; Audit; Data visualization; Computer security; Information retrieval; Artificial intelligence","score_opus":0.027225201343472772,"score_gpt":0.35597988501045824,"score_spread":0.32875468366698546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922208991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074536656,0.00011470314,0.988533,0.00043478646,0.0019213224,0.0007978935,0.00002697178,0.00034238186,0.0003752335],"genre_scores_gemma":[0.8813972,0.000017516406,0.115383275,0.0021934821,0.0005094292,0.00003499409,0.00042625208,0.00002254715,0.000015317455],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977949,0.0002609546,0.000363683,0.0006659099,0.000507181,0.00040733194],"domain_scores_gemma":[0.99779505,0.00026843324,0.00019376936,0.0011025391,0.00044504227,0.00019519294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010965419,0.00020926469,0.0002581053,0.00010423733,0.00025085898,0.0005031822,0.0012064421,0.00014363219,0.00007669268],"category_scores_gemma":[0.00010010823,0.00022367436,0.00011437678,0.0007846269,0.000036998066,0.001025191,0.00024455905,0.00015887481,0.000082473794],"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":[0.000005398852,0.00019450007,0.0013917383,0.000038317154,0.000026235015,5.7036897e-7,0.0014778029,0.0093677025,0.0000065284535,0.9726348,0.011900743,0.0029556472],"study_design_scores_gemma":[0.00032781024,0.000104145205,0.0004087741,0.00003774625,0.000016811586,0.0000016718585,0.000024993113,0.70562756,0.000028795597,0.27915138,0.014058161,0.00021217126],"about_ca_topic_score_codex":0.000007771665,"about_ca_topic_score_gemma":0.000011723684,"teacher_disagreement_score":0.8739435,"about_ca_system_score_codex":0.0000761519,"about_ca_system_score_gemma":0.00014658213,"threshold_uncertainty_score":0.91211754},"labels":[],"label_agreement":null},{"id":"W2981518695","doi":"10.1016/j.cose.2019.101654","title":"Blockchain smart contracts formalization: Approaches and challenges to address vulnerabilities","year":2019,"lang":"en","type":"article","venue":"Computers & Security","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":277,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"IEEE Foundation","keywords":"Computer science; Correctness; Computer security; Smart contract; Domain (mathematical analysis); Model checking; Software engineering; Blockchain; Formal methods; State (computer science); Programming language","score_opus":0.027701418410819214,"score_gpt":0.2258013098955689,"score_spread":0.19809989148474969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981518695","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6859644,0.0026892037,0.26625085,0.039328247,0.00066240184,0.0019425845,0.000013510148,0.00091162685,0.002237202],"genre_scores_gemma":[0.9791971,0.00006836878,0.0192941,0.0012080843,0.000060803814,0.0001264571,0.0000032990583,0.000012227575,0.000029603052],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820834,0.00009172943,0.00027952646,0.0007804776,0.00021856272,0.0004213456],"domain_scores_gemma":[0.9983579,0.00019056746,0.00007224071,0.0010248106,0.00010353552,0.0002509645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053673174,0.00023665246,0.00032602073,0.00021193473,0.00019134136,0.00013734747,0.001028437,0.00016600435,0.000006882633],"category_scores_gemma":[0.000034576904,0.00023847168,0.00004996932,0.0004073512,0.0000800015,0.00017077761,0.000896098,0.00023040392,0.00006444736],"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":[0.000013094732,0.00018163609,0.00084756437,0.00012382105,0.000033921282,0.000003956778,0.01378178,0.000604494,0.000035205747,0.9274741,0.0010768234,0.05582362],"study_design_scores_gemma":[0.0022607269,0.001212996,0.025047855,0.0002430381,0.00002999012,0.00017726577,0.0017971181,0.52495444,0.0034601209,0.12832597,0.310236,0.002254438],"about_ca_topic_score_codex":0.000027097987,"about_ca_topic_score_gemma":0.00006231433,"teacher_disagreement_score":0.79914814,"about_ca_system_score_codex":0.00003928278,"about_ca_system_score_gemma":0.000033172662,"threshold_uncertainty_score":0.9724593},"labels":[],"label_agreement":null},{"id":"W3000358430","doi":"10.1016/j.cose.2020.101716","title":"A semantic-based classification approach for an enhanced spam detection","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Computer science; Categorization; Text categorization; Domain (mathematical analysis); Information retrieval; Bag-of-words model; Set (abstract data type); Semantic analysis (machine learning); Artificial intelligence; Data mining","score_opus":0.043767520729506536,"score_gpt":0.2557178883192978,"score_spread":0.21195036758979127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000358430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032129567,0.000021099344,0.9652181,0.0007409312,0.0006071453,0.00047683666,0.0000028772747,0.0006128065,0.00019063645],"genre_scores_gemma":[0.91576314,0.0000013141434,0.08274621,0.0010348267,0.00036032416,0.00005528903,0.000024624438,0.0000128591355,0.0000013865135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985601,0.000112482914,0.00021667326,0.0006610975,0.00020887917,0.00024081553],"domain_scores_gemma":[0.9990469,0.00007701892,0.00012935142,0.00044185846,0.000119868186,0.00018496842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025201813,0.00016582572,0.00017675044,0.00007270459,0.0002213869,0.00026549175,0.0006404109,0.00010556834,0.000001200564],"category_scores_gemma":[0.000042991604,0.00017733945,0.00010497628,0.0004970687,0.000031273783,0.0005710846,0.00006813784,0.00016890962,0.000008870608],"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":[0.00068581966,0.0015511166,0.0004012048,0.0010163701,0.00013808707,0.000006188731,0.021976283,0.020488707,0.3197951,0.024653919,0.00517503,0.6041122],"study_design_scores_gemma":[0.0005296177,0.00046141466,0.0011021743,0.000007016807,0.000009762298,0.0000021663614,0.000025089264,0.96201354,0.033357423,0.0011173618,0.0011650805,0.0002093292],"about_ca_topic_score_codex":0.000019481366,"about_ca_topic_score_gemma":0.0000064252586,"teacher_disagreement_score":0.94152486,"about_ca_system_score_codex":0.00005372889,"about_ca_system_score_gemma":0.000045915458,"threshold_uncertainty_score":0.72316927},"labels":[],"label_agreement":null},{"id":"W3008741522","doi":"10.1016/j.cose.2020.101753","title":"Optimization-based k-anonymity algorithms","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anonymity; Scalability; k-anonymity; Algorithm; Integer (computer science); Mathematical optimization; Optimization problem; Data mining; Mathematics; Database","score_opus":0.027431217347174994,"score_gpt":0.25176076041087214,"score_spread":0.22432954306369715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008741522","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00047003102,0.00011346783,0.90771955,0.08819785,0.0005869431,0.00021321562,0.00002251155,0.0023086732,0.0003677546],"genre_scores_gemma":[0.19086899,0.000011435297,0.8045769,0.004373743,0.0001128789,0.000010014266,0.000030859548,0.000014163258,0.0000010737233],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788654,0.00011628879,0.00030464944,0.00086516753,0.0004041507,0.00042323393],"domain_scores_gemma":[0.9942703,0.00024125652,0.00013664355,0.005012709,0.00011382058,0.00022522076],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00024444808,0.00024684292,0.00027413352,0.00009086586,0.00017408619,0.0002917666,0.02568506,0.00014388187,0.00003304795],"category_scores_gemma":[0.0036866784,0.00025956478,0.00010316312,0.0009221357,0.00013465197,0.00066174165,0.04066948,0.00039173517,0.00007339838],"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":[0.00001862956,0.00024268244,0.0009815339,0.00010367605,0.000062709696,0.0001850691,0.00045792002,0.07598037,0.00004300712,0.00408741,0.888687,0.029149957],"study_design_scores_gemma":[0.00041901137,0.00007618811,0.000096601085,0.000016689888,0.000004841996,0.000003832582,0.0000048412794,0.97615373,0.00067882525,0.017817562,0.004440781,0.00028709837],"about_ca_topic_score_codex":0.00002072557,"about_ca_topic_score_gemma":0.0000013591275,"teacher_disagreement_score":0.90017337,"about_ca_system_score_codex":0.00007217525,"about_ca_system_score_gemma":0.00012480076,"threshold_uncertainty_score":0.99998564},"labels":[],"label_agreement":null},{"id":"W3035713744","doi":"10.1016/j.cose.2020.101965","title":"Scalable and robust unsupervised android malware fingerprinting using community-based network partitioning","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Malware; Scalability; Android (operating system); System call; Mobile malware; Data mining; Android malware; Machine learning; Artificial intelligence; Computer security; Database; Operating system","score_opus":0.043769988153451164,"score_gpt":0.24858644599063612,"score_spread":0.20481645783718494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035713744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10015653,0.00014266529,0.89713687,0.0009824164,0.00018889473,0.00021085276,0.0000028725083,0.0011103088,0.000068618334],"genre_scores_gemma":[0.621015,0.000005761915,0.37615082,0.0026893246,0.000110002984,0.000007200077,0.0000055607807,0.000015741254,5.816246e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821144,0.00034948852,0.0003226456,0.00046980687,0.00021039063,0.00043625175],"domain_scores_gemma":[0.9987096,0.00026616422,0.00015701474,0.0005237507,0.00011116801,0.00023228141],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045212224,0.00023964765,0.00030074708,0.00007001062,0.00089377566,0.00034310683,0.0007183663,0.00009423743,0.000006425137],"category_scores_gemma":[0.00006573961,0.0002812992,0.000074215466,0.00067601167,0.000103915736,0.0006754225,0.00088140846,0.0006422737,0.0000041101102],"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":[0.0000880682,0.00027104167,0.01881499,0.0009473907,0.00009162528,0.00014118623,0.00884216,0.91642934,0.0029618752,0.0063065537,0.004877038,0.040228706],"study_design_scores_gemma":[0.0004000981,0.00012669398,0.00058832415,0.00016964633,0.000009151972,0.000020981437,0.00004338328,0.98944545,0.0033294747,0.003810378,0.0017111604,0.0003452318],"about_ca_topic_score_codex":0.00005944788,"about_ca_topic_score_gemma":0.000010988102,"teacher_disagreement_score":0.520986,"about_ca_system_score_codex":0.00006530611,"about_ca_system_score_gemma":0.000047343554,"threshold_uncertainty_score":0.99996394},"labels":[],"label_agreement":null},{"id":"W3042586362","doi":"10.1016/j.cose.2020.101956","title":"Assessing blockchain selfish mining in an imperfect network: Honest and selfish miner views","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Blockchain; Computer science; Proof-of-work system; Revenue; Cryptocurrency; Imperfect; Block (permutation group theory); Metric (unit); Computer security; Data mining; Business; Economics; Mathematics; Finance; Operations management","score_opus":0.027401121671337552,"score_gpt":0.26971621334726753,"score_spread":0.24231509167593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042586362","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88905245,0.00071589334,0.10278048,0.0061092908,0.00023087086,0.0003668426,0.00000244264,0.00061123085,0.00013049842],"genre_scores_gemma":[0.93406206,0.000030283985,0.06279262,0.0027923493,0.00025730487,0.000036367142,0.0000073830192,0.000020084559,0.0000015276871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727446,0.00027755072,0.0005000469,0.0010939743,0.00021738437,0.0006366138],"domain_scores_gemma":[0.998453,0.00020780406,0.00018182669,0.00075876544,0.00007922204,0.00031934006],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00076094404,0.00033735106,0.0005031933,0.00013357693,0.00029800003,0.00048489124,0.0012661811,0.00026557638,0.0000045149463],"category_scores_gemma":[0.000037415793,0.0003578519,0.00007561808,0.0012228705,0.00015464949,0.00054287916,0.00092872186,0.0005669092,0.0000060519856],"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":[0.00006747088,0.0019223014,0.23969115,0.0005439744,0.0002125294,0.0008092507,0.090449855,0.006010595,0.0021201144,0.09863426,0.045249652,0.51428884],"study_design_scores_gemma":[0.00077372836,0.00017808362,0.017674888,0.00006332656,0.000013955378,0.00006826642,0.00023043343,0.96825755,0.00012225895,0.0046966006,0.0073243887,0.00059649925],"about_ca_topic_score_codex":0.000037624257,"about_ca_topic_score_gemma":0.00017620303,"teacher_disagreement_score":0.96224695,"about_ca_system_score_codex":0.000047240097,"about_ca_system_score_gemma":0.00006942834,"threshold_uncertainty_score":0.99988735},"labels":[],"label_agreement":null},{"id":"W3047621094","doi":"10.1016/j.cose.2020.101994","title":"Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Saskatchewan","keywords":"Computer science; Scheme (mathematics); Ensemble learning; Artificial intelligence; Machine learning; Smart grid; Unsupervised learning; Labeled data; Data mining; Pattern recognition (psychology)","score_opus":0.048206534104030994,"score_gpt":0.2610741400248,"score_spread":0.212867605920769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047621094","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90583426,0.0008548702,0.09094299,0.00048232003,0.0011582857,0.00028036116,0.000018528859,0.00035681928,0.000071581315],"genre_scores_gemma":[0.997747,0.000030090021,0.00095797563,0.000642998,0.0005303213,0.00000355226,0.00006123181,0.000026568314,2.4607797e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982934,0.00033891734,0.00032093123,0.00040597082,0.0002641175,0.00037668605],"domain_scores_gemma":[0.9990689,0.0002976862,0.000065450666,0.00043486193,0.0000244982,0.0001085647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007315047,0.00021887396,0.00023816436,0.00009149339,0.00025597023,0.00011265477,0.0007225242,0.000102210666,0.00000523246],"category_scores_gemma":[0.00011583171,0.00019608317,0.000055445053,0.00059121416,0.00004829683,0.0002753479,0.00021527834,0.0010012385,0.000010583147],"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":[0.000051954692,0.0000607401,0.015938642,0.00039640197,0.00002879595,0.00008292849,0.010523155,0.96249366,0.0013819027,0.00002168873,0.0008119403,0.008208176],"study_design_scores_gemma":[0.00044247226,0.000080520935,0.001422089,0.00006785118,0.000013593967,0.000026515825,0.00029638008,0.9923053,0.0004456052,0.000022136277,0.004653572,0.00022396437],"about_ca_topic_score_codex":0.0002927952,"about_ca_topic_score_gemma":0.00046850016,"teacher_disagreement_score":0.09191277,"about_ca_system_score_codex":0.00007171704,"about_ca_system_score_gemma":0.000038746766,"threshold_uncertainty_score":0.799604},"labels":[],"label_agreement":null},{"id":"W3094138310","doi":"10.1016/j.cose.2020.102092","title":"A survey of machine learning techniques in adversarial image