{"id":"W4403512716","doi":"10.2139/ssrn.4953855","title":"Mixology: Order Flow Segmentation Design","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Efficiency and Management","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Order (exchange); Flow (mathematics); Computer science; Segmentation; Business; Mathematics; Artificial intelligence; Geometry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001588329,0.0007370077,0.0006599146,0.001141255,0.0008706126,0.002854504,0.001335489,0.0009366808,0.02365781],"category_scores_gemma":[0.004492004,0.0007075617,0.0007565615,0.001312125,0.0009349777,0.003171334,0.002063788,0.001407667,0.002878467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001192349,"about_ca_system_score_gemma":0.001736776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001126043,"about_ca_topic_score_gemma":0.001754128,"domain_scores_codex":[0.9990459,0.0002734835,0.00005953553,0.000216315,0.0002968015,0.0001079639],"domain_scores_gemma":[0.9987005,0.0004573763,0.0001007203,0.0003512604,0.0002715292,0.0001185286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008990475,0.0002640925,0.001555409,0.0003870921,0.00007923031,0.0001245025,0.0003998688,0.08872327,0.01572791,0.3781087,0.01339333,0.5003374],"study_design_scores_gemma":[0.0001639667,0.0002207196,0.0003897655,0.00006913351,0.00006250656,0.00008258605,0.0001516029,0.6671817,0.02100342,0.2766309,0.03400911,0.00003455751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006800197,0.0001028497,0.9835224,0.0001803046,0.00005212927,0.0001450724,0.0001445338,0.001501586,0.007550898],"genre_scores_gemma":[0.267646,0.0002387433,0.7164029,0.0002495331,0.00009431667,0.000416735,0.0005796117,0.0009975473,0.01337462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02365781,"threshold_uncertainty_score":0.07914323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01315105027742366,"score_gpt":0.2553444475239838,"score_spread":0.2421933972465601,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}