{"id":"W2594153125","doi":"10.1139/cjfr-2016-0296","title":"Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Bureau of Land Management","keywords":"Basal area; Lidar; Spatial correlation; Statistics; Estimator; Correlation; Forest inventory; Range (aeronautics); Spatial dependence; Spatial analysis; Correlation coefficient; Mathematics; Spatial ecology; Spatial variability; Environmental science; Forest management; Geography; Forestry; Remote sensing; Ecology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001298342,0.00006653791,0.0002075927,0.000820688,0.0002423016,0.00009680144,0.0004035404,0.00008867682,0.00007391688],"category_scores_gemma":[0.0005249307,0.00006095482,0.00008531819,0.0004607634,0.0004799829,0.001337652,0.00004659228,0.0002905324,0.000005571031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003087046,"about_ca_system_score_gemma":0.0004188837,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3000927,"about_ca_topic_score_gemma":0.5484516,"domain_scores_codex":[0.9986244,0.00009847279,0.0004041065,0.00008903956,0.0005409592,0.0002429586],"domain_scores_gemma":[0.9985945,0.0001582221,0.0004211435,0.0003753704,0.000196779,0.0002540266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002675655,0.00001101594,0.7210597,0.000004158735,0.00004081539,0.000003613712,0.000785396,0.2750547,0.00002459676,0.0002415367,0.0001137345,0.002633993],"study_design_scores_gemma":[0.0001675,0.00006519997,0.7877727,0.00004119202,0.0000410627,0.00000327896,0.0001552196,0.2097537,0.00006803138,0.001671231,0.0002228326,0.00003815618],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9777676,0.00001161043,0.01647536,0.0001497371,0.00004491381,0.0001587889,0.00004930838,0.00000107186,0.005341549],"genre_scores_gemma":[0.9993612,0.00001949726,0.0005152322,0.000005075778,0.00001654996,9.495978e-7,0.00001813837,0.000004713791,0.00005871321],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2483589,"threshold_uncertainty_score":0.7045681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0457203007526288,"score_gpt":0.2896096674621668,"score_spread":0.243889366709538,"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."}}