{"meta":{"query_hash":"8c8b9322bce3","filters":{"venue":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"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/8c8b9322bce3","api":"https://metacan.xera.ac/api/v1/cohort?venue=2018+1st+IEEE+International+Conference+on+Knowledge+Innovation+and+Invention+%28ICKII%29"},"results":[{"id":"W2904326537","doi":"10.1109/ickii.2018.8569113","title":"Tor Traffic Classification from Raw Packet Header using Convolutional Neural Network","year":2018,"lang":"en","type":"article","venue":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":36,"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":"Header; Computer science; Traffic classification; Encryption; Deep packet inspection; Convolutional neural network; Traffic generation model; Network packet; Artificial intelligence; Data mining; Machine learning; Computer network","score_opus":0.15613001727820044,"score_gpt":0.34243471211239584,"score_spread":0.1863046948341954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904326537","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":[0.773747,0.0005303035,0.20249163,0.00035004373,0.00030107689,0.00023899246,0.0035180831,0.0080496315,0.01077318],"genre_scores_gemma":[0.9614231,0.00025175026,0.027303817,0.000066448,0.00005906796,0.00007468143,0.0062162485,0.00008756059,0.0045173503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971145,0.000026957872,0.000017755914,0.00006526796,0.00008256885,0.000095902746],"domain_scores_gemma":[0.9995944,0.00007204477,0.000053113974,0.00005992005,0.00019449771,0.000026068265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032966555,0.0011278479,0.0005353841,0.0024287363,0.00039830626,0.0007168951,0.00063305936,0.00054930244,0.0012579999],"category_scores_gemma":[0.0012095713,0.00018010406,0.00053331174,0.0010842207,0.0002181707,0.0009171094,0.0004948739,0.00070771354,0.0007367135],"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":[0.0011440811,0.0010340472,0.03513236,0.00018878582,0.00018988634,0.00069851725,0.00010505299,0.23861301,0.040897958,0.0043607606,0.020681933,0.6569537],"study_design_scores_gemma":[0.000005293797,0.000030472213,0.002616549,0.000006030981,0.000015921356,0.00004839695,0.000025480815,0.9875795,0.0079765925,0.00093449716,0.0007510951,0.000010065366],"about_ca_topic_score_codex":0.009382467,"about_ca_topic_score_gemma":0.0076975995,"teacher_disagreement_score":0.009382467,"about_ca_system_score_codex":0.00089725305,"about_ca_system_score_gemma":0.000650993,"threshold_uncertainty_score":0.018655717},"labels":[],"label_agreement":null}]}