{"meta":{"query_hash":"8e85a570749c","filters":{"venue":"AI and Data Science Journal"},"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/8e85a570749c","api":"https://metacan.xera.ac/api/v1/cohort?venue=AI+and+Data+Science+Journal"},"results":[{"id":"W7117462108","doi":"10.61784/adsj3031","title":"FEDERATED LEARNING-AWARE MULTI-OBJECTIVE SCHEDULING FOR DISTRIBUTED EDGE-CLOUD ENVIRONMENTS","year":2025,"lang":"","type":"article","venue":"AI and Data Science Journal","topic":"Intelligent Tutoring Systems and Adaptive Learning","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 British Columbia","funders":"","keywords":"Scheduling (production processes); Range (aeronautics); Interoperability; Open source","score_opus":0.04089318880909045,"score_gpt":0.3233830809228182,"score_spread":0.28248989211372777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117462108","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":[0.007636187,0.0012155169,0.98449796,0.0013332067,0.004660436,0.0004323148,0.000121256,0.000036705314,0.00006639006],"genre_scores_gemma":[0.98208,0.00058094424,0.009618395,0.000384633,0.00072039384,0.00000957615,0.000057825295,0.00002013483,0.0065281196],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99532217,0.00023696094,0.00085382396,0.0016060503,0.0008060486,0.0011749229],"domain_scores_gemma":[0.9975188,0.00024307758,0.00059853354,0.00067815575,0.00050896674,0.0004525179],"candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0052623553,0.0004244766,0.0004835691,0.0004256444,0.0071246563,0.005273499,0.0027166503,0.00016237149,0.000018028592],"category_scores_gemma":[0.0016598139,0.00038445456,0.0001033441,0.0012775629,0.0007050001,0.00474169,0.0024516303,0.0014514114,0.000024615963],"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":[0.00060202676,0.0020838638,0.10018144,0.0006967374,0.0013372204,0.00035640565,0.00981087,0.058242545,0.034417074,0.059937995,0.009024696,0.7233091],"study_design_scores_gemma":[0.0011607283,0.00031929565,0.0054348344,0.000836257,0.00006133354,0.00013612714,0.0016302353,0.89789295,0.0016381706,0.00014264944,0.090285674,0.00046177098],"about_ca_topic_score_codex":0.00006270582,"about_ca_topic_score_gemma":0.0000074293293,"teacher_disagreement_score":0.97487956,"about_ca_system_score_codex":0.0003680905,"about_ca_system_score_gemma":0.0014609322,"threshold_uncertainty_score":0.99986076},"labels":[],"label_agreement":null}]}