forensics","year":2020,"lang":"en","type":"article","venue":"Computers & Security","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Università degli Studi di Siena","keywords":"Computer science; Adversarial system; Computer security; Artificial intelligence; Adversarial machine learning; Data science; Computer vision; Machine learning","score_opus":0.015543654172116022,"score_gpt":0.23149406308390408,"score_spread":0.21595040891178804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094138310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11276754,0.00010530784,0.88319933,0.0008464205,0.0010530174,0.00039298373,0.000019217945,0.00059357396,0.001022631],"genre_scores_gemma":[0.9573751,0.000008636591,0.042311557,0.00020469908,0.000066814406,0.0000036224624,0.00001729027,0.0000107584265,0.0000015187293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858207,0.00018192983,0.00033521757,0.0003877084,0.0002771021,0.00023598822],"domain_scores_gemma":[0.9991223,0.00021146647,0.0001519716,0.00026448345,0.00013020163,0.000119575816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034585217,0.0001563967,0.00030081638,0.000110182715,0.000029552131,0.00008215025,0.0006241352,0.0000699846,0.000002156353],"category_scores_gemma":[0.0004060613,0.00016503481,0.000067681765,0.0007924165,0.00009958527,0.0005888739,0.00055659394,0.0003272812,0.000008726702],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032768684,0.0003522358,0.035166804,0.00031374654,0.000086608314,0.00026226614,0.01593622,0.0004645617,0.0028660554,0.008540396,0.0043197786,0.93136364],"study_design_scores_gemma":[0.0014532151,0.0011403911,0.031686805,0.00012095754,0.000008656014,0.000019084366,0.00002990876,0.9185261,0.035683054,0.0072198575,0.0034848272,0.0006271066],"about_ca_topic_score_codex":0.0005951004,"about_ca_topic_score_gemma":0.00026361248,"teacher_disagreement_score":0.93073654,"about_ca_system_score_codex":0.000044091885,"about_ca_system_score_gemma":0.00005797538,"threshold_uncertainty_score":0.67299247},"labels":[],"label_agreement":null},{"id":"W3118512068","doi":"10.1016/j.cose.2020.102162","title":"A Formal Approach to Network Segmentation","year":2021,"lang":"en","type":"article","venue":"Computers & Security","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Firewall (physics); Segmentation; Distributed computing; Formalism (music); Computer network; Theoretical computer science; The Internet; Software-defined networking; Computer security; Artificial intelligence; Operating system","score_opus":0.013298513131506776,"score_gpt":0.2269586706110305,"score_spread":0.21366015747952372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118512068","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009036877,0.00039722177,0.9858925,0.0006509606,0.0013593587,0.00018758417,0.0000022452507,0.00032574777,0.0021475402],"genre_scores_gemma":[0.46028945,0.00001877499,0.53409797,0.0047596847,0.00069600006,0.00003085035,0.00004534629,0.000014338178,0.000047575326],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983315,0.0000958693,0.00023109015,0.00053769,0.0002740529,0.0005297978],"domain_scores_gemma":[0.99894524,0.00009928511,0.00006055663,0.00058076857,0.00010603301,0.00020812219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026651676,0.00017065564,0.00020603157,0.0000441496,0.00021492141,0.0003000194,0.0006444889,0.00006882409,0.0000065694103],"category_scores_gemma":[0.000014156662,0.00017631766,0.000098899305,0.00085224496,0.000018047635,0.0004983426,0.0007910158,0.00017039306,0.000048272035],"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":[0.000036244455,0.0007168216,0.0039633317,0.00010834542,0.00013975741,0.00015273607,0.0099994615,0.11350541,0.00008472127,0.2688482,0.31933644,0.28310853],"study_design_scores_gemma":[0.0012417458,0.00019125214,0.009429872,0.00009205245,0.000022611594,0.0001982691,0.000090728405,0.8914482,0.00038318866,0.026303142,0.069685504,0.000913418],"about_ca_topic_score_codex":0.000010998204,"about_ca_topic_score_gemma":0.0000068286554,"teacher_disagreement_score":0.7779428,"about_ca_system_score_codex":0.00005263908,"about_ca_system_score_gemma":0.00007257367,"threshold_uncertainty_score":0.71900254},"labels":[],"label_agreement":null},{"id":"W3174361912","doi":"10.1016/j.cose.2021.102371","title":"I-MAD: Interpretable malware detector using Galaxy Transformer","year":2021,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Queen's University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Defence Research and Development Canada; Nvidia","keywords":"Malware; Computer science; Interpretability; Artificial intelligence; Executable; Machine learning; Data mining; Detector; Pattern recognition (psychology); Computer security; Programming language","score_opus":0.012426153081469861,"score_gpt":0.25655896774125836,"score_spread":0.2441328146597885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174361912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026563242,0.0003972536,0.96965426,0.00027058474,0.0011768538,0.0002026683,0.000007935543,0.0011388668,0.00058835896],"genre_scores_gemma":[0.70143336,0.000027722424,0.2978431,0.0005194707,0.000092770904,0.000013265438,0.0000037343314,0.000022860497,0.000043678134],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978877,0.00012791123,0.0003702215,0.0007732494,0.00032429482,0.00051662524],"domain_scores_gemma":[0.99844277,0.00010111853,0.000111048685,0.0009020406,0.00025524452,0.00018778548],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018139274,0.00029043626,0.00034162262,0.00015086506,0.00022001444,0.00024847657,0.0009058147,0.0001342746,0.00006657361],"category_scores_gemma":[0.00004105364,0.00031981754,0.00020306058,0.00074663595,0.000070149814,0.0011080819,0.0003728666,0.00038269485,0.000026587773],"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":[0.00014629506,0.001010582,0.001657583,0.00092177093,0.00048723843,0.0022513622,0.01591453,0.00442491,0.19624887,0.04157808,0.010239061,0.7251197],"study_design_scores_gemma":[0.0008699283,0.00023797007,0.00058189844,0.0003048104,0.00003467952,0.0009423508,0.00009924999,0.2867857,0.6218852,0.043535944,0.04341492,0.0013073916],"about_ca_topic_score_codex":0.000032944856,"about_ca_topic_score_gemma":0.000029280973,"teacher_disagreement_score":0.72381234,"about_ca_system_score_codex":0.00019555657,"about_ca_system_score_gemma":0.00015271903,"threshold_uncertainty_score":0.9999254},"labels":[],"label_agreement":null},{"id":"W3189763337","doi":"10.1016/j.cose.2021.102432","title":"Achieve efficient position-heap-based privacy-preserving substring-of-keyword query over cloud","year":2021,"lang":"en","type":"article","venue":"Computers & Security","topic":"Cryptography and Data Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Substring; Computer science; Heap (data structure); Keyword search; Cloud computing; Encryption; Inverted index; Information retrieval; Theoretical computer science; Database; Data structure; Computer security; Algorithm; Search engine indexing","score_opus":0.00968859770412619,"score_gpt":0.23670410710597548,"score_spread":0.22701550940184928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189763337","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46477586,0.0006736338,0.5313085,0.0009871535,0.0014789345,0.00018629253,0.000062042556,0.00022803206,0.0002995909],"genre_scores_gemma":[0.95192426,0.000023582481,0.04703172,0.0006883674,0.00020243016,0.000007956601,0.00010374111,0.000016462332,0.0000014652095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969437,0.0003172419,0.00057281065,0.0009370733,0.0006512553,0.0005778949],"domain_scores_gemma":[0.9966622,0.00048143577,0.00025223513,0.0020578555,0.00027389996,0.00027237946],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048965705,0.0003370517,0.0004517524,0.00022557905,0.00027028352,0.00029035113,0.0020795015,0.00015555082,0.00005090235],"category_scores_gemma":[0.00009060138,0.00036772026,0.00038241124,0.0013815159,0.0001443242,0.0004303044,0.0019322314,0.00045562707,0.000014006983],"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":[0.00040448215,0.006951959,0.029778255,0.0015559422,0.0005280215,0.0013493244,0.0150086,0.020422803,0.007093679,0.85337144,0.044754263,0.018781224],"study_design_scores_gemma":[0.0037840204,0.0004102256,0.06792841,0.0008419421,0.000091894566,0.000114803166,0.00010572422,0.8051027,0.035707608,0.0682087,0.015863482,0.0018404977],"about_ca_topic_score_codex":0.00012019401,"about_ca_topic_score_gemma":0.000042744003,"teacher_disagreement_score":0.78516275,"about_ca_system_score_codex":0.000075298165,"about_ca_system_score_gemma":0.0002687248,"threshold_uncertainty_score":0.99987745},"labels":[],"label_agreement":null},{"id":"W3203444100","doi":"10.1016/j.cose.2021.102490","title":"Ransomware: Recent advances, analysis, challenges and future research directions","year":2021,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":248,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"King Saud University","keywords":"Ransomware; Phishing; Computer security; Computer science; Government (linguistics); Internet privacy; Malware; Coronavirus disease 2019 (COVID-19); The Internet; World Wide Web; Medicine","score_opus":0.03219693488085095,"score_gpt":0.32720324401240236,"score_spread":0.2950063091315514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203444100","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000703872,0.27439925,0.7051175,0.014658085,0.0013836309,0.0002796391,0.000008181676,0.001241297,0.0022085095],"genre_scores_gemma":[0.070055306,0.7416912,0.18672223,0.00048272952,0.0008118542,0.00009918203,0.0000207614,0.000029011335,0.00008772393],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99749225,0.00044813682,0.00025156912,0.00092841696,0.0004625476,0.00041704674],"domain_scores_gemma":[0.99786943,0.00025625806,0.00007324221,0.00096529885,0.0006434841,0.00019229534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006008054,0.00018889003,0.000325015,0.00041028063,0.00038092467,0.00016016803,0.00056702225,0.000118149925,0.000016594326],"category_scores_gemma":[0.000060297854,0.00019895274,0.00011252055,0.0024235062,0.000119889904,0.0007021664,0.00065623113,0.0005345442,0.0000064529763],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000060312486,0.00010352443,0.000033897944,0.000037355076,0.00009865804,0.000059372756,0.0016065189,0.000018980443,0.000049728977,0.029619042,0.000657619,0.96770924],"study_design_scores_gemma":[0.00019691627,0.00007012106,0.0018086045,0.00001830795,0.000025675752,0.000060135168,0.00021029443,0.0026537727,0.0027655207,0.03142603,0.9605184,0.00024617877],"about_ca_topic_score_codex":0.00000867087,"about_ca_topic_score_gemma":0.00025279116,"teacher_disagreement_score":0.9674631,"about_ca_system_score_codex":0.00011537213,"about_ca_system_score_gemma":0.00007960091,"threshold_uncertainty_score":0.8113057},"labels":[],"label_agreement":null},{"id":"W3207050365","doi":"10.1016/j.cose.2021.102498","title":"A survey of remote attestation in Internet of Things: Attacks, countermeasures, and prospects","year":2021,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Research Council; China Scholarship Council; University of Waterloo; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Computer security; Adaptability; Adversarial system; Botnet; Protocol (science); Internet of Things; Service (business); The Internet; World Wide Web; Business","score_opus":0.017333953810619803,"score_gpt":0.2688090880780141,"score_spread":0.2514751342673943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207050365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22677736,0.00034528074,0.77235633,0.00007649568,0.00015308584,0.0001392798,0.0000035119244,0.00008879462,0.000059875856],"genre_scores_gemma":[0.8942489,0.00004026727,0.10559567,0.00008976255,0.0000062373583,0.000001632952,0.000006229796,0.0000060263587,0.000005285713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987524,0.00018113368,0.00035065188,0.00035999843,0.0002158283,0.00013994914],"domain_scores_gemma":[0.9988182,0.00017894142,0.00021977456,0.00037344923,0.00036865936,0.00004096598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005389621,0.00011368808,0.00026370265,0.00011804579,0.000016151014,0.000036077647,0.00033059306,0.000057710473,0.0000014528662],"category_scores_gemma":[0.0001954398,0.00012636022,0.000029790895,0.0004934477,0.00007687864,0.00044447402,0.00035011303,0.0001544346,4.375235e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024032049,0.0011457439,0.17590319,0.0020457404,0.00015711755,0.00041978242,0.04090398,0.00026604888,0.013631884,0.020294646,0.0041286503,0.7408629],"study_design_scores_gemma":[0.0011019983,0.00038789524,0.40657637,0.00082050305,0.0000070861233,0.00010033347,0.000036574213,0.439969,0.123268105,0.026688462,0.00058018445,0.00046349948],"about_ca_topic_score_codex":0.00071418437,"about_ca_topic_score_gemma":0.0003317108,"teacher_disagreement_score":0.7403994,"about_ca_system_score_codex":0.000053172436,"about_ca_system_score_gemma":0.00007164215,"threshold_uncertainty_score":0.51528203},"labels":[],"label_agreement":null},{"id":"W3209428779","doi":"10.1016/j.cose.2021.102511","title":"Power jacking your station: In-depth security analysis of electric vehicle charging station management systems","year":2021,"lang":"en","type":"article","venue":"Computers & Security","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Office of Advanced Cyberinfrastructure; Natural Sciences and Engineering Research Council of Canada; Ontario Arts Council; Concordia University; National Science Foundation","keywords":"Computer security; Computer science; Cyber-physical system; Firmware; Leverage (statistics); Grid; Vulnerability assessment; Vulnerability (computing); Implementation; Vulnerability management; Critical infrastructure; Telecommunications","score_opus":0.008312177583636468,"score_gpt":0.22573767002711898,"score_spread":0.2174254924434825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209428779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97907096,0.0021983632,0.016159855,0.00004016313,0.0008704395,0.00021483863,0.00002473231,0.00014492491,0.0012756974],"genre_scores_gemma":[0.9992293,0.0003363052,0.00023537767,0.000033277916,0.00005265327,0.000011447634,0.00007823539,0.000017164712,0.0000062022887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830276,0.00012213118,0.00048416707,0.00035145364,0.0003778233,0.00036168535],"domain_scores_gemma":[0.99926996,0.000096318196,0.00009773788,0.0003294829,0.00012324905,0.00008324814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031498756,0.00019304178,0.00041556894,0.00055381574,0.000071950875,0.00008149818,0.00021115539,0.00008417352,0.000024429597],"category_scores_gemma":[0.0000130117005,0.0002245224,0.00013536678,0.002567808,0.000025338122,0.00024516744,0.000079877966,0.0002520942,0.0000065438894],"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":[0.00003340351,0.0003875556,0.035082255,0.0014094188,0.0017236691,0.00036407606,0.028384103,0.91640955,0.0023881418,0.010313601,0.0010970298,0.0024072172],"study_design_scores_gemma":[0.00044496902,0.00002286423,0.086266175,0.00012662665,0.00015897608,0.000004845474,0.0012537121,0.90897983,0.0014514669,0.0003427889,0.00066256657,0.00028516832],"about_ca_topic_score_codex":0.00008453741,"about_ca_topic_score_gemma":0.00014449478,"teacher_disagreement_score":0.051183917,"about_ca_system_score_codex":0.00017570262,"about_ca_system_score_gemma":0.000026307056,"threshold_uncertainty_score":0.91557574},"labels":[],"label_agreement":null},{"id":"W4221004701","doi":"10.1016/j.cose.2022.102684","title":"Chameleon: Optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Benchmark (surveying); Particle swarm optimization; Artificial intelligence; Feature selection; Anomaly detection; Ensemble learning; Machine learning; Data mining; Intrusion detection system; Feature (linguistics); Selection (genetic algorithm); Pattern recognition (psychology)","score_opus":0.017397742512871798,"score_gpt":0.2785705815780907,"score_spread":0.26117283906521893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221004701","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05513221,0.00071850175,0.94076174,0.00042666163,0.0019922564,0.00061794394,0.000002053914,0.00033138963,0.000017231152],"genre_scores_gemma":[0.42323,0.00003784475,0.5759458,0.00032798457,0.00034620587,0.00007552322,0.0000066273706,0.00001734597,0.000012662471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976512,0.00066683936,0.00029960126,0.0006952273,0.00021860046,0.00046851282],"domain_scores_gemma":[0.9989339,0.00024167812,0.00023692496,0.00031693754,0.00013764256,0.00013292537],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0012946825,0.0002283971,0.0002996811,0.000111362315,0.001577682,0.00027221531,0.00035311063,0.00011506054,0.000013030167],"category_scores_gemma":[0.00003153106,0.00026531128,0.00012307602,0.001067767,0.000040854527,0.00063812156,0.00050724216,0.00038731316,6.245523e-7],"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":[0.00015346354,0.00007892737,0.000034300774,0.00002479739,0.00003897644,0.0000018224487,0.0008815262,0.93607736,0.0017091236,0.0021189975,0.0006945137,0.05818618],"study_design_scores_gemma":[0.00088996335,0.00041970168,0.000053478252,0.000011061651,0.000028965016,0.00013212374,0.000028109273,0.9812835,0.005374602,0.0051699886,0.006319312,0.00028919565],"about_ca_topic_score_codex":0.000045924942,"about_ca_topic_score_gemma":0.000018001208,"teacher_disagreement_score":0.36809778,"about_ca_system_score_codex":0.00019940692,"about_ca_system_score_gemma":0.00005108831,"threshold_uncertainty_score":0.9999799},"labels":[],"label_agreement":null},{"id":"W4224284423","doi":"10.1016/j.cose.2022.102718","title":"AdStop: Efficient flow-based mobile adware detection using machine learning","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","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":"Seneca Polytechnic","funders":"","keywords":"Computer science; Android (operating system); Overhead (engineering); Malware; False positive paradox; Software deployment; Mobile device; Artificial intelligence; Generalizability theory; False positive rate; Machine learning; Real-time computing; Embedded system; Computer security; Operating system","score_opus":0.008861128997836054,"score_gpt":0.23755382619734455,"score_spread":0.2286926971995085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224284423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12630734,0.0002077584,0.8702482,0.00004913702,0.0010585662,0.00038545832,0.000009288489,0.0017123049,0.000021950047],"genre_scores_gemma":[0.90138364,0.0000020535294,0.098144375,0.00024762563,0.000064731306,0.0001136244,0.000010339487,0.000025072497,0.000008565148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774617,0.00035046358,0.00030528687,0.00069815293,0.0005020945,0.00039782395],"domain_scores_gemma":[0.9988323,0.000121961515,0.00020908039,0.0006091056,0.000106052554,0.00012151268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044079032,0.0002452205,0.00023970667,0.00031148945,0.0010425339,0.000110636705,0.00085924735,0.00005644872,0.000042268006],"category_scores_gemma":[0.000030191175,0.00029094322,0.00015326128,0.00091954914,0.000048644866,0.00022103984,0.0010434024,0.0007463537,0.0000071314525],"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":[0.000022776512,0.00013526765,0.00009095574,0.00002259873,0.000008649445,0.000037628855,0.0006799901,0.94376945,0.0023257763,0.00011948393,0.000036580346,0.052750852],"study_design_scores_gemma":[0.00033655085,0.00038040496,0.000038235205,0.000012755201,0.0000061352393,0.00007113862,0.00003262822,0.95695263,0.026643356,0.00043966816,0.014781906,0.0003045599],"about_ca_topic_score_codex":0.000114272516,"about_ca_topic_score_gemma":0.000015020807,"teacher_disagreement_score":0.77507627,"about_ca_system_score_codex":0.0005453992,"about_ca_system_score_gemma":0.00007752539,"threshold_uncertainty_score":0.9999543},"labels":[],"label_agreement":null},{"id":"W4281772732","doi":"10.1016/j.cose.2022.102778","title":"Everything you control is not everything: Achieving intention-concealed visit on social networks","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; University of Guelph","funders":"Ministry of Public Security of the People's Republic of China; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Tel aviv; Computer science; Decoy; Control (management); Quality (philosophy); Internet privacy; Social media; Process (computing); Computer security; World Wide Web; Artificial intelligence","score_opus":0.013878887776892349,"score_gpt":0.23712297187249387,"score_spread":0.22324408409560154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281772732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11254284,0.00018044136,0.8797497,0.0035193914,0.0030221122,0.00027434493,0.000012234272,0.00042469447,0.00027422025],"genre_scores_gemma":[0.9879443,0.0000052577325,0.0013436348,0.009917694,0.0006215384,0.000022054632,0.000019659155,0.000027980197,0.00009786021],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960097,0.0005807328,0.00070443575,0.0010444614,0.00091918785,0.0007414781],"domain_scores_gemma":[0.99845773,0.0003384753,0.00044519734,0.00044169664,0.00015851387,0.00015841352],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013923172,0.00039928293,0.0006180344,0.00023391868,0.0015480986,0.00058081996,0.002058789,0.00012201774,0.000111013935],"category_scores_gemma":[0.0000382321,0.00042805637,0.0006136249,0.00062786246,0.000073452706,0.0005106994,0.0011844368,0.0013139623,0.000028494447],"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":[0.00034750754,0.0010697703,0.0014335179,0.000094295785,0.0013121034,0.00046596548,0.06945116,0.344652,0.00008484836,0.44544357,0.07024826,0.06539699],"study_design_scores_gemma":[0.0009774555,0.00013901736,0.00079412415,0.000044839897,0.000047148886,0.00003057505,0.0002371207,0.9917587,0.000009741313,0.00010994693,0.0053904857,0.0004608339],"about_ca_topic_score_codex":0.0000454421,"about_ca_topic_score_gemma":0.00000817852,"teacher_disagreement_score":0.8784061,"about_ca_system_score_codex":0.00033679113,"about_ca_system_score_gemma":0.00007296489,"threshold_uncertainty_score":0.99981713},"labels":[],"label_agreement":null},{"id":"W4283689178","doi":"10.1016/j.cose.2022.102813","title":"Fuzzing vulnerability discovery techniques: Survey, challenges and future directions","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Fuzz testing; Computer science; Vulnerability (computing); Computer security; Data science; Programming language","score_opus":0.027466111279944364,"score_gpt":0.26526658250549223,"score_spread":0.23780047122554787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283689178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20015018,0.04405108,0.65521854,0.01729004,0.0076819942,0.0017655073,0.00015168289,0.07099304,0.0026979374],"genre_scores_gemma":[0.9281892,0.0009785054,0.06992544,0.00043832298,0.00030631834,0.0001156177,0.000014511169,0.000020079524,0.0000119673805],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977232,0.00068078184,0.00024046666,0.00073498103,0.0003085829,0.00031194455],"domain_scores_gemma":[0.9981545,0.0007107791,0.000110414076,0.0008489032,0.00007386194,0.000101509315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001666219,0.00021510506,0.00026365262,0.00015487803,0.0007106764,0.00021303132,0.0008717395,0.00006187845,0.000003045762],"category_scores_gemma":[0.00010174239,0.00022340678,0.000075731754,0.00041276377,0.00008937602,0.00052063784,0.0016764167,0.0005220245,8.333115e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001784904,0.00042904227,0.007395054,0.00009330314,0.000039632145,0.000040608098,0.009422458,0.000010142341,0.00001576888,0.01987474,0.018048512,0.94461286],"study_design_scores_gemma":[0.000638139,0.0011071545,0.2492018,0.00012185736,0.000030133499,0.00071136706,0.00028165284,0.032794874,0.000374642,0.38641107,0.32620338,0.002123941],"about_ca_topic_score_codex":0.00030749015,"about_ca_topic_score_gemma":0.000055293716,"teacher_disagreement_score":0.94248897,"about_ca_system_score_codex":0.00012128385,"about_ca_system_score_gemma":0.000059014885,"threshold_uncertainty_score":0.9110264},"labels":[],"label_agreement":null},{"id":"W4283718025","doi":"10.1016/j.cose.2022.102820","title":"Attributes impacting cybersecurity policy development: An evidence from seven nations","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"European Union Agency for Network and Information Security","keywords":"Computer security; Cyber threats; Cybercrime; Business; The Internet; Cloud computing; Key (lock); Internet privacy; Computer science","score_opus":0.02789513501764273,"score_gpt":0.27799959387276707,"score_spread":0.25010445885512433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283718025","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7247615,0.0005289023,0.2624605,0.0058597242,0.0021723828,0.00070424814,0.00015426766,0.0014329134,0.0019255651],"genre_scores_gemma":[0.97284603,0.00001394644,0.024869688,0.001792629,0.00024220113,0.000056103785,0.00014355114,0.0000147819,0.000021091053],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672824,0.0004487034,0.00061385415,0.00067258335,0.0008957103,0.0006409123],"domain_scores_gemma":[0.99763954,0.00047849515,0.00033520552,0.00097579585,0.00020805879,0.0003628824],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010280496,0.00031674755,0.00030810837,0.00047053385,0.0020758375,0.00055075524,0.0025445072,0.00007849992,0.00013492246],"category_scores_gemma":[0.0002039094,0.00036419564,0.00013498038,0.0016367967,0.000064437794,0.0033303432,0.002184365,0.0006482117,0.000080407735],"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":[0.000071607355,0.0013486637,0.0077192066,0.00010536318,0.00027227873,0.00011752387,0.57464916,0.0044306023,0.00024521886,0.30881506,0.016049521,0.086175814],"study_design_scores_gemma":[0.0026320508,0.00050929934,0.06509234,0.0002330202,0.000044083674,0.0002588503,0.0045157857,0.6208798,0.0034341167,0.05312318,0.24605502,0.0032224292],"about_ca_topic_score_codex":0.0012684059,"about_ca_topic_score_gemma":0.00031055926,"teacher_disagreement_score":0.61644924,"about_ca_system_score_codex":0.0006634052,"about_ca_system_score_gemma":0.0008711891,"threshold_uncertainty_score":0.999881},"labels":[],"label_agreement":null},{"id":"W4286784859","doi":"10.1016/j.cose.2022.102830","title":"Robust stacking ensemble model for darknet traffic classification under adversarial settings","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Stacking; Adversarial system; Computer network; Data mining; Artificial intelligence; Chemistry","score_opus":0.038992476415059193,"score_gpt":0.24800426935136402,"score_spread":0.20901179293630484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286784859","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06416231,0.000071212366,0.93245935,0.0014346306,0.0010887652,0.00034199437,0.000019238772,0.0003031377,0.00011937034],"genre_scores_gemma":[0.95914185,0.0000023306407,0.039312854,0.0010761546,0.00022766691,0.00005229129,0.000087822584,0.000023839788,0.00007521176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738455,0.00017786198,0.00049485185,0.00087207893,0.00053200015,0.0005386328],"domain_scores_gemma":[0.9988007,0.00021018091,0.0002953169,0.00042440923,0.00014743245,0.00012199179],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009520197,0.00026161323,0.00033777312,0.00017670981,0.0007683548,0.000304465,0.0013939369,0.0000772286,0.000016597247],"category_scores_gemma":[0.000021590024,0.0002911512,0.00029717883,0.00041745865,0.000045698922,0.00041152898,0.0006246576,0.00041420566,0.0000058814994],"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":[0.000019437404,0.00010949608,0.0000024874944,0.000017728704,0.000048880236,0.0000034345398,0.0050812983,0.8207573,0.000034189972,0.15656991,0.012150233,0.0052056005],"study_design_scores_gemma":[0.0006556776,0.00009241259,0.00001651406,0.000012930323,0.00003295597,0.00001288377,0.00040116903,0.9948077,0.000012413271,0.00071184884,0.00291114,0.00033236336],"about_ca_topic_score_codex":0.000007101169,"about_ca_topic_score_gemma":0.000030207051,"teacher_disagreement_score":0.89497954,"about_ca_system_score_codex":0.00027940233,"about_ca_system_score_gemma":0.00016307161,"threshold_uncertainty_score":0.99995404},"labels":[],"label_agreement":null},{"id":"W4309724303","doi":"10.1016/j.cose.2022.103014","title":"A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks","year":2022,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Computer science; Deep learning; Malware; Computer security; Artificial intelligence; Internet of Things; Network packet; Node (physics); Attack surface; Convolutional neural network; Routing (electronic design automation); Computer network; Machine