{"id":"W2552015230","doi":"10.1111/itor.12353","title":"Waiting‐time estimation in walk‐in clinics","year":2016,"lang":"en","type":"article","venue":"International Transactions in Operational Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimation; Computer science; Service (business); Waiting list; Line (geometry); Simple (philosophy); Operations research; Medicine; Business; Mathematics; Marketing","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00526851,0.0001422508,0.0002137605,0.00145703,0.0004315042,0.0000466064,0.0003153061,0.0002942171,0.006171486],"category_scores_gemma":[0.001795872,0.0001201103,0.00004621615,0.001003517,0.0001001794,0.000776688,0.00002652755,0.001144245,0.0009826082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001698674,"about_ca_system_score_gemma":0.001394482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001085874,"about_ca_topic_score_gemma":0.005088392,"domain_scores_codex":[0.9955362,0.001259332,0.001245079,0.000467838,0.0009106354,0.0005809236],"domain_scores_gemma":[0.9960011,0.002715541,0.00006955441,0.0002246866,0.0008852801,0.000103803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008872342,0.001856404,0.1903422,0.0001199929,0.00005653657,0.0000947052,0.007219332,0.5429457,0.004085544,0.08316957,0.001018011,0.1682048],"study_design_scores_gemma":[0.003660881,0.00008974233,0.06031452,0.00103628,0.000001928326,0.00000590545,0.0009337304,0.9221319,0.0001009396,0.00378815,0.007650351,0.0002856637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7846253,0.00007719422,0.09209565,0.09039796,0.001656165,0.002726205,0.000205603,0.0001023976,0.02811352],"genre_scores_gemma":[0.981576,0.000311288,0.006162692,0.0003043746,0.000152789,0.0007749707,0.00009444625,0.00002773131,0.01059576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3791862,"threshold_uncertainty_score":0.9997953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1985990726936576,"score_gpt":0.5656721314651179,"score_spread":0.3670730587714602,"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."}}