{"id":"W2175629264","doi":"10.1139/cjfr-2014-0202","title":"Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Forest inventory; Sampling design; Sampling (signal processing); Sample (material); Stratified sampling; Computer science; Copula (linguistics); Population; Statistics; Forest cover; Environmental science; Mathematics; Forestry; Geography; Forest management; Econometrics; Ecology","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":[0.00250217,0.00008436669,0.000122006,0.0001593961,0.0005183672,0.0002041329,0.0004253628,0.00005329121,0.00004229305],"category_scores_gemma":[0.001031664,0.00006233791,0.00005680704,0.0004208561,0.0004807146,0.00007955819,0.00003156721,0.0003787232,0.00001586958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004022577,"about_ca_system_score_gemma":0.0003929457,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07298629,"about_ca_topic_score_gemma":0.4670261,"domain_scores_codex":[0.9985909,0.000126229,0.0002411335,0.0001480571,0.0003480445,0.0005456076],"domain_scores_gemma":[0.9981664,0.001100093,0.00007569777,0.0002248544,0.00006817195,0.0003647256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004583523,0.00005034463,0.6418719,0.00003330998,0.00002007603,0.000008998537,0.004850045,0.2643347,0.001623226,0.0335816,0.04273055,0.01084946],"study_design_scores_gemma":[0.0008002328,0.0003997683,0.3705592,0.0001870019,0.0000147613,0.0000496067,0.006651248,0.04544759,0.0003112199,0.1082532,0.4669923,0.0003338497],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773025,0.0001004709,0.01561475,0.003373607,0.00009531554,0.000260725,0.00001324005,0.000003118656,0.003236258],"genre_scores_gemma":[0.9977983,0.000004616949,0.001872347,0.0000527536,0.0001097817,0.000004644514,0.000004020581,0.0000134999,0.0001400339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4242618,"threshold_uncertainty_score":0.9331868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04877361298144618,"score_gpt":0.3201066591529223,"score_spread":0.2713330461714761,"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."}}