{"meta":{"query_hash":"8a144f749737","filters":{"venue":"Digital Threats Research and Practice"},"cohort_total":17,"direct_labels_cover":0,"predictions_cover":17,"exported":17,"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/8a144f749737","api":"https://metacan.xera.ac/api/v1/cohort?venue=Digital+Threats+Research+and+Practice"},"results":[{"id":"W2890128393","doi":"10.1145/3372802","title":"The Sorry State of TLS Security in Enterprise Interception Appliances","year":2020,"lang":"en","type":"preprint","venue":"Digital Threats Research and Practice","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":9,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer security; Transport Layer Security; Downgrade; Network security; Malware; Web application security; Man-in-the-middle attack; Encryption; Computer network; Database; World Wide Web; Web service; Web development","score_opus":0.0965448298557313,"score_gpt":0.3915280326285037,"score_spread":0.2949832027727724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890128393","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9551359,0.00027870235,0.035330273,0.00038808683,0.000012072082,0.000089989946,0.00023045797,0.001231245,0.0073032808],"genre_scores_gemma":[0.9913771,0.00016422493,0.0068833055,0.000052633004,0.0000077476,0.00003168108,0.00020906783,0.00011951584,0.001154672],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982344,0.000301126,0.000065014836,0.00026932434,0.0008089303,0.00032122355],"domain_scores_gemma":[0.9962852,0.0010812762,0.0006424946,0.0009480846,0.00090412004,0.00013874561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015992816,0.00039190202,0.00042993407,0.001737959,0.0009009495,0.002082743,0.0010214229,0.000938962,0.0011768232],"category_scores_gemma":[0.0032700326,0.00037977012,0.00053738727,0.0015557159,0.0019238072,0.0037027206,0.0009642407,0.0012597266,0.00040646148],"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":[0.000685004,0.0007713423,0.46359643,0.00048736716,0.00028586644,0.0025499586,0.0058588204,0.21360071,0.07341886,0.060026176,0.0069490266,0.17177041],"study_design_scores_gemma":[0.000030045743,0.0005841773,0.3043018,0.00025137997,0.00016648797,0.001920307,0.0047765067,0.6059692,0.041107144,0.023607822,0.017128447,0.00015672199],"about_ca_topic_score_codex":0.0062625986,"about_ca_topic_score_gemma":0.005420178,"teacher_disagreement_score":0.0062625986,"about_ca_system_score_codex":0.0017403249,"about_ca_system_score_gemma":0.0010647178,"threshold_uncertainty_score":0.012626946},"labels":[],"label_agreement":null},{"id":"W2970130226","doi":"10.1145/3468526","title":"A Chosen Random Value Attack on WPA3 SAE Authentication Protocol","year":2021,"lang":"en","type":"preprint","venue":"Digital Threats Research and Practice","topic":"Advanced Authentication Protocols Security","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Protocol (science); Computer science; Authentication protocol; Value (mathematics); Authentication (law); Computer security; Computer network; Medicine","score_opus":0.21197430654203295,"score_gpt":0.49699686810297017,"score_spread":0.2850225615609372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970130226","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25602224,0.00052909175,0.7083914,0.0019483288,0.00029642397,0.00048641284,0.00025807432,0.0031716574,0.028896336],"genre_scores_gemma":[0.96128076,0.00024481298,0.031834047,0.00027945967,0.00004627166,0.00018657939,0.00015874476,0.00010004014,0.0058692736],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9946266,0.0019572622,0.00034364732,0.0004485292,0.0020644406,0.00055956136],"domain_scores_gemma":[0.9958912,0.0016775838,0.00042910484,0.0014707287,0.00038927566,0.00014206422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019871688,0.0006574024,0.0006369247,0.0007652376,0.0009902909,0.0012275471,0.0008876493,0.0018918872,0.0022752115],"category_scores_gemma":[0.006996404,0.00032554846,0.0010548166,0.0007994408,0.0014854897,0.0033767247,0.0023697906,0.0017587871,0.001080251],"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":[0.0022284526,0.0002912281,0.007161997,0.0004460932,0.00046758103,0.009871205,0.0031921323,0.057029907,0.14937021,0.5987601,0.015094104,0.15608698],"study_design_scores_gemma":[0.0002445655,0.000713861,0.00297692,0.00012887537,0.00028433502,0.009610055,0.0004303481,0.6111778,0.1921819,0.13741429,0.04463108,0.00020597408],"about_ca_topic_score_codex":0.00048089414,"about_ca_topic_score_gemma":0.00020568355,"teacher_disagreement_score":0.0022752115,"about_ca_system_score_codex":0.00068149413,"about_ca_system_score_gemma":0.0005606544,"threshold_uncertainty_score":0.010509312},"labels":[],"label_agreement":null},{"id":"W3044848499","doi":"10.1145/3374136","title":"Threats