{"id":"W3201334366","doi":"10.3390/drones5030099","title":"Leveraging AI to Estimate Caribou Lichen in UAV Orthomosaics from Ground Photo Datasets","year":2021,"lang":"en","type":"article","venue":"Drones","topic":"Lichen and fungal ecology","field":"Agricultural and Biological Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"Environment and Climate Change Canada; University of Waterloo; Queen's University; Government of Canada; University of Ottawa","keywords":"Lichen; Vegetation (pathology); Convolutional neural network; Remote sensing; Artificial neural network; Environmental science; Random forest; Limiting; RGB color model; Cartography; Computer science; Artificial intelligence; Geography; Ecology; Engineering; Biology","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":[0.0003064887,0.0004905419,0.0003138563,0.002102649,0.0002017274,0.0006057468,0.0003119251,0.000332891,0.0007119953],"category_scores_gemma":[0.0009423542,0.0001671756,0.0002824466,0.001261647,0.0001791359,0.000762554,0.0003773693,0.000254053,0.0004774077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004319281,"about_ca_system_score_gemma":0.0002283581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01457724,"about_ca_topic_score_gemma":0.04178754,"domain_scores_codex":[0.9998174,0.00002548125,0.000008740973,0.00007111707,0.0000436299,0.00003362464],"domain_scores_gemma":[0.9996655,0.00009514705,0.00005289674,0.00004549811,0.0001170952,0.00002375985],"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":[0.0002332348,0.0002531083,0.3486889,0.000286685,0.0002756923,0.0002452695,0.0004239901,0.2127258,0.06181459,0.0005398578,0.001676613,0.3728364],"study_design_scores_gemma":[0.000004670105,0.00005348312,0.1566816,0.0000317848,0.00004516092,0.00007952374,0.0003060496,0.8319209,0.008913876,0.0004142665,0.001527495,0.00002130197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9371905,0.0005178305,0.05657857,0.00004863498,0.0000234342,0.00003890387,0.000993779,0.001249408,0.003358937],"genre_scores_gemma":[0.9723331,0.0001242825,0.02546797,0.00001924776,0.00000713801,0.00001770304,0.001260185,0.00004270905,0.000727639],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01457724,"threshold_uncertainty_score":0.02898479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0217454759731764,"score_gpt":0.2692744141739204,"score_spread":0.247528938200744,"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."}}