{"id":"W1861434861","doi":"10.1139/cjfr-2013-0521","title":"Design-based treatment of missing data in forest inventories using canopy heights from aerial laser scanning","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Calibration; Statistics; Sampling (signal processing); Terrain; Weighting; Variance (accounting); Tree canopy; Sample (material); Forest inventory; Environmental science; Sampling design; Laser scanning; Variable (mathematics); Missing data; Canopy; Remote sensing; Mathematics; Computer science; Geography; Forest management; Cartography; Laser; Filter (signal processing); Agroforestry","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":[0.03651185,0.00110244,0.001433917,0.001650648,0.0006226603,0.0009183971,0.001785832,0.001849269,0.001183326],"category_scores_gemma":[0.07437463,0.0009465498,0.001716521,0.001600301,0.001413014,0.000992432,0.00140295,0.001054403,0.0002600934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009554869,"about_ca_system_score_gemma":0.001626574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009382984,"about_ca_topic_score_gemma":0.001482043,"domain_scores_codex":[0.9635758,0.03160543,0.0007385571,0.001553387,0.002019214,0.0005074862],"domain_scores_gemma":[0.9419516,0.04680299,0.004634951,0.004638698,0.001697178,0.0002746579],"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":[0.001114279,0.0004629022,0.03305231,0.001218392,0.001197339,0.0005577191,0.0006353406,0.6364521,0.01126562,0.04158914,0.00156747,0.2708873],"study_design_scores_gemma":[0.0002185867,0.001214331,0.009900661,0.00007390655,0.0002794316,0.0003184305,0.00008527271,0.9475864,0.007751614,0.03057994,0.001900289,0.00009108903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01390058,0.0001094825,0.9854648,0.00004082828,0.00001942691,0.0001263211,0.00007755852,0.0001336595,0.0001273293],"genre_scores_gemma":[0.3834203,0.000310535,0.6130347,0.0001335965,0.0000953369,0.001466383,0.0006437147,0.00008621247,0.0008092507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03651185,"threshold_uncertainty_score":0.1930954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.135830775387994,"score_gpt":0.341592393630726,"score_spread":0.205761618242732,"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."}}