{"id":"W2069855989","doi":"10.1117/12.2067506","title":"Mapping tree species in a boreal forest area using RapidEye and Lidar data","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Innovates; Agriculture and Agri-Food Canada; Alberta Environment and Protected Areas; University of Lethbridge","funders":"","keywords":"Lidar; Remote sensing; Random forest; Taiga; Environmental science; Digital elevation model; Ranging; Forest inventory; Support vector machine; Canopy; Forestry; Geography; Forest management; Computer science; Agroforestry; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000315745,0.0002799317,0.0001578955,0.001488367,0.0004573972,0.0003421218,0.0003362098,0.0001886643,0.0004955778],"category_scores_gemma":[0.0004278522,0.0001129579,0.000177767,0.001129008,0.0001830776,0.0002311037,0.0002756173,0.0001085542,0.0001260384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009866633,"about_ca_system_score_gemma":0.001006373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4789262,"about_ca_topic_score_gemma":0.8074139,"domain_scores_codex":[0.9998037,0.00001630355,0.000007984781,0.00003280974,0.00009997844,0.00003923188],"domain_scores_gemma":[0.9997332,0.00004518353,0.00003010943,0.0000122565,0.0001426217,0.0000365521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005664205,0.0002811626,0.739512,0.0001994216,0.00008988549,0.0007603902,0.0007613281,0.01688877,0.06013152,0.0002619375,0.0009752962,0.1795719],"study_design_scores_gemma":[0.00001611954,0.00009119234,0.9732218,0.00001754595,0.00003775953,0.0002049155,0.001212946,0.02034144,0.003762144,0.00007997475,0.0009877819,0.00002634321],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996905,0.00009261564,0.0009112569,0.00001518455,0.000002910354,0.0000234758,0.0007302417,0.00005600652,0.001263307],"genre_scores_gemma":[0.9902107,0.00009579032,0.007579641,0.000008445871,0.000002414504,0.00001180227,0.001403398,0.000004191329,0.0006837543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4789262,"threshold_uncertainty_score":0.952277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02158365367826949,"score_gpt":0.230106743590714,"score_spread":0.2085230899124445,"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."}}