{"meta":{"query_hash":"64e0369eaba3","filters":{"venue":"MEDIAGRO"},"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/64e0369eaba3","api":"https://metacan.xera.ac/api/v1/cohort?venue=MEDIAGRO"},"results":[{"id":"W2564303651","doi":"10.31942/md.v12i2.1614","title":"ANALISIS PERAMALAN (Forecasting) PRODUKSI KARET (Hevea Brasiliensis) DI PT PERKEBUNAN NUSANTARA IX KEBUN SUKAMANGLI KABUPATEN KENDAL","year":2016,"lang":"id","type":"article","venue":"MEDIAGRO","topic":"Management and Optimization Techniques","field":"Business, Management and Accounting","cited_by":9,"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":"Natural rubber; Quarter (Canadian coin); Hevea brasiliensis; Ton; Agricultural science; Production (economics); Environmental science; Autoregressive integrated moving average; Mathematics; Toxicology; Engineering; Operations management; Agricultural economics; Statistics; Geography; Biology; Economics; Time series; Chemistry","score_opus":0.022827805683994954,"score_gpt":0.21797554086741858,"score_spread":0.19514773518342363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564303651","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":[0.98148715,0.00113354,0.0019369037,0.00014838114,0.00002676083,0.000034073153,0.0048483727,0.00006947292,0.010315311],"genre_scores_gemma":[0.9932407,0.0007562707,0.0014903538,0.000012101635,0.000008902981,0.00001518997,0.0016584806,0.000007317961,0.0028107027],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971336,0.00002679233,0.000027395694,0.000062213105,0.00014267843,0.000027541526],"domain_scores_gemma":[0.9994869,0.0001727851,0.000103818034,0.000028244292,0.00017629938,0.00003186473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003582585,0.00030588155,0.0002096139,0.0013673693,0.00019131452,0.0007913479,0.00018915458,0.00016289958,0.0020441797],"category_scores_gemma":[0.0006914562,0.00011009776,0.00029600706,0.0016823066,0.000168222,0.00038305437,0.00014792773,0.0002868333,0.00039161387],"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":[0.00025013127,0.00012863197,0.8262722,0.00048528955,0.00018704297,0.0011094041,0.0019179226,0.004861871,0.014351679,0.0011987813,0.0031054767,0.14613153],"study_design_scores_gemma":[0.000003905707,0.00017427086,0.9651074,0.00008847559,0.0000827704,0.00038238644,0.002689014,0.0114197675,0.004780592,0.0003484121,0.014895505,0.000027451517],"about_ca_topic_score_codex":0.041431893,"about_ca_topic_score_gemma":0.06761533,"teacher_disagreement_score":0.041431893,"about_ca_system_score_codex":0.0005220519,"about_ca_system_score_gemma":0.00032472456,"threshold_uncertainty_score":0.08238149},"labels":[],"label_agreement":null}]}