{"id":"W2340423775","doi":"10.5539/cis.v9n2p82","title":"Empirical Study on How to Set Prices for Cruise Cabins Based on Improved Quantum Particle Swarm Optimization","year":2016,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Cruise Tourism Development and Management","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Cruise; Particle swarm optimization; Computer science; Process (computing); Set (abstract data type); Mathematical optimization; Function (biology); Swarm behaviour; Quantum; Operations research; Cruise control; Dynamic pricing; Artificial intelligence; Algorithm; Economics; Microeconomics; Control (management); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056938,0.00007462106,0.00007485296,0.0001790808,0.0005768845,0.0004360156,0.0002186376,0.00001856057,0.000008228534],"category_scores_gemma":[0.0001662438,0.00005041345,0.0000165052,0.0004496517,0.000105186,0.001971744,0.00006698359,0.00002015155,0.00001757149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008284595,"about_ca_system_score_gemma":0.0001163904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001098178,"about_ca_topic_score_gemma":0.000007882298,"domain_scores_codex":[0.9990119,0.00002494658,0.0001543804,0.0001689116,0.0004096842,0.0002302263],"domain_scores_gemma":[0.9993792,0.0001140369,0.00007027189,0.0001237867,0.0001688638,0.0001439072],"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":[0.000770152,0.001269955,0.02543243,0.00008276032,0.00003808113,0.000002210068,0.285717,0.05716391,0.0002931199,0.04216271,0.06623028,0.5208374],"study_design_scores_gemma":[0.002328983,0.001730607,0.07301126,0.00004993923,0.00001040317,1.048805e-7,0.004762948,0.7915195,0.0005724459,0.000111879,0.1254994,0.0004025274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2898965,5.816925e-7,0.6954185,0.01193711,0.0003911538,0.001164535,0.000003879546,0.00006984535,0.001117897],"genre_scores_gemma":[0.9929299,0.000003481893,0.00471554,0.002124073,0.00006328258,0.00006475563,0.000001577085,0.000002036443,0.0000953037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7343556,"threshold_uncertainty_score":0.4436988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04684097120360402,"score_gpt":0.3391720007459499,"score_spread":0.2923310295423459,"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."}}