{"id":"W4382394571","doi":"10.32920/23593239.v1","title":"A Thick Green Line: Extracting Public Space from Infrastructure","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Amenity; Public space; Pedestrian; Downtown; Geography; Population; Realm; Space (punctuation); Urban design; Transport engineering; Architectural engineering; Economic geography; Civil engineering; Urban planning; Regional science; Engineering; Sociology; Political science; Computer science","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":[0.0004711301,0.0006227915,0.0001951029,0.0007847784,0.0009257552,0.00301871,0.0006895714,0.0009583329,0.0161286],"category_scores_gemma":[0.001484695,0.0003135841,0.0004709084,0.000668601,0.002341201,0.002487865,0.00266069,0.000856973,0.002760278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008802661,"about_ca_system_score_gemma":0.0009682124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003216403,"about_ca_topic_score_gemma":0.006712391,"domain_scores_codex":[0.9994633,0.000208212,0.00001401478,0.00008883925,0.0001673743,0.00005825237],"domain_scores_gemma":[0.9996159,0.0001112501,0.00002875724,0.0001340212,0.00006905974,0.00004100662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001596221,0.00009316678,0.002106778,0.0002561786,0.00002148101,0.0004498906,0.003139172,0.02678168,0.0133793,0.7271854,0.0249028,0.2015245],"study_design_scores_gemma":[0.00007508128,0.0002509608,0.002906729,0.0002140791,0.00004626805,0.000655787,0.006168306,0.135292,0.02219978,0.4324783,0.3996152,0.0000974759],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03942423,0.0003074553,0.8376451,0.0035074,0.0002553227,0.0001863723,0.0003032324,0.0009713835,0.1173995],"genre_scores_gemma":[0.4400387,0.0005162102,0.4887579,0.0005010862,0.00009551552,0.0003242395,0.0004765912,0.0004995418,0.0687902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0161286,"threshold_uncertainty_score":0.05395555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03477403884431809,"score_gpt":0.2297196963205288,"score_spread":0.1949456574762107,"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."}}