to Online Advertising and Countermeasures","year":2020,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Nokia (Canada)","funders":"","keywords":"Online advertising; The Internet; Native advertising; Advertising; Advertising research; Business; Internet privacy; Transparency (behavior); Revenue; Brainstorming; Advertising campaign; Computer science; Computer security; Marketing; World Wide Web","score_opus":0.17212302339777757,"score_gpt":0.4375860902328107,"score_spread":0.2654630668350331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3044848499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14011507,0.12848803,0.4236212,0.041348353,0.0031128987,0.0016616491,0.00031854687,0.001966832,0.25936744],"genre_scores_gemma":[0.85171455,0.0465528,0.0813062,0.0035924304,0.001270416,0.00039674045,0.00019115477,0.00009554936,0.014880309],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.995572,0.0016273264,0.00023570949,0.0003935873,0.0017942208,0.00037716466],"domain_scores_gemma":[0.9890809,0.0065300157,0.0012454488,0.001223621,0.0016969391,0.00022299972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030941379,0.0010767976,0.00052552496,0.0052007264,0.0019226879,0.0048130313,0.001291356,0.003398605,0.002067925],"category_scores_gemma":[0.011718905,0.0004510984,0.00090348645,0.0015622097,0.002722985,0.0063447813,0.0021524716,0.0025193153,0.00087377056],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009913203,0.0003860562,0.009919992,0.0017222605,0.00008089963,0.0014339304,0.0043258765,0.011873506,0.007700828,0.39468157,0.01645335,0.55132264],"study_design_scores_gemma":[0.000048867558,0.0008018158,0.010891016,0.006549444,0.00029373236,0.0115848575,0.012375767,0.11292882,0.03137411,0.42018184,0.39268234,0.00028737797],"about_ca_topic_score_codex":0.00087600725,"about_ca_topic_score_gemma":0.000660608,"teacher_disagreement_score":0.0052007264,"about_ca_system_score_codex":0.0012626312,"about_ca_system_score_gemma":0.0014660864,"threshold_uncertainty_score":0.016363561},"labels":[],"label_agreement":null},{"id":"W3114029772","doi":"10.1145/3416124","title":"Securing Applications against Side-channel Attacks through Resource Access Veto","year":2020,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Concordia University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Permission; Android (operating system); Computer science; Password; SwIPe; Side channel attack; Computer security; Access control; Accelerometer; Pointer (user interface); Mobile device; Operating system; Cryptography; Computer hardware","score_opus":0.2139835394519516,"score_gpt":0.4519402485151255,"score_spread":0.2379567090631739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114029772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2821766,0.0037311069,0.5877029,0.0013938812,0.000628495,0.0018404813,0.00040234305,0.09489754,0.027226632],"genre_scores_gemma":[0.95289034,0.0006195571,0.035621487,0.0010010847,0.00019559085,0.00045988717,0.0002443839,0.0023138179,0.006653851],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99310267,0.0015522607,0.0006626915,0.00094674516,0.0023488358,0.0013867678],"domain_scores_gemma":[0.97907215,0.0044129076,0.0021729732,0.012137978,0.0015693113,0.00063457876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031195856,0.0018745913,0.001253353,0.0013194524,0.0012666266,0.0029565266,0.0024398724,0.0019460716,0.003729156],"category_scores_gemma":[0.0149136335,0.0013567946,0.0010800116,0.00044155083,0.0027747417,0.0067305397,0.008977373,0.0037901464,0.0027669917],"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":[0.0046547065,0.0014572672,0.044569485,0.0018176901,0.000527432,0.005483306,0.006440609,0.02657343,0.3068655,0.08186617,0.03517487,0.48456952],"study_design_scores_gemma":[0.0004322388,0.0020678563,0.020334518,0.0011735617,0.0006752624,0.006976214,0.0012503405,0.41698736,0.35478044,0.06063882,0.13390173,0.00078164134],"about_ca_topic_score_codex":0.00087384635,"about_ca_topic_score_gemma":0.00073727116,"teacher_disagreement_score":0.003729156,"about_ca_system_score_codex":0.00062360876,"about_ca_system_score_gemma":0.0015499583,"threshold_uncertainty_score":0.016498148},"labels":[],"label_agreement":null},{"id":"W3125882489","doi":"10.1145/3419474","title":"Stealthy Attacks against Robotic Vehicles Protected by Control-based Intrusion Detection Techniques","year":2021,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Smart Grid Security and Resilience","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intrusion detection system; Computer science; Computer security; Software; Real-time