{"id":"W3111887689","doi":"10.1139/cjfr-2020-0518","title":"Improving living biomass C-stock loss estimates by combining optical satellite, airborne laser scanning, and NFI data","year":2021,"lang":"en","type":"preprint","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Svenska Forskningsrådet Formas","keywords":"Estimator; Environmental science; Stock (firearms); Lidar; National park; Remote sensing; Climate change; Carbon stock; Statistics; Meteorology; Mathematics; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004533359,0.000775391,0.0005970197,0.001961362,0.0002635887,0.0008900181,0.0006808139,0.0003634825,0.000704203],"category_scores_gemma":[0.00766401,0.0003497059,0.0004853272,0.001550924,0.000300825,0.001287028,0.0009673744,0.0002887319,0.0003601501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005218046,"about_ca_system_score_gemma":0.0005201731,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007186256,"about_ca_topic_score_gemma":0.01873645,"domain_scores_codex":[0.9986571,0.0005750448,0.0001024379,0.0002800322,0.0003238581,0.00006155059],"domain_scores_gemma":[0.9967008,0.001352848,0.0005954044,0.0007270765,0.0005753553,0.00004850707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001189737,0.0001974131,0.529317,0.0001571885,0.0004253382,0.00006391311,0.0001575767,0.1435463,0.01404784,0.001122474,0.0005783358,0.3102677],"study_design_scores_gemma":[0.00002742468,0.000242261,0.3299202,0.00004799701,0.000172797,0.0001916533,0.0001706584,0.6480379,0.01353351,0.003934163,0.00366611,0.00005535185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.461138,0.000397865,0.5339544,0.00009702895,0.00001649803,0.0001442513,0.001139857,0.0005486898,0.002563443],"genre_scores_gemma":[0.7622132,0.000139797,0.2351473,0.00004715915,0.00002138082,0.0001102017,0.001611153,0.00005063121,0.0006591434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9928138,"threshold_uncertainty_score":0.02397501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04957680006451126,"score_gpt":0.3201146409646042,"score_spread":0.2705378409000929,"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."}}