{"id":"W2024255690","doi":"10.1109/igarss.2014.6946688","title":"Tree species identification and subsequent health determination from mobile LiDAR data","year":2014,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Lidar; Tree (set theory); Identification (biology); Computer science; Tree health; Decision tree; Remote sensing; Data mining; Geography; Mathematics; Ecology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004019289,0.0004512125,0.0002803713,0.002186066,0.0003782978,0.0005469008,0.000400203,0.0005295448,0.001193753],"category_scores_gemma":[0.0009574234,0.0001899996,0.0002840986,0.0005472229,0.000198914,0.0005392228,0.0005190196,0.0003368242,0.0006680746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001863179,"about_ca_system_score_gemma":0.0002695156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002002799,"about_ca_topic_score_gemma":0.005377945,"domain_scores_codex":[0.9997669,0.00002707105,0.00001502067,0.00005664785,0.00009546302,0.00003886721],"domain_scores_gemma":[0.9994586,0.0001280513,0.00007381516,0.00005611969,0.0002480366,0.00003524862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003962789,0.0002655539,0.1549657,0.0003765798,0.0000730107,0.0007454986,0.001013959,0.01160737,0.3560875,0.002198424,0.00123331,0.4710367],"study_design_scores_gemma":[0.00004243838,0.001180993,0.3461362,0.0002262978,0.0001724986,0.003035536,0.002958295,0.261453,0.360094,0.006584906,0.01793198,0.0001839681],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4697554,0.0005046437,0.5212852,0.0001425936,0.00009253636,0.0004241453,0.001169609,0.00082474,0.00580115],"genre_scores_gemma":[0.7213531,0.0003501415,0.2741795,0.00008961164,0.00003834059,0.0001570484,0.0008345839,0.00004681532,0.002950822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002186066,"threshold_uncertainty_score":0.003993511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02775511761001108,"score_gpt":0.2723007661113629,"score_spread":0.2445456485013519,"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."}}