{"meta":{"query_hash":"2de13139c608","filters":{"venue":"EKEV Akademi Dergisi"},"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/2de13139c608","api":"https://metacan.xera.ac/api/v1/cohort?venue=EKEV+Akademi+Dergisi"},"results":[{"id":"W4302014143","doi":"10.17753/sosekev.1140004","title":"RESEARCHING THE FUTURE OF BITCOIN MARKET WITH MACHINE LEARNING METHOD: ANAPPLICATION ON THE CASE OF TURKEY","year":2022,"lang":"en","type":"article","venue":"EKEV Akademi Dergisi","topic":"Blockchain Technology Applications and Security","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":"Closing (real estate); Cryptocurrency; Currency; Quarter (Canadian coin); Digital currency; Artificial intelligence; Economics; Commerce; Computer science; Humanities; Monetary economics; Geography; Computer security; Finance; Art","score_opus":0.011855363592904946,"score_gpt":0.27373561494236875,"score_spread":0.2618802513494638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302014143","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":[0.76200765,0.06660583,0.087147914,0.014806746,0.0004966262,0.00015251017,0.00051191245,0.0001519711,0.06811879],"genre_scores_gemma":[0.96680284,0.01558975,0.012646661,0.00021759368,0.00022468825,0.000030231837,0.0001974707,0.000019627181,0.004271064],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994286,0.0002595268,0.000033124146,0.000078568366,0.00011877382,0.0000813896],"domain_scores_gemma":[0.9986142,0.0009373773,0.000136274,0.00005074803,0.00022153468,0.00003985584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012349407,0.0004010289,0.0004522255,0.002185463,0.0006969094,0.002559311,0.00048725342,0.0010588715,0.002011635],"category_scores_gemma":[0.0022421468,0.00016432955,0.0005926874,0.002382312,0.0007665823,0.0027517541,0.0006221318,0.0010219641,0.00020837883],"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":[0.00030702757,0.00041582252,0.08801551,0.0013569942,0.0002375933,0.0087814685,0.0024802599,0.15376347,0.0023114628,0.41434902,0.013467685,0.3145137],"study_design_scores_gemma":[0.00003205949,0.00019608745,0.043529633,0.0008738808,0.00012896316,0.0015889138,0.004029109,0.7852073,0.0025321646,0.11349089,0.04824415,0.00014684198],"about_ca_topic_score_codex":0.009408158,"about_ca_topic_score_gemma":0.00782769,"teacher_disagreement_score":0.009408158,"about_ca_system_score_codex":0.0014002275,"about_ca_system_score_gemma":0.0007808953,"threshold_uncertainty_score":0.018706799},"labels":[],"label_agreement":null}]}