{"id":"W2585546872","doi":"10.1016/j.ecoinf.2017.01.005","title":"Regional mapping of vegetation structure for biodiversity monitoring using airborne lidar data","year":2017,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":156,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Alberta Ministry of Agriculture and Forestry; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Alberta","keywords":"Lidar; Vegetation (pathology); Biodiversity; Disturbance (geology); Habitat; Environmental science; Remote sensing; Ecology; Geography; Vegetation classification; Physical geography; Taiga; Forestry; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000358684,0.0002427193,0.0002338498,0.002723664,0.0002382376,0.0006427621,0.0002927961,0.0001779235,0.001625731],"category_scores_gemma":[0.0007386404,0.0001443731,0.0002786998,0.001983613,0.00008916829,0.0005558059,0.0005365325,0.0001880303,0.0005317276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002609305,"about_ca_system_score_gemma":0.0005645617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007963683,"about_ca_topic_score_gemma":0.01743725,"domain_scores_codex":[0.99985,0.00002700139,0.00001069478,0.00004750855,0.00004513524,0.00001957405],"domain_scores_gemma":[0.9996338,0.00006629252,0.00006908324,0.00007345671,0.0001233759,0.00003398462],"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":[0.0002131832,0.0003374013,0.193787,0.000301111,0.0001722923,0.0001904282,0.0006034971,0.02626444,0.0808332,0.002408918,0.007080573,0.687808],"study_design_scores_gemma":[0.00006372542,0.0002902109,0.6407658,0.0001621076,0.000319003,0.0004523,0.001416214,0.2869444,0.03594778,0.004299715,0.02926743,0.00007127998],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7865838,0.001227017,0.1869353,0.0003219197,0.00003340883,0.0002447971,0.01049099,0.002901243,0.0112615],"genre_scores_gemma":[0.8318214,0.0003804745,0.1615709,0.00003458686,0.00001773932,0.0001072674,0.004619542,0.00009131884,0.0013568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007963683,"threshold_uncertainty_score":0.01583469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1077449500767912,"score_gpt":0.3004580195573822,"score_spread":0.1927130694805909,"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."}}