{"meta":{"query_hash":"cd3c7c5839b2","filters":{"venue":"International Journal of Digital Crime and Forensics"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/cd3c7c5839b2","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Digital+Crime+and+Forensics"},"results":[{"id":"W1968925786","doi":"10.4018/ijdcf.2013100104","title":"Audio Watermarking Scheme Using IMFs and HHT for Forensic Applications","year":2013,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"Advanced Steganography and Watermarking Techniques","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":"University of Northern British Columbia","funders":"","keywords":"Digital watermarking; Computer science; Hilbert–Huang transform; Watermark; Audio signal; Authentication (law); Speech recognition; SIGNAL (programming language); Fidelity; Frame (networking); Transformation (genetics); Computer security; Artificial intelligence; Computer vision; Image (mathematics); Speech coding; Telecommunications; Filter (signal processing)","score_opus":0.020115213863521265,"score_gpt":0.2751941016228266,"score_spread":0.2550788877593053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968925786","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":[0.059318233,0.00023974564,0.9392772,0.00035530602,0.00019161405,0.00016460287,0.0000104508135,0.000030406261,0.00041243993],"genre_scores_gemma":[0.7726426,0.00003355319,0.22702804,0.00012457768,0.00013458809,0.000007660163,0.0000043209698,0.000007084023,0.000017570224],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992429,0.000005304103,0.00029065908,0.00013625041,0.00018657515,0.00013829893],"domain_scores_gemma":[0.9990393,0.00006915434,0.00020929819,0.00010078614,0.00050591043,0.000075550975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008739718,0.00010574064,0.00013660362,0.00015578445,0.000078164805,0.000513502,0.00033374783,0.000040036273,6.5662533e-7],"category_scores_gemma":[0.00002033505,0.000085949156,0.00008267721,0.00006235251,0.00009713204,0.0018482617,0.0001627585,0.00008592902,4.891341e-7],"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":[0.00007065576,0.00009597025,0.011209611,0.00005722103,0.00037741844,0.00003460452,0.00065427064,0.000017900702,0.009026243,0.1754729,0.001121698,0.8018615],"study_design_scores_gemma":[0.0010028589,0.00028969298,0.0023193152,0.00017972427,0.000034113946,0.0015652368,0.00009191194,0.016814958,0.015759079,0.9461328,0.015404926,0.00040541586],"about_ca_topic_score_codex":0.0000022151087,"about_ca_topic_score_gemma":2.1010973e-7,"teacher_disagreement_score":0.8014561,"about_ca_system_score_codex":0.000020235324,"about_ca_system_score_gemma":0.000021043243,"threshold_uncertainty_score":0.49517116},"labels":[],"label_agreement":null},{"id":"W2023802354","doi":"10.4018/jdcf.2010100102","title":"Spatio-Temporal Just Noticeable Distortion Model Guided Video Watermarking","year":2010,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"Advanced Steganography and Watermarking 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":"Toronto Metropolitan University","funders":"","keywords":"Watermark; Digital watermarking; Computer science; Human visual system model; Just-noticeable difference; Luminance; Artificial intelligence; Computer vision; Distortion (music); Image (mathematics); Bandwidth (computing)","score_opus":0.025336832365517294,"score_gpt":0.2801453200958328,"score_spread":0.2548084877303155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023802354","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":[0.24931884,0.000029154025,0.7463022,0.0005030818,0.0010481838,0.000044819866,0.000010117002,0.0000533308,0.002690285],"genre_scores_gemma":[0.9331927,0.0000142975305,0.06637519,0.00013551905,0.00019540639,8.524203e-7,0.000011097336,0.000007760021,0.00006721626],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99891824,0.0000075869184,0.00040231372,0.00014379446,0.0003853567,0.00014272502],"domain_scores_gemma":[0.99893403,0.00003340