learning","score_opus":0.013963285965198356,"score_gpt":0.24890159273210366,"score_spread":0.2349383067669053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309724303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09060842,0.0004259802,0.905454,0.0007165041,0.001750889,0.0005453677,0.0000013187218,0.00045575472,0.000041755757],"genre_scores_gemma":[0.96394324,0.000007925364,0.033898845,0.0016124033,0.00031149274,0.00017439146,0.000013912526,0.000029717019,0.000008082432],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968727,0.0006172564,0.00051293074,0.0008448181,0.0003904637,0.00076178473],"domain_scores_gemma":[0.9980702,0.0009659684,0.00027210428,0.0004385442,0.00009622565,0.0001569609],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001247734,0.00029684213,0.0003795347,0.00028817847,0.0015849491,0.00038556507,0.0009578028,0.00019110375,0.00004881083],"category_scores_gemma":[0.0001361065,0.0003570451,0.00019393717,0.001634471,0.000043678247,0.00025662518,0.0007466106,0.0018065359,0.0000045671695],"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":[0.0001687828,0.00022137637,0.0005853068,0.00003373989,0.00002733439,0.00005410052,0.0030739242,0.87501115,0.00003742727,0.0033637984,0.0006993226,0.116723746],"study_design_scores_gemma":[0.000913882,0.00037935533,0.00037438152,0.00006209153,0.000006195836,0.000026423124,0.00011532787,0.9822858,0.00024139484,0.006863772,0.008327597,0.00040379155],"about_ca_topic_score_codex":0.000084635314,"about_ca_topic_score_gemma":0.00013786711,"teacher_disagreement_score":0.8733348,"about_ca_system_score_codex":0.0003232248,"about_ca_system_score_gemma":0.00007416552,"threshold_uncertainty_score":0.9998882},"labels":[],"label_agreement":null},{"id":"W4314446179","doi":"10.1016/j.cose.2023.103097","title":"2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Interconnectivity; Single point of failure; Intrusion detection system; Internet of Things; Computer security; Scheme (mathematics); Federated learning; Key exchange; Protocol (science); Industrial Internet; Key (lock); Artificial intelligence; Machine learning; Computer network; Public-key cryptography","score_opus":0.030017790191188158,"score_gpt":0.25650691082665483,"score_spread":0.22648912063546667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4314446179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41181806,0.000036036745,0.58088183,0.002163435,0.0015344527,0.00055799243,0.000013255413,0.002991377,0.0000035367273],"genre_scores_gemma":[0.97520685,0.000021607877,0.024439849,0.000053818443,0.000116995565,0.000057303918,0.00007619626,0.000024735968,0.0000026353134],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975388,0.00027680845,0.00041030062,0.00084920553,0.00033570122,0.0005891756],"domain_scores_gemma":[0.9971581,0.0004563407,0.00023558337,0.0019019643,0.000111822585,0.00013620305],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0006642824,0.00029377904,0.0003741572,0.00030446984,0.0005753936,0.00060983334,0.0055319816,0.00032766865,0.0000018399911],"category_scores_gemma":[0.0032927939,0.00029170772,0.00009007886,0.0009211652,0.00010139625,0.00027234794,0.01739844,0.0004907997,0.000012649972],"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":[0.0013314695,0.00055804924,0.0070749074,0.001799615,0.0005294107,0.0002318857,0.0011795985,0.0028485942,0.03454114,0.005813292,0.123332374,0.82075965],"study_design_scores_gemma":[0.002424262,0.00024159376,0.0009770254,0.0001752147,0.00001695323,0.000009642593,0.000020005107,0.9625278,0.021596476,0.007826424,0.003849078,0.00033548332],"about_ca_topic_score_codex":0.00003996485,"about_ca_topic_score_gemma":0.000021063483,"teacher_disagreement_score":0.95967925,"about_ca_system_score_codex":0.00019066641,"about_ca_system_score_gemma":0.000075363496,"threshold_uncertainty_score":0.9999535},"labels":[],"label_agreement":null},{"id":"W4322123789","doi":"10.1016/j.cose.2023.103159","title":"CKDAN: Content and keystroke dual attention networks with pre-trained models for continuous authentication","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Keystroke dynamics; Computer science; Keystroke logging; Authentication (law); Password; Biometrics; Artificial intelligence; Asset (computer security); Focus (optics); Computer security; Machine learning; S/KEY","score_opus":0.02783251963617532,"score_gpt":0.23935632985303135,"score_spread":0.211523810216856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322123789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29784727,0.00009707148,0.6985547,0.001540521,0.0005322049,0.00088436046,0.000011917908,0.00050089747,0.00003108464],"genre_scores_gemma":[0.9959594,0.000026113792,0.0032118054,0.00019781911,0.00012999124,0.00013565194,0.00008136634,0.000019370595,0.00023845508],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810606,0.00011609025,0.00039458377,0.00064275874,0.000311059,0.00042943546],"domain_scores_gemma":[0.9986403,0.00018176425,0.000196079,0.00055133505,0.00024764604,0.00018288012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005752673,0.00022829004,0.00031904786,0.00015790615,0.00024249371,0.00039130004,0.0004619911,0.000102443395,0.0000010613064],"category_scores_gemma":[0.000017402752,0.00020598545,0.0000983728,0.00039859532,0.00009560994,0.0005624601,0.0002088639,0.0001302185,0.000007700074],"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":[0.000581689,0.0014282876,0.011139017,0.0012601822,0.00107343,0.00009272126,0.27159178,0.007042021,0.0030872582,0.62751114,0.027724246,0.04746823],"study_design_scores_gemma":[0.0012073882,0.0001883753,0.013249049,0.00007605273,0.000026712438,0.000028038061,0.00017002114,0.97856987,0.000019764659,0.00512457,0.0010865564,0.00025358438],"about_ca_topic_score_codex":0.000033364555,"about_ca_topic_score_gemma":0.000025381607,"teacher_disagreement_score":0.9715279,"about_ca_system_score_codex":0.000040041647,"about_ca_system_score_gemma":0.00003638465,"threshold_uncertainty_score":0.8399843},"labels":[],"label_agreement":null},{"id":"W4381486732","doi":"10.1016/j.cose.2023.103350","title":"CPID: Insider threat detection using profiling and cyber-persona identification","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Concordia University","funders":"","keywords":"Persona; Computer science; Profiling (computer programming); Identification (biology); Computer security; Software deployment; Context (archaeology); Scalability; Insider; Workstation; Network security; Human–computer interaction; Software engineering; Operating system","score_opus":0.02549383295948198,"score_gpt":0.2580228893925076,"score_spread":0.23252905643302563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381486732","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74281085,0.00016499577,0.25416958,0.00028238594,0.0016751522,0.00023220653,0.0000015183732,0.0006110391,0.000052262298],"genre_scores_gemma":[0.99511814,0.000077577446,0.0043116785,0.00016706911,0.00028032548,0.000011197697,0.0000067680985,0.000013575696,0.00001365342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830896,0.00013940064,0.00030512482,0.0006164715,0.0002958975,0.00033416305],"domain_scores_gemma":[0.9991083,0.000102872276,0.0001394091,0.00043154394,0.00010266324,0.00011521505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055265706,0.00018449052,0.00018352385,0.00026754156,0.0005205161,0.00036961268,0.00034590627,0.00012609569,0.000003637122],"category_scores_gemma":[0.000032098065,0.00019686649,0.0000702618,0.0010866798,0.00007014249,0.0010042281,0.00040057988,0.00025788182,0.000052470932],"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":[0.000106366446,0.000327511,0.0052922587,0.0004900096,0.00021500292,0.0001521627,0.022804042,0.008677134,0.13627195,0.04449293,0.0030755815,0.77809507],"study_design_scores_gemma":[0.00025002533,0.000052877742,0.005215802,0.000039965576,0.000010530741,0.00006664256,0.000059676437,0.96052337,0.016657611,0.015956476,0.0009136866,0.00025335915],"about_ca_topic_score_codex":0.00012299803,"about_ca_topic_score_gemma":0.000056656154,"teacher_disagreement_score":0.9518462,"about_ca_system_score_codex":0.000085952575,"about_ca_system_score_gemma":0.000028501585,"threshold_uncertainty_score":0.8027982},"labels":[],"label_agreement":null},{"id":"W4385336663","doi":"10.1016/j.cose.2023.103409","title":"XMal: A lightweight memory-based explainable obfuscated-malware detector","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Obfuscation; Malware; Computer science; Feature (linguistics); Code (set theory); Popularity; Detector; Process (computing); Computer security; Static analysis; Field (mathematics); Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology); Operating system; Programming language","score_opus":0.011152351624905405,"score_gpt":0.24366278297380276,"score_spread":0.23251043134889735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385336663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025360884,0.00014952233,0.95937675,0.0015240477,0.0015747767,0.0005934125,0.000012598066,0.0107028885,0.0007050964],"genre_scores_gemma":[0.90430105,0.00002430917,0.09385408,0.0010478962,0.0002279781,0.00020781357,0.000025072171,0.00006219222,0.0002496191],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969516,0.00016830595,0.00045818053,0.001039884,0.0005371532,0.00084489374],"domain_scores_gemma":[0.9975386,0.00028992924,0.00020578805,0.0014275605,0.00024897614,0.00028911408],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046774262,0.00040997917,0.0004150093,0.0005968843,0.000422743,0.00027457273,0.00189869,0.00018795111,0.0000473416],"category_scores_gemma":[0.00009041811,0.0004238747,0.00022092625,0.0022971928,0.00010283666,0.00084061216,0.00084417395,0.00041431832,0.0003809949],"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":[0.00031603136,0.0015951289,0.0015126563,0.00180153,0.0004149699,0.0048376224,0.014347619,0.017898712,0.017929612,0.06634604,0.37155172,0.50144833],"study_design_scores_gemma":[0.0016490482,0.0006754262,0.0012824503,0.00023267923,0.000023438271,0.000082852566,0.00009612099,0.55950636,0.19604953,0.03312199,0.20548995,0.0017901561],"about_ca_topic_score_codex":0.000040304374,"about_ca_topic_score_gemma":0.000021504211,"teacher_disagreement_score":0.87894017,"about_ca_system_score_codex":0.00020693899,"about_ca_system_score_gemma":0.00014530428,"threshold_uncertainty_score":0.9998213},"labels":[],"label_agreement":null},{"id":"W4386827564","doi":"10.1016/j.cose.2023.103482","title":"Transferable adversarial distribution learning: Query-efficient adversarial attack against large language models","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Overfitting; Machine learning; Language model; Artificial intelligence; Leverage (statistics); Regularization (linguistics); Adversarial system; Black box; Artificial neural network","score_opus":0.013819036654897237,"score_gpt":0.26314159993484443,"score_spread":0.2493225632799472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386827564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1620036,0.000077150995,0.8301702,0.000843987,0.0035602706,0.00044783205,0.00005924939,0.0019937477,0.0008439381],"genre_scores_gemma":[0.9946965,0.000033310735,0.0034640892,0.00022762579,0.00078267034,0.000023508754,0.0005953584,0.000048444257,0.00012851799],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953467,0.0006479961,0.00059860625,0.0012096309,0.0009306322,0.0012664164],"domain_scores_gemma":[0.99799997,0.00039314508,0.00020949502,0.00090965704,0.00015923662,0.00032850963],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015031156,0.0005092872,0.00056425785,0.00025634642,0.0008761312,0.00035651514,0.0017349732,0.00029805145,0.00002716241],"category_scores_gemma":[0.00020466406,0.00055136473,0.00035771544,0.00159628,0.00011498885,0.00090272183,0.0011200324,0.0012526659,0.00024776527],"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":[0.0000629591,0.00014196856,0.00010376234,0.000044215234,0.00005752589,0.00017507574,0.008713703,0.9593881,0.000091408794,0.022074316,0.004887242,0.0042597298],"study_design_scores_gemma":[0.0023122048,0.0001011113,0.00020932948,0.00005795177,0.000023956207,0.000009341307,0.000337566,0.9801105,0.000105504616,0.00081617385,0.015323952,0.0005923685],"about_ca_topic_score_codex":0.000106607964,"about_ca_topic_score_gemma":0.000020006839,"teacher_disagreement_score":0.83269286,"about_ca_system_score_codex":0.00032604276,"about_ca_system_score_gemma":0.00019723747,"threshold_uncertainty_score":0.9996938},"labels":[],"label_agreement":null},{"id":"W4388562145","doi":"10.1016/j.cose.2023.103594","title":"SHRIMPS: A framework for evaluating multi-user, multi-modal implicit authentication systems","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; University of Waterloo","funders":"","keywords":"Computer science; Authentication (law); Session (web analytics); Modalities; Modal; Human–computer interaction; Identification (biology); Sensor fusion; Data mining; Computer security; Machine learning; World Wide Web","score_opus":0.07717239579828841,"score_gpt":0.3672327914804051,"score_spread":0.2900603956821167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388562145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0732455,0.00027353797,0.91846025,0.0010938749,0.0036825733,0.0017495785,0.000036359193,0.0014449959,0.000013324991],"genre_scores_gemma":[0.9157834,0.0000165717,0.08292398,0.00027312036,0.000260641,0.00041350903,0.00006570429,0.00004354758,0.00021949387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964164,0.00033669086,0.0008091397,0.0010514173,0.00061159226,0.0007747558],"domain_scores_gemma":[0.99671394,0.00077440264,0.00037743058,0.0014286289,0.0004143463,0.00029126453],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017247791,0.00036301254,0.00048192902,0.00034119893,0.00049325736,0.00077506015,0.0018615822,0.0002502932,0.0000037312016],"category_scores_gemma":[0.0003646369,0.00037417104,0.00026103036,0.001109675,0.000069112306,0.00054627145,0.000556447,0.00031025667,0.00032930545],"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":[0.000046703863,0.0012266905,0.0026680762,0.0013955458,0.00037161086,0.000021196125,0.25527975,0.0012461519,0.0026597225,0.7122546,0.011191257,0.01163872],"study_design_scores_gemma":[0.0011118128,0.00009923006,0.004092515,0.00019069074,0.00002489517,0.0000135846385,0.00040460954,0.9820463,0.0000851025,0.0073563578,0.004153291,0.00042157914],"about_ca_topic_score_codex":0.000095775446,"about_ca_topic_score_gemma":0.000015189542,"teacher_disagreement_score":0.98080015,"about_ca_system_score_codex":0.00014285179,"about_ca_system_score_gemma":0.0001285897,"threshold_uncertainty_score":0.999871},"labels":[],"label_agreement":null},{"id":"W4389990302","doi":"10.1016/j.cose.2023.103666","title":"Influences of displaying permission-related information on web single sign-on login decisions","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Login; Permission; Sign (mathematics); Computer science; Single sign-on; World Wide Web; Computer security; Internet