{"id":"W4323536505","doi":"10.1177/23998083231159905","title":"Developing a national dataset of bicycle infrastructure for Canada using open data sources","year":2023,"lang":"en","type":"article","venue":"Environment and Planning B Urban Analytics and City Science","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Simon Fraser University","funders":"","keywords":"Cycling; Equity (law); Transport engineering; Sample (material); Transport infrastructure; Transportation infrastructure; Critical infrastructure; Infrastructure planning; Green infrastructure; Business; Computer science; Geography; Engineering; Environmental planning; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001306579,0.00006326244,0.0001076002,0.00005763194,0.0007882399,0.0001548727,0.0006441145,0.00002455046,0.00001621918],"category_scores_gemma":[0.0001127446,0.0000581725,0.000006181323,0.0002858348,0.0005569206,0.000440602,0.0003579264,0.00004466987,1.028099e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008149713,"about_ca_system_score_gemma":0.0008490243,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05717489,"about_ca_topic_score_gemma":0.02577829,"domain_scores_codex":[0.9989366,0.00001280933,0.0001520138,0.0003059331,0.0003982437,0.000194428],"domain_scores_gemma":[0.9995717,0.0000908012,0.00009168945,0.0001407887,0.000019702,0.00008535457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005264652,0.000003556712,0.9943931,0.00001346297,0.00000825491,0.000001003159,0.0009969106,0.0006032874,0.00007705418,0.00051757,0.003119833,0.000260767],"study_design_scores_gemma":[0.0001763842,0.000009984229,0.9014159,0.00003130335,0.00002471698,2.393139e-7,0.002360381,0.03954707,0.00007094841,0.001907817,0.05427555,0.0001797552],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997141,0.00007513208,0.0009126107,0.0002248613,0.00004816804,0.0001323229,0.001353619,0.00000463733,0.0001076631],"genre_scores_gemma":[0.9973442,0.00002390993,0.002053599,0.00008311802,0.00004432549,9.203088e-7,0.0003759474,0.000002257622,0.00007173346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09297718,"threshold_uncertainty_score":0.9919987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1336275195737109,"score_gpt":0.3605975854426943,"score_spread":0.2269700658689834,"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."}}