{"id":"W4410171428","doi":"10.3390/f16050783","title":"Deep Learning-Based Urban Tree Species Mapping with High-Resolution Pléiades Imagery in Nanjing, China","year":2025,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"China; Aerial imagery; High resolution; Geography; Tree (set theory); Remote sensing; Forestry; Cartography; Environmental science; Archaeology; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001399474,0.0001701432,0.0001580185,0.00009460009,0.0001535351,0.00005907064,0.0001711712,0.00008690276,0.0001055691],"category_scores_gemma":[0.0000790191,0.0001248597,0.00004105562,0.0005926096,0.0001888984,0.0001477626,0.00008116964,0.0002716505,0.00008366221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003100117,"about_ca_system_score_gemma":0.00001278618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007079695,"about_ca_topic_score_gemma":0.01470539,"domain_scores_codex":[0.9988434,0.00007220074,0.0001691679,0.0003310017,0.0002595617,0.0003247189],"domain_scores_gemma":[0.9996296,0.0000469271,0.00007843175,0.0001938299,0.000006686138,0.00004455952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003856161,0.00005765834,0.7763492,0.00001966619,0.000008182858,0.00004151303,0.0004744864,0.2108622,0.004199977,0.00009496615,0.004826272,0.003027325],"study_design_scores_gemma":[0.0003761224,0.00004925535,0.9630567,0.0001427114,0.000006628867,0.000003559471,0.00007521962,0.030227,0.001172074,0.00016832,0.004560749,0.0001616002],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770985,0.00006754268,0.00607888,0.0005505709,0.0001188404,0.0002040152,6.119201e-7,0.0001032846,0.01577774],"genre_scores_gemma":[0.9918181,0.000003569663,0.00328158,0.00007480004,0.00004452689,0.000002668288,0.00001848508,0.00001163703,0.004744604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1867076,"threshold_uncertainty_score":0.8205955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004161629148082688,"score_gpt":0.1832082364396762,"score_spread":0.1790466072915935,"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."}}