computing; Intrusion; Actuator; Control (management); Command and control; Artificial intelligence; Operating system","score_opus":0.035959230158529934,"score_gpt":0.3288910315929537,"score_spread":0.29293180143442377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125882489","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6417048,0.00048346087,0.3448616,0.00043546534,0.00009137455,0.00020264852,0.000061871244,0.0048348876,0.007323984],"genre_scores_gemma":[0.98345184,0.000061095765,0.015840229,0.000058148453,0.000006120671,0.000023801891,0.000021564372,0.000030699,0.0005065613],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989023,0.0002302668,0.000069286085,0.00013446492,0.0005124624,0.00015114002],"domain_scores_gemma":[0.9973283,0.0008340593,0.0005957096,0.0008004854,0.0003749646,0.00006644537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006000781,0.0005123397,0.0004185993,0.0007297168,0.00036955738,0.0005251286,0.0005434756,0.00047274586,0.00048992975],"category_scores_gemma":[0.0035552445,0.00016851242,0.00033308502,0.0002832248,0.0009544715,0.0009566565,0.0008493933,0.0004647222,0.00015131035],"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":[0.00058784656,0.0003648738,0.027738601,0.0003431512,0.00032234887,0.0014114272,0.0013608099,0.33546633,0.30072004,0.020192144,0.0034479334,0.3080445],"study_design_scores_gemma":[0.000037073827,0.00071426964,0.0075961137,0.00004876866,0.00007214775,0.0009452375,0.00027028008,0.8129139,0.16353601,0.0056185005,0.008200603,0.00004720874],"about_ca_topic_score_codex":0.0014132289,"about_ca_topic_score_gemma":0.0011515165,"teacher_disagreement_score":0.0014132289,"about_ca_system_score_codex":0.0004511504,"about_ca_system_score_gemma":0.00043878867,"threshold_uncertainty_score":0.0032733083},"labels":[],"label_agreement":null},{"id":"W3145691682","doi":"10.1145/3513025","title":"Analysis and Correlation of Visual Evidence in Campaigns of Malicious Office Documents","year":2022,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hatch (Canada)","funders":"Horizon 2020 Framework Programme; Generalitat de Catalunya; European Commission","keywords":"Computer science; Visual Basic for Applications; Malware; Task (project management); Payload (computing); Microsoft Office; Pipeline (software); Fingerprint (computing); Construct (python library); Database; Computer security; World Wide Web; Information retrieval; Operating system; Programming language; Engineering","score_opus":0.08699993772077706,"score_gpt":0.4363185021071302,"score_spread":0.3493185643863531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145691682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97970015,0.0010064037,0.012036538,0.00010710491,0.000052826355,0.00014422537,0.0012598247,0.0010218692,0.004671151],"genre_scores_gemma":[0.9880613,0.000296645,0.009230278,0.000026758178,0.000042271404,0.000031872216,0.0010173238,0.00009036882,0.0012030978],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99867475,0.00013272825,0.00009566524,0.00019271042,0.0007046702,0.00019943673],"domain_scores_gemma":[0.99140865,0.0031385482,0.0023892636,0.00073024427,0.0019386683,0.000394602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067603646,0.0005064829,0.0004574945,0.009256025,0.00041080077,0.0012399572,0.00026928782,0.0005146808,0.0014757442],"category_scores_gemma":[0.0065731644,0.00019941285,0.0002837827,0.002469647,0.00057386135,0.0009047528,0.00083847623,0.00044765687,0.0009932024],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002278868,0.0005148989,0.30885655,0.0013833763,0.00022458717,0.0046705026,0.0032715595,0.006733434,0.15810679,0.0028939212,0.007840907,0.50322473],"study_design_scores_gemma":[0.000043718956,0.0006422499,0.6628411,0.00041598233,0.00029426592,0.009641622,0.0028685376,0.09180036,0.21200904,0.0021224297,0.017150413,0.00017028431],"about_ca_topic_score_codex":0.0013323659,"about_ca_topic_score_gemma":0.0015310622,"teacher_disagreement_score":0.009256025,"about_ca_system_score_codex":0.00041514277,"about_ca_system_score_gemma":0.00040807817,"threshold_uncertainty_score":0.004936874},"labels":[],"label_agreement":null},{"id":"W3193794888","doi":"10.1145/3480468","title":"Risk-aware Fine-grained Access Control in Cyber-physical Contexts","year":2021,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Privacy, Security, and Data Protection","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Access control; Computer science; Context (archaeology); Heuristic; Authorization; Control (management); Physical access; Duration (music); Computer security; Risk analysis (engineering); Artificial