{"id":"W4414437589","doi":"10.3390/ijgi14100368","title":"Third Spaces to Represent Urban Greenery: A Study of Informal Green Spaces in a High-Density City Using Deep Learning and Geo-Weighted Analysis","year":2025,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Urban green space; Identification (biology); Urban spatial structure; Space (punctuation); Green infrastructure; Work (physics); Urban space; Sustainable development","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":[0.0005713449,0.0003223289,0.0003209053,0.001410168,0.0009170631,0.00171856,0.0007316637,0.0004591676,0.00105488],"category_scores_gemma":[0.0009927186,0.0002014988,0.0005990561,0.002004357,0.001374225,0.00129528,0.001249809,0.0006880505,0.0001397088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001673168,"about_ca_system_score_gemma":0.0007190843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06823651,"about_ca_topic_score_gemma":0.1272839,"domain_scores_codex":[0.9996926,0.00014438,0.000008851307,0.00005154197,0.0000344642,0.00006824471],"domain_scores_gemma":[0.9994106,0.0003189311,0.00006998027,0.00005195698,0.00007963437,0.00006894804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004622073,0.001664315,0.6854606,0.0003471479,0.0003115441,0.002045657,0.04384181,0.09400685,0.004438638,0.0327569,0.00405626,0.1306081],"study_design_scores_gemma":[0.00002330126,0.000163841,0.3395259,0.0000954391,0.00009764425,0.0002532882,0.07213376,0.568666,0.001747667,0.01143223,0.005761297,0.00009956441],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930236,0.00007462681,0.004900603,0.0001238441,0.000004013331,0.00002616831,0.0001313949,0.00002084124,0.001694796],"genre_scores_gemma":[0.9951302,0.00006523447,0.004167784,0.00001681708,0.000002290016,0.00001661377,0.0001099836,0.000009551083,0.0004816682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06823651,"threshold_uncertainty_score":0.1356786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008954382264756944,"score_gpt":0.2755348281124242,"score_spread":0.2665804458476673,"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."}}