{"id":"W4379165195","doi":"10.1007/978-3-031-31746-0_8","title":"Disclosing the Impact of Micro-level Environmental Characteristics on Dockless Bikeshare Trip Volume: A Case Study of Ithaca","year":2023,"lang":"en","type":"book-chapter","venue":"˜The œurban book series","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cycling; Built environment; Macro; Macro level; Micro level; Environmental impact assessment; Volume (thermodynamics); Geography; Computer science; Transport engineering; Engineering; Civil engineering; Forestry; Political science; Economic impact analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005131059,0.0004487836,0.0006724762,0.0000974142,0.000829613,0.00009451849,0.0007184449,0.0002409976,0.0006380336],"category_scores_gemma":[0.00001761339,0.0002780132,0.0004293055,0.00008524817,0.001374963,0.0002328319,0.0001295507,0.0004811867,0.00002629739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001895168,"about_ca_system_score_gemma":0.0002305048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004056092,"about_ca_topic_score_gemma":0.007229767,"domain_scores_codex":[0.9976462,0.0001432953,0.0007371327,0.0004534446,0.0006590525,0.0003609471],"domain_scores_gemma":[0.9981107,0.0001893181,0.0007732516,0.0007698068,0.0000593072,0.00009758198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001933239,0.002184649,0.6805515,0.0005207817,0.002648038,0.002332122,0.2849643,0.00001660785,0.0007232783,0.002537633,0.01307708,0.008510759],"study_design_scores_gemma":[0.002615215,0.004173325,0.8044392,0.001323892,0.002049871,0.000104326,0.09861112,0.0000156576,0.0002623539,0.002557661,0.08102588,0.002821448],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790151,0.0004650342,0.000001626782,0.00009323554,0.0003273667,0.001676125,0.004290191,0.00005741427,0.01407388],"genre_scores_gemma":[0.7613057,0.0001210562,0.000002218715,0.00001868918,0.0002648525,0.00002243901,0.00008909747,0.0000602121,0.2381158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2240419,"threshold_uncertainty_score":0.9999672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04731296517550398,"score_gpt":0.289290213139432,"score_spread":0.2419772479639281,"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."}}