{"id":"W1986061398","doi":"10.2495/safe070331","title":"Airport level of service perceptions before and after September 11: a neural network analysis","year":2007,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Aviation Industry Analysis and Trends","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Service (business); Level of service; Business; Airport security; Service level; Computer security; Transport engineering; Computer science; Marketing; Psychology; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004088798,0.0001392686,0.0002718274,0.00018694,0.000188497,0.00001789825,0.0001084357,0.00009155351,0.00639986],"category_scores_gemma":[0.000001349209,0.0001208213,0.0002331922,0.0005247283,0.00007570753,0.0000865887,0.000007385825,0.0001627175,0.0001335013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005630107,"about_ca_system_score_gemma":0.000003016135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002176756,"about_ca_topic_score_gemma":0.002414726,"domain_scores_codex":[0.9989542,0.00001583755,0.0004856699,0.0002763206,0.0000698974,0.0001980118],"domain_scores_gemma":[0.9992866,0.00003862495,0.0001925429,0.0004020516,0.000008802361,0.0000713886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004959812,0.0003964115,0.7297322,0.00001264519,0.001642722,0.000003791022,0.001086583,0.259691,0.000009627155,0.003613907,0.0001624981,0.003598953],"study_design_scores_gemma":[0.0001747593,0.00003632695,0.9869951,0.000004334137,0.000366682,0.000002346101,0.0002224424,0.007418185,0.00001704519,0.000696508,0.003930354,0.0001359315],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7587447,0.00008707822,0.2376579,0.0019861,0.00004903016,0.0001108924,0.0002665761,0.00001110153,0.001086637],"genre_scores_gemma":[0.995762,0.00002891118,0.0004379968,0.0004064205,0.00003977781,0.00003418914,0.00001727244,0.00001242061,0.003261016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2572628,"threshold_uncertainty_score":0.9945084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04498232331155838,"score_gpt":0.225450159764625,"score_spread":0.1804678364530666,"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."}}