{"id":"W4407357921","doi":"10.1016/j.ufug.2025.128725","title":"Measuring pedestrian-level street greenery visibility through space syntax and crowdsourced imagery: A case study in London, UK","year":2025,"lang":"en","type":"article","venue":"Urban forestry & urban greening","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Space syntax; Pedestrian; Visibility; Geography; Space (punctuation); Crowdsourcing; Cartography; Regional science; Environmental resource management; Remote sensing; Environmental planning; Computer science; Environmental science; Meteorology; Archaeology; World Wide Web","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":[0.0004085795,0.0002649596,0.000281718,0.0009569364,0.00100777,0.001477447,0.0004537812,0.0006901596,0.00362652],"category_scores_gemma":[0.002330731,0.0002890264,0.0002771571,0.001845108,0.001004697,0.001002028,0.001314004,0.0003989609,0.0005201205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002246413,"about_ca_system_score_gemma":0.001043504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2532166,"about_ca_topic_score_gemma":0.5139555,"domain_scores_codex":[0.9994831,0.000172254,0.00003027265,0.00008541582,0.0001179828,0.0001109674],"domain_scores_gemma":[0.998943,0.0004373614,0.0001729165,0.00008895532,0.0002628395,0.0000949192],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001527761,0.0006411266,0.6619563,0.001169173,0.0002469247,0.01893373,0.1906057,0.006136386,0.008649195,0.003831518,0.008938834,0.09736336],"study_design_scores_gemma":[0.00005805613,0.0003795876,0.8232546,0.000259273,0.00009292334,0.001624229,0.1593382,0.003476213,0.001377093,0.0007155072,0.009341061,0.00008331438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99597,0.00008907235,0.0003681176,0.0001179138,0.000004469552,0.00003080185,0.0004834202,0.000008220935,0.002927949],"genre_scores_gemma":[0.9974315,0.0001204808,0.0005595183,0.00002038651,0.000002698996,0.00002323174,0.0001857625,0.000008679475,0.001647721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2532166,"threshold_uncertainty_score":0.5034854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0466930167261069,"score_gpt":0.2774454905700443,"score_spread":0.2307524738439374,"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."}}