{"id":"W2791852688","doi":"10.3390/rs10020338","title":"Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Academy of Finland","keywords":"Biodiversity; Species richness; Remote sensing; Taiga; Hyperspectral imaging; Environmental science; Photogrammetry; Point cloud; Boreal; Deciduous; Forest ecology; Geography; Ecosystem; Ecology; Forestry; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002678049,0.000157864,0.0001515264,0.0001424834,0.0002975757,0.0001476739,0.00007250183,0.00004794273,0.00001082006],"category_scores_gemma":[0.000041694,0.0001437977,0.0000324982,0.0008023018,0.0004699884,0.00018448,0.00006627901,0.0001632854,0.00003905987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002279765,"about_ca_system_score_gemma":0.00002595068,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022687,"about_ca_topic_score_gemma":0.001952693,"domain_scores_codex":[0.9987766,0.0000578858,0.0001431396,0.0004200723,0.0002244481,0.00037781],"domain_scores_gemma":[0.9994471,0.0000607267,0.00009691573,0.0002564533,0.00001972497,0.000119069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007261209,0.00005421546,0.1298907,0.00001237255,0.00001188354,0.0001408985,0.001089196,0.000440217,0.02398859,0.000006689877,0.0002578766,0.8440347],"study_design_scores_gemma":[0.0008479557,0.0000702045,0.3996093,0.00008232263,0.00003110595,0.0001920586,0.0006517989,0.5872896,0.01011003,0.0002779491,0.0004725858,0.0003650729],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642451,0.00001507202,0.02652467,0.0005590335,0.00003870676,0.0001498005,9.361497e-7,0.0000732902,0.008393415],"genre_scores_gemma":[0.9230614,0.000002396866,0.07663164,0.0002353967,0.00004336761,4.711904e-9,0.000004657633,0.00001208153,0.000009045359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8436697,"threshold_uncertainty_score":0.9963641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241581352763619,"score_gpt":0.2424853276102755,"score_spread":0.2300695140826393,"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."}}