{"id":"W2087241579","doi":"10.1016/j.trd.2014.06.012","title":"Using the built environment to oversample walk, transit, and bicycle travel","year":2014,"lang":"en","type":"article","venue":"Transportation Research Part D Transport and Environment","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Northwest University; Washington State Department of Transportation; Research and Innovative Technology Administration; U.S. Department of Transportation","keywords":"Oversampling; Transit (satellite); Quarter (Canadian coin); Transport engineering; Mile; Public transport; Block (permutation group theory); Travel behavior; Geography; Computer science; Engineering; Telecommunications; Mathematics; Archaeology; Geodesy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002220077,0.0002495653,0.0002975436,0.0001174662,0.001400836,0.00006424039,0.0002753295,0.0001439409,0.0007938521],"category_scores_gemma":[0.000007710981,0.0002071514,0.00009822018,0.0002223886,0.001135776,0.0002804457,0.000006468116,0.0003584348,0.0000206437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008471806,"about_ca_system_score_gemma":0.00005651308,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008377714,"about_ca_topic_score_gemma":0.008798452,"domain_scores_codex":[0.9966465,0.0002435801,0.0004685695,0.0006951862,0.001172973,0.0007732373],"domain_scores_gemma":[0.9988424,0.0001742545,0.00005419448,0.0003601564,0.00002356154,0.0005454922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003092401,0.0004368766,0.9172665,0.0001402652,0.0001019053,0.00002513284,0.05175564,0.002408038,0.004319496,0.005363622,0.0001324955,0.01774081],"study_design_scores_gemma":[0.0005711727,0.0001107395,0.8524948,0.00002681459,0.0000761649,2.424109e-7,0.002890056,0.0001340522,0.0003854066,0.001287742,0.1417341,0.0002887228],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697053,0.0002553142,0.02453608,0.003505026,0.00006240907,0.001010113,0.00009166737,0.00003557925,0.0007985428],"genre_scores_gemma":[0.9971611,0.0009492846,0.0009089502,0.0001862255,0.0001479099,0.00009107335,0.00006491897,0.00002948971,0.000460994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1416016,"threshold_uncertainty_score":0.9998992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116112798046756,"score_gpt":0.3622504679946111,"score_spread":0.2506391881899355,"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."}}