{"id":"W2028986484","doi":"10.1139/x99-247","title":"Estimating mapped-plot forest attributes with ratios of means","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Plot (graphics); Ratio estimator; Forest plot; Forest inventory; Mathematics; Confidence interval; Forestry; Econometrics; Geography; Bias of an estimator; Forest management; Minimum-variance unbiased estimator","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009198546,0.00008419581,0.0001538297,0.0001659418,0.0003198138,0.00006284945,0.0003567013,0.00004722608,0.001201377],"category_scores_gemma":[0.0001397288,0.00006601682,0.0000474508,0.0005658278,0.0005793241,0.0001562033,0.0000134185,0.0003536479,0.0001426811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000215,"about_ca_system_score_gemma":0.0004656888,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.024804,"about_ca_topic_score_gemma":0.2620816,"domain_scores_codex":[0.9985055,0.00008905592,0.0002959811,0.0001320243,0.0005401146,0.000437293],"domain_scores_gemma":[0.9988647,0.000126431,0.00009349496,0.0002452046,0.0001200781,0.0005500933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005317204,0.00005114745,0.7803017,0.00002287796,0.00004011298,0.0001542004,0.00137467,0.1523828,0.0006948109,0.0006483847,0.01611478,0.04816139],"study_design_scores_gemma":[0.0008526661,0.0009250412,0.9174181,0.0003097263,0.00002842684,0.0006043371,0.0004815015,0.01631528,0.001200969,0.004801213,0.05677201,0.0002907593],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.978157,0.00004226234,0.001058618,0.001171987,0.00002216043,0.0001524901,0.000009164925,0.000003676304,0.01938258],"genre_scores_gemma":[0.9896571,0.000005562607,0.009235819,0.00002227997,0.00008229237,0.000001127106,0.000003552474,0.00001496357,0.0009773258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2372776,"threshold_uncertainty_score":0.9997116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03521479577842638,"score_gpt":0.2886279661092719,"score_spread":0.2534131703308455,"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."}}