intelligence; Business","score_opus":0.11587123307018454,"score_gpt":0.4651544534530713,"score_spread":0.3492832203828868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193794888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13399462,0.00032161313,0.86101437,0.00039706344,0.000038683756,0.00019630775,0.00016556519,0.0017708335,0.0021009955],"genre_scores_gemma":[0.8773097,0.00008408311,0.121789016,0.00007260275,0.000017635672,0.00006787784,0.00012379093,0.00005714885,0.00047803504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99424815,0.0018177433,0.0005381691,0.0013663898,0.0016148344,0.00041458182],"domain_scores_gemma":[0.9873102,0.0060261143,0.0020277596,0.0028941284,0.0011880198,0.0005537708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004314104,0.0005657773,0.00097369455,0.0015068853,0.0011239868,0.0023909858,0.0013694357,0.0009498481,0.0010089335],"category_scores_gemma":[0.014474506,0.00047469002,0.0010053297,0.00070422806,0.0019537795,0.0049254685,0.0030677405,0.002056149,0.0002606366],"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":[0.0005293909,0.0006315661,0.036702663,0.00030494411,0.00022071875,0.0004107007,0.001746298,0.59204423,0.025756216,0.05144922,0.0019868382,0.28821725],"study_design_scores_gemma":[0.000012948635,0.00006968222,0.0035050886,0.000026462263,0.000023501707,0.0001143384,0.00013440056,0.954318,0.0055477074,0.034796275,0.0014167993,0.000034807083],"about_ca_topic_score_codex":0.004491795,"about_ca_topic_score_gemma":0.006332989,"teacher_disagreement_score":0.004491795,"about_ca_system_score_codex":0.0014656557,"about_ca_system_score_gemma":0.002000261,"threshold_uncertainty_score":0.022815466},"labels":[],"label_agreement":null},{"id":"W3201826149","doi":"10.1145/3487060","title":"Lessons Learned: Analysis of PUF-based Authentication Protocols for IoT","year":2021,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","field":"Computer Science","cited_by":33,"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; Canada Research Chairs","keywords":"Authentication protocol; Computer science; Authentication (law); Computer security; Lightweight Extensible Authentication Protocol; Cryptographic protocol; Challenge–response authentication; Cryptography; Confidentiality; Internet of Things","score_opus":0.33917689553117003,"score_gpt":0.5122944211758724,"score_spread":0.1731175256447024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201826149","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013307023,0.04620923,0.8380223,0.027520722,0.0029374117,0.0002446789,0.00036842763,0.00054334966,0.0708469],"genre_scores_gemma":[0.4340393,0.12320665,0.38273084,0.010648337,0.009815304,0.0010537797,0.0008828068,0.0005914863,0.037031494],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.996846,0.00072662334,0.0002815214,0.00039337718,0.0015322346,0.00022023676],"domain_scores_gemma":[0.9906069,0.0058568553,0.0004047542,0.0010991961,0.0018894002,0.00014275078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034432968,0.0011993267,0.0011192892,0.002461213,0.0011266313,0.0031099305,0.0014197539,0.0031417697,0.005938699],"category_scores_gemma":[0.016111905,0.0006678346,0.001976309,0.0016431186,0.0030471177,0.011055131,0.0014348148,0.0060557104,0.002139007],"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":[0.000033856657,0.000053909076,0.0008812161,0.0006034908,0.000049328748,0.00038350408,0.0002888005,0.015990071,0.0015938093,0.91416657,0.008563735,0.057391677],"study_design_scores_gemma":[0.00001101172,0.0000919822,0.00045365168,0.0004888389,0.00003263533,0.0012123177,0.00018097355,0.07218615,0.0014759867,0.86993045,0.05387122,0.00006481416],"about_ca_topic_score_codex":0.0017713702,"about_ca_topic_score_gemma":0.0009603012,"teacher_disagreement_score":0.005938699,"about_ca_system_score_codex":0.0025964577,"about_ca_system_score_gemma":0.0022099412,"threshold_uncertainty_score":0.019866943},"labels":[],"label_agreement":null},{"id":"W3205695017","doi":"10.1145/3492327","title":"Detection of Anomalous Behavior of Smartphone Devices using Changepoint Analysis and Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Cistel Technology (Canada); University of Waterloo","funders":"","keywords":"Malware; Computer science; Spectrum analyzer; Software; Parametric statistics; Static analysis; Code (set theory); Data mining; Power consumption; Machine learning; Artificial intelligence; Power (physics); Operating