{"id":"W4319453991","doi":"10.1093/forestry/cpac055","title":"<i>sgsR</i>: a structurally guided sampling toolbox for LiDAR-based forest inventories","year":2023,"lang":"en","type":"article","venue":"Forestry An International Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Ontario Forest Research Institute; Ministry of Natural Resources and Forestry; Ministère des Ressources naturelles et des Forêts; Natural Resources Canada; Canadian Forest Service; University of British Columbia","funders":"","keywords":"Toolbox; Computer science; Sampling (signal processing); Workflow; Forest inventory; Sample (material); Key (lock); Plot (graphics); Data mining; Sampling design; Forest management; Environmental science; Database; Statistics; Mathematics","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":[0.007664594,0.002618081,0.001394402,0.002160816,0.000707815,0.003289612,0.00331989,0.001259358,0.0639227],"category_scores_gemma":[0.0318925,0.002258874,0.002443256,0.001603367,0.001261937,0.0028131,0.003909745,0.00317122,0.06178729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008055691,"about_ca_system_score_gemma":0.002248641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003006928,"about_ca_topic_score_gemma":0.004416,"domain_scores_codex":[0.9971035,0.0009430266,0.0003489947,0.0006863387,0.000699243,0.0002188655],"domain_scores_gemma":[0.9843164,0.009598317,0.001129639,0.002557063,0.001927509,0.0004711246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008771517,0.000190731,0.01097683,0.002927156,0.0008182813,0.0007990976,0.001066136,0.02860217,0.01322126,0.02515798,0.668378,0.2469851],"study_design_scores_gemma":[0.0007685509,0.0002367723,0.01123925,0.001007125,0.0002688892,0.001247979,0.0002051422,0.1994898,0.03790427,0.05257785,0.6944631,0.0005912248],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.002216662,0.0002297784,0.5501403,0.0003965099,0.0001987463,0.0002947327,0.02475898,0.4180432,0.003721139],"genre_scores_gemma":[0.03325877,0.0004394696,0.6836015,0.0008630844,0.000224208,0.002123885,0.03903019,0.2330747,0.007384201],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0639227,"threshold_uncertainty_score":0.2138427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.117837665477602,"score_gpt":0.4158691305396716,"score_spread":0.2980314650620695,"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."}}