privacy; Password; Mathematics","score_opus":0.043799123209830794,"score_gpt":0.3150856046396731,"score_spread":0.2712864814298423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389990302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99123687,0.000036381018,0.0005663131,0.001806818,0.0012458024,0.00039818193,0.00005485433,0.00036438837,0.0042904033],"genre_scores_gemma":[0.9993015,0.0001295336,0.00014762035,0.00021557005,0.00009962147,0.000009591147,0.00007737001,0.0000062462564,0.000012965401],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820215,0.00025711703,0.00038979115,0.00023208435,0.00060304365,0.00031581693],"domain_scores_gemma":[0.9985037,0.00068022555,0.00021155509,0.00033855374,0.00011713851,0.00014883657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007608132,0.0001438529,0.00019563499,0.00032106187,0.00071437913,0.00013381237,0.00058308884,0.00017460833,0.00004642918],"category_scores_gemma":[0.0016720267,0.00013287063,0.000092320275,0.0009886315,0.00020896476,0.00086717383,0.00027392077,0.00026726545,0.0002486278],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005578992,0.0015364741,0.009231257,0.00025006244,0.00017656732,0.000058027887,0.20786485,0.0067136297,0.0041003022,0.10401655,0.13267519,0.5328192],"study_design_scores_gemma":[0.0048235827,0.002377091,0.1767339,0.0029648133,0.00012196195,0.0000121447665,0.022229956,0.08807303,0.0037385002,0.3136729,0.38273075,0.0025213512],"about_ca_topic_score_codex":0.0004462078,"about_ca_topic_score_gemma":0.00010818739,"teacher_disagreement_score":0.5302979,"about_ca_system_score_codex":0.000095496456,"about_ca_system_score_gemma":0.00012751482,"threshold_uncertainty_score":0.5494499},"labels":[],"label_agreement":null},{"id":"W4390116303","doi":"10.1016/j.cose.2023.103674","title":"Improving adversarial transferability through hybrid augmentation","year":2023,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Transferability; Computer science; Adversarial system; Domain (mathematical analysis); Focus (optics); Masking (illustration); Artificial intelligence; Diversity (politics); Scaling; Frequency domain; Image (mathematics); Machine learning; Pattern recognition (psychology); Algorithm; Computer vision; Mathematics","score_opus":0.014672022562037942,"score_gpt":0.26638045364820656,"score_spread":0.25170843108616864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390116303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09944413,0.000023352955,0.89340466,0.0014218754,0.0034530237,0.0003253136,0.0000065801705,0.0014292098,0.0004918326],"genre_scores_gemma":[0.9520286,0.000008634998,0.047100857,0.00035241008,0.00041246304,0.00001694136,0.00004025005,0.000021358665,0.000018522465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735117,0.00031108042,0.00040800738,0.00085808797,0.0005120209,0.00055960915],"domain_scores_gemma":[0.99854237,0.00035307521,0.0001279295,0.0007669613,0.0000887694,0.00012089974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007764255,0.00027314288,0.00031140266,0.00012912296,0.00040713008,0.0002461488,0.0012829573,0.000083367166,0.000021763126],"category_scores_gemma":[0.0001296119,0.00029547003,0.0001755935,0.00084172294,0.000103193066,0.0014833815,0.00065089756,0.0004608872,0.00013632541],"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":[0.00026603832,0.0005624485,0.004614757,0.00059943023,0.0002975781,0.0006444529,0.05409615,0.20479347,0.0023785087,0.17721723,0.018998772,0.53553116],"study_design_scores_gemma":[0.0013093705,0.00010024934,0.0020540592,0.00002474649,0.000017213008,0.0000138737605,0.00008707914,0.96288604,0.0008521555,0.029168138,0.0030540947,0.00043301092],"about_ca_topic_score_codex":0.00028134952,"about_ca_topic_score_gemma":0.000012298696,"teacher_disagreement_score":0.8525844,"about_ca_system_score_codex":0.00016742159,"about_ca_system_score_gemma":0.00010960353,"threshold_uncertainty_score":0.99994975},"labels":[],"label_agreement":null},{"id":"W4396558186","doi":"10.1016/j.cose.2024.103883","title":"Navigating quantum security risks in networked environments: A comprehensive study of quantum-safe network protocols","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":53,"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 New Brunswick; Université de Montréal","funders":"","keywords":"Computer science; Computer security; Protocol (science); Quantum computer; Quantum cryptography; Quantum network; Cryptographic protocol; Quantum; Cryptography; Quantum information; Physics","score_opus":0.02635697674877876,"score_gpt":0.3160726994727151,"score_spread":0.28971572272393636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396558186","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85448927,0.0010652113,0.13470715,0.00031965796,0.0015600452,0.007405569,0.00000971973,0.00041363479,0.000029718069],"genre_scores_gemma":[0.9919177,0.000020073698,0.0066534635,0.00017766077,0.000526839,0.0006471029,0.00000980749,0.000045948556,0.0000013692857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945249,0.0009257577,0.0012336646,0.0014654135,0.0008156331,0.0010346458],"domain_scores_gemma":[0.9972174,0.0008404874,0.00037228822,0.0012640455,0.0000701038,0.00023567154],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009121465,0.0006220146,0.0009425012,0.0001450778,0.00029826522,0.00040539828,0.0019457225,0.00019358506,0.0000073805973],"category_scores_gemma":[0.000027702074,0.00059104373,0.00025314797,0.00163988,0.00018254056,0.00042741426,0.0016904013,0.0018266614,0.000021082371],"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":[0.00015104515,0.0041248687,0.010015576,0.0009768426,0.00048206048,0.0015140302,0.08251516,0.75893885,0.00013398126,0.025175815,0.0026364697,0.11333528],"study_design_scores_gemma":[0.0011428909,0.0009376684,0.007825943,0.0016274687,0.000017820266,0.000046452904,0.00032837663,0.963209,0.000025689798,0.021456096,0.002806287,0.00057635887],"about_ca_topic_score_codex":0.0006256915,"about_ca_topic_score_gemma":0.000044119522,"teacher_disagreement_score":0.20427008,"about_ca_system_score_codex":0.00013710672,"about_ca_system_score_gemma":0.00010503691,"threshold_uncertainty_score":0.9996541},"labels":[],"label_agreement":null},{"id":"W4399358245","doi":"10.1016/j.cose.2024.103936","title":"FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Boosting (machine learning); Federated learning; Vulnerability (computing); Convergence (economics); Similarity (geometry); Computer security; Machine learning; Artificial intelligence","score_opus":0.01843278694387394,"score_gpt":0.26595516966738575,"score_spread":0.24752238272351182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399358245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10228551,0.0005272841,0.8899059,0.0007443438,0.002103013,0.00028059832,0.0000030790782,0.0030233907,0.0011268425],"genre_scores_gemma":[0.9228103,0.0000057776815,0.076183006,0.0005228834,0.00030148693,0.000022634078,0.00003359889,0.00006337364,0.000056902085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962472,0.00038603897,0.0006345897,0.0012795243,0.0006334654,0.0008192004],"domain_scores_gemma":[0.9980494,0.0006417007,0.00021547213,0.000681559,0.00014599822,0.00026586294],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009166175,0.000510347,0.0005263361,0.0002885967,0.0006821595,0.0016853726,0.0012572154,0.00022334183,0.000006555145],"category_scores_gemma":[0.00014698088,0.00050756556,0.00027780817,0.00093018543,0.000091580994,0.0010135418,0.0006535642,0.0014270947,0.00009292089],"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":[0.000012613454,0.000030809257,0.0008253053,0.00028862664,0.00006440798,0.00036688437,0.0012988355,0.9784727,0.000054947755,0.007343075,0.0018021632,0.009439609],"study_design_scores_gemma":[0.0004383841,0.00006473857,0.0000759987,0.0005330215,0.000020775466,0.000032419717,0.00006961669,0.99590766,0.0000641103,0.00032609137,0.0018995513,0.0005676241],"about_ca_topic_score_codex":0.000021235353,"about_ca_topic_score_gemma":0.000011866009,"teacher_disagreement_score":0.8205248,"about_ca_system_score_codex":0.00047571879,"about_ca_system_score_gemma":0.0003353559,"threshold_uncertainty_score":0.9997376},"labels":[],"label_agreement":null},{"id":"W4400065514","doi":"10.1016/j.cose.2024.103971","title":"Detecting command injection vulnerabilities in Linux-based embedded firmware with LLM-based taint analysis of library functions","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Security and Verification in Computing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Firmware; Computer science; Taint checking; Operating system; Embedded system; Software","score_opus":0.01266985832429048,"score_gpt":0.2408840868425788,"score_spread":0.22821422851828832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400065514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3469951,0.00025812333,0.6509009,0.0005327797,0.00038960975,0.00019495652,0.000015273266,0.00054495,0.00016833274],"genre_scores_gemma":[0.9818613,0.000002071736,0.017677981,0.00025964392,0.00008462745,0.000027857894,0.000060237508,0.000017493787,0.000008793381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771935,0.0003387717,0.0005622767,0.0007169079,0.00034292103,0.00031979688],"domain_scores_gemma":[0.99762625,0.0012413646,0.00016718684,0.00074931094,0.00011743281,0.00009847385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005541398,0.00025824152,0.00043687693,0.001248974,0.00023408904,0.00043415354,0.00064957363,0.00011982603,0.0000314],"category_scores_gemma":[0.000047581834,0.00024287587,0.0002389772,0.004594671,0.00013437751,0.0006824512,0.00016885121,0.00049294706,0.000004088874],"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":[0.00011101485,0.0006767782,0.025581902,0.00077099534,0.0005671656,0.000058796744,0.0135204205,0.926952,0.000054424454,0.0111527955,0.0012398438,0.019313848],"study_design_scores_gemma":[0.0003626939,0.00027907788,0.0055610733,0.0002682577,0.00009631995,0.000004882478,0.00036093907,0.99046016,0.00086456747,0.00078955025,0.0006799907,0.00027247457],"about_ca_topic_score_codex":0.00017884081,"about_ca_topic_score_gemma":0.00024090735,"teacher_disagreement_score":0.6348662,"about_ca_system_score_codex":0.00011964809,"about_ca_system_score_gemma":0.0003268826,"threshold_uncertainty_score":0.9904191},"labels":[],"label_agreement":null},{"id":"W4400647844","doi":"10.1016/j.cose.2024.103994","title":"SCL-CVD: Supervised contrastive learning for code vulnerability detection via GraphCodeBERT","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Software Engineering Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Vulnerability (computing); Code (set theory); Artificial intelligence; Computer security; Programming language","score_opus":0.014764084794374605,"score_gpt":0.2703110922352167,"score_spread":0.25554700744084213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400647844","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13021119,0.00048182462,0.8648963,0.00033771605,0.0019987458,0.00050617865,0.0000073555175,0.0015353893,0.000025298732],"genre_scores_gemma":[0.9859835,0.000010904292,0.013518755,0.00006908082,0.00024875972,0.00010431068,0.000011038853,0.0000311319,0.0000225143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99762976,0.00019834001,0.00029236334,0.00090073963,0.000393957,0.0005848478],"domain_scores_gemma":[0.9960163,0.0030193368,0.00003288043,0.00050632376,0.00021858161,0.0002065433],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010375256,0.00026130132,0.00028471582,0.00026521142,0.00027302376,0.0004886792,0.00082274585,0.00013016653,0.0000072495286],"category_scores_gemma":[0.00052572944,0.00026543974,0.00021665482,0.00080826745,0.00009293089,0.00057648256,0.00031989443,0.0006800435,0.00003291217],"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":[0.00023410907,0.00057398307,0.0097758,0.0021978216,0.0007156884,0.00021861675,0.016473932,0.023889406,0.017231178,0.017268037,0.0051285503,0.90629286],"study_design_scores_gemma":[0.00043269584,0.00024063903,0.006711882,0.00007972636,0.0000113327005,0.000028572877,0.000009865686,0.9752111,0.0045722583,0.0059076436,0.0064736186,0.00032065262],"about_ca_topic_score_codex":0.00008590385,"about_ca_topic_score_gemma":0.000035213125,"teacher_disagreement_score":0.9513217,"about_ca_system_score_codex":0.00023391734,"about_ca_system_score_gemma":0.00011441668,"threshold_uncertainty_score":0.9999798},"labels":[],"label_agreement":null},{"id":"W4401344934","doi":"10.1016/j.cose.2024.104034","title":"IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networks","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Research and Productivity Council; University of New Brunswick","funders":"Canadian Institute of Planners; National Research Council Canada","keywords":"Internet of Things; Computer science; Intrusion detection system; Network packet; Computer network; Computer security","score_opus":0.01585681615057286,"score_gpt":0.26240555412709843,"score_spread":0.24654873797652557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401344934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10991727,0.0009155544,0.8816241,0.0013637161,0.0049099852,0.00052397785,0.0000025784998,0.00062136387,0.00012145919],"genre_scores_gemma":[0.98956305,0.00010887252,0.008879538,0.00047958374,0.00083836715,0.00007350331,0.0000121610365,0.000022435974,0.000022515671],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977617,0.00018292955,0.00046255553,0.00085468526,0.00026027762,0.000477813],"domain_scores_gemma":[0.99870044,0.00044966154,0.00008573364,0.00054796296,0.000092148664,0.00012406474],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007118401,0.00024142202,0.0002496714,0.00040226904,0.00033469833,0.00056875707,0.00059305,0.00017849296,0.000014344531],"category_scores_gemma":[0.000047562306,0.00025636706,0.00017745442,0.0013025571,0.00005437068,0.0005019949,0.00038059868,0.0005346581,0.000020891051],"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":[0.00008376695,0.00019673094,0.00016261198,0.00021845447,0.000067955436,0.00007938981,0.0077012344,0.05444074,0.00078558,0.026405882,0.015443548,0.8944141],"study_design_scores_gemma":[0.00035387362,0.0001144036,0.00068187166,0.00018338644,0.000009057311,0.000038463957,0.000034503708,0.94577914,0.0014025256,0.029251572,0.021873906,0.00027726436],"about_ca_topic_score_codex":0.00013473762,"about_ca_topic_score_gemma":0.0002400622,"teacher_disagreement_score":0.89413685,"about_ca_system_score_codex":0.00021364797,"about_ca_system_score_gemma":0.00005766525,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W4401947202","doi":"10.1016/j.cose.2024.104080","title":"RSSI-based attacks for identification of BLE devices","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":3,"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 du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Agence Nationale de la Recherche","keywords":"Computer science; Identification (biology); Computer security; Internet