{"id":"W4400229876","doi":"10.1016/j.rse.2024.114283","title":"Influence of temperate forest autumn leaf phenology on segmentation of tree species from UAV imagery using deep learning","year":2024,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université de Montréal","funders":"Canada First Research Excellence Fund; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Temperate climate; Temperate forest; Phenology; Temperate rainforest; Deciduous; Segmentation; Evergreen; Convolutional neural network; Tree (set theory); Temperate deciduous forest; Deep learning; Remote sensing; Environmental science; Ecology; Biology; Geography; Artificial intelligence; Computer science; Ecosystem; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002317269,0.0002784064,0.0004146328,0.0001011108,0.00008389371,0.00002034023,0.0001467867,0.0001482615,0.000106951],"category_scores_gemma":[0.00007424907,0.0002420651,0.0001368404,0.0002270801,0.000600691,0.000166577,0.00017116,0.0002910192,0.00006818978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003971156,"about_ca_system_score_gemma":0.00001290585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054786,"about_ca_topic_score_gemma":0.0000590727,"domain_scores_codex":[0.9978058,0.0001721649,0.0006196569,0.0005188515,0.0005900162,0.0002935095],"domain_scores_gemma":[0.998991,0.0001925784,0.0003644256,0.0003755545,0.00001177371,0.00006472974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002048721,0.00002148793,0.001412739,0.00002682933,0.00003268422,0.00001432956,0.0005868119,0.3973826,0.5766351,0.000004715901,0.00001231021,0.02384994],"study_design_scores_gemma":[0.0003444004,0.0002753191,0.2207821,0.0006344703,0.0001363413,0.00003429666,0.0004705531,0.2556004,0.5203633,0.0004319388,0.0005556691,0.0003711882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992617,0.0001356722,0.005707862,0.00008422129,0.0001309909,0.0002090489,0.000007265951,0.00003747778,0.001070514],"genre_scores_gemma":[0.9582765,0.0001676773,0.04118819,0.00002602902,0.00005500618,3.299337e-8,0.00001985021,0.00003462545,0.0002320961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2193694,"threshold_uncertainty_score":0.9871129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087091791572903,"score_gpt":0.2163336196699363,"score_spread":0.2054627017542072,"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."}}