system","score_opus":0.10152677251184548,"score_gpt":0.39555381262197675,"score_spread":0.29402704011013125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205695017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6260864,0.0009605374,0.3557783,0.00047632132,0.00012331808,0.00041145663,0.0019183992,0.010150113,0.004095179],"genre_scores_gemma":[0.9459895,0.00027017074,0.051468097,0.000059546554,0.000027523269,0.00011678924,0.00074315525,0.000103181505,0.0012220799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916255,0.000082055245,0.00007209652,0.00023720023,0.00038519214,0.000060949616],"domain_scores_gemma":[0.9974412,0.0009533569,0.0006181931,0.00031513657,0.00059169595,0.000080496466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003871547,0.0008526434,0.00064603984,0.004124905,0.00025765956,0.00073806976,0.0004898137,0.00045003387,0.0009623061],"category_scores_gemma":[0.0029722997,0.00020394746,0.00051233044,0.001708692,0.000298295,0.00095672,0.00037347662,0.0005291659,0.00063627487],"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":[0.00049903325,0.00054431317,0.16878167,0.000548956,0.00028281676,0.0018048243,0.0006912452,0.02561328,0.09168349,0.0014523311,0.004420416,0.70367765],"study_design_scores_gemma":[0.000030255842,0.0008157972,0.19931172,0.00012598753,0.00014124783,0.0031973182,0.00051418325,0.68606246,0.09818337,0.004080118,0.0074280347,0.00010959001],"about_ca_topic_score_codex":0.0016183456,"about_ca_topic_score_gemma":0.0020925705,"teacher_disagreement_score":0.004124905,"about_ca_system_score_codex":0.00043785584,"about_ca_system_score_gemma":0.00026693533,"threshold_uncertainty_score":0.0032192469},"labels":[],"label_agreement":null},{"id":"W3206777893","doi":"10.1145/3450972","title":"Informing Cyber Threat Intelligence through Dark Web Situational Awareness: The AZSecure Hacker Assets Portal","year":2021,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":25,"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":"National Science Foundation","keywords":"Hacker; Cyberspace; Situation awareness; Computer security; Scrutiny; Internet privacy; Cybercrime; Business; Computer science; World Wide Web; Engineering; The Internet; Political science; Law","score_opus":0.15484244695384128,"score_gpt":0.4235943036928133,"score_spread":0.26875185673897206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206777893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87209,0.0007347729,0.051451072,0.005730546,0.000102090344,0.0004841726,0.003905829,0.0070540756,0.058447514],"genre_scores_gemma":[0.95094067,0.0004542153,0.042772476,0.00034252292,0.000065638575,0.000087558634,0.0021825717,0.00017357488,0.002980721],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991974,0.00042698838,0.000042037515,0.00008179153,0.00018562526,0.000066233326],"domain_scores_gemma":[0.9957599,0.0018976047,0.0005718492,0.0009274445,0.00037459566,0.00046863954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014802804,0.00024160682,0.00018249011,0.0029523394,0.00054435205,0.0037924214,0.0003051676,0.00049161573,0.0016640763],"category_scores_gemma":[0.0042501027,0.00020807942,0.00012689266,0.0020424975,0.00067469093,0.005203428,0.0034023572,0.00092492515,0.0006470171],"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":[0.00049019774,0.0012456463,0.38279387,0.0004301756,0.00013029751,0.0015629047,0.021995421,0.004638986,0.016794471,0.028117478,0.03207094,0.5097297],"study_design_scores_gemma":[0.00012305142,0.00081840606,0.4503656,0.0008011092,0.00020691718,0.0022504213,0.0487625,0.10915439,0.033233248,0.08454805,0.2694436,0.00029268765],"about_ca_topic_score_codex":0.0022687907,"about_ca_topic_score_gemma":0.0038581425,"teacher_disagreement_score":0.0037924214,"about_ca_system_score_codex":0.00030629477,"about_ca_system_score_gemma":0.0007922529,"threshold_uncertainty_score":0.007828593},"labels":[],"label_agreement":null},{"id":"W4226126496","doi":"10.1145/3477403","title":"Randomized Moving Target Approach for MAC-Layer Spoofing Detection and Prevention in IoT Systems","year":2022,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Telus (Canada); York University","funders":"","keywords":"Spoofing attack; Computer science; Adversary; Authentication (law); Computer security; SIGNAL (programming language); Wireless; Computer network; Identity (music); Cryptography; Physical layer; IP address spoofing; Telecommunications; Internet