{"id":"W650171906","doi":"","title":"Cycling data and indicators: a critical ingredient in assigning priority for cycling","year":2005,"lang":"en","type":"article","venue":"","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cycling; TRIPS architecture; Work (physics); Transport engineering; Journey to work; Engineering; Kilometer; Geography; Public transport; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1061681,0.001080716,0.001899865,0.01474132,0.00322844,0.01053007,0.002423005,0.002012987,0.0035139],"category_scores_gemma":[0.1969288,0.001421133,0.0005471574,0.01425046,0.003070254,0.01226213,0.005023027,0.007695698,0.00111775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004778178,"about_ca_system_score_gemma":0.01019198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01529887,"about_ca_topic_score_gemma":0.01694113,"domain_scores_codex":[0.9537426,0.02622452,0.007133973,0.001614481,0.009980333,0.001303951],"domain_scores_gemma":[0.8207945,0.09740695,0.01233306,0.009881229,0.05416156,0.005422704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005589655,0.0003035464,0.1230169,0.003289735,0.0001986983,0.0004345422,0.01651654,0.007408869,0.00349002,0.1255629,0.05995779,0.6592615],"study_design_scores_gemma":[0.0001348986,0.0008853045,0.1392855,0.00772074,0.0002761783,0.0006255353,0.05604675,0.02303571,0.006127748,0.2326951,0.5325005,0.0006661084],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07640437,0.00871229,0.7114984,0.09403264,0.005006737,0.005947836,0.008576825,0.002403846,0.08741695],"genre_scores_gemma":[0.3629849,0.002452901,0.6223304,0.002631581,0.001039602,0.001663487,0.003162476,0.0004480836,0.003286674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1061681,"threshold_uncertainty_score":0.5614773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06356421644867159,"score_gpt":0.410107141171327,"score_spread":0.3465429247226554,"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."}}