{"id":"W7118630692","doi":"10.5281/zenodo.18168574","title":"Dynamic Pricing Models in Telecom: Implementation of Real Time, Dynamic Pricing Strategies through Artificial Intelligence","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Advanced Data and IoT Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Marriott International (Canada)","funders":"","keywords":"Dynamic pricing; Revenue management; Software deployment; Pricing strategies; Reinforcement learning; Key (lock); Process (computing); Revenue; Big data","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":[],"consensus_categories":[],"category_scores_codex":[0.0001791385,0.0001198222,0.0001561121,0.0001763186,0.0002657601,0.0001775118,0.0003892928,0.00005745642,0.0008316032],"category_scores_gemma":[0.00007950346,0.0001394697,0.00002568894,0.0007275814,0.00007182215,0.0007301219,0.0003965943,0.000206158,0.0002168397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002108031,"about_ca_system_score_gemma":0.000007212993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002877927,"about_ca_topic_score_gemma":0.00001188034,"domain_scores_codex":[0.9988745,0.00006345838,0.0003553182,0.0002501046,0.0001775227,0.0002790919],"domain_scores_gemma":[0.9994255,0.00001901257,0.00006341727,0.0002941462,0.0001707032,0.00002720729],"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":[0.00002050549,0.00007501583,0.000002846061,0.0002200847,0.00004387898,0.00002789017,0.003034126,0.1851212,0.330905,0.02678995,0.0006683252,0.4530912],"study_design_scores_gemma":[0.0004474613,0.0003177445,0.001163515,0.0002224961,0.00003152188,0.00009670889,0.03336803,0.822154,0.0817228,0.04872806,0.01101375,0.0007338604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4412937,0.0001875048,0.5340323,0.0001317296,0.00008602068,0.0004256263,0.0001879639,0.001711775,0.02194341],"genre_scores_gemma":[0.9916552,0.0006865736,0.006403047,0.00000511271,0.000008471205,7.385117e-8,0.0008281686,0.0003899778,0.00002340263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6370329,"threshold_uncertainty_score":0.9105473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0317254492106625,"score_gpt":0.2883746178775328,"score_spread":0.2566491686668703,"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."}}