{"id":"W3156225490","doi":"10.1093/forestry/cpab011","title":"Modelling growing stock volume of forest stands with various ALS area-based approaches","year":2021,"lang":"en","type":"article","venue":"Forestry An International Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Narodowe Centrum Badań i Rozwoju","keywords":"Mean squared error; Random forest; Canopy; Forest inventory; Mathematics; Statistics; Forestry; Stock (firearms); Environmental science; Forest management; Geography; Computer science; Machine learning","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":[0.0009112087,0.0005531315,0.0002852633,0.001099635,0.0001353886,0.0006394129,0.000650345,0.0003728408,0.000643804],"category_scores_gemma":[0.001743145,0.000237107,0.0006582403,0.000878357,0.0001603521,0.0006112092,0.0002825893,0.0002643996,0.000204792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005347479,"about_ca_system_score_gemma":0.0003340482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01287327,"about_ca_topic_score_gemma":0.01364993,"domain_scores_codex":[0.9997168,0.00007698133,0.00001940958,0.00008416209,0.00007887816,0.00002373095],"domain_scores_gemma":[0.9990618,0.0005131203,0.0001389106,0.0000719468,0.0001851431,0.00002907637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001298527,0.00009421774,0.1087486,0.00006901551,0.0001413545,0.00007385537,0.00009486909,0.8538517,0.004054361,0.0003842731,0.0001331057,0.03222484],"study_design_scores_gemma":[0.000004473281,0.00003927888,0.03088067,0.000007901789,0.00001578455,0.00002284732,0.00002811823,0.9671441,0.001542915,0.0001701609,0.0001287846,0.00001500881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561464,0.0001392041,0.04158144,0.00002534879,0.00000798882,0.00004763844,0.0006276498,0.0003397691,0.001084545],"genre_scores_gemma":[0.9776564,0.00004032055,0.02157281,0.000006729603,0.000002901358,0.00004596205,0.0004188352,0.00002374069,0.000232255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01287327,"threshold_uncertainty_score":0.02559668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09082593023264346,"score_gpt":0.32431978097618,"score_spread":0.2334938507435366,"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."}}