{"id":"W2910358728","doi":"10.1111/gean.12188","title":"A Measure of Competitive Access to Destinations for Comparing Across Multiple Study Regions","year":2019,"lang":"en","type":"article","venue":"Geographical Analysis","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Destinations; Measure (data warehouse); Competition (biology); Supply and demand; Business; Supply side; Population; Inequality; Transport engineering; Computer science; Geography; Economics; Microeconomics; Tourism; Mathematics; Commerce","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003123413,0.0003806685,0.0005236953,0.006110331,0.001557867,0.001734646,0.0009779008,0.0003932064,0.007061873],"category_scores_gemma":[0.01194262,0.0001305449,0.0009283469,0.009544098,0.0008855222,0.001303949,0.002635651,0.0005762966,0.000424155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001601968,"about_ca_system_score_gemma":0.002125437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1002415,"about_ca_topic_score_gemma":0.1862372,"domain_scores_codex":[0.9973477,0.00101705,0.0003139576,0.0003248062,0.0007910058,0.0002053723],"domain_scores_gemma":[0.9932202,0.002397476,0.001579216,0.0007301344,0.001584221,0.0004887634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001413451,0.0001743098,0.9182417,0.0003792951,0.0005456418,0.0001612422,0.004423614,0.001663585,0.001189197,0.02175066,0.00420556,0.0471238],"study_design_scores_gemma":[0.00002400101,0.0002244854,0.97126,0.00009512593,0.0001452078,0.0001968724,0.004898606,0.003277217,0.0006187784,0.004117259,0.01510198,0.0000405727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.878067,0.0008162825,0.05200142,0.0004057941,0.00007174512,0.00088217,0.02094986,0.0001813967,0.04662427],"genre_scores_gemma":[0.9701628,0.0001723722,0.02268342,0.00004879801,0.00002250194,0.0009001013,0.004292974,0.00003837102,0.00167859],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.1002415,"threshold_uncertainty_score":0.199316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05735584174762461,"score_gpt":0.3731842936007159,"score_spread":0.3158284518530913,"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."}}