Protocol","score_opus":0.1132527249028037,"score_gpt":0.3760468571103661,"score_spread":0.2627941322075624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226126496","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0089985505,0.00015865073,0.98954433,0.000077238095,0.00001963507,0.00003794813,0.000008397379,0.00015592141,0.0009992922],"genre_scores_gemma":[0.79029876,0.0003493758,0.2076022,0.00015386623,0.000036037836,0.00011137155,0.000032237844,0.000043239914,0.0013728754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985025,0.00059868954,0.000052541858,0.00021755762,0.0004918167,0.00013693591],"domain_scores_gemma":[0.997964,0.0012790741,0.00022515042,0.0002572363,0.0002172262,0.000057297526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016280023,0.00063963543,0.0006662358,0.00061741925,0.0003787348,0.00068030274,0.0008609495,0.000855233,0.0014430467],"category_scores_gemma":[0.004627552,0.00022860082,0.0005818357,0.00048276645,0.00084446376,0.001394731,0.0010301061,0.0012609535,0.0003055784],"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":[0.00031122612,0.00012134764,0.0012750247,0.0002124646,0.00010684022,0.00023892456,0.00018198346,0.7377593,0.04464823,0.10599501,0.0010735116,0.108076155],"study_design_scores_gemma":[0.000007265913,0.000100535835,0.00012364562,0.000007865419,0.00001061348,0.00008706403,0.000010828398,0.9888841,0.004215244,0.005998599,0.0005446705,0.000009623898],"about_ca_topic_score_codex":0.0005820495,"about_ca_topic_score_gemma":0.00049159554,"teacher_disagreement_score":0.0016280023,"about_ca_system_score_codex":0.00076380634,"about_ca_system_score_gemma":0.00070093386,"threshold_uncertainty_score":0.008609772},"labels":[],"label_agreement":null},{"id":"W4284886658","doi":"10.1145/3497862","title":"Toward Improving the Security of IoT and CPS Devices: An AI Approach","year":2022,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":15,"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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Higher Education and Scientific Research","keywords":"Computer science; Malware; Anomaly detection; Artificial intelligence; Histogram; Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Power (physics); Transformation (genetics); Convolutional neural network; Data mining; Image (mathematics); Real-time computing; Computer security","score_opus":0.12151095957414831,"score_gpt":0.39796211795317005,"score_spread":0.27645115837902173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284886658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026340252,0.0039438237,0.9516385,0.0045312364,0.0003218648,0.00021705143,0.00019169487,0.0025355094,0.010280038],"genre_scores_gemma":[0.5180549,0.0048529147,0.46826604,0.0013408945,0.0004352114,0.00019697743,0.00058829813,0.00022807524,0.0060367505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881953,0.00019860789,0.0000581942,0.00023030238,0.00058415474,0.00010915846],"domain_scores_gemma":[0.9974131,0.00095041154,0.00039279778,0.00051800295,0.0006282391,0.00009743784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015553042,0.0016736119,0.0009044334,0.0030233702,0.0006233191,0.002467814,0.0013731807,0.0016034021,0.0018402955],"category_scores_gemma":[0.0050582797,0.00050276256,0.0010077788,0.0011139691,0.0016442303,0.0047550057,0.0021256336,0.0028367254,0.0010810441],"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":[0.00017133162,0.0004985945,0.0104434015,0.0007589448,0.00022892973,0.00025100628,0.00028671586,0.10972956,0.04432811,0.056556407,0.0102214245,0.76652545],"study_design_scores_gemma":[0.00001784268,0.0003019087,0.0037347206,0.00023024628,0.00007880808,0.00035190058,0.00035600842,0.88766855,0.019633964,0.06852881,0.019045403,0.000051808158],"about_ca_topic_score_codex":0.001910154,"about_ca_topic_score_gemma":0.001492467,"teacher_disagreement_score":0.0030233702,"about_ca_system_score_codex":0.0010839641,"about_ca_system_score_gemma":0.0013641001,"threshold_uncertainty_score":0.008225381},"labels":[],"label_agreement":null},{"id":"W4285798199","doi":"10.1145/3499428","title":"CDNs’ Dark Side: Security Problems in CDN-to-Origin Connections","year":2022,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Man-in-the-middle attack; Computer security; Transport Layer Security; Certificate; Denial-of-service attack; Lightweight Directory Access Protocol; End user; The Internet; Server; Internet access; World Wide Web; Computer network; Authentication (law)","score_opus":0.13019357261167375,"score_gpt":0.3970022846225135,"score_spread":0.2668087120108398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285798199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9305134,0.001079638,0.04833293,0.0015193608,0.00022830068,0.00041377777,0.00018619416,0.0018583165,0.015868183],"genre_scores_gemma":[0.98419845,0.00024045334,0.013057174,0.00038432435,0.000033172197,0.00006211252,0.00014609219,0.0002196463,0.0016585103],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9901599,0.0021996428,0.0004957252,0.001426425,0.0043668714,0.0013514346],"domain_scores_gemma":[0.9804637,0.00783244,0.0020200675,0.004446487,0.004426774,0.0008106122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047087944,0.0009555352,0.0006940008,0.0015298328,0.0031177