privacy","score_opus":0.014923253227962912,"score_gpt":0.27454885074878677,"score_spread":0.25962559752082387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401947202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043171495,0.0005901228,0.95397866,0.0007289947,0.0010753206,0.00016389375,0.000008418565,0.00021187392,0.00007121393],"genre_scores_gemma":[0.98963934,0.000002825907,0.009948097,0.0001936264,0.00013653307,0.000012020015,0.000019799401,0.000009522435,0.00003824209],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859565,0.000052446907,0.00044303064,0.00046112415,0.0002303917,0.00021734339],"domain_scores_gemma":[0.99912405,0.00025183358,0.00013306722,0.0002758477,0.00015848364,0.000056694837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005742654,0.00013858825,0.00022008969,0.00016530887,0.00008000627,0.00032294888,0.00079390965,0.00006318147,0.0000074556738],"category_scores_gemma":[0.000018139533,0.00012772364,0.0002440948,0.00040450174,0.000043936903,0.00030326695,0.00010640461,0.00011006714,0.000020238502],"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":[0.000018076213,0.00030022013,0.00019195139,0.0012494045,0.0003039916,0.000021360602,0.00789411,0.035940852,0.0006283068,0.8703525,0.025831394,0.057267834],"study_design_scores_gemma":[0.00012374109,0.00004864246,0.00013968302,0.000110034176,0.000022168393,0.000002070582,0.00002158408,0.98880273,0.0015817978,0.00044434564,0.008566991,0.0001362019],"about_ca_topic_score_codex":0.000009416143,"about_ca_topic_score_gemma":0.000024738278,"teacher_disagreement_score":0.9528619,"about_ca_system_score_codex":0.000037520393,"about_ca_system_score_gemma":0.00007698989,"threshold_uncertainty_score":0.5208419},"labels":[],"label_agreement":null},{"id":"W4402644087","doi":"10.1016/j.cose.2024.104120","title":"Entity and relation extractions for threat intelligence knowledge graphs","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Topic Modeling","field":"Computer Science","cited_by":28,"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 Windsor","funders":"","keywords":"Computer science; Knowledge graph; Relation (database); Natural language processing; Computer security; Knowledge management; Artificial intelligence; Data mining","score_opus":0.0321096837671894,"score_gpt":0.30012742349680904,"score_spread":0.2680177397296196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402644087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018588636,0.003586424,0.9740356,0.00071775226,0.0021003494,0.0002217768,0.0000031923284,0.00034940374,0.00039682677],"genre_scores_gemma":[0.9401423,0.00012753095,0.059498295,0.000036936723,0.00011753663,0.00001786025,0.0000034146153,0.000006757585,0.000049402406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990463,0.00003510746,0.0001829018,0.00046604878,0.00009205171,0.00017756347],"domain_scores_gemma":[0.9992467,0.00027561537,0.000027446706,0.00031486468,0.000059610065,0.00007572262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022459067,0.000113739836,0.0001088231,0.0001244113,0.00016642609,0.00031541623,0.00031871023,0.00006335223,0.0000029236498],"category_scores_gemma":[0.00001940756,0.00011387754,0.0000764855,0.00026391872,0.000041453917,0.0006776982,0.00019999754,0.00016568619,0.000016948788],"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":[0.00000147533,0.000033015473,0.00015964224,0.000091681955,0.000015898282,0.000004147514,0.0026070336,0.00016475873,0.000036210316,0.867868,0.00077339815,0.12824474],"study_design_scores_gemma":[0.00004276567,0.00002470123,0.00050895894,0.000045989404,0.00000802348,0.000021813334,0.000010943065,0.7531135,0.00009416962,0.23887864,0.0071467482,0.000103720085],"about_ca_topic_score_codex":0.00002144813,"about_ca_topic_score_gemma":0.000023957033,"teacher_disagreement_score":0.9215536,"about_ca_system_score_codex":0.000044331573,"about_ca_system_score_gemma":0.00004357121,"threshold_uncertainty_score":0.46437913},"labels":[],"label_agreement":null},{"id":"W4402754310","doi":"10.1016/j.cose.2024.104114","title":"Empowering 5G SBA security: Time series transformer for HTTP/2 anomaly detection","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); Concordia University","funders":"","keywords":"Anomaly detection; Computer science; Series (stratigraphy); Computer security; Real-time computing; Data mining; Geology","score_opus":0.007295658256371165,"score_gpt":0.23665994959667128,"score_spread":0.22936429134030012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402754310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09358673,0.0015745071,0.8919723,0.0019988336,0.0066724136,0.0008312206,0.000024925417,0.001972315,0.0013667713],"genre_scores_gemma":[0.99321944,0.00012778673,0.0051126326,0.00033843375,0.00089625816,0.00007852987,0.000016272752,0.000040762105,0.00016988315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752283,0.000103225924,0.00046975687,0.0009294063,0.0003555431,0.00061925227],"domain_scores_gemma":[0.9988498,0.0002018848,0.00007913727,0.00054655736,0.00012646796,0.0001961205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005670887,0.0003768405,0.000366019,0.00028859204,0.00047259705,0.00087714166,0.0007512693,0.00023270432,0.00004507798],"category_scores_gemma":[0.000019788366,0.00038664902,0.00035273843,0.00084384595,0.00009763751,0.002163267,0.00013800347,0.00046137988,0.00015328046],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005467154,0.000586662,0.00004329313,0.0021061697,0.0006814589,0.00026355273,0.042672995,0.0010190874,0.028001724,0.12203447,0.046006687,0.7560372],"study_design_scores_gemma":[0.00042835806,0.0006091073,0.00006354416,0.00018945895,0.00003517437,0.00020231158,0.0000332201,0.6229733,0.023929533,0.045569077,0.30534494,0.00062195095],"about_ca_topic_score_codex":0.00004166613,"about_ca_topic_score_gemma":0.0000898967,"teacher_disagreement_score":0.8996327,"about_ca_system_score_codex":0.00014873556,"about_ca_system_score_gemma":0.000084047824,"threshold_uncertainty_score":0.99985856},"labels":[],"label_agreement":null},{"id":"W4403561221","doi":"10.1016/j.cose.2024.104160","title":"NTLFlowLyzer: Towards generating an intrusion detection dataset and intruders behavior profiling through network and transport layers traffic analysis and pattern extraction","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Profiling (computer programming); Intrusion detection system; Intrusion; Traffic analysis; Offender profiling; Data mining; Computer network; Operating system","score_opus":0.015166182974327804,"score_gpt":0.2689959710158228,"score_spread":0.253829788041495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403561221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57395065,0.0008463745,0.42385757,0.0001123426,0.0007345067,0.00022283132,0.00003458224,0.0002383738,0.0000027719204],"genre_scores_gemma":[0.98433363,0.00069366954,0.013954643,0.00022097807,0.0005047236,0.000027814709,0.00024414578,0.00001925066,0.0000011587064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975831,0.00021514688,0.0004339988,0.0010845438,0.00029692365,0.0003862732],"domain_scores_gemma":[0.9991623,0.00008590646,0.00011100583,0.00039298437,0.000049074264,0.00019873212],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063391303,0.00032222937,0.00035445506,0.00025521402,0.0005784595,0.00070782757,0.00023553427,0.00020221298,0.000008805248],"category_scores_gemma":[0.0000064485357,0.0003239815,0.0000824595,0.0010181107,0.000120487726,0.0020930741,0.00021454577,0.00051045336,0.0000012556582],"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":[0.000027864748,0.000080466314,0.0011125719,0.00015922621,0.00017897546,0.00008335406,0.004501195,0.009186317,0.0012292905,0.0002603556,0.00018255229,0.98299783],"study_design_scores_gemma":[0.00025346706,0.00024128526,0.004647772,0.000063427324,0.00029546546,0.00016240306,0.00008885322,0.99082553,0.00081008277,0.00042516438,0.0017922074,0.00039435015],"about_ca_topic_score_codex":0.00034981777,"about_ca_topic_score_gemma":0.0011317319,"teacher_disagreement_score":0.9826035,"about_ca_system_score_codex":0.00006268384,"about_ca_system_score_gemma":0.00003296092,"threshold_uncertainty_score":0.9999212},"labels":[],"label_agreement":null},{"id":"W4404094142","doi":"10.1016/j.cose.2024.104156","title":"Role of cybersecurity for a secure global communication eco-system: A comprehensive cyber risk assessment for satellite communications","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Satellite Communication Systems","field":"Engineering","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":"University of New Brunswick","funders":"","keywords":"Computer security; Computer science; Communications satellite; Cyber threats; Satellite; Telecommunications; Engineering","score_opus":0.01645980187132789,"score_gpt":0.28914719168582387,"score_spread":0.272687389814496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404094142","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13523991,0.49659222,0.3065623,0.001773615,0.004599066,0.015820518,0.011553139,0.0069596358,0.020899588],"genre_scores_gemma":[0.9331778,0.005641763,0.05960741,0.000030791,0.00010093602,0.0006254511,0.0007387879,0.0000728535,0.0000041956882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975077,0.0004353195,0.0009785902,0.0004094473,0.0002633213,0.00040564124],"domain_scores_gemma":[0.9944909,0.0016732707,0.00024622836,0.0028723644,0.00056729093,0.00014989634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000744439,0.00039164265,0.0006383407,0.00014998802,0.00030396195,0.00021248429,0.0017024211,0.00024310697,0.0000044387716],"category_scores_gemma":[0.000028349235,0.00043503792,0.00042182894,0.000520173,0.00020736856,0.0003308758,0.00044533462,0.00042836936,0.000017429145],"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":[0.00019850055,0.0009130141,0.0063731926,0.014110285,0.004225975,0.0000037379668,0.03964488,0.007183524,0.0025000826,0.73486346,0.0144416215,0.17554171],"study_design_scores_gemma":[0.00075726904,0.00007558488,0.0024390495,0.00069339864,0.0001883922,0.000020893041,0.0017554277,0.5749356,0.00018976472,0.011383792,0.40710172,0.00045908184],"about_ca_topic_score_codex":0.0002245353,"about_ca_topic_score_gemma":0.0002496262,"teacher_disagreement_score":0.7979379,"about_ca_system_score_codex":0.0006474464,"about_ca_system_score_gemma":0.00010042268,"threshold_uncertainty_score":0.99981016},"labels":[],"label_agreement":null},{"id":"W4404296167","doi":"10.1016/j.cose.2024.104187","title":"Dynamic trigger-based attacks against next-generation IoT malware family classifiers","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Malware; Computer science; Internet of Things; Computer security","score_opus":0.03447076430568409,"score_gpt":0.2829498113158103,"score_spread":0.24847904701012624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404296167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030180665,0.0010696531,0.9594512,0.0015423365,0.0031405175,0.0004262763,0.000017143568,0.0037877834,0.00038444783],"genre_scores_gemma":[0.8813271,0.00007421492,0.11646339,0.0017215364,0.00016381544,0.00006445799,0.000048578626,0.000043874144,0.000093027855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737257,0.00015633006,0.00045113955,0.0010719432,0.0004517454,0.0004962468],"domain_scores_gemma":[0.9984604,0.00015898315,0.00011150798,0.0009252086,0.00014604625,0.00019781366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033312856,0.0003824271,0.0003146992,0.00043339017,0.00026278742,0.00079758145,0.0010672382,0.00021044233,0.000009966526],"category_scores_gemma":[0.000030022595,0.00039727724,0.00023813732,0.0010113157,0.00011194256,0.000869412,0.00029660773,0.0005463642,0.0000784297],"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":[0.000032051885,0.00026295235,0.000037807058,0.00039261207,0.00011173729,0.00054139184,0.0014035827,0.012877518,0.042754747,0.014133963,0.06515809,0.86229354],"study_design_scores_gemma":[0.00023383676,0.00014214385,0.00010107148,0.000115652736,0.000009336552,0.000014963918,0.000015911728,0.93869174,0.006196424,0.0024838864,0.051547114,0.00044789695],"about_ca_topic_score_codex":0.000014508611,"about_ca_topic_score_gemma":0.000018985309,"teacher_disagreement_score":0.9258143,"about_ca_system_score_codex":0.00045168662,"about_ca_system_score_gemma":0.00022773388,"threshold_uncertainty_score":0.9998479},"labels":[],"label_agreement":null},{"id":"W4405467377","doi":"10.1016/j.cose.2024.104272","title":"Evaluation framework for quantum security risk assessment: A comprehensive strategy for quantum-safe transition","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":19,"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 New Brunswick; Université de Montréal","funders":"","keywords":"Computer science; Quantum; Risk analysis (engineering); Computer security; Business; Quantum mechanics; Physics","score_opus":0.030072223620043567,"score_gpt":0.33326368270097917,"score_spread":0.3031914590809356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405467377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07088488,0.0019840389,0.91719115,0.0024780054,0.004173446,0.0021637292,0.0002972589,0.00078723143,0.000040250903],"genre_scores_gemma":[0.83645976,0.000062538544,0.1617195,0.0003937811,0.0009422033,0.00023218321,0.00014015539,0.00004837098,0.0000015108677],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9953874,0.00063732476,0.0007064729,0.0015153587,0.000923492,0.0008299958],"domain_scores_gemma":[0.99544907,0.0023258852,0.00026530406,0.00086677534,0.00084586965,0.00024711475],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0022596917,0.00056005,0.000602026,0.0002981263,0.0007393952,0.0011291144,0.0011070055,0.0003188383,0.000011191382],"category_scores_gemma":[0.0001264751,0.00054293865,0.00058309943,0.0007328424,0.00012689174,0.0006551127,0.00021679605,0.00092496356,0.0000121390785],"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":[0.000069018206,0.00040930317,0.000009490311,0.0008895052,0.00042386074,0.000025725347,0.011213149,0.07390529,0.00008258792,0.6900748,0.0057653547,0.21713191],"study_design_scores_gemma":[0.00055957044,0.00046437175,0.00016314382,0.0002455428,0.00010429817,0.000021014335,0.00007150198,0.54773736,0.000043936318,0.44693422,0.0032960495,0.0003589918],"about_ca_topic_score_codex":0.00006343785,"about_ca_topic_score_gemma":0.000011688755,"teacher_disagreement_score":0.7655749,"about_ca_system_score_codex":0.00023355919,"about_ca_system_score_gemma":0.00052501366,"threshold_uncertainty_score":0.9999078},"labels":[],"label_agreement":null},{"id":"W4406900157","doi":"10.1016/j.cose.2025.104358","title":"A comprehensive review of current trends, challenges, and opportunities in text data privacy","year":2025,"lang":"en","type":"review","venue":"Computers & Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Current (fluid); Computer science; Data science; Internet privacy; Information