{"id":"W2169765847","doi":"10.1016/j.rse.2008.04.018","title":"Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories","year":2008,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Estimator; Forest inventory; Mean squared error; Variance (accounting); Remote sensing; Computer science; Parametric statistics; k-nearest neighbors algorithm; Random forest; Statistics; Data mining; Environmental science; Mathematics; Geography; Forest management; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002407114,0.0002735271,0.0003182994,0.00007345831,0.0008734386,0.0000248062,0.0002349182,0.000133031,0.000006254545],"category_scores_gemma":[0.00008066752,0.0002897204,0.0001051389,0.0001519316,0.0006714134,0.0001394918,0.0001856258,0.0001125209,0.000006332968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002216699,"about_ca_system_score_gemma":0.00005324001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006279801,"about_ca_topic_score_gemma":0.0002327818,"domain_scores_codex":[0.9980335,0.00003865723,0.0004678538,0.0007487819,0.0003323816,0.0003787992],"domain_scores_gemma":[0.9980119,0.000247343,0.0002713384,0.001251847,0.00002324829,0.000194307],"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":[0.00009878311,0.0001719581,0.001124424,0.0001277948,0.00005349802,0.000002459385,0.0004547887,0.9453468,0.0190528,0.00008825805,0.001268976,0.03220943],"study_design_scores_gemma":[0.0006132856,0.00006612658,0.002311395,0.00004731792,0.0001260123,0.00004219253,0.0000711366,0.9846302,0.0008882277,0.001367569,0.00956188,0.0002746616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2034829,0.00003396108,0.7942151,0.0003316684,0.00004153253,0.001400171,0.0002757799,0.00006106645,0.0001577429],"genre_scores_gemma":[0.5555978,0.00001652599,0.4438755,0.00006208506,0.00006882266,0.000001530154,0.0002397569,0.0000492327,0.00008873014],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3521149,"threshold_uncertainty_score":0.9999555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07243775103388896,"score_gpt":0.2840479181966289,"score_spread":0.21161016716274,"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."}}