{"id":"W4283386036","doi":"10.1016/j.landurbplan.2022.104505","title":"A room with a view: Automatic assessment of window views for high-rise high-density areas using City Information Models and deep transfer learning","year":2022,"lang":"en","type":"article","venue":"Landscape and Urban Planning","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Window (computing); Computer science; Valuation (finance); Real estate; Quarter (Canadian coin); Visibility; Transferability; Scalability; Transfer of learning; Scale (ratio); Deep learning; Urban planning; Artificial intelligence; Environmental resource management; Geography; Machine learning; Cartography; Environmental science; Civil engineering; Business; Meteorology; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.000384237,0.000139579,0.0002829039,0.00006207961,0.0004633623,0.00003728976,0.000066589,0.00003782039,0.00009683992],"category_scores_gemma":[0.000003184354,0.0001170205,0.00002492799,0.0001368456,0.0000348893,0.0004823499,0.00007744701,0.0001857564,4.227951e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006039308,"about_ca_system_score_gemma":0.00002414047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003362776,"about_ca_topic_score_gemma":0.0001117174,"domain_scores_codex":[0.9990063,0.00006691759,0.0002454332,0.0001883663,0.000264003,0.0002289748],"domain_scores_gemma":[0.9996202,0.00005820688,0.0001186955,0.0001033133,0.00001005536,0.00008954231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007258162,0.00002832602,0.9560772,0.0001003962,0.00002002787,0.000002837454,0.004282173,0.03766051,0.0001459122,0.00008443122,0.0001709119,0.001354657],"study_design_scores_gemma":[0.001520042,0.0005552169,0.4584801,0.00006959065,0.00009393267,0.00002916114,0.001101059,0.536934,0.00002333796,0.0001719406,0.0007942215,0.0002273514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551258,0.0002824882,0.04392621,0.00006226411,0.00002116394,0.0003974428,0.0000163841,0.00002844744,0.0001397911],"genre_scores_gemma":[0.9951646,0.000021511,0.004539497,0.0001346259,0.00001561313,0.00003601654,0.00004091221,0.00001024906,0.00003694376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4992735,"threshold_uncertainty_score":0.4771958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02138299534671318,"score_gpt":0.2510418761881525,"score_spread":0.2296588808414394,"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."}}