{"id":"W2735448406","doi":"10.14358/pers.83.7.501","title":"Northern Conifer Forest Species Classification Using Multispectral Data Acquired from an Unmanned Aerial Vehicle","year":2017,"lang":"en","type":"article","venue":"Photogrammetric Engineering & Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources","keywords":"Remote sensing; Multispectral image; Point cloud; Aerial photography; Geography; Artificial intelligence; Forest inventory; RGB color model; Digital camera; Photogrammetry; Image resolution; Cartography; Computer science; Forestry; Forest management","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002446813,0.0002319359,0.0001207472,0.0008004424,0.0002879419,0.0002309876,0.0001360305,0.0000763553,0.0008211128],"category_scores_gemma":[0.000352014,0.00007358628,0.000113153,0.00047931,0.0001262376,0.0002303075,0.0001576878,0.00006770901,0.0001957019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004138643,"about_ca_system_score_gemma":0.000399404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04594339,"about_ca_topic_score_gemma":0.185922,"domain_scores_codex":[0.9998875,0.00001615216,0.000006594871,0.00003385375,0.00003791667,0.00001806022],"domain_scores_gemma":[0.9998164,0.00003152597,0.00002413282,0.00002135421,0.00009345513,0.0000132985],"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":[0.0002833412,0.0001788988,0.2558614,0.0000955997,0.00004694553,0.0001986142,0.0005129523,0.01963055,0.1512401,0.0004071588,0.001362811,0.5701817],"study_design_scores_gemma":[0.00002337009,0.0001928198,0.7330736,0.00001839869,0.00004151657,0.0001998364,0.0007369888,0.222948,0.03961141,0.0002927951,0.002837782,0.00002357968],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9702804,0.00003943302,0.02595383,0.00001778248,0.000004907553,0.00007416126,0.0004206012,0.0002951402,0.002913923],"genre_scores_gemma":[0.9554741,0.00003564909,0.04277344,0.000009387478,0.000002348688,0.00002971505,0.0004908439,0.00001033121,0.001174044],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04594339,"threshold_uncertainty_score":0.09135193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0571693978298255,"score_gpt":0.2820179204930103,"score_spread":0.2248485226631848,"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."}}