{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006870198,0.0002813176,0.0001139725,0.001955746,0.000351812,0.0006726486,0.0002003377,0.0001332132,0.02151389],"category_scores_gemma":[0.0002899418,0.000122933,0.0002023087,0.003956086,0.0001518602,0.0002439547,0.0004812925,0.0001051232,0.002692448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005117111,"about_ca_system_score_gemma":0.0005262889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1252767,"about_ca_topic_score_gemma":0.2742591,"domain_scores_codex":[0.999876,0.00001496106,0.000005116203,0.00001898559,0.00005202472,0.00003294722],"domain_scores_gemma":[0.999851,0.00001372765,0.00002880383,0.000008817406,0.00007216161,0.00002550601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004454746,0.000116935,0.6398512,0.0006871019,0.00009918517,0.0008675677,0.008844706,0.003618801,0.003644465,0.003177274,0.1464905,0.1921569],"study_design_scores_gemma":[0.000008351956,0.00003569649,0.8681884,0.0001093587,0.00001551404,0.0003520986,0.006721298,0.0007720953,0.000390664,0.0002635373,0.1231226,0.00002042921],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.6217514,0.0007888192,0.00199688,0.0002333993,0.00006032614,0.0002929884,0.1656162,0.0004399189,0.2088201],"genre_scores_gemma":[0.9040307,0.0008028328,0.004018661,0.00003745153,0.00001631982,0.0002384547,0.04159225,0.00007716288,0.04918609],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1252767,"threshold_uncertainty_score":0.2490951,"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."}}