{"id":"W4214695740","doi":"10.3390/rs14051181","title":"Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Natural Resources Canada; Canadian Space Agency; U.S. Forest Service","keywords":"Remote sensing; Lidar; Forest inventory; Environmental science; Synthetic aperture radar; Satellite; Taiga; Altimeter; Forest management; Forestry; Geography; Agroforestry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003351219,0.0001382004,0.0002089287,0.00005348377,0.0002870625,0.00002214707,0.0001840888,0.00003104732,0.00001122604],"category_scores_gemma":[0.00006138537,0.0001535888,0.00002094067,0.0003953211,0.0001038094,0.00008108018,0.0004186634,0.0002200084,8.224517e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005721644,"about_ca_system_score_gemma":0.0001304331,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7657419,"about_ca_topic_score_gemma":0.8542564,"domain_scores_codex":[0.9986444,0.00008410191,0.000274516,0.0004257202,0.0002577174,0.000313511],"domain_scores_gemma":[0.9991401,0.00008454881,0.0001440831,0.0005448346,0.000009784488,0.00007665366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005880563,0.00005462972,0.09754673,0.00005957367,0.00005506823,0.0001411571,0.001932721,0.07822324,0.6250462,0.000006836576,0.0002697712,0.1966053],"study_design_scores_gemma":[0.0003813601,0.00001658893,0.03060357,0.00003310716,0.00001553388,0.0001236482,0.0007647668,0.9625927,0.002191066,0.00009428374,0.0029589,0.0002245268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947386,0.00008147452,0.004064616,0.000345478,0.00007241646,0.0002272075,0.00003983136,0.00001675083,0.0004136049],"genre_scores_gemma":[0.9734597,0.000007357252,0.02634761,0.00006134215,0.00002862866,4.848947e-9,0.00004205867,0.00002101929,0.000032286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8843694,"threshold_uncertainty_score":0.6263171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02545002842065808,"score_gpt":0.2341687672348275,"score_spread":0.2087187388141694,"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."}}