{"id":"W7116312157","doi":"10.1016/j.ufug.2025.129235","title":"Crafting a walkable and ridable urban green space system: Human-centered park network planning supported by active leisure travel trajectories","year":2025,"lang":"en","type":"article","venue":"Urban forestry & urban greening","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Space (punctuation); Network planning and design; Node (physics); Cycling; Ecological network; Street network; Global Positioning System; Construct (python library); Urban planning","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":[0.0007735319,0.0002409606,0.0002606684,0.0008393487,0.001403444,0.002658276,0.0009146952,0.0005265009,0.006959948],"category_scores_gemma":[0.002462616,0.0002331981,0.0003337783,0.0008649069,0.0004655839,0.001875846,0.002151004,0.0005067842,0.0005148874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001622598,"about_ca_system_score_gemma":0.006234425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04154764,"about_ca_topic_score_gemma":0.1637056,"domain_scores_codex":[0.9995359,0.0002003903,0.00001519856,0.0000562822,0.00006674772,0.0001254691],"domain_scores_gemma":[0.9993209,0.0001242977,0.00008105096,0.00006221241,0.0001280595,0.0002836064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004802155,0.001869109,0.1017526,0.0003286963,0.0001653219,0.0006892692,0.007769735,0.4262583,0.005080673,0.06135119,0.0130164,0.3812384],"study_design_scores_gemma":[0.0001467084,0.001062218,0.05051097,0.0001665307,0.0001387869,0.000176431,0.0559314,0.8122668,0.003265029,0.04509171,0.03111651,0.0001269484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8397842,0.0001391208,0.1211376,0.001939023,0.00005726649,0.0008683968,0.0005142677,0.0005106397,0.03504941],"genre_scores_gemma":[0.9576508,0.00005793043,0.03962171,0.00002150049,0.000003487839,0.0001200289,0.0001519277,0.00002938292,0.002343206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04154764,"threshold_uncertainty_score":0.08261162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01504923167369684,"score_gpt":0.2457157736553222,"score_spread":0.2306665419816254,"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."}}