{"id":"W2615436872","doi":"10.5558/tfc2017-012","title":"Unmanned aerial systems for precision forest inventory purposes: A review and case study","year":2017,"lang":"en","type":"review","venue":"The Forestry Chronicle","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Point cloud; Forest inventory; Lidar; Canopy; Forestry; Percentile; Remote sensing; Environmental science; Laser scanning; Photogrammetry; Tree (set theory); Scale (ratio); Aerial survey; Forest management; Geography; Computer science; Cartography; Mathematics; Statistics; Laser; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001335604,0.0007036837,0.0006524184,0.003460887,0.0003428712,0.001262623,0.0009288737,0.001086384,0.001942882],"category_scores_gemma":[0.002331819,0.000386262,0.0005718561,0.005156547,0.0005563062,0.001780734,0.0005436033,0.0009386918,0.0009954657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006627489,"about_ca_system_score_gemma":0.001461961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004877158,"about_ca_topic_score_gemma":0.007263173,"domain_scores_codex":[0.9992974,0.000117601,0.0001251128,0.00010636,0.0003055943,0.00004801855],"domain_scores_gemma":[0.9972856,0.001606007,0.0002939336,0.00007445802,0.0006646708,0.00007533363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002887458,0.00005982273,0.0008701247,0.01402327,0.00005790205,0.0002620163,0.0001822484,0.001027781,0.0009894822,0.004092834,0.009680438,0.9687251],"study_design_scores_gemma":[0.000006132564,0.0001955727,0.005321717,0.01565959,0.0001521689,0.001446424,0.0004323079,0.0008679038,0.001114861,0.001747618,0.9730047,0.00005112096],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008168097,0.9940058,0.001019626,0.000241126,0.0001392872,0.00002405526,0.00003409879,0.0000127006,0.003706515],"genre_scores_gemma":[0.005419536,0.9917783,0.001592726,0.0002525994,0.0001549931,0.00002194242,0.0000685688,0.000007438725,0.0007038084],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004877158,"threshold_uncertainty_score":0.009697497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07979881129730466,"score_gpt":0.3583843376047239,"score_spread":0.2785855263074192,"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."}}