{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002223496,0.0003846177,0.0003192038,0.0003379293,0.0001978691,0.0001747843,0.0005361771,0.0003368812,0.0003217612],"category_scores_gemma":[0.00005360485,0.0003408665,0.0002209766,0.0002596017,0.00005171459,0.00006327469,0.0005002078,0.004895553,0.0002631441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001768444,"about_ca_system_score_gemma":0.002857716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002790286,"about_ca_topic_score_gemma":0.001503862,"domain_scores_codex":[0.9960178,0.0003063492,0.0004780703,0.0005232355,0.00038492,0.002289582],"domain_scores_gemma":[0.9991924,0.0000545638,0.000216828,0.0003461248,0.0001110916,0.00007901771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004945471,0.0000751092,0.000002885976,0.00005498861,0.0007582952,0.00003414996,0.0002222908,0.5114875,0.0001779948,0.3895239,0.001890603,0.09572283],"study_design_scores_gemma":[0.0004112521,0.0002433018,0.000008153742,0.0001042325,0.0003257675,0.0001939849,0.0005743908,0.01867274,0.0003096369,0.967783,0.01093331,0.0004402467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01636592,0.03209024,0.8955078,0.004582408,0.008571199,0.0007143287,0.000007659255,0.0004737223,0.04168675],"genre_scores_gemma":[0.899359,0.03186496,0.02116856,0.0009395385,0.003004276,0.0002442356,0.0002481133,0.0002849638,0.04288631],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8829932,"threshold_uncertainty_score":0.9999043,"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."}}