012,0.0003029014,0.00015145776,0.0004906411,0.00008755104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001786033,0.00011972961,0.00014099268,0.00018139057,0.00006622644,0.00046952072,0.0005783244,0.00005924302,0.0000016496958],"category_scores_gemma":[0.000053569685,0.00009803232,0.00010960938,0.00006946943,0.00008360759,0.002560985,0.00017488944,0.00023373973,0.0000012238845],"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":[0.00051047327,0.0005006663,0.08009776,0.00006794436,0.00053550635,0.00065654237,0.003793667,0.0021743367,0.011902793,0.29974058,0.010649621,0.58937013],"study_design_scores_gemma":[0.0013136434,0.00039689746,0.0034889798,0.0002109856,0.000060486476,0.0019452935,0.000060366277,0.12296594,0.068666875,0.77919894,0.020897396,0.0007942101],"about_ca_topic_score_codex":0.0000035576027,"about_ca_topic_score_gemma":0.000003590099,"teacher_disagreement_score":0.68387383,"about_ca_system_score_codex":0.000023456414,"about_ca_system_score_gemma":0.00004318943,"threshold_uncertainty_score":0.45275992},"labels":[],"label_agreement":null},{"id":"W2982055583","doi":"10.4018/ijdcf.2020010105","title":"A Deep Learning Framework for Malware Classification","year":2019,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Illinois at Urbana-Champaign; Zayed University; University of Waterloo; Damascus University; Concordia University; York University; Harbin Institute of Technology; University of Alberta; Amrita Vishwa Vidyapeetham University; Institut national de recherche en informatique et en automatique (INRIA); University of Memphis; McGill University; Simon Fraser University; University of Manitoba; University of Ontario Institute of Technology","keywords":"Malware; Computer science; Artificial intelligence; Convolutional neural network; Machine learning; Deep learning; Support vector machine; Computer security","score_opus":0.01844777074460633,"score_gpt":0.29361333477540125,"score_spread":0.2751655640307949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982055583","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":[0.009713513,0.000090032074,0.9882118,0.00040013512,0.00060202007,0.00007483074,0.0000025061079,0.00004632948,0.0008588371],"genre_scores_gemma":[0.83901864,0.000025929769,0.16060941,0.000104701954,0.00013015121,0.0000020177447,0.000002609134,0.000007034634,0.00009952022],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99929124,0.000006716427,0.00024289411,0.000119827346,0.00025026794,0.00008904328],"domain_scores_gemma":[0.998797,0.00016573815,0.00028104964,0.00009085453,0.0006187893,0.000046565954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000094432806,0.000073778836,0.000106318876,0.00012232913,0.00002814605,0.000274306,0.000353741,0.0000519157,0.0000033735007],"category_scores_gemma":[0.00023820958,0.000066496825,0.00007967687,0.00006449253,0.000024688889,0.0012692829,0.00008209564,0.00016553474,0.0000056875083],"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":[0.000083132654,0.000030695173,0.0027871549,0.000011636764,0.00006902171,0.000009648551,0.00031249062,0.00014109758,0.0004432286,0.4227307,0.00013571137,0.57324547],"study_design_scores_gemma":[0.00063642854,0.0008808978,0.003699534,0.00013124719,0.000011852186,0.00045768617,0.00022099627,0.019426355,0.006955209,0.9453676,0.02196783,0.00024437267],"about_ca_topic_score_codex":2.2358897e-7,"about_ca_topic_score_gemma":1.2885526e-7,"teacher_disagreement_score":0.8293051,"about_ca_system_score_codex":0.000041204203,"about_ca_system_score_gemma":0.000022594304,"threshold_uncertainty_score":0.27116618},"labels":[],"label_agreement":null},{"id":"W2982241386","doi":"10.4018/ijdcf.2020010104","title":"Evaluation of Autopsy and Volatility for Cybercrime Investigation","year":2019,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"Digital and Cyber Forensics","field":"Computer Science","cited_by":1,"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":"Plug-in; Computer science; Computer