{"id":"W4402474084","doi":"10.1109/ccece59415.2024.10667161","title":"Data Analytics for Resort Revenue Management","year":2024,"lang":"en","type":"article","venue":"","topic":"Cruise Tourism Development and Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Revenue management; Revenue; Analytics; Computer science; Data science; Data analysis; Business; Finance; Data mining","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":[0.002595566,0.001208155,0.0008067813,0.004261637,0.0006148398,0.004401962,0.001380326,0.0007225131,0.008306746],"category_scores_gemma":[0.009711906,0.0004336776,0.0009350685,0.004622082,0.0003441548,0.00354801,0.002023349,0.001465922,0.005960753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000786349,"about_ca_system_score_gemma":0.001180529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004416803,"about_ca_topic_score_gemma":0.003042464,"domain_scores_codex":[0.9979498,0.0004179325,0.0002818864,0.0004534128,0.0007741023,0.0001229277],"domain_scores_gemma":[0.9941519,0.001878461,0.000457228,0.001893189,0.001262639,0.000356627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008099668,0.0005091335,0.02808672,0.001701053,0.000332771,0.0005737787,0.0009744006,0.01772772,0.0168031,0.02311204,0.1861803,0.7231891],"study_design_scores_gemma":[0.0001495016,0.0002972481,0.03680018,0.001071396,0.0002063691,0.0008122382,0.002548978,0.3628018,0.03931575,0.08447306,0.4712719,0.0002516387],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04893388,0.004975324,0.589898,0.006114572,0.001269369,0.001852008,0.1502152,0.1606776,0.03606399],"genre_scores_gemma":[0.3835405,0.002935136,0.463418,0.001105164,0.0004907302,0.0010888,0.1352862,0.003548418,0.008586948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008306746,"threshold_uncertainty_score":0.02778882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1493741090575861,"score_gpt":0.4016846706261321,"score_spread":0.252310561568546,"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."}}