{"id":"W4230240973","doi":"10.4095/301177","title":"Commercial Land Use: Pedestrian Strips","year":2010,"lang":"en","type":"report","venue":"","topic":"Urban Design and Spatial Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; STRIPS; Geography; Environmental science; Computer science; Artificial intelligence; Archaeology","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":[0.0002241627,0.0002945914,0.0005074016,0.0001846578,0.00006193362,0.0001345599,0.0001943685,0.0006282008,0.002229574],"category_scores_gemma":[0.000100963,0.0002533196,0.0002257096,0.0001587883,0.00002340524,0.00007530066,0.00002823792,0.001043539,0.0001610124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008980188,"about_ca_system_score_gemma":0.0001680453,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007539888,"about_ca_topic_score_gemma":0.02362267,"domain_scores_codex":[0.9987242,0.00002198845,0.0003423605,0.000199452,0.0004592897,0.0002527482],"domain_scores_gemma":[0.9992228,0.00008004972,0.00005206508,0.0003870479,0.00012427,0.0001337838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004653387,0.00002699712,0.02127731,0.000178685,0.0005195141,0.00006078371,0.00002832694,0.0002599127,0.000162987,0.00001220016,0.9519643,0.02550432],"study_design_scores_gemma":[0.0001334209,0.00001371836,0.007082117,0.00002143431,0.0002423586,0.0000135102,0.000002105591,0.001850892,0.00009534542,0.000008107143,0.9901222,0.0004147842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02577013,0.001737816,0.01571918,0.0001407583,0.008473469,0.0006650032,0.0004415602,0.00235568,0.9446964],"genre_scores_gemma":[0.702143,0.005419761,0.001975768,0.0001042942,0.007245366,0.00004810806,0.0015071,0.0003823225,0.2811743],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.6763728,"threshold_uncertainty_score":0.9999919,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05200198407540552,"score_gpt":0.2462362161096689,"score_spread":0.1942342320342634,"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."}}