{"id":"W4382394965","doi":"10.32920/23593239","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; Realm; Population; Space (punctuation); Urban sprawl; Transport engineering; Urban design; Architectural engineering; Regional science; Urban planning; Civil engineering; Cartography; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001456313,0.0003992619,0.0004937617,0.0002714162,0.00005691531,0.000260354,0.000482725,0.000629077,0.00149888],"category_scores_gemma":[0.000101804,0.0003669295,0.0002655426,0.000323375,0.00002031963,0.0001059062,0.0004191951,0.001432394,0.0003959566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001104023,"about_ca_system_score_gemma":0.00005954009,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006941454,"about_ca_topic_score_gemma":0.003223191,"domain_scores_codex":[0.9984461,0.00003937676,0.0003789477,0.0004606572,0.0003212828,0.0003536397],"domain_scores_gemma":[0.9988709,0.0001605568,0.00008690324,0.0006446565,0.00007600203,0.0001609437],"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":[0.00002723893,0.00005679414,0.01724758,0.0009875902,0.005659101,0.0002780412,0.00323364,0.6617719,0.007961585,0.000954485,0.2297802,0.07204187],"study_design_scores_gemma":[0.0001847168,0.00001042474,0.00758473,0.0001235133,0.0002570255,0.000001886677,0.0001882264,0.9610462,0.0005782871,0.01476281,0.01431771,0.0009444488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.177814,0.002417626,0.7540258,0.008761452,0.00581526,0.0009829538,0.0005360821,0.01314587,0.03650088],"genre_scores_gemma":[0.9707201,0.0002863016,0.01231784,0.0001106217,0.001658347,0.00004313597,0.0005081061,0.0001912527,0.01416436],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.792906,"threshold_uncertainty_score":0.9998783,"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."}}