{"meta":{"query_hash":"1e004a36ce91","filters":{"venue":"Trust, Security And Privacy In Computing And Communications"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/1e004a36ce91","api":"https://metacan.xera.ac/api/v1/cohort?venue=Trust%2C+Security+And+Privacy+In+Computing+And+Communications"},"results":[{"id":"W2184486309","doi":"10.1109/trustcom-bigdatase-ispa.2015.436","title":"RLTE: A Reinforcement Learning Based Trust Establishment Model","year":2015,"lang":"en","type":"article","venue":"Trust, Security And Privacy In Computing And Communications","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":6,"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 Ottawa","funders":"","keywords":"Honesty; Reputation; Reinforcement learning; Computer science; Trustworthiness; Order (exchange); Reinforcement; Work (physics); Artificial intelligence; Psychology; Social psychology; Computer security; Business; Law; Political science; Engineering; Finance","score_opus":0.07272068516223065,"score_gpt":0.32425605605119723,"score_spread":0.2515353708889666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184486309","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":[0.03641086,0.00046747454,0.9505129,0.0010221713,0.00012441627,0.0001571153,0.00024309602,0.000762102,0.010299943],"genre_scores_gemma":[0.92929804,0.0003930205,0.060340382,0.00015871167,0.00005135791,0.00029131555,0.00022363839,0.000042282947,0.009201163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988518,0.00039562877,0.00007945408,0.00027400223,0.0002440792,0.0001550499],"domain_scores_gemma":[0.99828136,0.00082899764,0.0002758712,0.000087779816,0.00037422212,0.0001517537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013147778,0.0007734387,0.001006508,0.00043691523,0.00057504314,0.0013157611,0.0021724622,0.0012688598,0.004456678],"category_scores_gemma":[0.004084887,0.00036248725,0.0006918224,0.0003943532,0.0007136158,0.0017174785,0.0013082598,0.0018159222,0.00073983165],"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":[0.00017776029,0.00014333356,0.001981267,0.000111738525,0.00008723534,0.00036727733,0.00019285706,0.9222893,0.0014016769,0.03331407,0.002197601,0.037735824],"study_design_scores_gemma":[0.00001799634,0.000040748488,0.000120194636,0.000006226336,0.000014878797,0.000033121556,0.0000103701295,0.9933774,0.00014467178,0.005452262,0.0007745018,0.0000076502465],"about_ca_topic_score_codex":0.010327017,"about_ca_topic_score_gemma":0.007483251,"teacher_disagreement_score":0.010327017,"about_ca_system_score_codex":0.0014362412,"about_ca_system_score_gemma":0.0016835773,"threshold_uncertainty_score":0.0205338},"labels":[],"label_agreement":null},{"id":"W2188485401","doi":"10.1109/trustcom-bigdatase-ispa.2015.466","title":"CaptureMe: Attacking the User Credential in Mobile Banking Applications","year":2015,"lang":"en","type":"article","venue":"Trust, Security And Privacy In Computing And Communications","topic":"User Authentication and Security Systems","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":"École de Technologie Supérieure","funders":"","keywords":"Credential; Password; Computer science; Android (operating system); Mobile banking; Computer security; Optical character recognition; Mobile device; World Wide Web; Artificial intelligence; Operating system; Image (mathematics)","score_opus":0.05278747971358873,"score_gpt":0.32407302842793245,"score_spread":0.27128554871434374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188485401","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":[0.8520457,0.0012325733,0.12696552,0.00072676654,0.00014811254,0.00024379698,0.0001643329,0.0019051491,0.016568087],"genre_scores_gemma":[0.99375546,0.00009519229,0.00436971,0.000114188806,0.000009751511,0.000014569254,0.000024389092,0.00001730353,0.0015993606],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987489,0.00034163604,0.000046330308,0.00012388811,0.00054252346,0.00019666445],"domain_scores_gemma":[0.9979716,0.0007670405,0.00023767902,0.0007389385,0.0002050143,0.00007972168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041490048,0.00048773226,0.00034634062,0.00061484624,0.0005458655,0.000725938,0.00031618934,0.0013873719,0.0015380181],"category_scores_gemma":[0.0029545114,0.00019821823,0.0003805038,0.00026839713,0.00065216795,0.0013842955,0.0010973508,0.00077496405,0.00040485218],"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":[0.002492475,0.00039890135,0.03015287,0.0006518867,0.00030322283,0.009198211,0.0025387013,0.009215289,0.51652485,0.02467277,0.009221214,0.39462975],"study_design_scores_gemma":[0.000068112444,0.0022308314,0.058009315,0.00028151352,0.00027517354,0.029337337,0.0008914561,0.19603994,0.67312115,0.007745773,0.03177952,0.00021992395],"about_ca_topic_score_codex":0.00042008876,"about_ca_topic_score_gemma":0.00059898983,"teacher_disagreement_score":0.0015380181,"about_ca_system_score_codex":0.00023654323,"about_ca_system_score_gemma":0.00013679783,"threshold_uncertainty_score":0.0051451325},"labels":[],"label_agreement":null},{"id":"W2189191503","doi":"10.1109/trustcom-bigdatase-ispa.2015.563","title":"Similarity Measure Based on Low-Rank Approximation for Highly Scalable Recommender Systems","year":2015,"lang":"en","type":"article","venue":"Trust, Security And Privacy In Computing And Communications","topic":"Recommender Systems and Techniques","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":"Toronto Metropolitan University","funders":"","keywords":"Collaborative filtering; Recommender system; Scalability; Singular value decomposition; Computer science; Low-rank approximation; Sparse matrix; Similarity (geometry); Matrix decomposition; Computation; Rank (graph theory); Similarity measure; Data mining; Approximation algorithm; Theoretical computer science; Artificial intelligence; Machine learning; Algorithm; Mathematics; Database","score_opus":0.07831400695735763,"score_gpt":0.3127641550735404,"score_spread":0.23445014811618275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189191503","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":[0.007943579,0.00047134914,0.99041486,0.00010068513,0.0000407551,0.000043320986,0.000060293973,0.00025099333,0.00067404966],"genre_scores_gemma":[0.45123005,0.0010373223,0.5434694,0.00018202579,0.00024022066,0.0002534885,0.0007093847,0.00008443699,0.002793617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975562,0.0006880826,0.00016692192,0.00042344973,0.0010482706,0.000116969735],"domain_scores_gemma":[0.99645376,0.0017615763,0.00031489128,0.00058293017,0.00078801916,0.0000987751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019087965,0.00077810226,0.0019419731,0.0014301778,0.00063573907,0.0014019909,0.0014958149,0.0011318842,0.0018742888],"category_scores_gemma":[0.009157538,0.00036049087,0.0008516244,0.0021375145,0.0006082307,0.0023130795,0.0009860266,0.001452741,0.00088696665],"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":[0.00021862761,0.00019609205,0.0019117957,0.00028330882,0.00018762088,0.00017856121,0.00015201308,0.69313574,0.008572802,0.050729327,0.0056680655,0.23876603],"study_design_scores_gemma":[0.000006803958,0.000032023956,0.00015206564,0.0000040001605,0.000007790287,0.000026756787,0.000007800717,0.99363315,0.0004716955,0.005130011,0.0005199747,0.000007864913],"about_ca_topic_score_codex":0.005638587,"about_ca_topic_score_gemma":0.00376876,"teacher_disagreement_score":0.005638587,"about_ca_system_score_codex":0.0009151001,"about_ca_system_score_gemma":0.0009853429,"threshold_uncertainty_score":0.011211574},"labels":[],"label_agreement":null}]}