{"id":"W4385350115","doi":"10.3390/f14081541","title":"Assessing Site Productivity via Remote Sensing—Age-Independent Site Index Estimation in Even-Aged Forests","year":2023,"lang":"en","type":"article","venue":"Forests","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry","funders":"Ontario Ministry of Natural Resources and Forestry; Ministry of Natural Resources","keywords":"Site index; Lidar; Productivity; Environmental science; Estimation; Taiga; Forest management; Mean squared error; Index (typography); Forest inventory; Silviculture; Percentile; Field (mathematics); Logging; Statistics; Forestry; Geography; Remote sensing; Agroforestry; Mathematics; Computer science; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002034611,0.0004628872,0.0003084498,0.001756702,0.000201207,0.0004700141,0.0004788938,0.0002903479,0.0004011924],"category_scores_gemma":[0.002997608,0.0002434893,0.0004741381,0.001090674,0.000173825,0.0007752927,0.0003911378,0.0001898048,0.0001861851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003366224,"about_ca_system_score_gemma":0.0002498873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01008018,"about_ca_topic_score_gemma":0.02503229,"domain_scores_codex":[0.9995595,0.00009366102,0.00003180575,0.0001495395,0.0001169175,0.0000485561],"domain_scores_gemma":[0.9984211,0.0004979527,0.0004685406,0.0002004868,0.0003210181,0.00009090699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001071283,0.00008098572,0.9202609,0.00005013124,0.0001010456,0.0001359251,0.0001659853,0.03300017,0.00762465,0.0001973632,0.0001728994,0.03810282],"study_design_scores_gemma":[0.000006620378,0.0001272151,0.8698338,0.00001303836,0.00004323695,0.0001871834,0.0001508744,0.1262514,0.00243463,0.00038635,0.0005424658,0.00002313678],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9763654,0.0001897378,0.02207123,0.000007788453,0.000005996685,0.00003384689,0.0006220779,0.0000819919,0.0006219798],"genre_scores_gemma":[0.9809875,0.00006556139,0.01750018,0.000007865135,0.000005716362,0.00002589787,0.001196606,0.00001306922,0.0001975511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01008018,"threshold_uncertainty_score":0.02004302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650430028934752,"score_gpt":0.2813845401357606,"score_spread":0.2648802398464131,"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."}}