{"id":"W2994956501","doi":"10.1139/cjfr-2019-0139","title":"Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory","year":2019,"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; Forest inventory; Statistics; Mean squared error; Small area estimation; Variance (accounting); National forest; Estimation; Environmental science; Stock (firearms); Mathematics; Stratification (seeds); Sample (material); Econometrics; Forest management; Geography; Forestry; 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":[0.00179021,0.00007466542,0.0001318361,0.0005456961,0.00009882318,0.00004318752,0.0002842401,0.00007990326,0.0002402032],"category_scores_gemma":[0.0005619022,0.00006902395,0.00005281245,0.0004825461,0.0001781571,0.0001139178,0.00001159386,0.0003249024,0.0001120309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000665043,"about_ca_system_score_gemma":0.0009729377,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01387749,"about_ca_topic_score_gemma":0.1757376,"domain_scores_codex":[0.9984385,0.0001468853,0.000365823,0.0001543412,0.0006319362,0.0002624932],"domain_scores_gemma":[0.9988488,0.0002314227,0.000161672,0.0002140509,0.0002817823,0.0002622553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001207907,0.0001518102,0.6811495,0.00002451311,0.000008205812,0.00002481674,0.0009659614,0.2877253,0.007009982,0.001163903,0.001105265,0.02055],"study_design_scores_gemma":[0.0005287576,0.0005426902,0.8992419,0.0001582354,0.000003235379,0.00002580069,0.0001634577,0.09323812,0.0005554463,0.004178651,0.001267131,0.00009663849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9890965,0.000019105,0.0004527732,0.00057164,0.00005358204,0.0003439744,0.00001016728,0.000002060477,0.009450178],"genre_scores_gemma":[0.9985036,0.000001931254,0.001206144,0.00003840276,0.00002006127,0.000002574407,0.00002029913,0.000009955682,0.0001970653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2180924,"threshold_uncertainty_score":0.9926892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04063415040230413,"score_gpt":0.2914152034377119,"score_spread":0.2507810530354078,"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."}}