{"id":"W4321995317","doi":"10.5194/egusphere-egu23-9996","title":"Direct upscaling of national forest inventory aboveground biomass of Canada with Sentinel and ALOS PALSAR observations","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Forest inventory; Random forest; Environmental science; Satellite; Remote sensing; Disturbance (geology); Statistics; Mathematics; Geography; Computer science; Geology; Machine learning; Forest management; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006747729,0.001039532,0.0003703566,0.00157918,0.0008012414,0.0008940626,0.001099746,0.0002905567,0.001164692],"category_scores_gemma":[0.001524265,0.000329068,0.0007913818,0.002439593,0.0003820791,0.000848548,0.0006551186,0.0006180224,0.0004429733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005127043,"about_ca_system_score_gemma":0.00967393,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9441481,"about_ca_topic_score_gemma":0.9582482,"domain_scores_codex":[0.9995024,0.00002537263,0.00001268158,0.0001402147,0.0002074609,0.0001118647],"domain_scores_gemma":[0.9992253,0.0000600873,0.00004566264,0.00007985228,0.0005232101,0.00006602106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002213729,0.000219612,0.551089,0.0003195896,0.0004856798,0.0003279357,0.0003689943,0.229082,0.01205254,0.001122009,0.01670195,0.1880093],"study_design_scores_gemma":[0.00005477007,0.00005044506,0.4187377,0.0001075878,0.0001678307,0.00008257952,0.0006574493,0.5533352,0.007656943,0.0007639874,0.01827092,0.0001143858],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9299499,0.001170794,0.03347932,0.0004347274,0.0001340984,0.0001262392,0.02290331,0.003279249,0.00852234],"genre_scores_gemma":[0.9446883,0.0004488117,0.02900425,0.0001314337,0.00002423727,0.00004985026,0.0232117,0.0002770245,0.002164427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05585188,"threshold_uncertainty_score":0.1123616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03953022850536123,"score_gpt":0.2374807201557527,"score_spread":0.1979504916503915,"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."}}