privacy; Engineering","score_opus":0.2794450641724881,"score_gpt":0.39540393815842256,"score_spread":0.11595887398593446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406900157","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.3001787e-7,0.98947287,0.0022351788,0.00545021,0.00090036425,0.0008115063,0.00051076413,0.00033113247,0.00028784983],"genre_scores_gemma":[7.791654e-7,0.98446596,0.014470248,0.00016894467,0.000048395825,0.00005718439,0.00076187006,0.000022121261,0.0000044855374],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99494106,0.00077132083,0.0014355596,0.0018287549,0.00048825744,0.0005350589],"domain_scores_gemma":[0.9767498,0.0011993247,0.0008833373,0.020896897,0.0001433217,0.00012734537],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0009642994,0.00075448444,0.0031993852,0.0009956755,0.000058575184,0.00009437616,0.055996325,0.0003177249,0.000006199368],"category_scores_gemma":[0.0029327236,0.00069716846,0.00023855522,0.0009917808,0.0003192179,0.0007659071,0.26411125,0.001133394,0.0000040508185],"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":[4.8091607e-7,0.00007448291,1.578107e-7,0.20729554,0.000047572223,0.000029139823,0.000045830457,7.969152e-9,1.7181936e-9,0.002576415,0.1068468,0.6830836],"study_design_scores_gemma":[0.00013327821,0.000028318607,0.0000043357863,0.23776089,0.00010787188,0.000037053436,0.000004367817,0.0008809592,4.4678355e-8,0.007358098,0.7532836,0.0004011413],"about_ca_topic_score_codex":0.000016736181,"about_ca_topic_score_gemma":0.0000061151914,"teacher_disagreement_score":0.68268245,"about_ca_system_score_codex":0.00012917715,"about_ca_system_score_gemma":0.00059885986,"threshold_uncertainty_score":0.99954796},"labels":[],"label_agreement":null},{"id":"W4406979724","doi":"10.1016/j.cose.2025.104352","title":"Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Safeguarding; Computer security; Computer network; Medicine","score_opus":0.006667412404014703,"score_gpt":0.2311578254226256,"score_spread":0.22449041301861092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406979724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9614545,0.0049903747,0.025288505,0.00082768366,0.00033058418,0.004840914,0.0000109022885,0.0010086704,0.0012478401],"genre_scores_gemma":[0.9966188,0.00012793887,0.0024652067,0.00023692405,0.000053503143,0.00043811995,0.000020985013,0.000027922912,0.000010588483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881727,0.00012782088,0.00031402515,0.00031726563,0.00009585646,0.00032777016],"domain_scores_gemma":[0.9990211,0.000220927,0.000045005796,0.0005289568,0.00006355382,0.00012044833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025021465,0.00025290603,0.00034605732,0.00009899646,0.00018561266,0.00019174321,0.00028383906,0.00014781156,0.000008373574],"category_scores_gemma":[0.000028709585,0.00028109798,0.000053754447,0.0002327891,0.000130361,0.00017401103,0.0003539783,0.00042731085,0.0000045510624],"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":[0.0002948484,0.00057954685,0.041301265,0.0051923855,0.0026349353,0.00034070268,0.01569707,0.5040125,0.0050665173,0.039961845,0.10724611,0.27767226],"study_design_scores_gemma":[0.0010072656,0.00004711667,0.005839112,0.0006120104,0.00004225782,0.00004726873,0.00008381693,0.9497762,0.0006830115,0.00175922,0.039734524,0.000368253],"about_ca_topic_score_codex":0.000023684539,"about_ca_topic_score_gemma":0.000058139405,"teacher_disagreement_score":0.44576365,"about_ca_system_score_codex":0.000100294834,"about_ca_system_score_gemma":0.0000263046,"threshold_uncertainty_score":0.9999641},"labels":[],"label_agreement":null},{"id":"W4408932676","doi":"10.1016/j.cose.2025.104452","title":"A comprehensive review of security vulnerabilities in heavy-duty vehicles: Comparative insights and current research gaps","year":2025,"lang":"en","type":"review","venue":"Computers & Security","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","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 Windsor","funders":"FedDev Ontario; Mitacs; University of Windsor","keywords":"Current (fluid); Computer security; Computer science; Business; Engineering","score_opus":0.0644500230276165,"score_gpt":0.3718681093338216,"score_spread":0.3074180863062051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408932676","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007833019,0.9946086,0.00006490917,0.000022927694,0.00093543384,0.0029251908,0.00013532629,0.0001598287,0.0003645215],"genre_scores_gemma":[0.0016699743,0.99742216,0.00016296485,0.000036594167,0.00018526505,0.00022734216,0.00023837172,0.00005450975,0.000002823224],"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","domain_scores_codex":[0.99383265,0.0020627498,0.0016426487,0.00094248634,0.0006941505,0.00082529575],"domain_scores_gemma":[0.99521196,0.0028233589,0.00024111447,0.0010105056,0.00045802712,0.00025503614],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0011072893,0.0008971383,0.004482501,0.000714703,0.00012840104,0.0000758873,0.0008169989,0.000413374,0.000012765249],"category_scores_gemma":[0.00009627349,0.0008505576,0.000506581,0.0017257755,0.0005469054,0.00018310544,0.00078251027,0.0031982497,0.000014553142],"study_design_candidate":"systematic_review","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":[0.000010729142,0.00019077695,0.0000038854964,0.81842434,0.0002567943,0.00003975222,0.0033513608,0.001833443,1.00052446e-7,0.00082156126,0.020933628,0.15413363],"study_design_scores_gemma":[0.00025220268,0.000057665708,0.00000617301,0.29437125,0.00013701656,0.000016934866,0.000069528054,0.013267748,0.0000016812014,0.0010876992,0.6902264,0.0005057511],"about_ca_topic_score_codex":0.00006690736,"about_ca_topic_score_gemma":0.000082300554,"teacher_disagreement_score":0.66929275,"about_ca_system_score_codex":0.0005593993,"about_ca_system_score_gemma":0.00039392812,"threshold_uncertainty_score":0.99939454},"labels":[],"label_agreement":null},{"id":"W4408992490","doi":"10.1016/j.cose.2025.104456","title":"Security risk assessment in IoT environments: A taxonomy and survey","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":13,"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 TÉLUQ; University of Regina","funders":"","keywords":"Computer science; Taxonomy (biology); Internet of Things; Computer security; Data science; Ecology","score_opus":0.013860397758621372,"score_gpt":0.24886792276397496,"score_spread":0.23500752500535357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408992490","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5156667,0.0004922921,0.47577518,0.0005496785,0.005400879,0.00052422617,0.0000033803788,0.00014013692,0.0014474841],"genre_scores_gemma":[0.9839396,0.00006901161,0.015205962,0.000500049,0.00021877198,0.000026018086,0.000009304062,0.000008868768,0.000022451639],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99731326,0.00061667676,0.0004510333,0.0008479441,0.00024035628,0.00053073326],"domain_scores_gemma":[0.99844486,0.0005181198,0.00016069034,0.00070468226,0.00003306558,0.00013859254],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015647594,0.0002932052,0.00039579542,0.00026213742,0.0002466193,0.00031461043,0.00096149254,0.00012301713,0.0000018681766],"category_scores_gemma":[0.000056676246,0.00031384692,0.000075268516,0.00062170124,0.00011032393,0.0003124782,0.0015828515,0.0006362493,0.000010635612],"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":[0.000025897605,0.00073514367,0.86830753,0.0001158158,0.0001232547,0.00007445401,0.0029107952,0.0003323585,0.000023088718,0.0067085577,0.024990696,0.09565238],"study_design_scores_gemma":[0.0010096716,0.00006311483,0.6422047,0.00009270757,0.00000952549,0.0000055311975,0.000018033288,0.3033855,0.000050808496,0.011598606,0.041170742,0.00039108522],"about_ca_topic_score_codex":0.0008214453,"about_ca_topic_score_gemma":0.0001278024,"teacher_disagreement_score":0.46827284,"about_ca_system_score_codex":0.00021461814,"about_ca_system_score_gemma":0.00013179776,"threshold_uncertainty_score":0.99993134},"labels":[],"label_agreement":null},{"id":"W4412455291","doi":"10.1016/j.cose.2025.104581","title":"CSFuzzer: A grey-box fuzzer for network protocol using context-aware state feedback","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Major Scientific and Technological Innovation Project of Shandong Province; Natural Science Foundation of Shandong Province; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Fuzz testing; Context (archaeology); Protocol (science); State (computer science); Computer network; Computer security; Operating system; Algorithm; Software","score_opus":0.017292268293880215,"score_gpt":0.28025442315465576,"score_spread":0.26296215486077557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412455291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054842336,0.00010972478,0.9623544,0.00088462874,0.00279709,0.027548954,0.000011128009,0.00049969164,0.0003101267],"genre_scores_gemma":[0.72953445,0.000035271692,0.21099946,0.014027019,0.002934982,0.04163609,0.000038716298,0.00012755452,0.0006664653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997085,0.00023242756,0.00063480064,0.00091850763,0.0003109682,0.0008183023],"domain_scores_gemma":[0.998042,0.00027760683,0.0002753328,0.00085922464,0.00036252482,0.00018329395],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066911214,0.00039282918,0.0004904365,0.00018462108,0.0007255885,0.00054477237,0.0012482086,0.00019339041,0.000016484139],"category_scores_gemma":[0.000031283897,0.0003992605,0.00027973406,0.0010748897,0.00012527697,0.00077794195,0.0008424476,0.00042165027,0.000020057221],"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":[0.001129817,0.0009965642,0.0017106109,0.0014701057,0.00053675316,0.00006116404,0.0056048464,0.02919404,0.00027198088,0.15517604,0.27708462,0.52676344],"study_design_scores_gemma":[0.0018982022,0.00020187764,0.00032533292,0.0003734634,0.000019420802,0.000016874206,0.000018238725,0.65202904,0.00054939307,0.09170531,0.25237894,0.000483922],"about_ca_topic_score_codex":0.00008638257,"about_ca_topic_score_gemma":0.00009812995,"teacher_disagreement_score":0.75135493,"about_ca_system_score_codex":0.00018780134,"about_ca_system_score_gemma":0.00023112763,"threshold_uncertainty_score":0.9998459},"labels":[],"label_agreement":null},{"id":"W4412650851","doi":"10.1016/j.cose.2025.104607","title":"Cyber risk communication during vessel incident management: A case study","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Risk and Safety Analysis","field":"Decision Sciences","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":"Memorial University of Newfoundland","funders":"Høgskulen på Vestlandet","keywords":"Computer science; Computer security; Risk management; Incident management; Risk analysis (engineering); Business; Finance","score_opus":0.027215619470577566,"score_gpt":0.350944328732459,"score_spread":0.32372870926188146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412650851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98609304,0.0004470236,0.009563061,0.00069635327,0.00025950064,0.00036023388,0.0000053137232,0.00007076821,0.0025046733],"genre_scores_gemma":[0.99799085,0.00034837818,0.0011243342,0.00011150556,0.000023090777,0.000018168417,0.0000027456329,0.000005400229,0.0003755384],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969526,0.0009070032,0.00068187923,0.00055245287,0.0006891061,0.00021695129],"domain_scores_gemma":[0.99704003,0.00065611204,0.00026866328,0.0017574388,0.00019288814,0.00008485569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024710794,0.00016246414,0.000334544,0.00046898157,0.0008125819,0.00037466752,0.0012389973,0.000049623468,0.00004488627],"category_scores_gemma":[0.00015841701,0.00013875875,0.00018049746,0.0015573318,0.00008225458,0.00029455745,0.0012679374,0.00027184916,0.00010558266],"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":[0.00017818491,0.002791663,0.602583,0.000060102226,0.0014412487,0.005494211,0.056561187,0.009557193,0.0000057788025,0.0059179463,0.028506486,0.286903],"study_design_scores_gemma":[0.0031524843,0.00008304151,0.7483812,0.0000939763,0.0005996329,0.00018259366,0.0474386,0.060144793,0.000035842146,0.12456943,0.014714749,0.0006036454],"about_ca_topic_score_codex":0.0014909623,"about_ca_topic_score_gemma":0.0013946379,"teacher_disagreement_score":0.28629935,"about_ca_system_score_codex":0.000084431646,"about_ca_system_score_gemma":0.00001879561,"threshold_uncertainty_score":0.6249805},"labels":[],"label_agreement":null},{"id":"W4413241022","doi":"10.1016/j.cose.2025.104619","title":"Corrigendum to “Evaluation framework for quantum security risk assessment: A comprehensive strategy for quantum-safe transition” [Computers &amp; Security, 150, 104272]","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Université de Montréal","funders":"","keywords":"Computer science; Computer security; Quantum computer; Transition (genetics); Quantum; Chemistry; Quantum mechanics; Physics","score_opus":0.04603175982929746,"score_gpt":0.3381319900981257,"score_spread":0.29210023026882825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413241022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043552477,0.00031556757,0.93719673,0.004326514,0.008268704,0.0047506443,0.0005266325,0.00067249854,0.00039022937],"genre_scores_gemma":[0.84479004,0.00007243879,0.14739464,0.0060519436,0.00035654716,0.0007712107,0.0005086913,0.0000457113,0.000008770295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.993851,0.0006708811,0.0015157519,0.001586935,0.0011186361,0.0012568035],"domain_scores_gemma":[0.99381244,0.0012830182,0.0007127847,0.0016703636,0.0019698697,0.000551523],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0019340322,0.0008670175,0.0010393052,0.0009996415,0.0012579209,0.0012382236,0.0023118388,0.000516661,0.000038562812],"category_scores_gemma":[0.00018536618,0.0009503061,0.0009109318,0.0020835449,0.00021030169,0.001525013,0.00050261215,0.0009213965,0.000048012862],"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":[0.00019523683,0.00057281816,0.000112096364,0.0005449559,0.00034047573,0.0000028993952,0.011635391,0.0051887236,0.00002546948,0.8986135,0.06969321,0.013075235],"study_design_scores_gemma":[0.0016324504,0.0003718835,0.0006683962,0.00021088384,0.00010239972,0.000008006347,0.00036232613,0.5296637,0.00006902221,0.4321306,0.034178123,0.0006022216],"about_ca_topic_score_codex":0.00013433133,"about_ca_topic_score_gemma":0.00010470431,"teacher_disagreement_score":0.8012376,"about_ca_system_score_codex":0.00036995366,"about_ca_system_score_gemma":0.0006693803,"threshold_uncertainty_score":0.9997986},"labels":[],"label_agreement":null},{"id":"W4414136034","doi":"10.1016/j.cose.2025.104651","title":"Fuzzy to clear: Elucidating the threat hunter cognitive process and cognitive support needs","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","field":"Computer Science","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":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Process (computing); Cognition; Work (physics); Fuzzy cognitive map; Honeypot; Fuzzy logic","score_opus":0.00807541147782273,"score_gpt":0.2662565501271994,"score_spread":0.25818113864937664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414136034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49577942,0.00021389392,0.42207536,0.007865516,0.0012655889,0.0014195814,0.000034871446,0.00048590198,0.07085985],"genre_scores_gemma":[0.9853889,0.000007729962,0.00058699475,0.013800408,0.00006696359,0.000041956835,0.000014487663,0.0000072478415,0.000085353],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99843526,0.000114825525,0.00034791295,0.00039773696,0.00029787282,0.00040639966],"domain_scores_gemma":[0.99870485,0.00033668402,0.00011210478,0.00031707424,0.00037933787,0.00014995513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048037598,0.00024434322,0.00024476185,0.0002223063,0.00047775652,0.0005462271,0.0007710282,0.0000836924,0.000013591626],"category_scores_gemma":[0.00009106999,0.00019622689,0.000075175594,0.00093473,0.00014105007,0.0008410479,0.0008512329,0.0003492565,0.00008207718],"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":[0.00026582266,0.00037453984,0.0077657187,0.000373971,0.00037153577,0.000054634318,0.2793045,0.00003218563,0.000014983638,0.34609586,0.015055036,0.35029122],"study_design_scores_gemma":[0.02056616,0.003996976,0.25266075,0.0059473617,0.00089608034,0.0008366851,0.10392436,0.2524414,0.019468322,0.26099464,0.07040276,0.007864534],"about_ca_topic_score_codex":0.000027684584,"about_ca_topic_score_gemma":0.000026565027,"teacher_disagreement_score":0.48960942,"about_ca_system_score_codex":0.00003119101,"about_ca_system_score_gemma":0.00012740788,"threshold_uncertainty_score":0.80019003},"labels":[],"label_agreement":null},{"id":"W4414920902","doi":"10.1016/j.cose.2025.104696","title":"Operationalizing cybersecurity knowledge: Design, implementation &amp; evaluation of a knowledge management system for CACAO playbooks","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Digital Technology Supercluster; Research and Innovation Foundation; European Commission","keywords":"Interoperability; Workflow; Automation; Operationalization; Orchestration; Software deployment; Standardization; Key (lock)","score_opus":0.1170461048327486,"score_gpt":0.3783388227340357,"score_spread":0.26129271790128705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414920902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0645456,0.0019663668,0.8985391,0.00037131595,0.0043799137,0.0047203954,0.00006096429,0.0002991592,0.025117176],"genre_scores_gemma":[0.99460065,0.000010302015,0.003942893,0.0001723767,0.0004364084,0.0003071499,0.00043639002,0.000018085144,0.00007576422],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820817,0.00008017069,0.00058836746,0.0004849898,0.00034924716,0.00028906393],"domain_scores_gemma":[0.9978876,0.00013570167,0.0002704515,0.00037890847,0.0013123379,0.000014986569],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020504042,0.00024908074,0.000306017,0.0004395246,0.00033796806,0.00023447865,0.00046468226,0.000089391964,0.000059556056],"category_scores_gemma":[0.000041787353,0.0002566173,0.00011738079,0.0006462148,0.000064687236,0.0007580879,0.0003754671,0.000094782765,0.000054906963],"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":[0.00019780896,0.00064053835,0.001160581,0.0076778512,0.0004086136,0.0000014675968,0.0015329282,0.0016434367,0.0004028222,0.71251196,0.12027925,0.15354276],"study_design_scores_gemma":[0.006171665,0.00004064434,0.005915088,0.0026619758,0.0019561583,0.000004656006,0.0038647573,0.4780728,0.00416732,0.02976566,0.46612167,0.0012576148],"about_ca_topic_score_codex":0.00016651314,"about_ca_topic_score_gemma":0.00041437836,"teacher_disagreement_score":0.930055,"about_ca_system_score_codex":0.00023422114,"about_ca_system_score_gemma":0.00012579854,"threshold_uncertainty_score":0.9999886},"labels":[],"label_agreement":null},{"id":"W4416399167","doi":"10.1016/j.cose.2025.104749","title":"A formal approach for security pattern enforcement in software architecture","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Information and Cyber Security","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":"University of Ottawa; Université du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software security assurance; Security testing; Usability; Computer security model; Enforcement; Software; Security service; Software architecture; Security information and event management","score_opus":0.007592628939522522,"score_gpt":0.2313591017741829,"score_spread":0.22376647283466036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416399167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006725279,0.0001233458,0.98581827,0.0007040296,0.00077005225,0.0010184252,0.000025059962,0.00031968675,0.0044958834],"genre_scores_gemma":[0.9375796,0.000008033375,0.058189195,0.003838162,0.00007915923,0.00017086971,0.00009165087,0.000009812498,0.000033544664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976604,0.00009565349,0.00059227395,0.00057632953,0.00032997128,0.00074536924],"domain_scores_gemma":[0.99866897,0.00014148027,0.00014821057,0.0007726519,0.00013442311,0.00013428772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065993203,0.0003293393,0.00038270475,0.00037669443,0.00025579636,0.00029139995,0.0015089219,0.00017267559,0.00000947984],"category_scores_gemma":[0.000038934504,0.0003254467,0.0002195072,0.00065657665,0.00006519864,0.0008869984,0.00087217835,0.00045522756,0.000007730002],"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":[0.00013018998,0.0010901,0.0030140548,0.001621376,0.00015073128,0.000019360845,0.054974467,0.0073655066,0.0000067263622,0.5304729,0.022922017,0.37823257],"study_design_scores_gemma":[0.0035367394,0.00016616542,0.0018405452,0.0001413018,0.000016018263,0.000020543894,0.00025008628,0.8562168,0.00033072423,0.08481194,0.051901136,0.0007680327],"about_ca_topic_score_codex":0.00010324091,"about_ca_topic_score_gemma":0.000085805375,"teacher_disagreement_score":0.9308543,"about_ca_system_score_codex":0.00016711102,"about_ca_system_score_gemma":0.00016216999,"threshold_uncertainty_score":0.9999198},"labels":[],"label_agreement":null},{"id":"W4416679660","doi":"10.1016/j.cose.2025.104759","title":"Plug and prey: Exploiting design flaws to hijack EV charging stations","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Electric Vehicles and Infrastructure","field":"Engineering","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":"Concordia University","funders":"Canada Foundation for Innovation","keywords":"Exploit; Software deployment; Host (biology); Threat model; Plug and play; Power (physics); Plug-in; Inductive charging","score_opus":0.00775121980768788,"score_gpt":0.2131196533605201,"score_spread":0.2053684335528322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416679660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40852505,0.00069048407,0.58875364,0.00034156494,0.00034741504,0.00026384435,0.000006040915,0.00032837442,0.0007435728],"genre_scores_gemma":[0.98002756,0.00005314127,0.019426556,0.00035326072,0.00008650541,0.0000137661855,0.0000050109225,0.000015300524,0.000018899389],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993204,0.000025673364,0.00015569873,0.00018213701,0.00007248197,0.00024360699],"domain_scores_gemma":[0.9996404,0.00009352476,0.00001561054,0.0001374938,0.000030791274,0.000082165025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009648681,0.00013029712,0.00014511106,0.00010639589,0.00011211413,0.00008354391,0.00012184618,0.000049584833,0.000010942468],"category_scores_gemma":[0.000014850738,0.000141335,0.000025839428,0.00028568503,0.000012782872,0.00012471089,0.000046285248,0.00017680807,0.000006036579],"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":[0.000044300607,0.00006873438,0.005310966,0.0009714822,0.0003933558,0.00003899725,0.022047779,0.34888127,0.01651428,0.021684714,0.24075551,0.34328863],"study_design_scores_gemma":[0.00054514984,0.00006628061,0.008426657,0.00024371047,0.000030702155,0.000009280746,0.00018073917,0.95538956,0.0094742915,0.010946958,0.014248865,0.00043781596],"about_ca_topic_score_codex":0.000009146086,"about_ca_topic_score_gemma":0.000004881934,"teacher_disagreement_score":0.6065083,"about_ca_system_score_codex":0.00006236084,"about_ca_system_score_gemma":0.0000182211,"threshold_uncertainty_score":0.57634735},"labels":[],"label_agreement":null},{"id":"W640039046","doi":"10.1016/j.cose.2015.05.009","title":"Reconciling user privacy and implicit authentication for mobile devices","year":2015,"lang":"en","type":"article","venue":"Computers & Security","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Internet privacy; Computer security; Authentication (law)","score_opus":0.034651572469217136,"score_gpt":0.28793528210270175,"score_spread":0.2532837096334846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W640039046","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62840766,0.000551807,0.36779013,0.0011655908,0.00090017065,0.0008050795,0.0000068184722,0.00027924203,0.00009352289],"genre_scores_gemma":[0.98564506,0.000009287142,0.013637084,0.00042235153,0.00012262141,0.000089409914,0.000012218049,0.000011087714,0.000050868843],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857473,0.00008748242,0.0003330793,0.0005113756,0.00021980691,0.00027352542],"domain_scores_gemma":[0.99855936,0.00016735651,0.00014819264,0.00061709626,0.0002562414,0.00025175713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006640832,0.00016107122,0.00022189149,0.00009608741,0.00013689883,0.0003747019,0.00075144204,0.00007686701,0.0000017405633],"category_scores_gemma":[0.000058012374,0.00015899431,0.00006570145,0.00020087627,0.000044431632,0.00061089103,0.00030747347,0.00009083397,0.00002619465],"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":[0.000064858694,0.0007473996,0.019471366,0.0007481742,0.00023051606,0.000008787349,0.6491851,0.000057209996,0.0009979993,0.2265519,0.03238546,0.06955122],"study_design_scores_gemma":[0.0014116926,0.00023108385,0.0044513033,0.00007300911,0.000026575644,0.000042416,0.0004546368,0.757117,0.00054908847,0.032991286,0.20214646,0.0005054941],"about_ca_topic_score_codex":0.00003101401,"about_ca_topic_score_gemma":0.000015803142,"teacher_disagreement_score":0.75705975,"about_ca_system_score_codex":0.000058884903,"about_ca_system_score_gemma":0.00008098442,"threshold_uncertainty_score":0.64836},"labels":[],"label_agreement":null},{"id":"W7081970936","doi":"10.1016/j.cose.2025.104656","title":"An anomaly detection based approach for continuous authentication with smartwatch inertial sensors","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Geochemistry and Geologic Mapping","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Authentication (law); Discriminative model; Smartwatch; Pipeline (software); Anomaly detection; Convolutional neural network; Inertial measurement unit","score_opus":0.006668589315742503,"score_gpt":0.21624973356404012,"score_spread":0.20958114424829763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7081970936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16100292,0.000011166326,0.8365682,0.0009436456,0.00021295744,0.0003314062,0.0000019315494,0.00023504508,0.00069274736],"genre_scores_gemma":[0.9294478,2.5210923e-7,0.070078135,0.0002481999,0.0000671019,0.000051060964,0.000028214583,0.0000027150604,0.00007648445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878633,0.00008588468,0.00019075,0.0005497473,0.00012319672,0.00026406816],"domain_scores_gemma":[0.9989636,0.00008987117,0.00009410214,0.0005665712,0.00021277058,0.00007306988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002983913,0.00016141421,0.00018583989,0.00007927835,0.00020720829,0.00015587438,0.0005465849,0.00009717867,0.0000016218437],"category_scores_gemma":[0.000033292672,0.00014661596,0.00006144784,0.00035139194,0.000063065796,0.00020898336,0.00007793736,0.0001243919,0.0000010532266],"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":[0.0026591162,0.008811864,0.051094156,0.004582555,0.0009981669,0.00010904302,0.021777716,0.29308018,0.15485194,0.12404761,0.013155913,0.32483172],"study_design_scores_gemma":[0.00071509014,0.00016736846,0.0025818185,0.000020060628,0.000017453076,0.0000072789057,0.00003682783,0.9726667,0.018720562,0.0015687363,0.00331079,0.00018730902],"about_ca_topic_score_codex":0.000036520298,"about_ca_topic_score_gemma":0.000010442681,"teacher_disagreement_score":0.7684449,"about_ca_system_score_codex":0.000035649296,"about_ca_system_score_gemma":0.00006668057,"threshold_uncertainty_score":0.5978825},"labels":[],"label_agreement":null},{"id":"W7082625108","doi":"10.1016/j.cose.2025.104658","title":"PRIVIUM: A differentiated privacy-privilege model for user security and safety in the metaverse","year":2025,"lang":"en","type":"article","venue":"Computers & Security","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Metaverse; Privilege (computing); Anonymity; Threat model; Variety (cybernetics); Access control","score_opus":0.013083125505643093,"score_gpt":0.2372322495136014,"score_spread":0.2241491240079583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7082625108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18645447,0.00017663362,0.79990274,0.010645302,0.0003522309,0.0008691988,0.000016011227,0.00015582294,0.001427586],"genre_scores_gemma":[0.9908064,0.000027935776,0.007811656,0.0011455626,0.000028567558,0.00004902851,0.000014315533,0.0000027759766,0.00011374082],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826074,0.00016239403,0.0003400999,0.0006373891,0.00017929792,0.00042009127],"domain_scores_gemma":[0.99848205,0.00038899007,0.00009815863,0.0008648441,0.00009782445,0.00006814975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072467554,0.0002446628,0.00031744153,0.00009198222,0.00023297468,0.00017920618,0.0015790719,0.00013067127,0.0000034853042],"category_scores_gemma":[0.00013317588,0.00019165041,0.00011664739,0.00043718313,0.00009944282,0.0002591707,0.0011440847,0.00031639836,0.0000016310298],"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":[0.0003204274,0.0018987857,0.016065577,0.0017596778,0.00043062132,0.00008977957,0.08240761,0.00898468,0.00056507543,0.82410616,0.0524237,0.010947879],"study_design_scores_gemma":[0.0009274211,0.00002139101,0.0043696365,0.000056828078,0.000018883136,0.0000069909447,0.00006174294,0.8436277,0.00017747039,0.1355896,0.014942353,0.00019996711],"about_ca_topic_score_codex":0.000020537223,"about_ca_topic_score_gemma":0.00004347242,"teacher_disagreement_score":0.83464307,"about_ca_system_score_codex":0.00003942541,"about_ca_system_score_gemma":0.00007366242,"threshold_uncertainty_score":0.7815277},"labels":[],"label_agreement":null}]}