security; Operating system","score_opus":0.028609485453444158,"score_gpt":0.27570102898582316,"score_spread":0.247091543532379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982241386","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":[0.97524923,0.00021848502,0.017517185,0.00034075012,0.00065099815,0.00014357349,0.000029137103,0.000008537182,0.005842107],"genre_scores_gemma":[0.9964884,0.0000063285343,0.003286439,0.000070870476,0.000067773275,9.699536e-7,0.000008227478,0.0000045821507,0.00006642733],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986874,0.0000152167995,0.0003646856,0.00012820392,0.0007217093,0.000082785555],"domain_scores_gemma":[0.9975617,0.00010030086,0.00026613232,0.000102192986,0.0019024692,0.00006721155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058058137,0.00008634351,0.00015529917,0.00009590703,0.000015439158,0.00022942293,0.00024114206,0.00003864966,0.0000020213477],"category_scores_gemma":[0.00020407124,0.000072327675,0.00007213474,0.00006347758,0.000090922054,0.0018074913,0.0001024534,0.000058763544,0.0000011025692],"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":[0.00008627437,0.00008056099,0.02581022,0.000034471705,0.00027419758,0.0000027389292,0.0009877327,0.000035269502,0.0008146419,0.20310372,0.0006122653,0.7681579],"study_design_scores_gemma":[0.0021445537,0.00066643907,0.03549189,0.00012284845,0.000102437814,0.00019700475,0.00013364905,0.041748643,0.0075809746,0.9100753,0.0015184062,0.00021784361],"about_ca_topic_score_codex":0.0000038462535,"about_ca_topic_score_gemma":0.0000017769802,"teacher_disagreement_score":0.76794004,"about_ca_system_score_codex":0.00003361759,"about_ca_system_score_gemma":0.00013098167,"threshold_uncertainty_score":0.2949437},"labels":[],"label_agreement":null},{"id":"W3102818033","doi":"10.4018/ijdcf.2021010101","title":"Drone Forensics","year":2020,"lang":"en","type":"article","venue":"International Journal of Digital Crime and Forensics","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Sheridan College; Ontario Tech University","funders":"","keywords":"Drone; Law enforcement; Computer science; Hacker; Computer security; Digital forensics; Internet privacy; Crime scene; Criminology; Law; Political science; Sociology","score_opus":0.030520474431533585,"score_gpt":0.22644946576865138,"score_spread":0.1959289913371178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102818033","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":[0.9705312,0.0006321045,0.0016172398,0.0023673319,0.00087924366,0.000040636587,0.00014580916,0.00001621855,0.02377022],"genre_scores_gemma":[0.9981171,0.000049474707,0.00061770645,0.0006560796,0.00044583404,2.2826764e-8,0.00003487468,0.0000024737874,0.00007642716],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99928325,0.000007374904,0.00022886849,0.00007783999,0.00030456902,0.00009807896],"domain_scores_gemma":[0.9994445,0.000046116595,0.00011783705,0.000029150315,0.00020883749,0.00015358279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067813795,0.00007914063,0.00012544951,0.00002494064,0.000029807548,0.00019538283,0.0001691235,0.000030509105,0.00008813777],"category_scores_gemma":[0.00008402414,0.00005696884,0.000071681425,0.000050265164,0.00006636132,0.00057874597,0.000014699761,0.000119471886,0.000035368186],"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":[0.0003373559,0.000015936572,0.33103424,0.000014022723,0.00021251722,0.00033392818,0.0011256658,0.00021191554,0.00009869619,0.000638257,0.009397864,0.6565796],"study_design_scores_gemma":[0.0040610223,0.0034186924,0.83649856,0.00021185439,0.0001403241,0.0021869524,0.0038776316,0.0086919395,0.0024081918,0.055806257,0.0815871,0.001111503],"about_ca_topic_score_codex":0.000011066846,"about_ca_topic_score_gemma":0.00001015067,"teacher_disagreement_score":0.6554681,"about_ca_system_score_codex":0.000002500622,"about_ca_system_score_gemma":0.00002449947,"threshold_uncertainty_score":0.23231219},"labels":[],"label_agreement":null}]}