{"id":"W4416949903","doi":"10.1038/d41586-025-03931-7","title":"Data drive city transportation forwards","year":2025,"lang":"en","type":"article","venue":"Nature","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Urban planning; Data collection; Public transport; Key (lock)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009287039,0.0005588591,0.0003356154,0.002096222,0.000655422,0.002518101,0.0005562145,0.0007973767,0.01541284],"category_scores_gemma":[0.01061567,0.0003111046,0.0005610997,0.00484215,0.0003298413,0.002495545,0.0009436323,0.001103557,0.007822108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324339,"about_ca_system_score_gemma":0.001584268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05544861,"about_ca_topic_score_gemma":0.06875918,"domain_scores_codex":[0.9991094,0.0002213556,0.00005159406,0.0002153267,0.0003317972,0.00007046849],"domain_scores_gemma":[0.9957201,0.001247231,0.0004186452,0.000769916,0.001680281,0.000163813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0005080207,0.0001548525,0.2184319,0.0007008463,0.0005279923,0.0002189038,0.001263135,0.05366084,0.001927863,0.04003432,0.4699199,0.2126513],"study_design_scores_gemma":[0.00009330236,0.0001201122,0.1245556,0.0004404092,0.0003520674,0.0002000882,0.004414784,0.1322637,0.004847642,0.05262905,0.6799257,0.0001575705],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3549991,0.002750849,0.05352531,0.02246668,0.002944859,0.0002492335,0.4292877,0.006671081,0.1271052],"genre_scores_gemma":[0.781844,0.001807881,0.01955144,0.0008354984,0.0003420449,0.0002050756,0.1628783,0.0007200942,0.03181559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05544861,"threshold_uncertainty_score":0.1102517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0207148946378408,"score_gpt":0.3642624392088838,"score_spread":0.343547544571043,"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."}}