{"meta":{"query_hash":"1dfbbb30a83d","filters":{"venue":"International Journal of Internet, Broadcasting and Communication"},"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/1dfbbb30a83d","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Journal+of+Internet%2C+Broadcasting+and+Communication"},"results":[{"id":"W3180352537","doi":"","title":"FAST-ADAM in Semi-Supervised Generative Adversarial Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Internet, Broadcasting and Communication","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"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":"","keywords":"Discriminator; Generator (circuit theory); Computer science; Benchmark (surveying); Generative grammar; Adversarial system; Stability (learning theory); Machine learning; Artificial intelligence; Convergence (economics); Artificial neural network; Power (physics)","score_opus":0.010661342132262996,"score_gpt":0.23808583896119653,"score_spread":0.22742449682893354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180352537","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":[0.010367011,0.0006176806,0.98597604,0.00026177696,0.000046728535,0.000030957308,0.000049305352,0.00072174106,0.0019288855],"genre_scores_gemma":[0.74221843,0.0009414252,0.24833827,0.0004341582,0.00011752904,0.00023211197,0.0003451163,0.000406923,0.0069660316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936837,0.00029246052,0.000032887783,0.00014606433,0.00010930556,0.000050851977],"domain_scores_gemma":[0.9987326,0.00088855287,0.00009739532,0.000116463154,0.00011791614,0.00004708814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015202363,0.0011789589,0.0010163946,0.00029005963,0.0002723241,0.00061230187,0.0010464875,0.0010329674,0.001413855],"category_scores_gemma":[0.0029934708,0.0006082855,0.000647973,0.0002772469,0.001356737,0.000915735,0.0011837104,0.0022878337,0.0005160769],"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":[0.000055187807,0.000017026257,0.00037413143,0.000059420225,0.00003609715,0.00006471619,0.00004186113,0.9610426,0.0017861772,0.012660416,0.001101236,0.0227612],"study_design_scores_gemma":[0.0000032201679,0.000011612305,0.00003233479,0.0000043525424,0.000002612436,0.000014781343,0.0000016134953,0.995282,0.000523873,0.0038144663,0.00030604695,0.0000030646927],"about_ca_topic_score_codex":0.0018843854,"about_ca_topic_score_gemma":0.0018445712,"teacher_disagreement_score":0.0018843854,"about_ca_system_score_codex":0.00066370366,"about_ca_system_score_gemma":0.00074824545,"threshold_uncertainty_score":0.008039832},"labels":[],"label_agreement":null}]}