{"id":"W4307934663","doi":"10.1093/forestry/cpac043","title":"Tree detection and diameter estimation based on deep learning","year":2022,"lang":"en","type":"article","venue":"Forestry An International Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Tree (set theory); Artificial intelligence; Unavailability; Deep learning; Segmentation; Lidar; Minimum bounding box; Machine learning; Block (permutation group theory); Scalability; Data mining; Artificial neural network; Pattern recognition (psychology); Image (mathematics); Remote sensing; Geography; Database; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004138834,0.001308323,0.0008623016,0.001427859,0.0002573016,0.0008318583,0.001835511,0.000883787,0.001905158],"category_scores_gemma":[0.001315375,0.0003886978,0.0007158854,0.001116557,0.0003529783,0.001127041,0.0008922755,0.0008983206,0.001381014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007045064,"about_ca_system_score_gemma":0.0006844361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009692856,"about_ca_topic_score_gemma":0.01727337,"domain_scores_codex":[0.9994755,0.0000438092,0.0000220818,0.0002497315,0.0001203424,0.00008861229],"domain_scores_gemma":[0.9993913,0.0001715782,0.00008285006,0.000127825,0.0001734954,0.0000529977],"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":[0.0004875318,0.0003844185,0.01540917,0.0002837757,0.0001536539,0.0001927169,0.00008416807,0.4527592,0.03110921,0.002135385,0.01604854,0.4809522],"study_design_scores_gemma":[0.000007042504,0.00002080792,0.001628898,0.00001428533,0.000008785257,0.00003275132,0.00001137914,0.9912301,0.005051685,0.001164756,0.0008211976,0.000008373642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3286306,0.002060105,0.6313584,0.0005172364,0.00024382,0.000115849,0.006701875,0.02325068,0.007121471],"genre_scores_gemma":[0.770527,0.0004049352,0.2113072,0.0002464374,0.00006223389,0.00007875997,0.0133332,0.0003470568,0.003693092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009692856,"threshold_uncertainty_score":0.01927286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02637205978948826,"score_gpt":0.3301137374855241,"score_spread":0.3037416776960358,"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."}}