474,0.0033523776,0.0019537942,0.0026538027,0.0021921478],"category_scores_gemma":[0.022899946,0.0006781704,0.00055571424,0.0014657358,0.0036629357,0.009233925,0.003187887,0.0037400739,0.000672656],"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":[0.0032153972,0.0028888818,0.2240835,0.001396232,0.0004107325,0.017585853,0.016111521,0.18057252,0.09868109,0.12970632,0.034689303,0.29065865],"study_design_scores_gemma":[0.00042713736,0.002388612,0.060845368,0.0009793845,0.00052701106,0.017021319,0.010400083,0.58995914,0.20431875,0.04646354,0.0661159,0.00055374106],"about_ca_topic_score_codex":0.011251206,"about_ca_topic_score_gemma":0.0066754543,"teacher_disagreement_score":0.011251206,"about_ca_system_score_codex":0.0040106056,"about_ca_system_score_gemma":0.00179406,"threshold_uncertainty_score":0.029099107},"labels":[],"label_agreement":null},{"id":"W4294238081","doi":"10.1145/3559768","title":"APTHunter: Detecting Advanced Persistent Threats in Early Stages","year":2022,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Provenance; Adversarial system; Graph; Compromise; Computer security; Data mining; Theoretical computer science; Artificial intelligence","score_opus":0.09393009053068718,"score_gpt":0.39494365097289563,"score_spread":0.30101356044220845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294238081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09936485,0.0007746758,0.64563036,0.00082450703,0.00026202743,0.0008669675,0.0043132314,0.24397735,0.003986086],"genre_scores_gemma":[0.51870644,0.00043209927,0.46312255,0.0004857491,0.000082143815,0.00040085847,0.0066477284,0.0058634533,0.0042589684],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777347,0.00031027815,0.00014813874,0.0005432539,0.0010964798,0.00012844008],"domain_scores_gemma":[0.9930112,0.0028544767,0.0009224641,0.002283897,0.00066855265,0.00025939604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018180462,0.0014116034,0.0007290811,0.0024708384,0.00060519745,0.0014840771,0.0016506355,0.0010676602,0.0019969218],"category_scores_gemma":[0.011686608,0.00068922946,0.00085625995,0.00093640503,0.00096599106,0.0044685947,0.002551149,0.001971056,0.0009696508],"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":[0.0014174202,0.0010230795,0.06797296,0.0010958283,0.0004292434,0.0020103008,0.0023736348,0.07772291,0.11637724,0.015737634,0.054495454,0.6593444],"study_design_scores_gemma":[0.000105984676,0.00043621453,0.010117764,0.00010362495,0.00013040092,0.00085711986,0.0002067821,0.8169081,0.11725878,0.02255188,0.031178975,0.0001444409],"about_ca_topic_score_codex":0.003344326,"about_ca_topic_score_gemma":0.0048922882,"teacher_disagreement_score":0.003344326,"about_ca_system_score_codex":0.00070823944,"about_ca_system_score_gemma":0.0013350091,"threshold_uncertainty_score":0.009614885},"labels":[],"label_agreement":null},{"id":"W4377103668","doi":"10.1145/3592623","title":"Asm2Seq: Explainable Assembly Code Functional Summary Generation for Reverse Engineering and Vulnerability Analysis","year":2023,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Software Engineering Research","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Queen's University","funders":"","keywords":"Computer science; Reverse engineering; Firmware; Source code; Assembly language; Automatic summarization; Vulnerability (computing); Code review; Code (set theory); Static program analysis; Leverage (statistics); Software; Process (computing); Context (archaeology); Software engineering; Artificial intelligence; Programming language; Software development; Computer security; Operating system","score_opus":0.1742418370316224,"score_gpt":0.39432737323836237,"score_spread":0.22008553620673996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377103668","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04095696,0.0010890163,0.72778153,0.00070279435,0.00037745084,0.0007320956,0.030566795,0.19376093,0.0040325057],"genre_scores_gemma":[0.13888796,0.0003826679,0.75417036,0.00040464295,0.00013523415,0.00088201667,0.09349472,0.0058724363,0.0057699685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890804,0.00025393878,0.00009086213,0.0003868913,0.00028379727,0.00007646646],"domain_scores_gemma":[0.9966272,0.0014122095,0.00026985767,0.0008131506,0.00078045635,0.0000972435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011648529,0.002462589,0.0006603137,0.0029350522,0.0005642287,0.0009635,0.0018095183,0.0013554139,0.009511383],"category_scores_gemma":[0.0075865514,0.0004981758,0.0013322174,0.0013022132,0.0004584151,0.001707744,0.0016486022,0.0014077794,0.005161437],"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":[0.0007554044,0.00035798308,0.007751052,0.0014468916,0.0002066531,0.00093774084,0.00082653674,0.02903477,0.041298334,0.006006016,0.15153341,0.7598452],"study_design_scores_gemma":[0.00025559665,0.0005678038,0.007251113,0.00013989976,0.00017384574,0.0010019896,0.00040956054,0.77182007,0.08632068,0.020591069,0.1113254,0.0001429465],"about_ca_topic_score_codex":0.004723047,"about_ca_topic_score_gemma":0.010287296,"teacher_disagreement_score":0.009511383,"about_ca_system_score_codex":0.0007660277,"about_ca_system_score_gemma":0.0014552369,"threshold_uncertainty_score":0.031818748},"labels":[],"label_agreement":null},{"id":"W4412792239","doi":"10.1145/3754456","title":"Every