{"meta":{"query_hash":"893ba5bb5cf6","filters":{"venue":"World Journal of Information Technology "},"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/893ba5bb5cf6","api":"https://metacan.xera.ac/api/v1/cohort?venue=World%C2%A0Journal%C2%A0of%C2%A0Information%C2%A0Technology%C2%A0"},"results":[{"id":"W4400835982","doi":"10.61784/wjit3001","title":"SUMMAGAN: ENHANCING WEB NEWS SUMMARIZATION THROUGH GENERATIVE ADVERSARIAL NETWORKS","year":2024,"lang":"en","type":"article","venue":"World Journal of Information Technology ","topic":"Computational and Text Analysis Methods","field":"Social Sciences","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 Waterloo","funders":"","keywords":"Automatic summarization; Adversarial system; Generative grammar; Computer science; Generative adversarial network; World Wide Web; Information retrieval; Artificial intelligence; Deep learning","score_opus":0.01302985140628786,"score_gpt":0.3145772466450879,"score_spread":0.30154739523880003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400835982","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":[0.017735304,0.0009091952,0.97165525,0.0003822997,0.00018385919,0.0001082915,0.00045591945,0.0051934356,0.0033765207],"genre_scores_gemma":[0.59178746,0.0011151267,0.38612378,0.0009685164,0.00037334824,0.0003888344,0.0038494973,0.0011013512,0.014292203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954754,0.00017523959,0.000021983287,0.00011228079,0.000105187326,0.000037812548],"domain_scores_gemma":[0.9987413,0.0008190172,0.00010131889,0.00014364053,0.00015238587,0.000042304287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010682605,0.0012008883,0.00063933886,0.0006330485,0.00026046095,0.00075858866,0.00097151543,0.00066197716,0.0025073302],"category_scores_gemma":[0.0036346966,0.00030767912,0.0006372359,0.00050002645,0.0004027564,0.0013550384,0.0010403359,0.0015333067,0.0011806333],"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":[0.00019774739,0.00011327197,0.0009432011,0.00024093398,0.0001419465,0.00022207906,0.00019466617,0.72233695,0.018248346,0.0093431305,0.010255306,0.23776244],"study_design_scores_gemma":[0.000013087921,0.00006608211,0.00014007417,0.0000129352065,0.000019443487,0.00003669915,0.00001638054,0.9858591,0.0049005877,0.0060228696,0.0029021732,0.000010536618],"about_ca_topic_score_codex":0.0017174197,"about_ca_topic_score_gemma":0.003237183,"teacher_disagreement_score":0.0025073302,"about_ca_system_score_codex":0.00049903773,"about_ca_system_score_gemma":0.00043621816,"threshold_uncertainty_score":0.008387864},"labels":[],"label_agreement":null}]}