{"id":"W4388092559","doi":"10.48550/arxiv.2310.18480","title":"Capacity, Collision Avoidance and Shopping Rate under a Social Distancing Regime","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Slowdown; Baseline (sea); Social distance; Trajectory; Computer science; Econometrics; Simulation; Economics; Coronavirus disease 2019 (COVID-19); Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000560912,0.0002928248,0.0007449202,0.0003706039,0.0004364624,0.0001861415,0.0003528352,0.0002686865,0.00009525565],"category_scores_gemma":[0.00004381065,0.0004008281,0.0002922193,0.0006004689,0.0001310446,0.000187934,0.0008412039,0.0004075955,0.0002029646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003409059,"about_ca_system_score_gemma":0.00003294423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00238277,"about_ca_topic_score_gemma":0.001125288,"domain_scores_codex":[0.9980434,0.0000555461,0.0004393084,0.001087102,0.00002517319,0.0003495329],"domain_scores_gemma":[0.9986526,0.00008637398,0.0006010835,0.0004889941,0.00006393514,0.0001069683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000408098,0.00003486504,0.0195242,0.0002853314,0.0004232209,0.00006951859,0.0005361348,0.05562024,0.00001609792,0.9226169,0.0008008545,0.00003181738],"study_design_scores_gemma":[0.001217588,0.00005094644,0.07923534,0.0003483538,0.0001947774,0.000004232041,0.001355733,0.3959018,0.000009013082,0.5089726,0.01100584,0.001703718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9314761,0.0003759465,0.06255116,0.00048226,0.0005092195,0.0002540225,0.0003074855,0.0001427711,0.003900998],"genre_scores_gemma":[0.9887394,0.000351001,0.00005846127,0.00004968391,0.000165755,0.000001636678,0.00003161045,0.00004253915,0.01055996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4136443,"threshold_uncertainty_score":0.9998444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1879243687625048,"score_gpt":0.1841262966925565,"score_spread":0.003798072069948366,"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."}}