Breath You Don’t Take: Deepfake Speech Detection Using Breath","year":2025,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science","score_opus":0.0860815806185097,"score_gpt":0.3941075366942001,"score_spread":0.3080259560756904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412792239","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69445336,0.0032603373,0.27363196,0.002441285,0.0010825266,0.0001951141,0.0028569645,0.009714017,0.012364399],"genre_scores_gemma":[0.93554986,0.00045660298,0.052177962,0.0005687439,0.00011351568,0.0000497802,0.0023770863,0.00021077608,0.008495658],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995346,0.00009327273,0.000021991014,0.00010942273,0.00015218944,0.00008850588],"domain_scores_gemma":[0.99917114,0.00038380915,0.00007374658,0.00011126611,0.00017598688,0.00008401947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066728424,0.0007737332,0.00046223658,0.0008342544,0.0003301222,0.0008410901,0.00052298023,0.00089096033,0.0018620968],"category_scores_gemma":[0.0027004594,0.00018962537,0.0003654473,0.00027373002,0.0004279571,0.0013120064,0.0012935725,0.0012888857,0.0015430211],"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":[0.0018133101,0.00047758827,0.028762395,0.00038488215,0.00016458513,0.0014988541,0.00063259964,0.044654682,0.06921334,0.0034822777,0.027467323,0.8214482],"study_design_scores_gemma":[0.00007455389,0.0005223973,0.015569822,0.000146277,0.00008682824,0.0013202449,0.0006475409,0.8645956,0.09418638,0.0073980135,0.0153600415,0.00009234254],"about_ca_topic_score_codex":0.0030126686,"about_ca_topic_score_gemma":0.005862778,"teacher_disagreement_score":0.0030126686,"about_ca_system_score_codex":0.00044235995,"about_ca_system_score_gemma":0.0005746183,"threshold_uncertainty_score":0.0062294006},"labels":[],"label_agreement":null},{"id":"W4417260753","doi":"10.1145/3785006","title":"Hybrid Machine Learning–Based Trust Management Approach to Secure the Mobile Crowdsourcing","year":2025,"lang":"en","type":"article","venue":"Digital Threats Research and Practice","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Queen's University; Queen's University Belfast","keywords":"Crowdsourcing; Credibility; Trust management (information system); Mobile computing; Mobile device; Reliability (semiconductor); Cornerstone; Variety (cybernetics)","score_opus":0.0421007979318442,"score_gpt":0.343206615330841,"score_spread":0.30110581739899683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417260753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040391292,0.0003563378,0.9544535,0.00084839325,0.00010174026,0.00015458127,0.00005551901,0.00053434167,0.0031042881],"genre_scores_gemma":[0.9377352,0.00012153573,0.058956854,0.00014722314,0.000087250446,0.00011652072,0.0000624416,0.000024592053,0.0027483096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963678,0.0014702802,0.00027115273,0.0006975547,0.0008006959,0.0003924965],"domain_scores_gemma":[0.99513984,0.002018441,0.00085730746,0.00045567268,0.0012795656,0.00024912288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037434353,0.00081452494,0.0013457009,0.0010409273,0.0012394913,0.0015105493,0.0018677509,0.0012931759,0.0018464352],"category_scores_gemma":[0.007767446,0.00038445953,0.00084527966,0.00076414994,0.001063485,0.0026369472,0.0016948736,0.0015088494,0.00053295196],"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":[0.00054167456,0.00047026755,0.008132507,0.00024351168,0.0003386582,0.00062865915,0.0008594948,0.7031112,0.0064706574,0.033347726,0.004544469,0.24131115],"study_design_scores_gemma":[0.0000073575725,0.000040498195,0.00027194843,0.000008022509,0.000018440942,0.00003560532,0.000039231425,0.9934355,0.0005046149,0.0050955843,0.00053338433,0.0000098598875],"about_ca_topic_score_codex":0.0059219953,"about_ca_topic_score_gemma":0.0047809863,"teacher_disagreement_score":0.0059219953,"about_ca_system_score_codex":0.002045688,"about_ca_system_score_gemma":0.0018562353,"threshold_uncertainty_score":0.019797444},"labels":[],"label_agreement":null}]}