{"id":"W4406110876","doi":"10.1016/j.geomat.2025.100047","title":"Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data","year":2025,"lang":"en","type":"article","venue":"GEOMATICA","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Canopy; Lidar; Biomass (ecology); Tree canopy; Tree (set theory); Field (mathematics); Forestry; Environmental science; Geography; Agroforestry; Remote sensing; Mathematics; Ecology; Biology; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0003068481,0.0003229158,0.0001789472,0.000342038,0.0001826127,0.0003988926,0.0004765273,0.0003124415,0.0005770744],"category_scores_gemma":[0.0004099394,0.000229021,0.0003440036,0.0003290551,0.0001993323,0.0003140278,0.0002458104,0.0001693189,0.0001153939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008816368,"about_ca_system_score_gemma":0.0004227075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06294511,"about_ca_topic_score_gemma":0.09267014,"domain_scores_codex":[0.9999149,0.00001836092,0.000003936418,0.00003410315,0.00001123801,0.00001751082],"domain_scores_gemma":[0.9998279,0.00008189123,0.0000346389,0.00001394667,0.00002240911,0.00001915655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001284626,0.0001394137,0.2053855,0.00005235774,0.00008092213,0.000231488,0.0001761568,0.7751601,0.007660315,0.0004899841,0.000149945,0.01034549],"study_design_scores_gemma":[0.000007167396,0.00004228056,0.06383541,0.00000568636,0.0000110639,0.00003765147,0.00005969872,0.9354545,0.0003316733,0.0001264369,0.00008002113,0.000008409908],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972441,0.00002291254,0.00237533,0.00001183426,9.659693e-7,0.000006107909,0.00007986469,0.00001702144,0.0002417526],"genre_scores_gemma":[0.9979977,0.0000160301,0.0016649,0.000003607068,8.039986e-7,0.000006893975,0.0001105521,0.000003887359,0.0001955968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06294511,"threshold_uncertainty_score":0.1251574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02368099813972172,"score_gpt":0.2510508609399585,"score_spread":0.2273698628002368,"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."}}