{"id":"W2116763923","doi":"10.1139/cjfr-2014-0152","title":"Integrating remote sensing and past inventory data under the new annual design of the Swiss National Forest Inventory using three-phase design-based regression estimation","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Forest inventory; Variance (accounting); Sample (material); Sampling (signal processing); Simple random sample; Sampling design; Sample size determination; Mathematics; Regression; Standard error; Environmental science; Computer science; Econometrics; Geography; Forest management; Forestry; Population; Telecommunications; Demography","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":[0.007044197,0.000737624,0.0008492696,0.0006827718,0.0003034547,0.0008155886,0.001126725,0.0006656969,0.001030814],"category_scores_gemma":[0.01052574,0.0005817705,0.001177648,0.001147531,0.0004316895,0.0006290124,0.0006940888,0.0009131699,0.0003117191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005418401,"about_ca_system_score_gemma":0.00131338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005340687,"about_ca_topic_score_gemma":0.006517181,"domain_scores_codex":[0.9965952,0.002064344,0.0001361611,0.0006214112,0.0004452386,0.0001375851],"domain_scores_gemma":[0.9955296,0.002519113,0.000612833,0.0005682581,0.0007023641,0.00006791706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0006385071,0.0004800703,0.03808774,0.0003210323,0.0004983834,0.0001203336,0.0001939003,0.6738358,0.02001828,0.01129925,0.001177199,0.2533295],"study_design_scores_gemma":[0.00005539503,0.0002392229,0.007561767,0.000009156787,0.00005876145,0.00003674656,0.00001449239,0.9857441,0.002554442,0.002640229,0.001053838,0.00003181375],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04125759,0.00007718935,0.9577658,0.00002966049,0.00002023833,0.0001604194,0.0001569851,0.000254839,0.0002772328],"genre_scores_gemma":[0.3542335,0.0001242768,0.642334,0.00005552648,0.00004696481,0.000759165,0.001402576,0.00006653011,0.0009774407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007044197,"threshold_uncertainty_score":0.03725374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1384464071576812,"score_gpt":0.3606119927987541,"score_spread":0.2221655856410729,"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."}}